WO2025097619A1

WO2025097619A1 - Communication method and communication apparatus

Info

Publication number: WO2025097619A1
Application number: PCT/CN2024/079983
Authority: WO
Inventors: Yiqun Ge; Nghia Trung DOAN; Wuxian Shi; Tayebeh LOTFI MAHYARI; Xiaoyan Bi; Jianglei Ma
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2023-11-07
Filing date: 2024-03-04
Publication date: 2025-05-15
Anticipated expiration: 2026-05-07

Abstract

Embodiments of the present application provide a communication method and communication apparatus. In the method, the downlink channel space can be divided into M sub-channels. The BS may transmit K sets of reference signals corresponding to K (K≤M) sub-channels in the M sub-channels to the UE. With receiving the K sets of reference signals, the UE may transmit a channel state information (CSI) corresponding to the K sets of reference signals to the BS, wherein the CSI comprises channel estimation of a reference sub-channel and a relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels within the K sub-channels. Then, the BS can reconstruct the downlink channel based on the CSI. In this way, the UE does not need to transmit channel estimation of the entire downlink channel to the BS, which reduces the communication resources occupied by the channel estimate transmission.

Description

COMMUNICATION METHOD AND COMMUNICATION APPARATUS

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to United States Patent Application No. 635,9679, 4, filed with the United States Patent and Trademark Office on November 07, 2023, entitled “Downlink Multiple-Input Multiple-Output (DL-MIMO) Channel Feedback in Ultra-Low-Dimensional Equivalent Space” , which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

Embodiments of the present application relate to the field of communications, and more specifically, to a communication method and a communication apparatus.

BACKGROUND

In a wireless communication system, it is important to acquire the characteristics of a channel. In order to estimate a channel, pilot signals known to both transmitting apparatus and receiving apparatus are transmitted. The receiving apparatus can estimate the channel by measuring the pilot signals transmitted by the transmitting apparatus and comparing the measurements with the known transmitted signals.

With the evolution of communication systems, channel bands become wider while the number of antennas or antenna ports keep increasing in communication systems, which increasing the computation resources and communication resources used for channel estimating.

SUMMARY

Embodiments of the present application provide a communication method and a communication apparatus. The technical solutions may reduce computation resources and communication resources used for channel estimating.

A first aspect of the disclosure involves a communication method applied at a user equipment side, comprising: receiving K sets of reference signal corresponding to K sub-channels of a downlink (DL) channel, K is a positive integer larger than 1; transmitting a channel state information (CSI) to a base station (BS) based on the K sets of reference signals; wherein the CSI comprises: a first information indicating the channel estimation for a first sub-channel among the K sub-channels, and a second information indicating a relationship between channel estimation of the first sub-channel and channel estimation of one or more sub-channels within the K sub-channels other than the first sub-channel.

In some embodiments of the first aspect, the BS may reconstruct the DL channel based on the CSI. For example, the BS may determine channel estimation of any sub-channel based on the channel estimation of the first sub-channel and the relationship between channel estimation of the first sub-channel and channel estimation of one or more sub-channels within the K sub-channels other than the first sub-channel.

In some embodiments of the first aspect, the second information may indicates relationship between channel estimation of the first sub-channel and channel estimation of other sub-channels within the K sub-channels other than the first sub-channel.

In some embodiments of the first aspect, the second information may indicates relationship between channel estimation of the first sub-channel and channel estimation of part of sub-channels within the K sub-channels other than the first sub-channel.

Due to the method involved in the first aspect, the UE may transmit a CSI comprise the first information and the second information to instruct the BS reconstruct the DL channel instead of transmitting the channel estimation of all sub-channels to the BS, which may reduce the amount of data transmitting from the UE to the BS during the channel estimation.

One or more embodiments according to the method in the first aspect, wherein: the K sub-channels are equally-sized.

In those embodiments, the relationship between channel estimation of the first sub-channel and channel estimation of sub-channels within the K sub-channels other than the first sub-channel may be simple due to the K sub-channels are equally-sized, which may reduce the computation resource need for determining the second information by the UE or reconstructing the DL channel by the BS.

One or more embodiments according to the method in the first aspect, wherein: the K sets of reference signals are respectively corresponding to the K sub-channels, and each set of reference signals are transmitted on the corresponding sub-channel.

One or more embodiments according to the method in the first aspect, wherein: the first information comprises a first channel coefficient vector (h_i) of the first sub-channel; the second information comprises a first transformation matrix (G_h) or a first transformation information indicating the first transformation matrix, and the first transformation matrix indicates a relationship between the first channel coefficient vector (h_i) and the channel coefficient vectors of one or more sub-channels within the K sub-channels other than the first channel coefficient vector (h_i) .

One or more embodiments according to the method in the first aspect, the first transformation matrix (G_h) may be determined by performing dynamic mode decomposition on the first channel coefficient vector and channel coefficient vectors of sub-channels within the K sub-channels other than the first channel coefficient vector, or using method such as Fourier transform, fast Fourier transform, deep neural networks, etc.

One or more embodiments according to the method in the first aspect, wherein: h_i+j=G_h ^jh_i, the first sub-channel is the i-th sub-channel in the K sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, i and j are integer, 1≤i+j≤K.

In those embodiments, the BS may determine any h_i+j based on h_i+j=G_h ^jh_i. That is, the BS may determine the channel estimation (e.g. channel coefficient vector) based on received DCI according to h_i+j=G_h ^jh_i.

One or more embodiments according to the method in the first aspect, wherein the first transformation information comprises one or more matrices determined by decomposing the first transformation matrix.

In those embodiments, the one or more matrices determined by decomposing the first transformation matrix may have smaller data amount than the first transformation matrix itself, which may reduce the communication resource needed for transmitting the first transformation matrix.

One or more embodiments according to the method in the first aspect, wherein: the one or more matrices comprise a first eigenvalue matrix (Ψ_h) and a first eigenvector matrix (Λ_h) ; the first eigenvalue matrix (Ψ_h) and the first eigenvector matrix (Λ_h) are determined by performing Eigen-decomposition on the first transformation matrix (G_h) .

One or more embodiments according to the method in the first aspect, wherein: the first information comprises a first low-dimension channel coefficient vector (c_i) corresponding to a first channel coefficient vector (h_i) of the first sub-channel; the second information comprises a second transformation matrix (G_c) or a second transformation information indicating the second transformation matrix, and the second transformation matrix indicates a relationship between the first low-dimension channel coefficient vector (c_i) and low-dimension channel coefficient vectors corresponding to the channel coefficient vectors of one or more sub-channels within the K sub-channels other than the first low-dimension channel coefficient vector (c_i) .

In those embodiments, the UE transmits a first low-dimension channel coefficient vector (c_i) corresponding to a first channel coefficient vector (h_i) of the first sub-channel instead of transmitting the first channel coefficient vector (h_i) , which may reduce the communication resource needed for transmitting the channel estimation of the firs sub-channel because the data amount of the first low-dimension channel coefficient vector (c_i) is smaller than the corresponding first channel coefficient vector (h_i) . Besides, the second transformation matrix (G_c) or the second transformation information also has smaller data amount that the aforementioned the first transformation matrix (G_h) or the first transformation information, which may further reduce the communication resource needed for channel estimation and reconstructing the DL channel.

One or more embodiments according to the method in the first aspect, he second transformation matrix (G_c) may be determined by performing dynamic mode decomposition on the first low-dimension channel coefficient vector and low-dimension channel coefficient vectors of one or more sub-channels within the K sub-channels other than the first low-dimension channel coefficient vector, or using method such as Fourier transform, fast Fourier transform, deep neural networks, etc.

One or more embodiments according to the method in the first aspect, wherein: c_i+j=G_c ^jc_i, the first sub-channel is the i-th sub-channel in the K sub-channels, c_i+j refers to the low-dimension channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, 1≤i+j≤K, i and j are integer.

In those embodiments, the BS may determine any c_i+j based on c_i+j=G_c ^jc_i. That is, the BS may determine the low-dimension channel estimation (e.g. channel coefficient vector or low-dimension channel coefficient vector) based on received DCI according to c_i+j=G_c ^jc_i.

One or more embodiments according to the method in the first aspect, wherein: the second transformation information comprises one or more matrices determined by decomposing the second matrix.

In those embodiments, the one or more matrices determined by decomposing the second transformation matrix may have smaller data amount than the second transformation matrix itself, which may reduce the communication resource needed for transmitting the second transformation matrix.

One or more embodiments according to the method in the first aspect, wherein: the one or more matrices comprises a second eigenvalue matrix (Ψ_c) and a second eigenvector matrix (Λ_c) ; the second eigenvalue matrix (Ψ_c) and the second eigenvector matrix (Λ_c) is determined by performing Eigen-decomposition on the second transformation matrix (G_c) .

One or more embodiments according to the method in the first aspect, wherein: low-dimension channel coefficient vector of u-th sub-channel in the K sub-channels is determined by compressing channel coefficient vector of the u-th sub-channel using one or more low-dimension matrices of the common basis of the DL channel, u is integer and 1≤u≤K.

For example, the low-dimension channel coefficient vector of the u-th sub-channel (c_u) in the K sub-channels and channel coefficient vector of the u-th sub-channel (h_u) may satisfy the formula that c_u=Θ^-1 h_u orwherein Θ is a low-dimension matrices of the common basis of the DL channel.

One or more embodiments according to the method in the first aspect, wherein: each of the one or more low-dimension matrices is using to compress channel coefficient vectors of one or more corresponding sub-channels in the K sub-channels.

One or more embodiments according to the method in the first aspect, wherein the method further comprises: receiving a compression information from the BS, wherein the compression information indicates the one or more low-dimension matrices of the common basis of the DL channel.

In those embodiments, the one or more low-dimension matrices of the common basis of the DL channel using for compressing channel coefficient vector is received from the BS. For example, the UE may receive information indicating the “Θ” in the description part.

One or more embodiments according to the method in the first aspect, wherein the compression information comprises the one or more low-dimension matrices, or inverse matrices or pseudoinverse matrices of the one or more low-dimension matrices.

For example, compression information may comprise “Θ” or “Θ^-1” orin the description part.

One or more embodiments according to the method in the first aspect, wherein the DL channel comprises M sub-channels, and M is an integer greater than or equal to K.

In those embodiments, the UE may estimate part sub-channels of the DL channel (that is K<M) , which may further reduce the communication resource for transmitting reference signals and computation resource for determining the CSI.

One or more embodiments according to the method in the first aspect, wherein the M sub-channels are determined by dividing the DL channel based on one or more of the following dimensions: frequency domain, time domain, or space domain.

In those embodiments, the space domain may refer to antennas or antenna ports of the BS, antennas or antenna ports of the UE, etc.

One or more embodiments according to the method in the first aspect, wherein there are Q sub-channels between the nth sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels, Q and n are integer, 0≤Q≤M/K, 1≤n≤K-1.

In those embodiments, the K sub-channels are non-continues sub-channels, that is, there are Q sub-channels between two adjacent sub-channel in K sub-channels, which may further reduce the communication resource for transmitting reference signals and computation resource for determining the CSI.

One or more embodiments according to the method in the first aspect, wherein the method further comprises: receiving a pattern information corresponding to the K sets of reference signals, wherein the pattern information indicates at least one of the following information of each reference signal in the K sets of reference signals: index of frequency intervals, index of time intervals, index of antennas or antenna ports of the BS, index of antennas or antenna ports of the UE, values, antenna port, or transmit power; and the receiving K sets of reference signals corresponding to K sub-channels of a DL channel comprising: receiving the K sets of reference signals based on the pattern information.

In those embodiments, the BS may transmit a pattern information corresponding to the K sets of reference signals (e.g. P in the description part) , hence the UE can receive the sets of reference signals based on the pattern information.

One or more embodiments according to the method in the first aspect, wherein: a first set of reference signals in the K sets of reference signals is the same as a second set of reference signals in the K sets of reference signals; or a first set of reference signals in the K sets of reference signals is different from any other sets of reference signals in the K sets of reference signals.

In those embodiments, the pattern of reference signals of different sub-channel may be same or different.

A second aspect of the disclosure involves a communication method applied at a base station side, comprising: transmitting K sets of reference signals corresponding to K sub-channels of a downlink (DL) channel to a user equipment (UE) , K is a positive integer larger than 1; receiving a channel state information (CSI) corresponding to the K sets of reference signals from the UE; wherein, the CSI comprises: a first information indicating the channel estimation for a first sub-channel among the K sub-channels, and a second information indicating a relationship between channel estimation of the first sub-channel and channel estimation of one or more sub-channels within the K sub-channels other than the first sub-channel.

Due to the method involved in the first aspect, the UE may transmit a CSI comprise the first information and the second information to instruct the BS reconstruct the DL channel instead of transmitting the channel estimation of all sub-channels to the BS, while the BS may transmit reference signals corresponding to part of sub-channels of the DL channel instead of transmitting the reference signals corresponding to all sub-channels to the UE, which reduce the amount of data transmitting between the UE to the BS during the channel estimation.

One or more embodiments according to the method in the second aspect, wherein the method further comprising: reconstructing the DL channel based on the CSI information.

One or more embodiments according to the method in the second aspect, wherein: the K sub-channels are equally-sized.

In those embodiments, the relationship between channel estimation of the first sub-channel and channel estimation of one or more sub-channels within the K sub-channels other than the first sub-channel may be simple due to the K sub-channels are equally-sized, which may reduce the computation resource need for determining the second information by the UE or reconstructing the DL channel by the BS.

One or more embodiments according to the method in the second aspect, wherein: the K sets of reference signals are respectively corresponding to the K sub-channels, and each set of reference signals are transmitted on the corresponding sub-channel.

One or more embodiments according to the method in the second aspect, wherein: the first information comprises a first channel coefficient vector (h_i) of the first sub-channel; the second information comprises a first transformation matrix (G_h) or a first transformation information indicating the first transformation matrix, and the first transformation matrix indicates a between the first channel coefficient vector (h_i) and the channel coefficient vectors of one or more sub-channels within the K sub-channels other than the first channel coefficient vector (h_i) .

One or more embodiments according to the method in the second aspect, wherein: the reconstructing the DL channel based on the CSI, comprising: determining channel coefficient vectors corresponding to the K sub-channels by h_i+j=G_h ^jh_i, wherein the first sub-channel is the i-th sub-channel in the K sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, i and j are integer, 1≤i+j≤K.

One or more embodiments according to the method in the second aspect, wherein the first transformation information comprises one or more matrices determined by decomposing the first transformation matrix.

One or more embodiments according to the method in the second aspect, wherein: the one or more matrices comprise a first eigenvalue matrix (Ψ_h) and a first eigenvector matrix (Λ_h) ; the first eigenvalue matrix (Ψ_h) and the first eigenvector matrix (Λ_h) are determined by performing Eigen-decomposition on the first transformation matrix (G_h) .

One or more embodiments according to the method in the second aspect, wherein: the reconstructing the DL channel based on the CSI, comprising: determining channel coefficient vectors corresponding to the K sub-channels by h_i+j=Ψ₁Λ₁ ^jΨ₁ ^-1h_i, wherein the first sub-channel is the i-th sub-channel in the K sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, i and j are integer, 1≤i+j≤K.

One or more embodiments according to the method in the second aspect, wherein: the first information comprises a first low-dimension channel coefficient vector (c_i) corresponding to a first channel coefficient vector (h_i) of the first sub-channel; the second information comprises a second transformation matrix (G_c) or a second transformation information indicating the second transformation matrix, and the second transformation matrix indicates a relationship between the second low-dimension channel coefficient vector (c_i) and the low-dimension channel coefficient vectors of one or more sub-channels within the K sub-channels other than the second low-dimension channel coefficient vector (c_i) .

One or more embodiments according to the method in the second aspect, wherein: the reconstructing the DL channel based on the CSI, comprising: determining channel coefficient vectors corresponding to the K sub-channels by h_i+j=UG_c ^jc_i, wherein, h_i+j refers to channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, U refers to a common basis of the DL channel, i and j are integer, 1≤i+j≤K.

One or more embodiments according to the method in the second aspect, wherein the second transformation information comprises one or more matrices determined by decomposing the second transformation matrix.

One or more embodiments according to the method in the second aspect, wherein: the one or more matrices comprises a second eigenvalue matrix (Ψ_c) and a second eigenvector matrix (Λ_c) ; the second eigenvalue matrix (Ψ_c) and the second eigenvector matrix (Λ_c) is determined by performing Eigen-decomposition on the second transformation matrix.

One or more embodiments according to the method in the second aspect, wherein: the reconstructing the DL channel based on the CSI, comprising: determining channel coefficient vectors corresponding to the K sub-channels by h_i+j=UΨ_cΛ_c ^jΨ_c ^-1c_i, wherein, h_i+j refers to channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, U refers to a common basis of the DL channel, i and j are integer, 1≤i+j≤K.

One or more embodiments according to the method in the second aspect, wherein the method further comprises: transmitting a compression information to the UE, wherein the compression information indicates one or more low-dimension matrices corresponding to the common basis of the DL channel, and each of the one or more low-dimension matrices is using for compressing channel coefficient vectors of one or more corresponding sub-channels in the K sub-channels.

One or more embodiments according to the method in the second aspect, wherein the compression information comprises the one or more low-dimension matrices, or inverse matrices or pseudoinverse matrices of the one or more low-dimension matrices.

One or more embodiments according to the method in the second aspect, wherein the DL channel comprises M sub-channels, M is an integer greater than or equal to K.

One or more embodiments according to the method in the second aspect, wherein the M sub-channels are determined by dividing the DL channel based on one or more of the following dimensions: frequency domain, time domain, or space domain.

One or more embodiments according to the method in the second aspect, wherein there are Q sub-channels between the nth sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels, Q and n are integer, 0≤Q≤M/K, 1≤n≤K-1.

One or more embodiments according to the method in the second aspect, wherein the method further comprises: transmitting a pattern information corresponding to the K sets of reference signals to the UE, wherein the pattern information indicates at least one of the following information of each reference signal in the K sets of reference signals: index of frequency intervals, index of time intervals, index of antennas or antenna ports of the BS, or index of antennas or antenna ports of the UE, values, antenna port, or transmit power.

One or more embodiments according to the method in the second aspect, wherein: a first set of reference signals in the K sets of reference signals is the same as a second set of reference signals in the K sets of reference signals; or a first set of reference signals in the K sets of reference signals is different from any other sets of reference signals in the K sets of reference signals.

A third aspect of the disclosure involves an apparatus, wherein the apparatus comprises a processor, wherein the processor is configured to execute one or more instructions stored in a memory, to enable the apparatus to implement any method the involved in the first aspect and the second aspect.

One or more embodiments of the apparatus in the third aspect, wherein the apparatus comprises the memory.

One or more embodiments of the apparatus in the third aspect, wherein the apparatus comprises a communication interface, configured to input and/or output information.

A fourth aspect of the disclosure involves an apparatus, wherein the apparatus comprises a function or unit to implement any method the involved in the first aspect and the second aspect.

A five aspect of the disclosure involves a communication system, comprising a transmitting apparatus and a receiving apparatus, wherein the transmitting apparatus performs the method according to any one of the first aspect, and the receiving apparatus performs the method according to any one of the second aspect.

A six aspect of the disclosure involves a computer readable storage medium, comprising one or more instructions, wherein when the instructions are run on a computer, the computer performs the method according to any one of the first aspect, or the method according to any one of the second aspect.

The detail explanation and beneficial effects of the third aspect to the sixth aspect may refer to the first aspect and the second aspect.

DESCRIPTION OF DRAWINGS

Fig. 1 illustrates a schematic of a communication system according to some embodiments of the disclosure.

Fig. 2 illustrates an example communication system according to some embodiments of the disclosure.

Fig. 3 illustrates examples of electric device and base station according to some embodiments of the disclosure.

Fig. 4 illustrates a basic module structure of a communication system according to some embodiments of the disclosure.

Fig. 5A illustrates an example ray tracing properties of CmWave according to some embodiments of the disclosure.

Fig. 5B illustrates an example ray tracing properties of MmWave according to some embodiments of the disclosure.

Fig. 6 illustrates an exemplary implementation of T-MIMO according to some embodiments of the disclosure.

Fig. 7 illustrates an example of determine reference signals according to some embodiments of the disclosure.

Fig. 8 illustrates a schematic of a dynamic mode decomposition method according to some embodiments of the disclosure.

Fig. 9A illustrates an example of periodic CSI-RS signaling diagram in an example communication system according to some embodiments of the disclosure.

Fig. 9B illustrates an example of aperiodic CSI-RS signaling diagram in an example communication system according to some embodiments of the disclosure.

Fig. 10A illustrates an example of periodic SRS signaling diagram in an example communication system according to some embodiments of the disclosure.

Fig. 10B illustrates an example of aperiodic SRS signaling diagram in an example communication system according to some embodiments of the disclosure.

Fig. 11 illustrates a scheme of antenna configuration in a T-MIMO communication system according to some embodiments of the disclosure.

Fig. 12 illustrates a finite state machine of a T-MIMO communication system according to some embodiments of the disclosure.

Fig. 13 illustrates a segmentation of T-MIMO channel according to some embodiments of the disclosure.

Fig. 14A-14D illustrates segmentations of T-MIMO channel based on different dimension according to some embodiments of the disclosure.

Fig. 15 illustrates a flow of determining the raw channel coefficient vectors according to some embodiments of the disclosure.

Fig. 16 illustrates a flow of determining the common basis U, the permutation matrix P and the compact representation of U (refers to Θ) according to some embodiments of the disclosure.

Fig. 17A illustrates a flow diagram of a communication method according to some embodiments of the disclosure.

Fig. 17B illustrates a method of periodic T-MIMO channel report signaling according to some embodiments of the disclosure.

Fig. 17C illustrates a method of aperiodic T-MIMO channel report signaling according to some embodiments of the disclosure.

Fig. 18A-18C illustrates the patterns of determining the transformation relationship between channel estimation of the reference sub-channel and other sub-channels of the downlink channel according to some embodiments of the disclosure.

Fig. 19A illustrates a flow diagram of a communication method according to some embodiments of the disclosure.

Fig. 19B illustrates a method of periodic T-MIMO channel report signaling according to some embodiments of the disclosure.

Fig. 19C illustrates a method of aperiodic T-MIMO channel report signaling according to some embodiments of the disclosure.

Fig. 20 illustrates a flow diagram of a communication method according to some embodiments of the disclosure.

Fig. 21 illustrates a schematic block diagram of an apparatus according to some embodiments of this disclosure.

DESCRIPTION OF EMBODIMENTS

The following describes technical solutions of the present application with reference to the accompanying drawings.

The technical solutions in embodiments of this application may be applied to multiple input multiple-output (MIMO) technology. And the technical solutions in embodiments of this application may be applied to various communication systems, such as a fifth generation (5G) wireless communication system, a new ratio (NR) wireless communication system, a long term evolution (LTE) system, an LTE frequency division duplex (FDD) system, an LTE time division duplex (TDD) system, a wireless local area network (WLAN) , a satellite communication system, or other evolving communication systems, such as a sixth generation (6G) wireless communication system.

For ease of understanding the embodiments of this application, communication systems are described below.

Referring to Fig. 1, as an illustrative example without limitation, a simplified schematic illustration of a communication system is provided. The communication system 100 comprises a radio access network 120. The radio access network 120 may be a next generation (e.g. sixth generation (6G) or later) radio access network, or a legacy (e.g. 5G, 4G, 3G or 2G) radio access network. One or more communication electric device (ED) 110a-120j (generically referred to as 110) may be interconnected to one another or connected to one or more network nodes (170a, 170b, generically referred to as 170) in the radio access network 120. A core network 130 may be a part of the communication system and may be dependent or independent of the radio access technology used in the communication system 100. Also, the communication system 100 comprises a public switched telephone network (PSTN) 140, the internet 150, and other networks 160.

Fig. 2 illustrates an example communication system 100. In general, the communication system 100 enables multiple wireless or wired elements to communicate data and other content. The purpose of the communication system 100 may be to provide content, such as voice, data, video, and/or text, via broadcast, multicast and unicast, etc. The communication system 100 may operate by sharing resources, such as carrier spectrum bandwidth, between its constituent elements. The communication system 100 may include a terrestrial communication system and/or a non-terrestrial communication system. The communication system 100 may provide a wide range of communication services and applications (such as earth monitoring, remote sensing, passive sensing and positioning, navigation and tracking, autonomous delivery and mobility, etc. ) . The communication system 100 may provide a high degree of availability and robustness through a joint operation of the terrestrial communication system and the non-terrestrial communication system. For example, integrating a non-terrestrial communication system (or components thereof) into a terrestrial communication system can result in what may be considered a heterogeneous network comprising multiple layers. Compared to conventional communication networks, the heterogeneous network may achieve better overall performance through efficient multi-link joint operation, more flexible functionality sharing, and faster physical layer link switching between terrestrial networks and non-terrestrial networks.

The terrestrial communication system and the non-terrestrial communication system could be considered sub-systems of the communication system. In the example shown, the communication system 100 includes electronic devices (ED) 110a-110d (generically referred to as ED 110) , radio access networks (RANs) 120a-120b, non-terrestrial communication network 120c, a core network 130, a public switched telephone network (PSTN) 140, the internet 150, and other networks 160. The RANs 120a-120b include respective base stations (BSs) 170a-170b, which may be generically referred to as terrestrial transmit and receive points (T-TRPs) 170a-170b. The non-terrestrial communication network 120c includes an access node 120c, which may be generically referred to as a non-terrestrial transmit and receive point (NT-TRP) 172.

Any ED 110 may be alternatively or additionally configured to interface, access, or communicate with any other T-TRP 170a-170b and NT-TRP 172, the internet 150, the core network 130, the PSTN 140, the other networks 160, or any combination of the preceding. In some examples, ED 110a may communicate an uplink and/or downlink transmission over an interface 190a with T-TRP 170a. In some examples, the EDs 110a, 110b and 110d may also communicate directly with one another via one or more sidelink air interfaces 190b. In some examples, ED 110d may communicate an uplink and/or downlink transmission over an interface 190c with NT-TRP 172.

The air interfaces 190a and 190b may use similar communication technology, such as any suitable radio access technology. For example, the communication system 100 may implement one or more channel access methods, such as code division multiple access (CDMA) , time division multiple access (TDMA) , frequency division multiple access (FDMA) , orthogonal FDMA (OFDMA) , or single-carrier FDMA (SC-FDMA) in the air interfaces 190a and 190b. The air interfaces 190a and 190b may utilize other higher dimension signal spaces, which may involve a combination of orthogonal and/or non-orthogonal dimensions.

The air interface 190c can enable communication between the ED 110d and one or multiple NT-TRPs 172 via a wireless link or simply a link. For some examples, the link is a dedicated connection for unicast transmission, a connection for broadcast transmission, or a connection between a group of EDs and one or multiple NT-TRPs for multicast transmission.

The RANs 120a and 120b are in communication with the core network 130 to provide the EDs 110a 110b, and 110c with various services such as voice, data, and other services. The RANs 120a and 120b and/or the core network 130 may be in direct or indirect communication with one or more other RANs (not shown) , which may or may not be directly served by core network 130, and may or may not employ the same radio access technology as RAN 120a, RAN 120b or both. The core network 130 may also serve as a gateway access between (i) the RANs 120a and 120b or EDs 110a 110b, and 110c or both, and (ii) other networks (such as the PSTN 140, the internet 150, and the other networks 160) . In addition, some or all of the EDs 110a 110b, and 110c may include functionality for communicating with different wireless networks over different wireless links using different wireless technologies and/or protocols. Instead of wireless communication (or in addition thereto) , the EDs 110a 110b, and 110c may communicate via wired communication channels to a service provider or switch (not shown) , and to the internet 150. PSTN 140 may include circuit switched telephone networks for providing plain old telephone service (POTS) . Internet 150 may include a network of computers and subnets (intranets) or both, and incorporate protocols, such as internet protocol (IP) , transmission control protocol (TCP) , user datagram protocol (UDP) . EDs 110a 110b, and 110c may be multimode devices capable of operation according to multiple radio access technologies, and incorporate multiple transceivers necessary to support such.

Fig. 3 illustrates another example of an ED 110 and a base station 170a, 170b and/or 170c. The ED 110 is used to connect persons, objects, machines, etc. The ED 110 may be widely used in various scenarios, for example, cellular communications, device-to-device (D2D) , vehicle to everything (V2X) , peer-to-peer (P2P) , machine-to-machine (M2M) , machine-type communications (MTC) , internet of things (IOT) , virtual reality (VR) , augmented reality (AR) , industrial control, self-driving, remote medical, smart grid, smart furniture, smart office, smart wearable, smart transportation, smart city, drones, robots, remote sensing, passive sensing, positioning, navigation and tracking, autonomous delivery and mobility, etc.

Each ED 110 represents any suitable end user device for wireless operation and may include such devices (or may be referred to) as a user equipment/device (UE) , a wireless transmit/receive unit (WTRU) , a mobile station, a fixed or mobile subscriber unit, a cellular telephone, a station (STA) , a machine type communication (MTC) device, a personal digital assistant (PDA) , a smartphone, a laptop, a computer, a tablet, a wireless sensor, a consumer electronics device, a smart book, a vehicle, a car, a truck, a bus, a train, or an IoT device, an industrial device, or apparatus (e.g. communication module, modem, or chip) in the forgoing devices, among other possibilities. Future generation EDs 110 may be referred to using other terms. The base station 170a and 170b is a T-TRP and will hereafter be referred to as T-TRP 170. Also shown in Fig. 3, a NT-TRP will hereafter be referred to as NT-TRP 172. Each ED 110 connected to T-TRP 170 and/or NT-TRP 172 can be dynamically or semi-statically turned-on (i.e., established, activated, or enabled) , turned-off (i.e., released, deactivated, or disabled) and/or configured in response to one of more of: connection availability and connection necessity.

In some embodiments, UE is also called user terminal, ED, terminal, transmit apparatus (when transmitting signal) , receive apparatus (when receiving signal) , etc.

The ED 110 includes a transmitter 201 and a receiver 203 coupled to one or more antennas 204. Only one antenna 204 is illustrated. One, some, or all of the antennas may alternatively be panels. The transmitter 201 and the receiver 203 may be integrated, e.g. as a transceiver. The transceiver is configured to modulate data or other content for transmission by at least one antenna 204 or network interface controller (NIC) . The transceiver is also configured to demodulate data or other content received by the at least one antenna 204. Each transceiver includes any suitable structure for generating signals for wireless or wired transmission and/or processing signals received wirelessly or by wire. Each antenna 204 includes any suitable structure for transmitting and/or receiving wireless or wired signals.

The ED 110 includes at least one memory 208. The memory 208 stores instructions and data used, generated, or collected by the ED 110. For example, the memory 208 could store software instructions or modules configured to implement some or all of the functionality and/or embodiments described herein and that are executed by the processing unit (s) 210. Each memory 208 includes any suitable volatile and/or non-volatile storage and retrieval device (s) . Any suitable type of memory may be used, such as random access memory (RAM) , read only memory (ROM) , hard disk, optical disc, subscriber identity module (SIM) card, memory stick, secure digital (SD) memory card, on-processor cache, and the like.

The ED 110 may further include one or more input/output devices (not shown) or interfaces (such as a wired interface to the internet 150 in Fig. 1) . The input/output devices permit interaction with a user or other devices in the network. Each input/output device includes any suitable structure for providing information to or receiving information from a user, such as a speaker, microphone, keypad, keyboard, display, or touch screen, including network interface communications.

The ED 110 further includes a processor 210 for performing operations including those related to preparing a transmission for uplink transmission to the NT-TRP 172 and/or T-TRP 170, those related to processing downlink transmissions received from the NT-TRP 172 and/or T-TRP 170, and those related to processing sidelink transmission to and from another ED 110. Processing operations related to preparing a transmission for uplink transmission may include operations such as encoding, modulating, transmit beamforming, and generating symbols for transmission. Processing operations related to processing downlink transmissions may include operations such as receive beamforming, demodulating and decoding received symbols. Depending upon the embodiment, a downlink transmission may be received by the receiver 203, possibly using receive beamforming, and the processor 210 may extract signaling from the downlink transmission (e.g. by detecting and/or decoding the signaling) . An example of signaling may be a reference signal transmitted by NT-TRP 172 and/or T-TRP 170. In some embodiments, the processor 276 implements the transmit beamforming and/or receive beamforming based on the indication of beam direction, e.g. beam angle information (BAI) , received from T-TRP 170. In some embodiments, the processor 210 may perform operations relating to network access (e.g. initial access) and/or downlink synchronization, such as operations relating to detecting a synchronization sequence, decoding and obtaining the system information, etc. In some embodiments, the processor 210 may perform channel estimation, e.g. using a reference signal received from the NT-TRP 172 and/or T-TRP 170.

In some embodiments, BS is also refers to gNB, STA, transmit apparatus, receiver apparatus, etc.

Although not illustrated, the processor 210 may form part of the transmitter 201 and/or receiver 203. Although not illustrated, the memory 208 may form part of the processor 210.

The processor 210, and the processing components of the transmitter 201 and receiver 203 may each be implemented by the same or different one or more processors that are configured to execute instructions stored in a memory (e.g. in memory 208) . Alternatively, some or all of the processor 210, and the processing components of the transmitter 201 and receiver 203 may be implemented using dedicated circuitry, such as a programmed field-programmable gate array (FPGA) , a graphical processing unit (GPU) , or an application-specific integrated circuit (ASIC) .

The T-TRP 170 may be known by other names in some implementations, such as a base station, a base transceiver station (BTS) , a radio base station, a network node, a network device, a device on the network side, a transmit/receive node, a node B, an evolved nodeB (eNodeB or eNB) , a home eNodeB, a next eneration nodeB (gNB) , a transmission point (TP) ) , a site controller, an access point (AP) , or a wireless router, a relay station, a remote radio head, a terrestrial node, a terrestrial network device, or a terrestrial base station, base band unit (BBU) , remote radio unit (RRU) , active antenna unit (AAU) , remote radio head (RRH) , central unit (CU) , distribute unit (DU) , positioning node, among other possibilities. The T-TRP 170 may be macro BSs, pico BSs, relay node, donor node, or the like, or combinations thereof. The T-TRP 170 may refer to the forging devices or apparatus (e.g. communication module, modem, or chip) in the forgoing devices.

In some embodiments, the parts of the T-TRP 170 may be distributed. For example, some of the modules of the T-TRP 170 may be located remote from the equipment housing the antennas of the T-TRP 170, and may be coupled to the equipment housing the antennas over a communication link (not shown) sometimes known as front haul, such as common public radio interface (CPRI) . Therefore, in some embodiments, the term T-TRP 170 may also refer to modules on the network side that perform processing operations, such as determining the location of the ED 110, resource allocation (scheduling) , message generation, and encoding/decoding, and that are not necessarily part of the equipment housing the antennas of the T-TRP 170. The modules may also be coupled to other T-TRPs. In some embodiments, the T-TRP 170 may actually be a plurality of T-TRPs that are operating together to serve the ED 110, e.g. through coordinated multipoint transmissions.

The T-TRP 170 includes at least one transmitter 252 and at least one receiver 254 coupled to one or more antennas 256. Only one antenna 256 is illustrated. One, some, or all of the antennas may alternatively be panels. The transmitter 252 and the receiver 254 may be integrated as a transceiver. The T-TRP 170 further includes a processor 260 for performing operations including those related to:preparing a transmission for downlink transmission to the ED 110, processing an uplink transmission received from the ED 110, preparing a transmission for backhaul transmission to NT-TRP 172, and processing a transmission received over backhaul from the NT-TRP 172. Processing operations related to preparing a transmission for downlink or backhaul transmission may include operations such as encoding, modulating, precoding (e.g. MIMO precoding) , transmit beamforming, and generating symbols for transmission. Processing operations related to processing received transmissions in the uplink or over backhaul may include operations such as receive beamforming, and demodulating and decoding received symbols. The processor 260 may also perform operations relating to network access (e.g. initial access) and/or downlink synchronization, such as generating the content of synchronization signal blocks (SSBs) , generating the system information, etc. In some embodiments, the processor 260 also generates the indication of beam direction, e.g. BAI, which may be scheduled for transmission by scheduler 253. The processor 260 performs other network-side processing operations described herein, such as determining the location of the ED 110, determining where to deploy NT-TRP 172, etc. In some embodiments, the processor 260 may generate signaling, e.g. to configure one or more parameters of the ED 110 and/or one or more parameters of the NT-TRP 172. Any signaling generated by the processor 260 is sent by the transmitter 252. Note that “signaling” , as used herein, may alternatively be called control signaling. Dynamic signaling may be transmitted in a control channel, e.g. a physical downlink control channel (PDCCH) , and static or semi-static higher layer signaling may be included in a packet transmitted in a data channel, e.g. in a physical downlink shared channel (PDSCH) .

A scheduler 253 may be coupled to the processor 260. The scheduler 253 may be included within or operated separately from the T-TRP 170, which may schedule uplink, downlink, and/or backhaul transmissions, including issuing scheduling grants and/or configuring scheduling-free ( “configured grant” ) resources. The T-TRP 170 further includes a memory 258 for storing information and data. The memory 258 stores instructions and data used, generated, or collected by the T-TRP 170. For example, the memory 258 could store software instructions or modules configured to implement some or all of the functionality and/or embodiments described herein and that are executed by the processor 260.

Although not illustrated, the processor 260 may form part of the transmitter 252 and/or receiver 254. Also, although not illustrated, the processor 260 may implement the scheduler 253. Although not illustrated, the memory 258 may form part of the processor 260.

The processor 260, the scheduler 253, and the processing components of the transmitter 252 and receiver 254 may each be implemented by the same or different one or more processors that are configured to execute instructions stored in a memory, e.g. in memory 258. Alternatively, some or all of the processor 260, the scheduler 253, and the processing components of the transmitter 252 and receiver 254 may be implemented using dedicated circuitry, such as a FPGA, a GPU, or an ASIC.

Although the NT-TRP 172 is illustrated as a drone only as an example, the NT-TRP 172 may be implemented in any suitable non-terrestrial form. Also, the NT-TRP 172 may be known by other names in some implementations, such as a non-terrestrial node, a non-terrestrial network device, or a non-terrestrial base station. The NT-TRP 172 includes a transmitter 272 and a receiver 274 coupled to one or more antennas 280. Only one antenna 280 is illustrated. One, some, or all of the antennas may alternatively be panels. The transmitter 272 and the receiver 274 may be integrated as a transceiver. The NT-TRP 172 further includes a processor 276 for performing operations including those related to: preparing a transmission for downlink transmission to the ED 110, processing an uplink transmission received from the ED 110, preparing a transmission for backhaul transmission to T-TRP 170, and processing a transmission received over backhaul from the T-TRP 170. Processing operations related to preparing a transmission for downlink or backhaul transmission may include operations such as encoding, modulating, precoding (e.g. MIMO precoding) , transmit beamforming, and generating symbols for transmission. Processing operations related to processing received transmissions in the uplink or over backhaul may include operations such as receive beamforming, and demodulating and decoding received symbols. In some embodiments, the processor 276 implements the transmit beamforming and/or receive beamforming based on beam direction information (e.g. BAI) received from T-TRP 170. In some embodiments, the processor 276 may generate signaling, e.g. to configure one or more parameters of the ED 110. In some embodiments, the NT-TRP 172 implements physical layer processing, but does not implement higher layer functions such as functions at the medium access control (MAC) or radio link control (RLC) layer. As this is only an example, more generally, the NT-TRP 172 may implement higher layer functions in addition to physical layer processing.

The NT-TRP 172 further includes a memory 278 for storing information and data. Although not illustrated, the processor 276 may form part of the transmitter 272 and/or receiver 274. Although not illustrated, the memory 278 may form part of the processor 276.

The processor 276 and the processing components of the transmitter 272 and receiver 274 may each be implemented by the same or different one or more processors that are configured to execute instructions stored in a memory, e.g. in memory 278. Alternatively, some or all of the processor 276 and the processing components of the transmitter 272 and receiver 274 may be implemented using dedicated circuitry, such as a programmed FPGA, a GPU, or an ASIC. In some embodiments, the NT-TRP 172 may actually be a plurality of NT-TRPs that are operating together to serve the ED 110, e.g. through coordinated multipoint transmissions.

The T-TRP 170, the NT-TRP 172, and/or the ED 110 may include other components, but these have been omitted for the sake of clarity.

One or more steps of the embodiment methods provided herein may be performed by corresponding units or modules, according to Fig. 4. Fig. 4 illustrates units or modules in a device, such as in ED 110, in T-TRP 170, or in NT-TRP 172. For example, a signal may be transmitted by a transmitting unit or a transmitting module. For example, a signal may be transmitted by a transmitting unit or a transmitting module. A signal may be received by a receiving unit or a receiving module. A signal may be processed by a processing unit or a processing module. Other steps may be performed by an artificial intelligence (AI) or machine learning (ML) module. The respective units or modules may be implemented using hardware, one or more components or devices that execute software, or a combination thereof. For instance, one or more of the units or modules may be an integrated circuit, such as a programmed FPGA, a GPU, or an ASIC. It will be appreciated that where the modules are implemented using software for execution by a processor for example, they may be retrieved by a processor, in whole or part as needed, individually or together for processing, in single or multiple instances, and that the modules themselves may include instructions for further deployment and instantiation.

Additional details regarding the EDs 110, T-TRP 170, and NT-TRP 172 are known to those of skill in the art. As such, these details are omitted here.

MIMO technology allows an antenna array of multiple antennas to perform signal transmissions and receptions to meet high transmission rate requirement. The above ED110 and T-TRP 170, and/or NT-TRP use MIMO to communicate over the wireless resource blocks. MIMO utilizes multiple antennas at the transmit apparatus and/or receive apparatus to transmit wireless resource blocks over parallel wireless signals. MIMO may beamform parallel wireless signals for reliable multipath transmission of a wireless resource block. MIMO may bond parallel wireless signals that transport different data to increase the data rate of the wireless resource block.

In recent years, a MIMO (large-scale MIMO) wireless communication system with the above T-TRP 170, and/or NT-TRP 172 configured with a large number of antennas has gained wide attentions from the academia and the industry. In the large-scale MIMO system, the T-TRP 170, and/or NT-TRP 172 is generally configured with more than ten antenna units (such as 128 or 256) , and serves for dozens of the ED 110 (such as 40) in the meanwhile. A large number of antenna units of the T-TRP 170, and NT-TRP 172 can greatly increase the degree of spatial freedom of wireless communication, greatly improve the transmission rate, spectrum efficiency and power efficiency, and eliminate the interference between cells to a large extent. The increase of the number of antennas makes each antenna unit be made in a smaller size with a lower cost. Using the degree of spatial freedom provided by the large-scale antenna units, the T-TRP 170, and NT-TRP 172 of each cell can communicate with many ED 110 in the cell on the same time-frequency resource at the same time, thus greatly increasing the spectrum efficiency. A large number of antenna units of the T-TRP 170, and/or NT-TRP 172 also enable each user to have better spatial directivity for uplink and downlink transmission, so that the transmitting power of the T-TRP 170, and/or NT-TRP 172 and an ED 110 is obviously reduced, and the power efficiency is greatly increased. When the antenna number of the T-TRP 170, and/or NT-TRP 172 is sufficiently large, random channels between each ED 110 and the T-TRP 170, and/or NT-TRP 172 can approach to be orthogonal, and the interference between the cell and the users and the effect of noises can be eliminated. The plurality of advantages described above enable the large-scale MIMO to have a magnificent application prospect.

A MIMO system may include a receiver connected to a receive (Rx) antenna, a transmitter connected to transmit (Tx) antenna, and a signal processor connected to the transmitter and the receiver. Each of the Rx antenna and the Tx antenna may include a plurality of antennas. For instance, the Rx antenna may have an ULA antenna array in which the plurality of antennas are arranged in line at even intervals. When a radio frequency (RF) signal is transmitted through the Tx antenna, the Rx antenna may receive a signal reflected and returned from a forward target.

A non-exhaustive list of possible unit or possible configurable parameters or in some embodiments of a MIMO system include:

Panel: unit of antenna group, or antenna array, or antenna sub-array which can control its Tx or Rx beam independently.

Beam: A beam is formed by performing amplitude and/or phase weighting on data transmitted or received by at least one antenna port, or may be formed by using another method, for example, adjusting a related parameter of an antenna unit. The beam may include a Tx beam and/or a Rx beam. The transmit beam indicates distribution of signal strength formed in different directions in space after a signal is transmitted through an antenna. The receive beam indicates distribution of signal strength that is of a wireless signal received from an antenna and that is in different directions in space. The beam information may be a beam identifier, or antenna port (s) identifier, or channel state information reference signal (CSI-RS) resource identifier, or SSB resource identifier, or sounding reference signals (SRS) resource identifier, or other reference signal resource identifier

MIMO technology represents an advanced wireless communication technique employing multiple antennas at both the transmitter and receiver ends, thereby enhancing the overall efficiency and performance of the radio link. The acronym MIMO stands for multiple-input multiple-output, signifying its capacity to capitalize on the multipath propagation of radio waves, facilitating the simultaneous transmission and reception of multiple data signals. Notably, MIMO technology finds extensive application in contemporary wireless standards such as wireless fidelity (Wi-Fi) , worldwide interoperability for microwave access (WiMAX) , long term evolution (LTE) , and 5G, underscoring its vital role in enabling high-speed and reliable wireless communication.

5G-NR massive MIMO technology represents a groundbreaking advancement in the realm of wireless communication, characterized by its integration of an extensive array of antennas at both the transmitting and receiving ends. This innovative approach enables the simultaneous transmission and reception of multiple data streams, significantly enhancing the overall data throughput and network capacity. By capitalizing on the multipath propagation of radio waves, 5G-NR massive MIMO technology ensures efficient and reliable data transfer, fostering seamless connectivity and improved spectral efficiency. Leveraging this technology, 5G-NR facilitates the deployment of high-speed and low-latency communication networks, catering to the burgeoning demand for enhanced mobile broadband services and supporting diverse applications such as virtual reality, augmented reality, and the internet of things (IoT) .

However, certain limitations accompany 5G NR massive MIMO technology. Factors such as an increase in system complexity, directly correlated to the escalated number of antennas in both the 5G base station (e.g. gNB) and UE, may result in heightened energy consumption and infrastructure costs, potentially posing challenges to its widespread implementation. Additionally, the deployment of massive MIMO systems may encounter obstacles related to interference management and spatial constraints, necessitating careful planning and optimization to mitigate potential performance degradation. Despite these challenges, the numerous benefits offered by 5G NR massive MIMO technology continue to position it as a promising solution for next-generation wireless communication networks.

One fundamental characteristic of 6G technology pertains to the significant escalation in the count of antenna ports within the base station or gNB. This notable enhancement facilitates a heightened degree of beamforming and spatial multiplexing, effectively bolstering the spectral efficiency and overall network capacity by separating space with a finer resolution. For instance, the integration of a substantial number of antenna ports in the antenna panel necessitates an operational frequency range for 6G spanning from 10GHz to 14GHz. Notably, this frequency range corresponds to a wavelength of approximately one centimeter, commonly referred to as the centimeter (cm) wave (cmWave) band. The utilization of the cmWave band in 6G technology offers reduced attenuation and diminished interference compared to the millimeter (mm) wave (mmWave) band utilized by its predecessor, 5G, thereby fostering improved data transmission capabilities and robust network performance.

The utilization of the 10GHz to 14GHz frequency band within the MIMO system of 6G technology offers a host of notable advantages. Firstly, the adoption of this frequency band, corresponding to the CmWave range, enables the implementation of a larger number of antenna ports within the base station, facilitating advanced beamforming and spatial multiplexing techniques. This, in turn, leads to enhanced spectral efficiency, allowing for the seamless transmission of a higher volume of data with increased reliability and reduced signal interference. Moreover, the characteristics of the CmWave band, characterized by lower attenuation and reduced susceptibility to environmental obstacles, contribute to the establishment of robust and reliable wireless communication networks. The reduced signal attenuation ensures improved signal propagation over extended distances, thereby fostering the development of more efficient and resilient communication infrastructures within the 6G MIMO system.

For example, as shown in Fig. 5A and Fig. 5B, the ray tracing properties of cmWave and mmWave is different. Obstacles can reflect cmWave emitted by a transmit apparatus, allowing the receive apparatus to get the information even if there is no direct line of sight that is, non-light-of-sight, (NLoS) . However, mmWave require a clear path between the transmit apparatus and the receive apparatus, that is, light-of-sight (LOS) to transmit the information effectively.

Technical problem and challenge

As shown in Fig. 6:

An exemplary implementation of Tera-bit-per-second MIMO (T-MIMO) within the 6G network, characterized by a base station equipped with 1024 antenna ports and user terminals featuring 16 antenna ports across a 500MHz bandwidth, introduces several noteworthy challenges. Primarily, from a performance perspective, the increased complexity associated with managing a substantial number of antenna ports and bandwidths necessitates meticulous system optimization to mitigate potential signal interference and ensure seamless data transmission. Additionally, the significant costs involved in the manufacturing, deployment, and maintenance of a sophisticated network architecture with a high volume of antenna ports and wide bandwidth pose a considerable economic challenge, requiring careful cost-benefit analysis to ensure the viability and sustainability of the 6G MIMO infrastructure.

Furthermore, the heightened complexity of the 6G T-MIMO system, attributed to the management and coordination of a large number of antenna ports, may result in increased operational intricacies, necessitating advanced signal processing and control mechanisms to facilitate efficient data handling and minimize performance bottlenecks. This increased overhead, in terms of both computational resources and energy consumption, demands the implementation of robust overhead management strategies to optimize the overall system performance. Moreover, the augmented air overhead, including the allocation of resources for pilots, control messages, and channel feedback, presents a significant challenge in the effective utilization of the available bandwidth. The need for efficient management and allocation of these additional resources requires the implementation of sophisticated air interface protocols and communication strategies, ensuring the optimized utilization of the spectrum and minimizing potential signal degradation. Addressing these challenges necessitates the development of comprehensive and adaptive approaches to enhance the overall efficiency and reliability of the 6G T-MIMO system.

In some embodiments, the pilot may also refer to reference signal, pilots signal, etc.

In some embodiments, the channel feedback may also refer to channel state information (CSI) .

Ray-Tracing-based channels

In the realm of wireless communication, a MIMO channel represents a sophisticated radio channel architecture that leverages the use of multiple antenna ports at both the transmit apparatus and receive apparatus to enhance the efficiency and dependability of data transmission. A radio channel, serving as the conduit for radio waves between a transmit apparatus’s antenna port and a receive apparatus’s antenna port, manifests at specific spatial locations through intricate interactions with the environment. These interactions include diverse phenomena such as reflection, diffraction, scattering, and fading, ultimately influencing the characteristics of the radio channel. The intricate dynamics of a radio channel at a given spatial location are contingent upon various factors, including the frequency, bandwidth, polarization, phase, and power of the radio waves, as well as the distance, angle, and geometric configuration of the transmit apparatus’s and receive apparatus’s antennas, alongside the unique attributes of the surrounding structures and mediums. Hence, the dynamics of a MIMO radio channel at a specific spatial point arise from the intricate interplay between the wireless communication environment and its encompassing elements. This encompasses both stationary constituents, such as buildings, terrain contours, and surface materials, as well as dynamic variables like weather conditions, moving trucks, and interference due to surrounding random events. To explicate, analyze, and simulate the characteristics of a MIMO radio channel at a given spatial location, the technique of ray tracing proves instrumental. Ray tracing simulates the propagation and interaction of electromagnetic waves within the environment. In this context, ray tracing enables the modeling of signal behaviors, encompassing reflection, refraction, scattering, and diffraction from various objects and surfaces. The manifestation of a MIMO radio channel stems from two distinct forms of ray tracing: random rays, emitted unpredictably from the transmit apparatus and receive apparatus, and deterministic rays, contingent upon the geometric attributes of the stationary environment.

The document 3rd generation partnership project (3GPP) 38.901 serves as a comprehensive guide outlining the specifications for channel modeling within the frequency range of 0.5 to 100 GHz. It delineates various scenarios, environments, propagation conditions, antenna configurations, and associated parameters essential for accurate channel modeling. Furthermore, the document delineates the systematic approach to creating clusters of radio rays through the amalgamation of stochastic channel modeling and ray tracing techniques. Notably, while the 3GPP 38.901 document primarily emphasizes the stochastic channel model, it acknowledges the optional utilization of the ray tracing model. The stochastic model relies on statistical parameters encompassing path loss, delay spread, angle spread, and more to characterize the propagation environment. In contrast, the ray tracing model operates based on the physical geometry of the surroundings, encompassing structures such as buildings, walls, and vegetation. Although the ray tracing model offers a more comprehensive representation of channel intricacies, including details and variations, it imposes greater computational demands and necessitates extensive input data. Conversely, the stochastic model faces certain limitations, notably its inability to account for blockage or shadowing effects induced by obstacles, thereby impacting signal quality and coverage. Moreover, the stochastic model falls short in capturing the spatial correlation of the channel, which holds critical significance for beamforming and spatial multiplexing (by precoding matrix) in massive MIMO techniques. Consequently, the ray tracing model emerges as a more favorable approach for 6G channel modeling, particularly in the context of high-frequency bands, such as those exceeding 6GHz, and dense urban scenarios. The analysis underscores the nuanced advantages and trade-offs associated with leveraging random clusters in the context of wireless channel modeling, further highlighting the significance of an integrated approach to accurately represent the deterministic intricacies of the wireless communication environment.

Dimension disaster of 6G T-MIMO

Upon achieving a significant scale, wherein 6G T-MIMO integrates an extensive array of more than one thousand antenna ports operating across a bandwidth of up to 500MHz, it facilitates the establishment of a substantial antenna array capable of concurrent transmission and reception of multiple data streams. While serving as a pivotal technology within the domain of 6G wireless communication, enhancing network capacity, coverage, and reliability, it simultaneously introduces a multitude of challenges. Notably, the adoption of such advanced T-MIMO configurations presents substantial overhead implications, signaling complexities, reference signal management intricacies, and heightened storage demands. Moreover, the amplified scale of the antenna array contributes to escalated system complexity, leading to potential latency concerns within the communication framework.

The operational overhead associated with the implementation of 6G T-MIMO primarily emanates from the essential requisites for meticulous CSI estimation across both UEs and base stations. This crucial process necessitates the transmission and reception of a considerable volume of pilot signals, thereby leading to substantial consumption of both bandwidth and power resources. Furthermore, the comprehensive feedback loop for channel estimation adds to the existing signaling overhead, emphasizing the intricate demands of the operational framework. Notably, the coordination of beamforming and precoding functionalities between the base station and UEs contributes to the amplified signaling complexities, underscoring the intricacies of signal processing within the system. The integration of reference signals to synchronize signal timing and frequency across the array of antennas further intensifies the operational overhead, reinforcing the multifaceted challenges inherent in the deployment of the advanced 6G T-MIMO system architecture.

The integration of a 6G T-MIMO framework incorporating an extensive array of one thousand antennas over a bandwidth of hundreds of megahertz (MHz) entails significant storage demands, operational complexities, and latency considerations. The requisite storage capacity is primarily directed towards accommodating the comprehensive storage requirements for precise CSI, as well as the intricate beamforming and precoding matrices for every user and base station within the system architecture. This necessitates a storage allocation of approximately terabytes (TB) -order, emphasizing the substantial storage demands associated with the intricate data sets and matrix computations integral to the operational framework.

A comprehensive strategic approach aimed at addressing these storage, complexity, and latency concerns is imperative to ensure the seamless integration and optimal performance of the 6G T-MIMO architecture within the broader 6G wireless communication landscape. Leveraging advanced storage solutions, optimized computational algorithms, and latency reduction strategies are pivotal in ensuring the efficient and streamlined operation of the intricate 6G T-MIMO system architecture within the evolving realm of wireless communication systems.

Imperfect uplink/downlink (UL/DL) reciprocity

A notable aspect of the 5G NR technology entails the utilization of UL/DL reciprocity to minimize overhead through channel sounding. This mechanism enables the base station to estimate the downlink channel state information by utilizing the uplink pilot-based transmission, thereby facilitating the concurrent use of identical beamforming vectors or precoding matrix for both uplink and downlink operations. This approach optimizes spectrum and resource utilization, particularly within TDD systems sharing the same frequency band for uplink and downlink communication.

To achieve UL/DL reciprocity, 5G NR integrates a versatile slot-based framework accommodating distinct subcarrier spacings, symbol durations, and cyclic prefix lengths tailored to varying numerologies. This adaptive architecture facilitates seamless alignment with diverse channel conditions and latency constraints. Additionally, 5G NR introduces innovative reference signals, including the SRS and the phase-tracking reference signal (PTRS) , pivotal in supporting reciprocity-based beamforming and phase compensation strategies.

The SRS serves as a periodic uplink transmission conveying critical channel insights from the user equipment to the base station. Leveraging this information, the base station executes reciprocity-based beamforming during downlink data transmission, such as physical downlink shared channel multi-user multiple-input multiple-output (PDSCH MU-MIMO) . On the other hand, the PTRS operates as an embedded reference signal within data symbols, actively monitoring and compensating for phase fluctuations attributed to hardware impairments and Doppler shifts.

Key 3GPP standard references for UL/DL reciprocity and sounding channel encompass:

technical specification (TS) 38.211: physical channels and modulation

TS 38.212: multiplexing and channel coding

TS 38.213: physical layer procedures for control

TS 38.214: physical layer procedures for data

While UL/DL reciprocity offers intrinsic benefits in TDD systems, ensuring alignment between uplink and downlink channels, it encounters performance degradation due to mismatches stemming from diverse channel conditions, including noise, interference, and fading. The presence of hardware differentials between base stations and UEs, alongside environmental factors such as temperature and humidity, contributes to these discrepancies. Notably, UL/DL reciprocity cannot be applied to FDD systems operating with distinct uplink and downlink frequency bands, given the non-reciprocal nature of the channels. FDD systems present distinct advantages, including lower latency, heightened spectral efficiency, and enhanced compatibility with legacy networks. However, they confront challenges concerning the acquisition of accurate downlink channel state information at the base station, critical for optimal massive MIMO functionality.

6G T-MIMO needs dimension reduction

The concept of massive dimension space pertains to a mathematical representation characterized by an extensive number of dimensions, ranging from infinite to uncountable. In contrast, dimensional reduction techniques serve to alleviate the complexities associated with such expansive spaces by projecting them onto lower-dimensional subspaces, often finite or countable in nature. These techniques find utility in diverse applications, spanning data analysis, equation resolution, and structural visualization within the realms of science and engineering. Nonetheless, dimensional reduction strategies are not without trade-offs, with potential compromises encompassing information loss, distortions in distances, and the potential introduction of noise. Consequently, the judicious selection of an appropriate dimensional reduction method and criterion assumes critical significance, underscoring its pivotal role in various scientific and engineering domains.

For instance, envision a bookshelf housing an infinite array of books, each comprising an infinite number of pages. Such an arrangement epitomizes a massive dimension space, wherein each book or page represents a distinct dimension. By applying dimensional reduction to this scenario, one could select a limited subset of relevant books or pages tailored to a specific purpose, such as studying a particular topic or discerning underlying patterns. This selective approach yields a lower-dimensional subspace, where each book or page remains a dimension, albeit in a reduced quantity. However, it is imperative to acknowledge the potential compromises associated with dimensional reduction, including the aforementioned loss of information, distortions in distances, and the introduction of noise, thereby emphasizing the nuanced decision-making process involved in choosing suitable dimensional reduction methods and criteria across diverse scientific and engineering disciplines.

Several notable methods employed for dimensional reduction include:

Principal component analysis (PCA) : This technique facilitates a linear mapping of the data to a lower-dimensional space, optimizing the variance of the data within the low-dimensional representation.

Kernel PCA: Employing the kernel trick, this approach enables nonlinear PCA, enhancing its capability to capture intricate and complex data patterns.

Graph-based kernel PCA: This method amalgamates graph theory and kernel techniques, facilitating PCA on datasets situated on nonlinear manifolds.

Singular value decomposition (SVD) : SVD serves to factorize a matrix into three distinct matrices, thereby enabling noise reduction, data compression, and the extraction of latent factors from the data.

Some of the methods above derive from eigen decomposition, which is the factorization of a matrix into its eigenvalues and eigenvectors. Eigenvalues are the scalars that satisfy the equation Aν = λν, where A is the matrix, ν is the eigenvector, and λ is the eigenvalue. Eigenvectors are the vectors that are only scaled by A, not rotated or distorted. By finding the eigenvalues and eigenvectors of a matrix, we can decompose it into a diagonal matrix of eigenvalues and a matrix of eigenvectors. This allows us to identify the principal components of the data, which are the directions of maximum variance. By projecting the data onto a subset of principal components, we can reduce its dimensionality while retaining most of its information.

For example, suppose we have a dataset of n points in d dimensions, represented by an n×d matrix X. We can compute the sample covariance matrix of X as S = (1/n) X^HX, which is a d×d symmetric matrix. Then, we can find the eigenvalues and eigenvectors of S using a numerical method such as power iteration or QR algorithm. The eigenvalues of S are non-negative and represent the variance of the data along each principal component. The eigenvectors of S are orthogonal and form a basis for the data space. We can sort the eigenvalues in descending order and select the k largest ones, along with their corresponding eigenvectors. These k eigenvalues and eigenvectors form the k principal components of the data. We can then project X onto these k principal components by multiplying X with a d×k matrix Q, whose columns are the k eigenvectors. The result is an n×k matrix Y, which is a lower-dimensional representation of X that captures most of its variance. The d×k matrix Q, whose columns are the k eigenvectors, contains the most important (principal) commonality between the dataset of the n points.

In some embodiments, QR algorithm refers to orthogonal triangular decomposition algorithm.

Deep learning-based dimensional reduction encounters significant challenges when applied to the extensive dimensional signal space characteristic of T-MIMO communication system (e.g. 6G T-MIMO) . As the system involves a vast number of antennas, such as 1024 at the base station and 16 at the terminals, and operates across a bandwidth of 500MHz, the deep learning models grapple with the tremendous complexity and computational requirements associated with processing such large-scale data. This complexity is compounded by the need for extensive training data to effectively capture the intricacies of the high-dimensional signal space. Furthermore, the high computational demands strain the hardware resources, leading to increased latency and processing times. Despite its capacity to handle complex patterns, the deep learning approach faces substantial limitations in terms of scalability and interpretability, hindering its efficacy in efficiently reducing the dimensions of the expansive T-MIMO communication system (e.g. 6G T-MIMO) signal space.

One of the challenges of 6G communication is to efficiently estimate the massive CSI in both uplink (UL) and downlink (DL) scenarios. A common approach is to use uniform reference signal (pilots) patterns, where the transmit apparatus sends a fixed number of pilot symbols in each coherence interval in each coherence frequency interval (for example, RB in 5G NR) . However, this method may not be optimal for 6G T-MIMO, as it requires a large amount of pilot overhead and may not capture the spatial diversity of the channel.

We proposed a novel method for estimating the T-MIMO channel using a sparse and non-uniform pilot pattern, instead of the conventional uniform pattern, for both UL and DL. Our method exploits the fact that the MIMO radio channels can be modeled by ray tracing, which implies some similarity among the channels at different spatial locations. This similarity allows us to reduce the dimensionality of the problem and optimize the pilot placement scheme for T-MIMO channel estimation or acquisition. However, we also acknowledge that there are some random factors in the ray propagation, such as the formation of random ray clusters. Therefore, we still need a small number (sparse) of pilots to capture these random effects in the T-MIMO channel.

Reference signal (pilot) placement is a method for selecting optimal locations for reference signals in high-dimensional MIMO channel. The goal is to use a small number of reference signals to capture the most relevant information from the MIMO channel and reconstruct the full state using a low-dimensional representation. QR-based reference-signal placement relies on two main steps: feature extraction and reference-signal selection.

Feature extraction is the process of finding a suitable basis for representing the MIMO channel state using a set of features that capture the dominant patterns or modes of variation in the data. One common way to do this is to apply SVD or proper orthogonal decomposition (POD) to find the eigen vectors of the channel data matrix A contributed by all UEs. The resulting eigen vectors correspond to the principal directions of variation of matrix A and can be ordered by their corresponding singular values or eigen values, which measure the amount of variance explained by each vector. By truncating the eigen vector matrix to retain only the most significant vectors, one can obtain a low-rank approximation of the data matrix A, that preserves most of the information and is called as common basis U.

Reference signal selection is the process of choosing a subset of candidate reference signal locations that maximize the information content or variance captured by the reference signals for all UEs.

In some embodiments, as shown in Fig. 7, the reference signal may be determined by a QR decomposition method (also refer to scheme 1) with column pivoting or pseudo-random placement strategy (also refer to scheme 2) .

As shown in Fig. 7:

One efficient way to do this is to use the QR decomposition with column pivoting, which is a matrix factorization technique that produces an orthogonal matrix Q and an upper triangular matrix R such that U^*= QRΠ^T, where U is the low-rank approximation or common basis of the data matrix in the feature extraction, Π is a permutation matrix, specifically a column-pivoting matrix of U^*. The column pivoting algorithm selects the columns of U^*that have the largest L2 norms and moves them to the left of U^*. Let P be a row-pivoting matrix of U constructed by selecting the first r rows of Π^T, where r is the rank of U. Let θ be a row-pivoted matrix of U, defined as θ=PU. The permutation matrix P can be used to identify the reference-signal locations that correspond to the selected rows of U. The QR-based reference-signal selection algorithm can be applied to the eigen vector matrix obtained from feature extraction to find the optimal reference-signal locations for reconstructing the channel state using a low-dimensional representation.

QR-based reference-signal placement has several advantages over other methods, such as random sampling or compressed sensing. It is data-driven, meaning that it adapts to the specific patterns and dynamics of the MIMO channel. It is computationally efficient, requiring only two ubiquitous matrix operations: SVD and QR decomposition. It is robust to noise and outliers, as it selects reference-signals based on their variance rather than their magnitude. It also provides a natural way to determine the number of reference-signals needed, as it depends on the rank of the data matrix or the eigen vector matrix.

Apparently, the number of reference-signals is determined by the size of matrix U, which in turn is determined by the number of the most significant vectors after truncated SVD on the collected data set. In general, if there is higher similarity among the MIMO data samples (for SVD) , the smaller number of the most significant vectors after truncated SVD, and the less reference-signals are needed (sparser) .

Moreover, in a DL MIMO channel, the base station (or gNB) has a large number of antenna ports compared to the UE. This creates an over-determined problem, where the number of equations is greater than the number of unknowns. In such a scenario, the gNB can exploit the spatial diversity and multiplexing gains of the MIMO channel to improve the data rate and reliability of the DL transmission. The gNB can also use beamforming techniques to focus the signal energy towards the desired UE and reduce the interference to other users. In 6G T-MIMO, the numbers of antenna ports between gNB is expected to be much larger than in 5G NR, reaching hundreds or thousands of antennas per gNB. This will enable ultra-high data rates, ultra-low latency, and ultra-reliable communication for 6G applications. Thus, the permuted matrix θ=PU is no longer a square matrix representing a determined problem but a rectangular matrix representing an over-determined one. Then, we can benefit from the spatial diversity and multiplexing gains of the MIMO channel by using a pseudo-random permutation matrix P rather than a QR-generated one in DL MIMO channel.

In some embodiments, the matrix U is called as common basis and the matrix Θ is called as compact matrix of the common basis (matrix) U in terms of the reference signal placement matrix P.

In some embodiments, pseudo-random placement strategy (scheme 2) means the BS may randomly select r’ rows of U to construct the permutation matrix P.

In some embodiments, a pseudo-random permutation matrix P whose size matches with the similarity among the channel data samples collected within a targeted environment can be achieved.

For example, instead of sending a sequence of reference signal positions to indicate the reference signal placement matrix P, the BS can simply sends a random seed, a random generator function, and number of the reference signals. After receiving them, a UE can compute out the reference signal placement matrix P. This would significantly reduce the air overhead, especially in a massive dimensional system like 6G-T-MIMO.

Dynamic mode decomposition (DMD)

In this disclosure, we will introduce a new technology called as DMD.

DMD is a data-driven technique for extracting spatiotemporal coherent structures from high-dimensional data. It can be used to analyze complex nonlinear dynamical systems, such as fluid flows, combustion, neuroscience, and epidemiology. DMD is based on the idea of decomposing a data matrix into a low-rank approximation that captures the dominant modes and frequencies of the underlying dynamics. DMD can also provide a linear model that approximates the nonlinear evolution of the system, which can be used for prediction, control, and optimization.

In some embodiments, extracting spatiotemporal coherent structures from high-dimensional data are also called as dominant modes and frequencies.

DMD is a technique that can help us understand complex systems that change over time and space, such as the flow of air around a wing, the spread of a disease, or the activity of neurons in the brain. DMD works by taking snapshots of the system at different times and arranging them into a matrix. Then, it finds a simpler matrix that is close to the original one, but has fewer rows and columns. This simpler matrix contains the main patterns and rhythms of the system, which are called modes and frequencies. DMD also gives us a formula that tells us how these modes and frequencies change over time, which can help us predict, control, or optimize the system.

One example of using DMD is to analyze the wake behind a cylinder in a fluid flow. This is a classic problem in fluid mechanics, where the flow becomes unstable and forms a periodic pattern of vortices, called a von Karman vortex street. By applying DMD to the snapshots of the flow field, we can identify the modes that correspond to the vortices and their frequencies. We can also use the formula given by DMD to predict how the flow will evolve in the future, or how it will change if we modify the cylinder shape or size.

DMD is closely related to the Koopman operator, which is an infinite-dimensional linear operator that acts on the space of observables of a nonlinear system. The Koopman operator can capture the global behavior of the system by mapping each observable to its future value at a given time. DMD approximates the Koopman operator by projecting the observables onto a finite-dimensional subspace spanned by snapshots of the system state. The resulting matrix can be diagonalized to obtain the DMD modes, which are eigenfunctions of the Koopman operator, and the DMD eigenvalues, which are the corresponding eigenvalues. The DMD modes and eigenvalues can reveal the dominant frequencies, growth rates, spatial patterns and nonlinear interactions of the system.

There may be different types of DMD algorithms. In some embodiments, as shown in Fig. 8, the basic steps of a generic DMD algorithm are:

Data snapshot matrix construction: create two data snapshot matrices, X and X', by assembling the data snapshots at subsequent time steps. Denote G a linear operator that satisfies X'=GX or equivalentlywhereis the Moore-Penrose pseudoinverse of X.

Perform SVD on X: decompose X = UΣV^H, where U and V are orthogonal matrices and Σ is a diagonal matrix of singular values.

Low-Rank truncation: reduce the rank of the matrices U, Σ, and V to retain the most significant modes while discarding negligible components.

DMD modes computation:

4.1. Compute the projection of G on to the column space of U^H as

4.2. Compute the eigendecomposition ofasto obtain theeigenvalues Λ ofand G share the same eigenvalues Λ.

4.3. Compute the eigenvectors of G asThe eigendecomposition of G is then given by GΨ=ΨΛ.

4.4. Compute the linear operator G based on the pair (Ψ, Λ) as G=ΨΛΨ^-1. Together, the pair (Ψ, Λ) represents the DMD of the data X and X'.

Through these computations, DMD effectively captures the underlying spatiotemporal dynamics of the system, making it a valuable tool for identifying coherent structures and analyzing the evolution of complex datasets. The DMD algorithm can be modified or extended in various ways to improve its performance or applicability. For example, one can use different norms or metrics to measure the distance between X and X', or use different basis functions to project the data on to a lower-dimensional space. One can also incorporate weighting factors to account for time-varying systems or non-uniform sampling rates.

In some embodiments, the linear operator G can be understood as linear transformation matrix that indicates a relationship how to change X to X’.

5G DL CSI-RS signaling schemes

RB: A resource block (RB) in 5G NR is a unit of frequency domain resource allocation that consists of 12 consecutive subcarriers with the same subcarrier spacing configuration. The subcarrier spacing can vary from 15 kHz to 240 kHz depending on the numerology used. An RB can span one or more orthogonal frequency division multiplexing (OFDM) symbols in the time domain, depending on the scheduling granularity. An RB is part of a resource grid, which is a two-dimensional matrix of resource elements that covers the entire bandwidth and time duration of a transmission. A resource element is the smallest unit of time-frequency resource that corresponds to one subcarrier and one OFDM symbol. A resource block group (RBG) consists of multiple contiguous Resource Blocks and is used for efficient resource management and allocation in 5G networks. The concept of RBGs enables flexible and dynamic allocation of resources to meet the diverse requirements of different services and applications in 5G communication.

Sub-channels: Sub-channels are groups of subcarriers that form the basic units of resource allocation in 5G NR. Subcarriers are the smallest frequency components of an OFDM signal, which is resource element (RE) used by 5G NR. Sub-channels can have different sizes and shapes depending on the numerology, bandwidth and configuration of the 5G NR system.

DL measurement by CSI: CSI stands for channel state information, which is a set of parameters that describe the condition of a wireless channel. CSI includes CQI, PMI, and RI, which are the channel quality indicator, the precoding matrix indicator, and the rank indicator, respectively. CQI measures the signal-to-noise ratio (SNR) of the channel, PMI selects a suitable precoding matrix for the downlink transmission, and RI determines the number of transmission layers or spatial streams that can be used for the downlink transmission. In 5G NR, CSI is computed by the UE based on the CSI-RS transmitted by the base station (gNB) . The UE then reports the CSI to the gNB as feedback. The gNB uses the CSI to optimize resource allocation and scheduling for the downlink data transmission. Depending on the codebook type and configuration, different methods are used to compute and report CSI in 5G NR. CSI is rough quantization rather than a compression (for the purpose of reconstruction) because it is based on a limited number of bits that can be transmitted in the feedback channel. The feedback channel has a finite capacity and bandwidth, so it cannot convey all the information about the channel state. Therefore, the UE has to quantize and compress the CSI before sending it to the gNB. This means that some information is lost or distorted in the process, and the gNB may not have an accurate or complete picture of the channel state. This can affect the performance and efficiency of the downlink transmission.

In some embodiments, the CSI-RS may be transmitted periodically or aperiodically.

As shown in Fig. 9A, the UE may transmit CSI-RS to the BS periodically. The flow of uplink estimation may comprise the following steps:

901a: BS transmits configurations to UE via RRC.

902a: BS transmits CSI-RS to UE.

903a: UE transmits CSI corresponding to the CIS-RS to BS.

904a: BS transmits CSI-RS to UE.

905a: UE transmits CSI corresponding to the CIS-RS to BS.

In some embodiments, step 902a-903a may be periodically repeated.

As shown in Fig. 9B, the UE may transmit CSI-RS to the BS aperiodically. The flow of uplink estimation may comprise the following steps:

901b: BS transmits configurations to UE via RRC.

902b: BS transmits CSI-RS trigger to UE.

903b: BS transmits CSI-RS to UE.

904b: UE transmits CSI corresponding to the CIS-RS to BS.

In some embodiments, the step 902b and 903b may repeat according a timing offset (e.g. a redefined or configured timing offset in X slots) .

In some embodiments, the step 902b and 904b may repeat according a timing offset (e.g. a redefined or configured timing offset in y slots) .

5G UL SRS signaling schemes

To facilitate understanding of the embodiments of this application, some terms about uplink channel estimation is described.

UL sounding by SRS: SRS are transmitted by the UE to the gNB in allocated time and frequency resources. The gNB uses these signals to estimate the uplink channel state information, such as path loss, delay spread, angle of departure, etc. The gNB then communicates the suitable uplink beamforming parameters to the UE using downlink control information (DCI) . The UE uses these parameters to perform beamforming for uplink transmission. Sounding signals are essential for enabling massive MIMO and beam management in 5G NR, which are key technologies for achieving high spectral efficiency and coverage.

Difference and relevance between beamforming and precoding matrix technologies: Beamforming and precoding matrix are two related but distinct concepts in 5G NR. Beamforming is the process of shaping and directing the radio waves from multiple antennas to a specific user or location. Precoding matrix is a set of coefficients that are applied to the data streams before they are transmitted by the antennas. Precoding matrix can be used to achieve different goals, such as spatial multiplexing, diversity, or array gain. In 5G NR, precoding matrix can be selected from a standardized codebook or designed adaptively based on channel state information. Beamforming and precoding matrix work together to optimize the performance of massive MIMO systems in 5G NR.

Precoding matrix: A precoding matrix is a matrix that transforms the data symbols before transmission over a wireless channel in 5G New Radio (NR) system. Precoding matrix can improve the spectral efficiency, reliability, and interference management of the system. In 5G NR, the precoding matrix can be selected from a set of predefined codebooks based on the CSI that the base station (gNB) acquires from the UE. The UE reports a transmitted precoding matrix indicator (TPMI) to indicate the preferred precoding matrix from the codebook. The codebook design follows the technical specifications 38-211 and 38-214 of the 3GPP. In theory, one way to generate a precoding matrix on each subcarrier is to use singular value decomposition (SVD) of the channel matrix on each subcarrier. SVD precoding diagonalizes the channel matrix by taking an SVD and removing the two unitary matrices through pre-and post-multiplication at the transmit apparatus and receive apparatus respectively. This method can achieve the optimal performance in terms of signal-to-noise ratio (SNR) or mutual information (MI) of the channel, but it requires perfect channel state information at the transmit apparatus, which is not realistic in practice. Therefore, other methods, including SRS-sounding (assuming UL/DL reciprocity) and CSI-RS feedback, hybrid precoding, and one precoding matrix for a RB group, can be used to reduce the complexity and feedback overhead of SVD precoding. Then, the quality of the precoding matrix given by a precoding technique is evaluated by comparing its correlation with the ground-truth precoding matrix (perfect channel information on each subcarrier) during the algorithm implementation stage. The correlation can be measured by the Frobenius norm of the difference between the two matrices. According to the experience, a correlation over 90%indicates a good precoding matrix approximation algorithm.

6G T-MIMO: In the context of 6G technology, T-MIMO, or Terabit-per-second Multiple-Input Multiple-Output, refers to a cutting-edge communication approach characterized by the utilization of a base station equipped with up to 1024 antennas and UEs integrated with up to 16 antennas. The technology operates over an expansive bandwidth of up to 500 MHz, effectively catering to high-speed data transmission requirements. Notably, the T-MIMO system operates within the frequency band ranging between 10GHz and 14GHz.

DCI: DCI is a critical component in the communication process between the network and UE within the 5G NR framework. It primarily carries information related to scheduling (allocating physical resources) for both downlink data (PDSCH) and uplink data (PUSCH) . The DCI helps in adjusting other parameters. DCI is utilized to transport downlink control information for one or more cells associated with a particular Radio Network Temporary Identifier (RNTI) . The coding steps involved include Information Element multiplexing, cyclic redundancy check (CRC) attachment, channel coding, and rate matching. DCI is encoded and modulated before being mapped to a specific slot in 5G NR. The DCI carries control information used for scheduling user data on physical downlink shared channel (PDSCH) on the downlink and physical uplink shared channel (PUSCH) on the uplink. DCI sends dynamic physical layer control messages from the base station to each UE. This information can be either system-wide or UE-specific, and it encompasses aspects of uplink and downlink data scheduling, HARQ management, power control, and other signaling.

UCI: is a crucial component in 5G NR that is carried by the physical uplink control channel (PUCCH) or physical uplink shared channel (PUSCH) depending on the scenario. UCI carries control signals from the UE to the base station in the uplink direction. It serves as a counterpart to DCI which travels from gNB to UE. It contains important control information such as HARQ feedback, CSI, and scheduling request (SR) . UCI is primarily carried by the PUCCH, but it can also be transported by the PUSCH under certain circumstances. This flexibility contrasts with DCI which is strictly carried by the physical downlink control channel (PDCCH) . The content, encoding, modulation, and mapping of UCI to the 5G NR slot via the PUCCH or PUSCH are critical aspects of how UCI functions within the 5G NR framework. The control information conveyed includes channel reports, HARQ-ACK, and scheduling requests.

PMI/RI/CQI: PMI in 5G NR is used for conveying information about the channel from the UE to the base station. PMI, along with the rank indicator (RI) that informs the base station about the number of transmission layers that the UE can reliably receive and channel quality indicator (CQI) that provides feedback on the downlink channel quality, forms part of the CSI feedback that the UE provides to the base station based on the CSI-RS it receives. The base station utilizes the reported CSI to configure various transmission parameters such as the target code rate, modulation scheme, number of layers, and MIMO precoding matrix for subsequent downlink transmissions. The PMI specifically helps in selecting the appropriate precoding matrix to be used for these transmissions, aiming to optimize the performance of the communication link.

In some embodiments, the SRS may be transmitted periodically or aperiodically.

As shown in Fig. 10A, the BS may transmit SRS to the UE periodically. The flow of uplink estimation may comprise the following steps:

1001a: BS transmits configurations to UE via RRC.

1002a: UE transmits SRS to BS.

1003a: BS transmits DCI corresponding to the SRS to UE.

BS may transmits DCI corresponding to the SRS to the UE, wherein the DCI may comprise UL-related configurations, DL MIMO precoding configurations, etc.

1004a: BS transmits downlink data. This step is optional.

1005a: UE transmit uplink data based on the DCI.

In some embodiments, step 1002a-1005a may be periodically repeated.

1006a: UE transmits SRS to BS.

As shown in Fig. 10B, the BS may transmit SRS to the UE aperiodically. The flow of uplink estimation may comprise the following steps:

1001b: BS transmits configurations to UE via RRC.

1002b: BS transmits SRS trigger signal to UE.

1003b: UE transmits SRS to BS.

1004b: BS transmits DCI corresponding to the SRS to UE.

1005b: BS transmits downlink data.

1006b: UE transmit uplink data based on the DCI. In some embodiments, the step 1002b and 1003b may repeat according a timing offset (e.g. a redefined or configured timing offset in X slots) .

But in 6G T-MIMO, antennas in UE and BS are much more than 5G MIMO, while band is much larger than 5G MIMO.

Illustratively, Fig. 11 shows a scheme of antenna configuration in 6G T-MIMO.

The present disclosure pertains to a system featuring a base station, alongside a multitude of UEs associated with the base station. Equipped with MIMO antennas, the system architecture demonstrates a notable disparity in antenna numbers, with the base station accommodating a significantly higher count compared to each UE. As an illustration, the base station configuration may integrate 1024 antenna ports, while individual UEs are equipped with 16 antenna ports. Leveraging this setup, the system facilitates high-capacity communication over an expansive bandwidth surpassing the capabilities of the 5G NR system, covering a range from 50MHz to 500MHz. Operating within the frequency bands of 10GHz to 15GHz, the system fosters centimeter-scale wavelength radio propagation, thereby enabling the attainment of nearly terabit-per-second throughput, signifying a significant advancement in data transmission capabilities.

Due to the increasing of amount of antennas and band, the estimation of the downlink channel and uplink channel may consume much more computation resources and communication resources in 6G T-MIMO.

The 6G T-MIMO channel poses a significant challenge for data-driven methods. These methods require the base station to collect a large number of data samples, each of which has the same size as the 6G T-MIMO channel. However, one sample data is already very large, and the UE cannot store the entire 6G massive MIMO channel on its device. Moreover, it is too costly to send them back to the base station over the uplink. Even if the base station can exploit the UL/DL reciprocity to obtain the channel information, it still faces the difficulty of storing and processing such a huge amount of data set. In particular, computing an SVD on the entire 6G T-MIMO channel is nearly impossible with current hardware capabilities. Therefore, we need to find alternative methods that can overcome these limitations and still achieve high performance in 6G T-MIMO systems.

In order to better realize the estimation of 6G T-MIMO downlink channel, embodiments of this application provide a communication method. In this method, the downlink channel space can be divided into M sub-channels. The BS may transmit K sets of reference signals corresponding to K (K≤M) sub-channels in the M sub-channels to the UE. With receiving the K sets of reference signals, the UE may determine the channel estimation corresponding to each sub-channel of the K sub-channels based on the K sets of reference signals set, and determine or detect the transformation relationship (transformation relationship may also refers to relationship) between channel estimation of a certain sub-channel (hereinafter referred to as the reference sub-channel) and channel estimation of other sub-channels within the K sub-channels. Then, the UE may transmit a CSI including an information indicating channel estimation of the reference sub-channel and an information indicating the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels within the K sub-channels the BS. Finally, the BS can obtain channel estimation of other sub-channels based on channel estimation of the reference sub-channel and the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels within the K sub-channels. In this way, the UE does not need to transmit channel estimation of the entire downlink channel to the BS, which reduces the communication resources occupied by the channel estimate transmission.

In some embodiments, the M sub-channels are equal-sized.

In some embodiments, sub-channel may also called unit.

In some embodiments, the information indicating channel estimation of the reference sub-channel may refer to the channel coefficient vector of the reference sub-channel, or information indicating the channel coefficient vector of the reference sub-channel (for example, one or more matrix obtained by estimating the channel coefficients on the reference signals) . Correspondingly, the aforementioned transformation relationship can be the transformation relationship between the channel coefficient vector of the reference sub-channel and channel coefficient vectors of other sub-channels in the K sub-channels.

In some embodiments, the information indicating channel estimation of the reference sub-channel may refer to a low-dimension (or low-rank, or low-dimension/low-rank version) channel coefficient vector of the reference sub-channel, or information indicating low-dimension (or low-rank , or low-dimension/low-rank version) channel coefficient vector of the reference sub-channel, (for example, one or more matrix obtained by projecting the channel coefficient vector of the reference sub-channel into a low-dimensional space, or decomposing the channel coefficient. ) . Correspondingly, the aforementioned transformation relationship can be the transformation relationship between low-dimension (or low-rank) channel coefficient vector of the reference sub-channel, and low-dimension (or low-rank) channel coefficient vectors of other sub-channels in the K sub-channels.

In some embodiments, a low-dimension/low rank vector of a certain vector is the vector obtained by reducing the dimensionality of the vector. For example, decomposing (e.g. (random) SVD decomposing, QR or QR-based decomposing, (random) POD decomposing, etc. ) a vector may obtain a low-dimension/low rank vector of the vector.

In some embodiments, the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels may be considered a number of dynamic modes, which may be determined or detected by DMD. For example, the transformation relationship between the channel coefficient vector of the reference sub-channel and channel coefficient vectors of other sub-channels in the K sub-channels may be determined or detected by performing DMD on the channel coefficient vectors of the K sub-channels. For another example, the transformation relationship between low-dimension channel coefficient vector of the reference sub-channel and low-dimension channel coefficient vectors of other sub-channels in the K sub-channels may be determined by performing DMD on the low-dimension channel coefficient vectors of the K sub-channels.

In other embodiments, the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels may also be determined through other methods, such as discrete Fourier transform (DFT) , fast Fourier transform (FFT) , deep neural networks (DNN) , etc., which are not limited here.

In some embodiments, the reference sub-channel refers to any sub-channel within the K sub-channels.

In this disclosure, we propose a novel method based on DMD.

Herein, we present a novel approach for channel feedback within 6G T-MIMO systems, aimed at mitigating data sample size and associated overhead. This method employs finer granularity through the division of the comprehensive 6G T-MIMO channel into contiguous units, ensuring a manageable data sample size compatible with the existing 5G framework. However, this partitioning results in a reduced sparsity of the reference signals, thereby increasing the overhead of CSI feedback when compared to the whole 6G T-MIMO channel. To address this challenge, we propose an application of DMD or similar detection methods over the contiguous units, enabling the identification of sparse channel patterns across the unit evolutions. DMD, known for extracting dominant modes from a system's temporal evolution, is adapted in this scenario for sub-channel-wise (herein for example frequential) evolution analysis, capturing channel variations over a multitude of sub-channels (herein for example diverse subcarriers) . The UE subsequently feeds back the channel representation of a single unit and the detected or determined transformation relationships. That is, dynamic modes among units to the base station, enabling complete 6G T-MIMO channel reconstruction or reconstruction of any sub-channel of interest as the BS wishes. Furthermore, our findings demonstrate that implementing DMD in a low-dimensional signal space further reduces computational complexity and feedback overhead, as the UE transmits the low-dimensional channel representations and dynamic modes to facilitate comprehensive channel reconstruction at the base station.

In some embodiments, the channel representation of a single unit may refer to the estimation of the reference sub-channel, for example, low-dimensional channel coefficient vector of the reference sub-channel.

In some embodiments, the method employs finer granularity through the division of the comprehensive 6G T-MIMO channel into equal-spaced (or equal-size) units (or sub-channels) .

Furthermore, the described system offers support for advanced beamforming and precoding matrix technologies in downlink, ensuring the efficient management and optimization of data transmission and reception processes. Before supporting advanced beamforming and precoding matrix, primarily, the pivotal objective of the system is to facilitate the acquisition of downlink channel information by the base station, a task that proves particularly challenging within the intricate framework of 6G T-MIMO, characterized by an exceptionally high-dimensional channel space. Overcoming this complexity, the system endeavors to devise methodologies that enable the extraction of downlink channel insights from multiple UEs, while maintaining costs comparable to those observed in 5G NR systems.

Diverging from the approach adopted in 5G NR, which typically assumes a lack of prior knowledge concerning downlink channels, the 6G T-MIMO system capitalizes on the wealth of existing environmental insights and channel-related information inspired from the ray-tracing channel model. Leveraging this extensive prior knowledge, the system employs a data-driven methodology, executed in real-time over the air, effectively across both the base station and UEs.

Operating within this framework, the system integrates a series of iterative processes, including data sample collection, in-depth learning from these data samples, the application of the acquired knowledge framework, and the subsequent collection of fresh data samples for fine tuning. Sequentially progressing through these stages, the system effectively transits from one operational state or process to another, continually refining its data-driven approach and optimizing its performance capabilities. This dynamic and adaptive methodology enables the system to remain responsive to evolving channel conditions, varying user requirements, and dynamic environmental factors.

The method in the disclosure may be applied to any of device/apparatus/chip/chipset, such as aforementioned UE, BS, ED 110, NT-TRP 172, receive apparatus, transmit apparatus etc. For easy understanding of the disclosure, the communication between BS and UE is adopt in the following description.

The method in the disclosure may be applied to any form of communication system. For easy understanding of the disclosure, 6G T-MIMO system is adopt in the following description.

Illustratively, Fig. 12 shows a finite state machine of 6G T-MIMO system.

As shown in Fig. 12:

The finite state machine of the proposed T-MIMO system, including the initial, data collection, in-depth learning, applying acquired channel knowledge, and fine-tuning states.

Initial state: In initial state, BS may transmit the information of how to segment the downlink channel, i.e. the pattern of segmenting the downlink channel, to UE (s) . In some embodiments, the BS may transmit an indicator of granularity (e.g. in some examples, a sub-channel is divided along the subcarriers direction) for segmenting the downlink channel into M sub-channels.

In response to the challenges posed by the complex and extensive 6G T-MIMO channel space, the system implements a strategic approach by segmenting the channel into M smaller, contiguous units. The division or partitioning scheme is orchestrated by the base station, which then disseminates this information to the UEs via a broadcast or multicast transmission during the initial states, preceding the data sample collection process.

In some embodiments, the BS may disseminate the information by broadcasting or multicasting in downlink.

In some embodiments, the 6G T-MIMO channel space may be segmented according to one or more of the following dimensions: frequency (subcarriers, RE, or RB) domain, Rx antenna port domain, Tx antenna port domain, time (OFDM symbol or TTI) domain. For example, Fig. 13 illustrates a segmentation of 6G T-MIMO channel. Let’s not consider timing domain for Fig 13 for the purpose to illustration, the downlink channel of the 6G T-MIMO may have three dimensions: frequency domain, Rx antenna port domain, Tx antenna port domain, each sub-channel (also refers to 6G T-MIMO channel unit or unit or other names) may comprise Rx antennas, Tx antenna andsubcarrier, wherein andare positive integer.

In some embodiments, the Rx antenna port domain may also refer to the antenna ports of UE in downlink.

In some embodiments, the Tx antenna port domain may also refer to the antenna ports of BS in downlink.

Data sample collection state (also refers to data collection state/process) : In this state, the BS may transmit reference signals to one or more UEs. The one or more UEs may send channel estimation of sub-channels corresponding to the reference signals to the BS. To facilitate the data sample collection process, the base station employs a strategic approach by transmitting multiple reference signals on the DL broadcast or multicast channel (s) . These reference signals enable the UEs to accurately estimate the channel coefficients of the sub-channels on which the reference signals are inserted and transmitted. The reference signal placement scheme adheres to either the established 5G NR framework or a newly defined methodology, which is predetermined and known to both the base station and UEs.

Upon receiving the reference signals, the UE diligently undertakes the task of estimating the channel coefficients for one, multiple, or all of the designated units on which the reference signals are inserted and transmitted, depending on its own processing capabilities and/or as scheduled by the base station. Subsequently, the UE communicates the estimated coefficients back to the base station.

In-depth learning state, BS may determine a common basis (U) of the downlink channel based on the received channel estimation of the sub-channels from the one or more UEs. Moreover, the BS may determine one or more row-pivoting matrix of U (refers to P) , wherein P indicates the placement pattern (also refers to placement scheme) of DL reference signals of the sub-channels of the downlink channel. And the BS may determine one or more low-dimension matrix (or compact matrix or compressing matrix) of U (refers to Θ) corresponding to each P according Θ=PU.

Upon the completion of data sample collection, signified by the reception of estimated channel coefficients from the UEs on the sub-channels, the base station initiates a data-driven process. This process is aimed at discerning the common basis among the gathered sub-channels from all participating UEs, subsequently culminating in the development of a unified and sparser reference signal placement scheme tailored for the sub-channels for the UEs in the environment.

The base station, following the completion of the data-driven process, proceeds to convey a concise representation of the established common basis (for example, the compact matrix Θ) of sub-channels and optimized reference signal placement scheme (for example, P or some method to generate P) of sub-channels to the UEs. This dissemination of crucial information occurs via a broadcast or multicast downlink transmission method to all the UEs within the environment and associated to that BS.

In some embodiments, all the sub-channels share the same compact matrix Θ and reference signal placement scheme P.

Applying acquired channel knowledge state (also refers to application state or applying state) : In this state, the BS may transmit a set of reference signals to UE (s) based on the optimized reference signal placement scheme on K sub-channels. The UE may determine the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels within the K sub-channels. Then, the UE may transmit a CSI including an information indicating channel estimation of the reference sub-channel and an information indicating the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels within the K sub-channels the BS. Finally, the BS can reconstruct any other sub-channel of the downlink channel based on the CSI as it wishes.

In some embodiments, the CSI may refer to T-MIMO report.

In the application process, the BS proceeds to transmit reference signals based on the optimized reference signal placement scheme on the K sub-channels of the downlink channels. Upon reception, the UEs estimate the channel coefficients on the transmitted reference signals into a channel coefficient vector on each of K sub-channels, and then detect the dynamic modes across the channel coefficient vectors of the K sub-channels. Subsequently, the UEs provide feedback to the stations, which includes the channel coefficient vector on a specific sub-channel of the K sub-channels and the detected dynamic modes. Leveraging this feedback, the base station reconstructs the channel coefficients of any sub-channel of downlink channel.

Fine-tuning state: In fine-tuning state, the BS may monitor the performance of the downlink channel. And the BS may repeat the data collection state, in-deep learning state and application state to further optimize the performance of the downlink channel while the performance of the downlink channel is low.

Furthermore, the base station continuously monitors the performance of the channel reconstruction for all UEs. If a degradation in performance is detected, the BS initiates a re-entry into the data sample collection process, either for selected UEs or a subset of UEs. This process aims to recalibrate the reference signal placement scheme and the common basis periodically or aperiodically to adapt to the evolving environmental conditions and ensure optimal system performance.

The reduced granularity at the unit level, a subset of the 6G T-MIMO channel, offers a significant technical advantage. With data samples provided at the unit granularity by UEs, complexity, storage, processing power, and feedback overhead on the air are all conserved. This reduction in granularity of sub-channel further aids the data-driven method employed at the base station, optimizing its processing capabilities.

The utilization of dynamic modes in the feedback process results in reduced overhead on the air. Leveraging the DMD evolution over the units of the channel, the BS can efficiently reconstruct channel estimation of any sub-channel (s) of 6G T-MIMO downlink channel. Comprising the mode-related information in the uplink feedback process has demonstrated significant efficiency gains. For instance, the DMD-based approach has shown a reduction in the feedback overhead by approximately 90%compared to conventional 5G NR methods. Moreover, the computational complexity is notably decreased by nearly 90%, leading to faster processing times and improved resource utilization. Additionally, the application of DMD has facilitated a 95%reduction in storage requirements, contributing to more streamlined data management and enhanced overall system performance.

In the embodiment shown in Fig. 12, the conventional assumption of uplink/downlink (UL/DL) reciprocity is strategically eschewed. By adopting this refined approach, the system capitalizes on the inherent disparities frequently witnessed between uplink and downlink channels in practical environments. This deviation from convention permits a more accurate and adaptive modulation of communication parameters, resulting in enhanced data throughput, reduced error rates, and bolstered overall system reliability. Furthermore, by not relying on the UL/DL reciprocity assumption, the method inherently guards against potential inaccuracies and inefficiencies stemming from asymmetric conditions, thus paving the way for a more robust and resilient wireless communication paradigm.

In the embodiment shown in Fig. 12, not only is the traditional assumption of uplink/downlink (UL/DL) reciprocity circumvented, but there is also an inherent compatibility with FDD modes of operation. Incorporating FDD support brings forth a multitude of benefits over TDD. Primarily, FDD's simultaneous uplink and downlink transmissions facilitate constant communication, thereby reducing latency and enhancing the real-time responsiveness of the system. Moreover, FDD inherently mitigates potential self-interference scenarios, which can be a challenge in TDD systems due to their alternating transmit and receive periods. This, in conjunction with our novel approach to UL/DL channel treatment, not only maximizes spectral efficiency but also ensures a more stable and interference-resistant communication framework.

In response to the segmentation of the 6G T-MIMO channel into sub-channels in the embodiment shown in Fig. 12, the system employs a strategic approach to address the complexities. To effectively manage the high-dimensional channel space, the system undertakes a meticulous process of dividing the space into smaller, contiguous units, with the partitioning scheme meticulously designed and orchestrated by the BS. This process involves a careful analysis of the unique characteristics of the channel, considering factors such as signal strength, propagation dynamics, and environmental influences. The resulting partitioning strategy is then communicated to the UEs through a comprehensive broadcast or multicast transmission, ensuring that each UE is well-informed and equipped to participate in the subsequent data sample collection process.

Segmenting a 6G T-MIMO channel into smaller units can be achieved through various techniques and methodologies, each catering to specific system requirements and operational constraints. Some of the alternatives include:

Frequency-Domain Partitioning: Segment the channel based on specific frequency bands or subcarriers, allowing for more focused analysis of individual frequency components and their respective characteristics.

In some embodiments, the DL channel may also be segmented based on RE, RB, or RGB in the frequency domain partitioning method.

For example, as shown in Fig. 14A, the downlink channel may be segmented only based on frequency domain. Consider the amount of subcarriers in the downlink channel is n_subcarriers and the amount of sub-channels of the downlink channel is M, each sub-channel may be corresponding to n_subcarriers/M subcarriers with all Rx antennas and Tx antennas.

Frequential-temporal segmentation: Partition the channel by considering both spatial and temporal factors, accounting for the dynamic changes in the channel over frequency and space.

Antenna grouping: Divide the channel based on the arrangement and grouping of antenna ports, forming subsets of antennas that exhibit coherent or similar propagation characteristics.

For example, as shown in Fig. 14B, the downlink channel may be segmented only based on Rx antenna ports domain. Consider the amount of Rx antenna ports of the UE is n_RxAnt and the amount of sub-channels of the downlink channel is M, each sub-channel may be corresponding to n_RxAnt /M Rx antennas with full band.

For example, as shown in Fig. 14C, the downlink channel may be segmented only based on Tx antenna ports domain. Consider the amount of Tx antenna ports of the UE is n_TxAnt and the amount of sub-channels of the downlink channel is M, each sub-channel may be corresponding to n_TxAnt /M Tx antennas with full band.

In some embodiments, the sub-channels may also be determined by segment the downlink channel with multiple dimensions (e.g. frequency domain, time domain, Rx antenna port domain, Tx antenna port domain) . For example, as shown in Fig. 14D, the downlink channel may be segmented by frequency domain, Tx antenna port domain, and Rx antenna port domain. Consider the amount of Rx antenna ports of the UE is n_RxAnt, the amount of Tx antenna ports of the UE is n_TxAnt, the amount of subcarriers in the downlink channel is n_subcarriers, and the downlink channel is segmented into M₁ (frequency domain) ×M₂ (Tx antenna port domain) ×M₃ (Rx antenna port domain) sub-channels, each sub-channel may be responding to n_subcarriers/M₁ subcarriers, n_TxAnt /M₂ Tx antennas and n_RxAnt /M₃ Rx antennas.

In some embodiments, the sub-channels may be determined by segmenting the downlink channel based on other dimension (or domain) such as timing (OFDM symbol, TTIs) , coding (spreading coding or random-mask coding) , which is not limited herein.

By leveraging these segmentation techniques, wireless systems can effectively manage and analyze the MIMO channel in a more granular and targeted manner, enhancing the system's overall performance and optimizing resource allocation.

Certainly, the dynamic nature of the segmentation strategy necessitates effective communication between the BS and UEs. Any alterations to the segmentation methodology should be carefully communicated to all relevant UEs within the system. This ensures that the UEs remain well-informed about any changes in the segmentation scheme, allowing for smooth transitions and consistent data handling throughout the network. Effective communication facilitates a cohesive and synchronized approach to data segmentation, enabling the system to adapt to evolving channel dynamics and optimize its operational efficiency in real-time.

Given the insight provided by the ray-tracing model, the system demonstrates a preference for frequency-domain partitioning. Leveraging the existing frequency domain segmentation schemes, such as 5G NR sub-channel (5G-sub-channel is a term that contains a number of RB groups, different from the sub-channels in this IPR) , RB group, and RB, enables the system to effectively manage and analyze the wireless MIMO channel with improved precision. Moreover, the ray-tracing model highlights the persistent dynamic modes evident across the frequential evolution, validating the effectiveness of frequency-domain partitioning in capturing and interpreting channel dynamics.

Capitalizing on the integration of sensing and communication (ISAC) paradigm, the system considers the involvement of sensors to provide an RF map for comprehensive ray tracing analysis. This integrated approach enables the base station to leverage the data from the RF map, facilitating a more accurate estimation of the channel characteristics. With this enhanced understanding, the system can generate a refined segmentation of the wireless MIMO channel, further optimizing the data processing and segmentation process. This synergistic collaboration between sensing and communication domains empowers the system to extract valuable insights from the environment, ensuring an efficient and precise segmentation strategy aligned with the specific requirements of the 6G T-MIMO system.

Indeed, the system utilizes the principle of "divide-and-conquer" , a classic strategy employed to manage complex and large-dimensional problems. Applying this approach, the segmentation of the 6G-T-MIMO channel into sub-channels aligns with this strategic method, effectively breaking down the intricate channel space into more manageable and coherent units.

In response to the data sample collection process in the embodiment shown in Fig. 12, the BS adopts a deliberate strategy that involves the transmission of multiple sets of the reference signals through the K sub-channels of the downlink channel. These meticulously designed (learned) the reference signal placement scheme (P) enables the UEs to accurately estimate the channel coefficients with as few reference signals as possible, thereby facilitating an effective and comprehensive data acquisition procedure. The reference signal placement scheme is carefully crafted on the data samples collected through the established 5G NR framework or a specifically tailored methodology that is pre-determined and well-known to both the base station and UEs.

The designed (learned) reference signal placement scheme has typically much sparser reference signals than a uniform and highly dense placement strategy akin to that of the 5G NR framework, which is used during the data sample collection stage. Although to learn the reference signal placement scheme from the data samples may entail initial costs, it is a one-time investment expected to remain effective (persist) over an extended period. Alternatively, beside a uniform and highly dense placement strategy akin to that of the 5G NR framework as an initial reference signal scheme during the data sample collection stage, the initial reference signal placement scheme can be generated through ray-tracing analysis, incorporating a redundant margin for added robustness and adaptability. Another alternative approach could involve the adoption of a legacy reference signal placement scheme derived from the previous-round data-driven methodology as initial reference signal scheme. This allows the system to build upon prior insights and leverage historical data.

During the data sample collection stage, upon the reception of these sets of the reference signals in the downlink with the pre-determined initial reference signal scheme (s) , the UE diligently embarks on the task of estimating the channel coefficients for the designated units, considering its processing capabilities and the scheduling set by the BS. Consequently, the UE transmits the estimated coefficients back to the base station.

In order to optimize resource utilization and accommodate diverse processing capabilities, UEs in the 6G T-MIMO system may employ alternative strategies to handle specific segments of the units instead of the entire set. This selective approach can be based on the device's current processing capability, considering factors such as available storage capacity, processing power, and RF capability. UEs may be programmed to prioritize specific units for channel estimation and data reporting, depending on their individual computational constraints and allocated resources. Alternatively, the BS can dynamically or randomly schedule the UEs to handle specific units based on the prevailing network conditions and traffic demands, ensuring a balanced distribution of processing tasks across the system.

The UE can implement additional compression techniques to further streamline the estimated channel coefficients, enhancing the data handling and processing capabilities within the 6G T-MIMO system. Various compression methodologies can be adopted, including but not limited to quantization, filtering, feature selection, and matrix factorization. Quantization enables the representation of high-precision data with reduced bit rates, effectively reducing the storage and processing requirements. Filtering techniques allow for the extraction of essential data components, eliminating redundant information and minimizing the data payload. Feature selection enables the identification and extraction of critical features, enhancing the signal-to-noise ratio and optimizing data utilization. Matrix factorization techniques facilitate the decomposition of complex data matrices into simpler structures, enabling efficient data representation and management. These compression strategies collectively contribute to the seamless and streamlined operation of the 6G T-MIMO system, ensuring optimal data handling and processing efficiency.

The strategic adoption of selective data handling mechanisms enables the UE to maintain the similar level of complexity compared with the mechanisms used in the prior arts, while simultaneously supporting the advanced functionalities and complexities associated with T-MIMO. By optimizing the allocation and granularity of resources and processing capabilities, the system can effectively balance the data processing requirements with the UE constraints, ensuring a seamless and efficient data acquisition and transmission process. This approach not only enhances the overall performance and functionality of the 6G T-MIMO system but also ensures a smooth transition from existing 5G NR technologies to the advanced capabilities of the next-generation 6G networks.

Illustratively, Fig. 15 is a schematic diagram of collecting the raw samples of channel coefficient vectors. As shown in Fig. 15, the schematic diagram comprise the following steps:

1501, BS construct reference signals with uniform and dense placement at K units (also called to K sub-channels) .

In some embodiments, reference signals with uniform and dense placement at K units may be a pre-negotiated initial reference signal placement scheme (s) ) for the K sub-channels.

In some embodiments, the information of how to segment the downlink channel into the K sub-channels have been pre-negotiated to the UE (s) .

In some embodiments, each sub-channel of K sub-channels corresponds to a unit (or sub-channel) in downlink channel.

In some embodiments, reference signal placement scheme corresponds to reference signal placement pattern.

In some embodiments, the BS constructs the reference signals with the pre-negotiated initial reference signal placement scheme or one of multiple pre-negotiated initial reference signal placement schemes for K sub-channels.

In some embodiments, the K sub-channels s (also refers to K units) may constitute the entire downlink channel.

In some embodiments, the K sub-channels may represent a portion of the downlink channel.

In some embodiments, the pre-negotiated initial reference signal placement scheme of the K sub-channels are the same

In some embodiments, the pre-negotiated initial reference signal placement schemes of at least some of the K sub-channels are different

1502, BS broadcasts the DL CSI-RS to UE.

In some embodiments, the BS may broadcast DL CSI-RS including the constructed reference signals in step 1501.

1503, UE performs channel sounding to obtain the channel responses at the K sub-channels.

UE (s) receiving the reference signals may perform channel sounding (also refers to channel estimating) to obtain the channel responses (or coefficients) at the K sub-channels.

In some embodiments, a UE may perform channel sounding of part of sub-channels within the K sub-channels. Hence a UE may perform channel sounding according to its available resource, which can reduce the impact on other tasks on the UE.

In some embodiments, multiple UEs associated with a BS may sounding the whole DL channel of the BS.

In some embodiments, a UE may perform channel sounding at all of the k units if there are enough available resource on the UE, which may improve the accuracy of the channel estimating.

In some embodiments, the channel responses on the reference signals of the K sub-channels may refer to {h₁, h₂, h₃, …h_k} , wherein h_i refers to the channel response on the reference signals of the i-th sub-channel in the K sub-channels.

1504, UE sends channel estimate feedback of uniform and dense DL reference signals (channel responses) to the BS.

UE may sends channel estimate feedback of uniform and dense DL reference signals determined in step 1503 to the BS.

1505, BS decodes and reconstruct DL channel estimation.

BS may decode the channel responses on the reference signals of the K sub-channels and reconstructs downlink channel estimation (raw channel coefficients for every radio resource within a sub-channel) for K sub-channels.

1506, BS performs channel data processing and save the raw channel coefficients of the K sub-channels. BS performs channel data processing (e.g. data augmentation, data cleaning, etc. ) and save the channel coefficients of the K sub-channels to a T-MIMO channel database. The raw channel coefficients of one sub-channel are used as one data sample to determine the common basis of the downlink channel in granularity of sub-channel.

With the raw channel coefficients the BS may determine the common basis of the downlink channel in granularity of sub-channel.

In response to in-depth learning in the embodiment shown in Fig. 12, the method or process includes the following steps at the base station:

The optional yet preferred data cleaning or augmentation process is instrumental in ensuring the dataset's reliability. By systematically excluding erroneous data, outliers, and exceptional data points, the BS ensures the accuracy and reliability of subsequent analytical procedures. Notably, two effective techniques for data cleaning encompass the application of statistical measures, such as the utilization of calculated mean for filling in missing values, and the identification of outliers based on data points that significantly deviate from the overall data pattern.

Upon detecting erroneous data samples, the BS identifies the corresponding UEs from which the data originated. It then proceeds to either request the retransmission of the estimated channel coefficients from the respective terminals or categorizes them as unreliable entities within the system.

The BS engages in a data-driven methodology concerning the data samples received from the UEs. This systematic approach involves the transformation of estimated (raw) channel coefficients into a vectorized format, facilitating the creation of a comprehensive data matrix comprising the resulting data vectors. Subsequently, the methodology employs Singular Value Decomposition (SVD) or proper orthogonal decomposition (POD) on the data matrix, thereby facilitating the extraction of its eigen vectors. For instance, consider a scenario where the BS, equipped with 1024 antennas, receives data samples from multiple UEs equipped with 16 antennas each across 12 RBs as a sub-channel (frequency-domain segmentation) . Upon vectorizing the estimated raw channel coefficients and forming a data matrix, the system proceeds to implement SVD or POD. Through this, the system extracts the eigen vectors, which are subsequently organized within an eigen vector matrix based on their corresponding singular values. This comprehensive sorting process allows for an efficient representation of the most significant vectors, contributing to the development of a common principal basis denoted as U, i.e. common basis. Additionally, in some implementations, the system may leverage the benefits of randomized SVD as an alternative to the conventional SVD method. This technique not only enables the efficient approximation of the dominant singular vectors of the data matrix but also significantly reduces computational complexity and storage requirements. By employing randomization algorithms, the system can swiftly compute an approximate low-rank factorization of the data matrix, leading to the extraction of the essential eigen vectors. Through this approach, the system can achieve comparable accuracy to the standard SVD method, making it particularly well-suited for handling large-scale datasets within resource-constrained environments. The utilization of randomized SVD enhances the system's computational efficiency, reduces storage overhead, and expedites the extraction of crucial data insights, thereby contributing to the streamlined data analysis process and overall system performance. Moreover, as a part of the outlined method, the eigen vector matrix undergoes a carefully executed truncation process, aimed at preserving solely the most crucial vectors that collectively constitute the identified principal basis U. By retaining these critical components, the system optimizes the data processing and analysis, thereby enhancing the overall performance and efficacy of the system in managing complex data sets.

In some embodiments, randomized SVD may also called random SVD or other names, which is not limited herein.

Upon establishing the common principal basis U, the base station proceeds to devise an optimal (designed or learned) reference-signal placement scheme (P) for this basis. Two alternative methods are considered for this purpose. The first approach entails a pivot-QR-based placement scheme, leveraging pivot-QR decomposition with U as the pivot. This method ensures an efficient and robust arrangement of reference signals, effectively optimizing the data processing and analysis within the system. The second method involves a pseudo-random placement strategy, facilitating a strategically randomized distribution of reference signals based on U. By introducing controlled randomness, this technique enhances the system's resilience to potential signal interference and improves overall data processing efficiency. For example, the number of the pseudo-random-based reference signals are related to the rank distribution of U. Both methods contribute to the development of an effective and streamlined reference-signal placement scheme, thereby enhancing the overall performance and functionality of the system. The reference-signal placement scheme can be succinctly represented using a permutation matrix P. Notably, the matrix Θ, obtained as the product of P and U, represents a significantly compressed version (or compact version) of the original common basis U. This reduction in size is a direct consequence of the relationship between the size of the basis U and the size of the data sample, the latter being determined by the partitioning (segmentation) unit employed. The compact representation of Θ, achieved through the permutation matrix P, allows for efficient and streamlined processing, enabling the system to handle large datasets while conserving computational and transmission overhead resources.

Illustratively, Fig. 16 shows a flow of determining the common basis U, the permutation matrix P and the compact representation of U (refers to Θ) .

With the raw channel coefficients of the K sub-channels from each UE into storage as training data set, the BS firstly checks out the raw channel coefficient from M UEs, vectorizes the raw channel coefficients of each sub-channel into a column vector, and form these columns into a matrix A. In some embodiments the channel coefficients of one sub-channel may be one column (also can be one row) .

In some embodiments, each column of the matrix A is the same length (has the same amount of elements) .

Secondly, the BS may determine the common basis U by performing SVD or randomized SVD or POD on the matrix A.

In some embodiments, the BS may determine the common basis U using other methods, for example, auto encoder by DNN, which is not limited herein.

Thirdly, the BS may determine, design, or learn the reference signal placement pattern P by performing pivot-based QR or pseudo-random selection on the common basis U of the downlink channel.

Then, the BS may determine the compact matrix Θ by Θ=PU.

In some embodiments, the BS may transmit Θ (or information indication Θ) , and P (or information indicating P) to the UE. In some embodiments, the BS may also determine inverse or pseudoinverse of the matrix Θ, and transmit the inverse or pseudoinverse of the matrix Θ to the UE instead of Θ. In some embodiments, compact representation of Θ may refers to a compression matrix, or a low-dimension/low-rank matrix of the common basis U.

The UEs proactively feedback their individually estimated channels to the BS with which they are associated to form a training data set. Given that these UEs operate within the proximity of a single base station, it logically ensues that their radio propagation channels are influenced by a congruent environmental paradigm. Drawing from ray-tracing model theory, it can be posited that a certain degree of similarity or coherence exists among their radio propagation channels. This inherent channel similarity can be encapsulated and represented within a common basis, denoted herein as U. Such a representation not only captures the underlying channel characteristics shared among the UEs but also facilitates the implementation of a sparser downlink reference signal placement scheme for the UEs.

The designed (or learned) reference signal placement scheme has usually far sparser reference signals than uniform reference signal placement one in 5G NR.

There is a strategic departure from constructing a common basis U and learning a reference signal placement scheme for the entire 6G T-MIMO channel space within a given environment. Instead, our method innovatively focuses on a sub-channel (or unit) level, providing a more feasible approach. Were one to embark on formulating a common basis U and reference signal placement scheme for the expanse of the entire 6G T-MIMO channel, the consequence would be a substantial overhead. Specifically, the BS would encounter profound inefficiencies when endeavoring to inform UEs of the common basis U, even its compact presentation of Θ, and associated learned reference signal placement scheme, given that their dimensions would be inherently commensurate with the vastness of the 6G T-MIMO channel space. In stark contrast, our refined methodology ensures that this alignment occurs at the sub-channel level, which is markedly smaller in comparison to the entire 6G T-MIMO channel. As a result of this precision-focused approach, the downlink messaging overhead remains in a realm that is comparable to the established benchmarks of 5G NR, thus ensuring optimal communication efficacy with minimized resource strain.

In the application of the acquired knowledge framework of the embodiment shown in Fig. 12, the BS embarks upon the transmission of reference signals, judiciously relying on an optimized (learned) reference signal placement scheme specifically tailored for sub-channels of the downlink channels in the current environment. Upon successful reception, UEs adeptly estimate the channel coefficients on the reference signals on each sub-channel, further discerning the dynamic modes spanned across the predefined sub-channels. In a subsequent procedural step, these UEs transmits feedback to the BS. This feedback encapsulates both the channel representation pertinent to a specific sub-channel of the sub-channels and the dynamic modes (or transformation relationship) across these sub-channels.

In some embodiments, the BS may transmit reference signals corresponding to each sub-channel of the downlink channel to the UE, so that the UE may determine the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels (e.g. the mode matrix G or matrix G below) .

For example, if the downlink channel is segmented into M sub-channels, the BS may transmit M sets of reference signals respectively corresponding to the M sub-channels to the UE.

Illustratively, Fig. 17A shows a flow diagram of a communication method.

As shown in Fig. 17A:

Alterative #1: the method includes the following steps:

Following the determination of the designed or learned reference-signal placement scheme, the BS initiates DL channel measurement for the purpose of communication other than data sample collection. In this phase, the BS efficiently communicates the designed or learned reference-signal placement scheme P and the compact representation of Θ to its UEs using a broadcast or multicast approach. Notably, all the UEs share the same designed reference-signal placement scheme and the same compact matrix Θ. If the designed reference-signal placement scheme is generated from a pseudo-random method, the BS simplifies the process by providing the pseudo-random seed, number of reference signals, and the function or method required for generating the pseudo-random reference-signal placement scheme.

In some embodiments, the reference-signal placement scheme P and the compact representation of Θ may be transmitted through DL broadcast channel or DL DCI.

The BS inserts reference signals, based on the designed reference-signal placement scheme, within each sub-channels of the K sub-channels of the downlink channel to UE.

Upon receiving the reference-signal placement scheme P and compact matrix Θ, the UE estimates the channel coefficients, h, using the provided reference signals on each sub-channel of the K sub-channels; h is vectorized into a column-wise vector, called channel coefficient vector, in our exemplary description.

Given the shared compact matrix Θ and placement scheme P across all the K sub-channels, each UE projects the channel coefficient vector h into a low-dimensional vector c by using the equation c = Θ^-1 h or (pseudo inverse of Θ, if Θ is not a square matrix) on each sub-channel of the K sub-channels. This projection results in a low-dimensional channel coefficient vector c, effectively condensing the representation of the channel coefficient vector h.

In some embodiments, refers to the pseudo inverse of matrix Θ.

Continuing from the previous process, the terminals detect dynamic modes across sub-channels, G_c, using variables c₁ on the first sub-channel to c_k on the K-th sub-channel corresponding to the lowest and highest sub-channels, respectively. Employing the DMD algorithm or other similar methods, the UEs compute essential dynamic modes, enabling efficient pattern identification and streamlined data interpretation such that c_j=G_c ^(j-1) c₁.

In some embodiments, c₁ to c_k refers to low-dimension (or low-rank) channel coefficient vectors of corresponding sub-channel (or unit) . For example, c_i (i=1, 2, …, K) refers to the low-dimension (or low-rank) channel coefficient vectors of i-th sub-channel of the K sub-channels, c₁ refers to the low-dimension (or low-rank) channel coefficient vectors of first sub-channel (or, lowest sub-channel) of the K sub-channels, c_K refers to the low-dimension (or low-rank) channel coefficient vectors of K-th sub-channel (or highest sub-channel , or last sub-channel) of the K sub-channels.

In some embodiments, if the reference sub-channel is the i-th sub-channel in the K sub-channels, the low-dimension channel coefficient vector of (i+j) th unit can be determined by c_i+j=G_c ^jc_i, the first sub-channel is the i-th sub-channel in the K sub-channels, c_i+j refers to the low-dimension channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, 1≤i+j≤K, i and j are integer.

The UEs provide feedback to the base station in the uplink, which includes both the information of the variable c₁ and the information of the mode matrix G_c.

In some embodiments, the UEs may transmit c₁ and mode matrix G_c to the BS.

In some embodiments, the UEs may transmit c₁ and a transformation information indicating mode matrix G_c to the BS.

In some embodiments, transformation information indicating mode matrix G_c may comprise one or more matrices determined by further decomposing the mode matrix G. For example, the UE may Eigen-decompose mode matrix G_c into eigenvectors Ψ and eigenvalues Λ according to GΨ=ΨΛ. Due to the data amount of Ψ and Λ is less than the mode matrix G_c, sending Ψ and Λinstead of G_c may reduce the consummation of communication resources.

In some embodiments, the eigenvectors Ψ changes slowly over time, while the eigenvalues Λ changes faster; the UE may only feedback the changed components of the G_c.

In some embodiments, if the UE finds the detected G_c not changing over time, the UE can feedback an indicator or information to the BS to ask the BS to keep using the previously feedback G_c.

With the reception of the low-dimensional channel coefficient vector c₁ and the mode matrix G_c, the BS possesses the capability to reconstruct the channel coefficients for any sub-channel across all M sub-channels, utilizing the formula c_j=G^j-1c₁.

In some embodiments, with the reception of low-dimensional channel coefficient vector c_i and the mode matrix G_c, the BS possesses the capability to reconstruct the channel coefficients for any sub-channel across all sub-channels, utilizing the formula c_i+j=G_c ^jc_i, 1≤i+j≤K, i and j are integer.

In some embodiments, the BS may periodically transmit reference signals to the UE, so that the UE may periodically determine channel estimation of the reference sub-channel and the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels.

Illustratively, Fig. 17B shows a method of periodic T-MIMO channel report signaling. As shown in Fig. 17B, the process includes the following steps:

1701, BS transmit UE-specific configurations to UE via RRC.

BS may transmit UE-specific configurations to UE via RRC (or other channel) .

In some embodiments, the UE-specific configurations may comprise information for initializing the procedure of DL channel estimation.

In some embodiments, the UE-specific configurations may predefined or transmitted to UE before, hence the step 1701 may be optional.

1702, BS broadcast/Multicast {P, Θ} .

BS may transmit P and Θ to UE by broadcasting or multicasting or unicasting way. Wherein P indicates the placement pattern of reference signals (also refers to pilot) corresponding to at least part of the K sub-channels, Θ indicates a low-dimension or compact matrix of the common basis U of the downlink channel, obtained in the in-depth learning stage.

In some embodiments, P may indicate at least one of the following information of reference signals: index of subcarrier intervals, index of time intervals, index of antenna ports of the BS, index of antenna ports of the UE, values, antenna port, or transmit power.

In some embodiments, the BS may transmit a compression information indicating Θ instead of transmitting Θ. For example, the BS may transmit inverse matrix or pseudoinverse matrix of Θ instead of transmitting Θ.

In some embodiments, different sub-channels may adopt different Θ, so that different sub-channels may adopt different P because P and Θ satisfy the formula Θ=PU.

In some embodiments, a Θ or P corresponding to the Θ may be corresponding one or more sub-channels. That is, multiple sub-channels may share the same Θ and P, or one sub-channel may adopts one Θ and one P.

In situations that different sub-channels adopting different Θ, the BS may transmit multiple Θs and multiple Ps to the UE.

1703, BS broadcasts reference signals.

BS may transmit (broadcast, multicast, or unicasting ways) reference signals in downlink based on P.

For example, consider that the downlink channel is segmented into M sub-channels, the BS may transmit M sets of reference signals respectively corresponding to the M sub-channels to the UE.

1704, UE transmit T-MIMO channel report {Ψ, diag (Λ) , c₁} to the BS.

With receiving the reference signals, the UE may determine the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels. Then the UE may transmit T-MIMO channel report (also refers to CSI) to the BS. Wherein, the T-MIMO channel report may comprise a first information indicating channel estimation for the reference sub-channel and a second information indicating a transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels.

In some embodiments, consider that the UE received M sets of reference signals corresponding to M sub-channels, the UE may estimate the channel coefficient vector of each channel within the M sub-channels. The UE may compress the determined channel coefficient vectors into low-dimension channel coefficient vector based on c_i=Θ^-1h_i, wherein c_i is the low-dimension channel coefficient vector of the i-th sub-channel in the M sub-channels, h_i is the coefficient vector of the i-th sub-channel in the M sub-channels.

Then, the UE may performing DMD on the low-dimension channel coefficient vectors corresponding to the M sub-channels based on c_HighUnits=Gc_LowUnits and transmit G_c (refers to the second information) and the low-dimension channel coefficient vector of the reference sub-channel (e.g. c_1, refers to the first information) to the UE. Wherein c_LowUnits= {c₁, c₂, c₃, ……, c_M-1} and c_HighUnits = {c₂, c₃, ……, c_M} .

In some embodiments, as shown in Fig. 18A, consider that the downlink channel is segmented based on the frequency domain. The UE may compute the c (c₁ to c_M) vectors for each sub-channel and obtain the dynamic mode in the frequency domain by performing DMD of the c vectors. As shown in Fig. 18B, consider that the downlink channel is segmented based on the Rx antenna port domain. The UE may compute the c (c₁ to c_M) vectors for each sub-channel and obtain the dynamic mode in the Rx antenna port domain by performing DMD of the c vectors. As shown in Fig. 18C, consider that the downlink channel is segmented based on the Tx antenna port domain. The UE may compute the c (c₁ to c_M) vectors for each sub-channel and obtain the dynamic mode in the Tx antenna port domain by performing DMD of the c vectors.

In some embodiments, consider that the downlink channel is segmented based on the timing domain. The UE may compute the c (c₁ to c_M) vectors for each sub-channel, i.e. time interval, and obtain the dynamic mode in the timing domain by performing DMD of the c vectors.

In some embodiments, the second information may comprise one or more matrices determined by decomposing the mode matrix G_c. For example, the UE may performing Eigen-decomposing on the mode matrix G_c to determine an eigenvector (Ψ_c) and an eigenvalue (Λ) . Then the second information may comprise Ψ_c and diag (Λ_c) . In some embodiments, the one or more matrices may be determined by decomposing the mode matrix G_c using other method (e.g. SVD, POD, etc. ) , which is not limited herein.

In some embodiments, consider that the UE received M sets of reference signals corresponding to M sub-channels, the UE may estimate the channel coefficient vector of each sub-channel within the M sub-channels. Then, the UE may performing DMD on the channel coefficient vectors corresponding to the M sub-channels based on h_HighUnits=G_hh_LowUnits and transmit G_h (refers to the second information) and the channel coefficient vector of the reference sub-channel (e.g. h_1, refers to the first information) to the UE. Wherein h_LowUnits = {h₁, h₂, h₃, ……, h_M-1} and h_HighUnits = {h₂, h₃, ……, h_M} and h_i is the channel coefficient vector of the i-th sub-channel in the M sub-channels.

In some embodiments, the second information may comprise one or more matrices determined by decomposing the mode matrix G_h. For example, the UE may performing Eigen-decomposing on the mode matrix G_h to determine an eigenvector (Ψ_h) and an eigenvalue (Λ_h) . Then the second information may comprise Ψ_h and diag (Λ_h) . In some embodiments, the one or more matrices may be determined by decomposing the mode matrix G_h using other method (e.g. SVD, POD, etc. ) , which is not limited herein.

In some embodiments, adopting the first sub-channel in the M sub-channels as the reference sub-channel is just an example. In other embodiments, the reference sub-channel is any sub-channel in the M sub-channels, which is not limited herein.

With the reception of T-MIMO channel report, the BS may reconstruct the downlink channel.

In some embodiment, consider that the T-MIMO channel report comprise mode matrix G_h (or Ψ_h and diag (Λ_h) ) and h_i, the downlink channel may be reconstructed by h_i+j=G_h ^jh_i or h_i+j=Ψ₁Λ₁ ^jΨ₁ ^-1h_i, , wherein the reference sub-channel is the i-th sub-channel in the M sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the M sub-channels, i and j are integer, 1≤i+j≤M.

In some embodiment, consider that the T-MIMO channel report comprise mode matrix G_c (or Ψ_c and diag (Λ_c) ) and c_i, the downlink channel may be reconstructed by h_i+j=UG_c ^jc_i or h_i+j=UΨ_cΛ_c ^jΨ_c ^-1c_i, , wherein the reference sub-channel is the i-th sub-channel in the M sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the M sub-channels, i and j are integer, 1≤i+j≤M.

1705, BS broadcast reference signals.

The detail may refer to step 1703.

1706, UE transmit T-MIMO channel report {Ψ, diag (Λ) , c₁}

The detail may refer to step 1704.

In some embodiments, the UE and BS may repeat step 1703 and step 1704 to keep tuning the downlink channel, which may improve the accuracy of channel estimation of the downlink channel.

In some embodiments, there are one or more other steps between two adjacent steps in step 1701-1706 or after step1706 or before step 1701.

In some embodiments, the BS may aperiodically transmit reference signals to the UE, so that the UE may aperiodically determine channel estimation of the reference sub-channel and the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels.

Illustratively, Fig. 17C shows a method of aperiodic T-MIMO channel report signaling. As shown in Figure 17C, the process includes the following steps:

1701′, BS transmit UE-specific configurations to UE via RRC.

BS may transmit UE-specific configurations to UE via RRC (or other channel) . The detail may refer to step 1701.

In some embodiments, the UE-specific configurations may predefined or transmitted to UE before, hence the step 1701′may be optional.

1702′, BS broadcast/Multicast {P, Θ} .

BS may transmit (broadcast/multicast/unicast ways) {P, Θ} . The detail may refer to step 1702.

1703′, BS transmit T-MIMO channel report trigger signal to UE.

BS transmit T-MIMO channel report trigger signal to UE to trigger channel estimation procedure.

1704′, BS broadcast reference signals.

After transmit T-MIMO channel report trigger signal to UE, BS may transmit (broadcast/multicast/unicast ways) reference signals based on P. The detail may refer to step 1703.

1705′, UE transmit T-MIMO channel report {Ψ, diag (Λ) , c₁} to the BS.

With receiving the reference signals, the UE may determine the may determine the transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels. Then the UE may transmit T-MIMO channel report (also refers to CSI) to the BS. Wherein, the T-MIMO channel report may comprise a first information indicating channel estimation for the reference sub-channel and a second information indicating a transformation relationship between channel estimation of the reference sub-channel and channel estimation of other sub-channels. The detail may refer to step 1704.

In some embodiments, the step 1703’ and 1704’ may repeat according a timing offset (e.g. a redefined or configured timing offset in X slots) .

In some embodiments, the step 1703’ to 1705’ may repeat according a timing offset (e.g. a redefined or configured timing offset in Y slots) .

In some embodiments, the BS may transmit reference signals corresponding to part of sub-channels of the downlink channel to the UE, so that the UE may perform channel estimation on part of the sub-channels to obtain channel estimation of the reference sub-channels and the transformation relationship between channel estimation of the reference sub-channels and channel estimation of other sub-channels.

For example, if the downlink channel is segmented into M sub-channels, the BS may select K sub-channels from the M sub-channels. Then the BS may transmit K sets of reference signals respectively corresponding to the K sub-channels to the UE.

In some embodiments, there are p-1 sub-channels between the n-th sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels, p-1 and n are integer, 1≤p≤M/K, 1≤n≤K-1.

Illustratively, Fig. 19A shows a flow diagram of a communication method.

As shown in Fig. 19A:

Alterative #2: the method includes the following steps:

Following the determination of the reference-signal placement scheme, the BS initiates DL channel measurement for the purpose of communication. In this phase, the BS efficiently communicates the reference-signal placement scheme and the compact representation of Θ to its UEs. Notably, all the UEs share the same reference-signal placement scheme and the same Θ, ensuring consistent and coordinated data processing. If the reference-signal placement scheme is generated from a pseudo-random method, the base station simplifies the process by providing the pseudo-random seed, number of reference signals, and the function required for generating the pseudo-random reference-signal placement scheme.

The BS transmits reference signals on the designed or learned reference-signal placement scheme, within selected K sub-channels. Rather than transmitting reference signals across every individual sub-channel of the M sub-channels, the BS strategically places these reference signals across periodic K sub-channels.

Upon receiving the designed reference-signal placement scheme, and the specific K sub-channels selected for the reference signal insertion, and compact matrix Θ, the UE estimates the channel coefficients, h, using the provided reference signals from each sub-channel of the K sub-channels;

Given the shared Θ and placement scheme P across all the periodic K sub-channels, UE computes the low-dimensional channel coefficient vector c using the equation c = Θ^-1 h or (pseudo inverse of Θ, if Θ is not a square matrix) where h represents the estimated channel coefficient vector derived from the reference signals on each sub-channel.

Continuing from the previous process, the UEs detect dynamic modes across periodic K units, G_c, using variables c₁ to c_k, corresponding to the lowest and highest units, respectively. Employing the DMD algorithm or similar methods, the UE computes essential dynamic modes, enabling efficient pattern identification and streamlined data interpretation such that c_j=G ^(j-1) /p c₁, where p is the periodicity of the K sub-channels selected for reference signal insertion across the M sub-channels.

The UEs provide feedback to the base station in the uplink, transmitting both the variable c₁ and the mode matrix G.

With the reception of variable c₁ and the mode matrix G, the base station possesses the capability to reconstruct the channel status across all M units, utilizing the formula c_j=G ^(j-1) /pc₁.

For example, consider that there are 3 sub-channels (refers p= 4) between the nth sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels. As shown in Fig. 19A, the UE may compute c₁, c₅, c₉, …, c_k corresponding to the 1st, 5th, 9th, …kth sub-channel in the K units. Then the UE may determine the dynamic mode of c₁, c₅, c₉, …, c_k by c_HighUnits=G_cc_LowUnits, wherein c_LowUnits = {c₁, c₅, c₉, …, c_k-4} and c_HighUnits = {c₅, c₉, …, c_k} . After the mode matrix G_c is determined, the UE may decomposes the mode matrix G_c in to eigenvector Ψ and eigenvalue Λ and transmit Ψ, Λ^1/4 and c₁ to the BS.

With reception of variable c₁ and the mode matrix G_c, the base station possesses the capability to reconstruct the channel coefficients of any sub-channel across all M sub-channels, utilizing the formulawhereinis channel estimation of the j-th sub-channel.

Illustratively, Fig. 19B shows a method of periodic T-MIMO channel report signaling. As shown in Fig. 19B, the process includes the following steps:

1901, BS transmit UE-specific configurations to UE via RRC.

BS may transmit UE-specific configurations to UE via RRC (or other channel) . The detail may refers to step 1701.

In some embodiments, the UE-specific configurations may predefined or transmitted to UE before, hence the step 1901 may be optional.

1902, BS broadcast/multicast {P, Θ} , unit sparity (also refers to sub-channel sparity) p etc.

BS may transmit (broadcast/multicast/unicast ways) P, Θ and unit sparity p to UE. Wherein P indicates the designed reference signal placement scheme (also refers to pilot) corresponding to at least part of the sub-channels, Θ indicates a low-dimension compact matrix of the common basis of the downlink channel, p indicates the space of sub-channels (units) to estimation (take one sub-channel every p sub-channel to estimation) .

In some embodiments, consider that the downlink channel is segmented into M sub-channels, P may indicate reference signals respectively corresponding to K sub-channels within the M sub-channels.

In some embodiments, the K sub-channels may be continues or non-continues.

For example, the nth sub-channel in the K sub-channels and the (n+1) th sub-channel are continues sub-channels.

For another example, there are p-1 sub-channels between the nth sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels. That is the references signals is p times sparse than the embodiment shown in Fig. 17A-17C, which reduce the resources used for channel estimation by at least p times.

In some embodiments, the BS may transmit information indicating p instead of p itself, such as index, p-1 etc.

In some embodiments, p may be a predefined value or transmitted to UE in other ways.

1903, BS broadcast reference signals.

BS may transmit (broadcast/multicast/unicast ways) reference signals based on P.

For example, consider that the downlink channel is segmented into M sub-channels, the BS may transmit K sets of reference signals respectively corresponding to the K sub-channels within the M sub-channels to the UE. In some embodiments, there are p-1 sub-channels between the nth sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels. That is the i-th sub-channel in the K sub-channels is ( (i-1) p+1) th sub-channel in the M sub-channels.

1904, UE transmit T-MIMO channel report {Ψ, diag (Λ) ^1/p, c₁} to the BS.

In some embodiments, consider that the UE received K sets of reference signals corresponding to K sub-channels and there are p-1 sub-channels between the nth sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels, the UE may estimate the channel coefficient vector of each channel within the K sub-channels. The UE may compress the estimated channel coefficient vectors into low-dimension channel coefficient vector based on c_i=Θ^-1h_i, wherein c_i is the low-dimension channel coefficient vector of the i-th sub-channel in the K sub-channels (also refers to the ( (i-1) p+1) th sub-channel in the M sub-channels) , h_i is the coefficient vector of the i-th sub-channel in the K sub-channels.

Then, the UE may performing DMD on the low-dimension channel coefficient vectors corresponding to the K sub-channels based on c_HighUnits=Gc_LowUnits and transmit G_c (refers to the second information) and the low-dimension channel coefficient vector of the reference sub-channel (e.g. c_1, refers to the first information) to the UE. Wherein c_LowUnits= {c₁, c₂, c₃, ……, c_K-1} and c_HighUnits = {c₂, c₃, ……, c_K} .

In some embodiments, due to the i-th sub-channel in the K sub-channels is the ( (i-1) p+1) th sub-channel in the M sub-channels, the DMD results of the low-dimension channel coefficient vectors corresponding to the M sub-channels may be G_c ^1/p (also refers to Ψ_c and diag (Λ_c) ^1/p) . So that the UE may transmit G_c ^1/p (or Ψ_c and diag (Λ_c) ^1/p) to the BS.

In some embodiments, the second information may comprise one or more matrices determined by decomposing the mode matrix G_c (or G_c ^1/p) . For example, the UE may perform Eigen-decomposing on the mode matrix G_c to determine an eigenvector (Ψ_c) and an eigenvalue (Λ) . Then the second information may comprise Ψ_c and diag (Λ_c) (or diag (Λ_c) ^1/p) . In some embodiments, the one or more matrices may be determined by decomposing the mode matrix G_c using other method (e.g. SVD, POD, etc. ) , which is not limited herein.

In some embodiments, consider that the UE received K sets of reference signals corresponding to K sub-channels, the UE may estimate the channel coefficient vector of each channel within the K sub-channels. Then, the UE may performing DMD on the channel coefficient vectors corresponding to the K sub-channels based on h_HighUnits=G_hh_LowUnits and transmit G_h (refers to the second information) and the channel coefficient vector of the reference sub-channel (e.g. h_1, refers to the first information) to the UE. Wherein h_LowUnits= {h₁, h₂, h₃, …, h_M-1} and h_HighUnits= {h₂, h₃, …, h_M} and h_i is the coefficient vector of the i-th sub-channel in the K sub-channels.

In some embodiments, due to the i-th sub-channel in the K sub-channels is the ( (i-1) p+1) th sub-channel in the M sub-channels, the DMD results of the channel coefficient vectors corresponding to the M sub-channels may be G_h ^1/p (also refers to Ψ_h and diag (Λ_h) ^1/p) . So that the UE may transmit G_h ^1/p (or Ψ_hand diag (Λ_h) ^1/p) to the BS.

In some embodiments, the second information may comprise one or more matrices determined by decomposing the mode matrix G_h (or G_h ^1/p) . For example, the UE may performing Eigen-decomposing on the mode matrix G_h to determine an eigenvector (Ψ_h) and an eigenvalue (diag (Λ_h) ) . Then the second information may comprise Ψ_h and diag (Λ_h) (or diag (Λ_h ^1/p) ) . In some embodiments, the one or more matrices may be determined by decomposing the mode matrix G_h using other method (e.g. SVD, POD, etc. ) , which is not limited herein.

In some embodiments, adopting the first sub-channel in the K sub-channels as the reference sub-channel is just an example. In other embodiments, the reference sub-channel is any sub-channel in the K sub-channels, which is not limited herein.

In some embodiment, consider that the T-MIMO channel report comprise mode matrix G_h (or G_h ^1/p, or {Ψ_h, diag (Λ_h) } , or {Ψ_h, diag (Λ_h) ^1/p } ) and h_i, any sub-channel of the downlink channel may be reconstructed by h_i+j=G_h ^j/ph_i or h_i+j=Ψ₁Λ₁ ^j/pΨ₁ ^-1h_i, , wherein the reference sub-channel is the i-th sub-channel in the M sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the M sub-channels, i and j are integer, 1≤i+j≤M.

In some embodiment, consider that the T-MIMO channel report comprise mode matrix G_c (or G_c ^1/p, or {Ψ_c and diag (Λ_c) } , or {Ψ_c and diag (Λ_c) ^1/p } ) and c_i, any sub-channel of the downlink channel may be reconstructed by h_i+j=UG_c ^j/pc_i or h_i+j=UΨ_cΛ_c ^j/pΨ_c ^-1c_i, wherein the reference sub-channel is the i-th sub-channel in the M sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the M sub-channels, i and j are integer, 1≤i+j≤M.

1905, BS broadcast reference signals.

BS may transmit (broadcast/multicast/unicast ways) reference signals. The detail may refer to step 1903.

1906, UE transmit T-MIMO channel report {Ψ, diag (Λ) ^1/p, c₁}

UE transmit T-MIMO channel report or CSI including {Ψ, diag (Λ) ^1/p, c₁} . The detail may refer to step 1904.

In some embodiments, the UE and BS may repeat step 1903 and step 1904 to keep tuning the downlink channel, which may improve the accuracy of channel estimation of the downlink channel.

According to the method above, the BS may transmit less reference signals and the UE may compute channel estimation of less sub-channels, which reduce the computing resources and communication resources consumed in the downlink channel estimation procedure.

Illustratively, Fig. 19C shows a method of aperiodic T-MIMO channel report signaling. As shown in Fig. 19C, the process includes the following steps:

1901′, BS transmit UE-specific configurations to UE via RRC.

In some embodiments, the UE-specific configurations may predefined or transmitted to UE before, hence the step 1901′may be optional.

1902′, BS broadcast/Multicast {P, Θ} , unit sparity p etc.

The detail may refer to step 1902.

1903′, BS transmit T-MIMO channel report trigger signal to UE.

1904′, BS broadcast reference signals.

After transmit T-MIMO channel report trigger signal to UE, BS may broadcast reference signals based on P. The detail may refer to step 1903.

1905′, UE transmit T-MIMO channel report {Ψ, diag (Λ) ^1/p, c₁} to the BS.

The detail may refer to step 1904.

In some embodiments, the step 1903’ and 1904’ may repeat according a timing offset (e.g. a redefined or configured timing offset in X slots) .

In some embodiments, the step 1903’ to 1905’ may repeat according a timing offset (e.g. a redefined or configured timing offset in Y slots) .

In method shown in Fig. 19A to Fig. 19C, BS transmits less reference signals while the UE estimates less sub-channels, which further reduces the resources consumed in downlink channel estimation.

It is noteworthy to mention within the context of the system's operation that the obligation for the UE to provide feedback, for example, CSI, specifically pertaining to c₁ is not mandatory. A UE may opt to relay an alternate variable from a different sub-channel, such as c_k. However, it remains imperative for the UE to explicitly notify the BS regarding the specific variable c_i that has been furnished as feedback to ensure accurate interpretation and subsequent system operations.

In some embodiments, c₁ refers to low dimension channel coefficient of the first sub-channel in the K sub-channels, c_i refers to low dimension channel coefficient of the i-th sub-channel in the K sub-channels.

In some embodiments, the notification information may be pre-negotiated or included in the CSI.

Furthermore, the UE retains the discretion to determine the direction and/or stepping for computing the dynamic mode -it may proceed either from c₁ to c_k or vice versa, from c_k to c₁. Regardless of the chosen direction and/or stepping, it remains imperative for the UE to provide explicit notification to the base station regarding the selected DMD direction to ensure synchronization and precision in processing. Alternatively, this directional preference can be orchestrated and scheduled directly by the base station.

Additionally, given the plurality of methodologies available for the computation of dynamic modes, each bearing subtle variances, it is essential to ensure system coherence. In scenarios where the system opts against a standardized approach and instead permits the use of multiple computational methodologies, the onus rests upon the UE to explicitly apprise the base station of the chosen method for dynamic mode computation. Conversely, should the need arise, the base station retains the prerogative to schedule and instruct the UE to adopt a specific computational methodology.

In relation to both Alternative #1 and Alternative #2 within the described system, it is pertinent to note that the procedural step, which employs the compact version matrix of the common basis, denoted as Θ, to project channel coefficients, h, onto variable c, can be optionally circumvented. In such instances, the computation of the dynamic mode would be performed directly over h.

The integration of low-dimensional projection in tandem with DMD across units markedly diminishes the computational complexity, storage requirements, and uplink channel state information (UL CSI) overhead inherent to the UE. With the availability of c and mode G as representative parameters, both the UE and the base station are effectively absolved from the necessity to retain the comprehensive 6G T-MIMO channel in its entirety.

Pursuant to the principles elucidated in Koopman theory, it is posited that the dynamic mode, denoted as G, exhibits a propensity toward sparsity and persistence. Such inherent characteristics of 'G' afford the potential for further compression, thereby optimizing its representation without compromising the integrity of the associated data.

The embodiments of this application further provides a communication method.

Fig. 20 illustrates a flow diagram of a communication method according to some embodiments of the disclosure. As shown in Fig. 20, the method comprising:

2001, BS transmits K sets of reference signals corresponding to K sub-channels to UE.

BS may transmit K sets of reference signals corresponding to K sub-channels of DL channel to UE.

In some embodiments, the BS may transmit the K sets of reference signals by broadcasting, multicasting or uncast ways.

In some embodiments, the DL channel may be segmented in to M (M≥K) sub-channels.

In some embodiments, the M sub-channels are segmented based on one or more of following dimensions: frequency domain, time domain, or space domain (e.g. antennas or antenna ports of the BS, antennas or antenna ports of the UE, etc. ) . The detail ways of segmenting the DL channel may refers to the embodiments shown in Fig. 13 and Fig. 14A-14D.

In some embodiments, the M sub-channels are equal-sized.

In some embodiments, each set of reference signals is corresponding to one sub-channel in the K sub-channels.

In some embodiments, the K sub-channels are continues sub-channels.

In some embodiments, there are p-1 sub-channels between the n-th sub-channel in the K sub-channels and the (n+1) -th sub-channel in the K sub-channels.

2002, UE transmits CSI corresponding to the K sets of reference signals to the BS.

With the K sets of reference signals, UE may determine or detect CSI corresponding to the K sets of reference signals and transmit the CSI to the BS.

In some embodiments, the CSI may comprise a first information indicating the channel estimation for a reference sub-channel among the K sub-channels, and a second information indicating a transformation relationship between the channel estimation of the reference sub-channel and the channel estimation of other sub-channels within the K sub-channels other than the reference sub-channel.

In some embodiments, the first information may comprise channel coefficient vector of a reference sub-channel, while the second information comprising a first transformation matrix (G_h) or a first transformation information indicating the first transformation matrix, and the first transformation matrix indicates a relationship between the reference channel coefficient vector (h_i) and the channel coefficient vectors of sub-channels within the K sub-channels other than the first channel coefficient vector (h_i) . In some embodiments, h_i+j=G_h ^jh_i, the reference sub-channel is the i-th sub-channel in the K sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, i and j are integer, 1≤i+j≤K.

In some embodiments, the first transformation information may comprises one or more matrices determined by decomposing the first transformation matrix. For example, the one or more matrices may comprise a first eigenvalue matrix (Ψ_h) and a first eigenvector matrix (Λ_h) , where in the first eigenvalue matrix (Ψ_h) and the first eigenvector matrix (Λ_h) are determined by performing Eigen-decomposition on the first transformation matrix (G_h) .

In some embodiments, the first information comprises a first low-dimension channel coefficient vector (c_i) corresponding to a first channel coefficient vector (h_i) of the reference sub-channel, while the second information comprises a second transformation matrix (G_c) or a second transformation information indicating the second transformation matrix, and the second transformation matrix indicates a relationship between the first low-dimension channel coefficient vector (c_i) and low-dimension channel coefficient vectors corresponding to the channel coefficient vectors of sub-channels within the K sub-channels other than the first low-dimension channel coefficient vector (c_i) . In some embodiments, c_i+j=G_c ^jc_i, the reference sub-channel is the i-th sub-channel in the K sub-channels, c_i+j refers to the low-dimension channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, 1≤i+j≤K, i and j are integer. In some embodiments, the second transformation information comprises one or more matrices determined by decomposing the second matrix. For example, the one or more matrices may comprise a second eigenvalue matrix (Ψ_c) and a second eigenvector matrix (Λ_c) wherein the second eigenvalue matrix (Ψ_c) and the second eigenvector matrix (Λ_c) is determined by performing Eigen-decomposition on the second transformation matrix (G_c) .

In some embodiments, the method for determining the CSI may refer to the embodiments shown in Fig. 17A or Fig. 19A, the aforementioned step 1704 or step 1904.

In some embodiments, the BS may transmit other information for determine the CSI to the UE before transmit the K sets of reference signals. For example, the BS may transmit the aforementioned common basis U, the compact matrix Θ (or information indicating Θ, such as Θ^-1 or) , the low-dimension matrix of the common basis U (such as the aforementioned matrix P or information indicating P) .

In some embodiments, the second information may be determined by aforementioned DMD, DFT, FFT, DNN method. The detail that determining the second information by DMD may refer to the embodiments shown in Fig. 17A or Fig. 19 A.

2003, BS reconstruct DL channel based on the CSI.

The BS may reconstruct DL channel based on the CSI. For example, the BS may determine the channel coefficient vectors of one or more sub-channels of DL channel. The ways the BS reconstruct the DL channel may refer to the embodiments shown in Fig. 17A or Fig. 19A, the aforementioned step 1704 or step 1904.

With the method shown in Fig. 20, the BS and the UE may transmit less information for estimating the DL channel, which may decrease the resource used for channel estimation and improve communication efficiency.

An embodiment of this application further provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions used to implement the method performed by the transmitting apparatus or the method performed by the receiving apparatus in the foregoing method embodiments.

For example, when the computer program is executed by a computer, the computer is enabled to implement the method performed by the transmitting apparatus or the method performed by the receiving apparatus in the foregoing method embodiments.

An embodiment of this application further provides a computer program product including instructions. When the instructions are executed by a computer, the computer is enabled to implement the method performed by the transmitting apparatus or the method performed by the receiving apparatus in the foregoing method embodiments.

An embodiment of this application further provides a communication system. The communication system includes the transmitting apparatus and the receiving apparatus in the foregoing embodiments.

For explanations and beneficial effects of related content of any communication apparatus provided above, refer to a corresponding method embodiment provided above. Details are not described herein again.

In some embodiments, at least parts of functions of UE or BS may be embedded into one or more chips or chipsets. The disclosure provides one or more chips or chipsets realizing at least parts of functions of UE or BS executing instructions or corresponding circuits.

Illustratively, referring to Fig. 21, Fig. 21 shows a schematic block diagram of an apparatus according to some embodiments of this disclosure. The apparatus 1000 includes a processor 1010. The processor 1010 is coupled to a memory 1020. The memory 1020 is configured to store a computer program or instructions and/or data. The processor 1010 is configured to execute the computer program or instructions and/or data stored in the memory 1020, so that the methods in the foregoing method embodiments are executed.

In some embodiments, the apparatus 1000 includes one or more processors 1010.

In some embodiments, as shown in Fig. 21, the apparatus 1000 may further include the memory 1020.

In some embodiments, the apparatus 1000 may include one or more memories 1020.

In some embodiments, the memory 1020 may be integrated with the processor 1010, or disposed separately from the processor 1010.

In some embodiments, as shown in Fig. 21, the apparatus 1000 may further include a communication interface 1030, and the communication interface 1030 is configured to communication with other apparatus/chips/device/chipset. For example, the processor 1010 is configured to receive a signal across a receiver or transmit a signal across a transmitter based on the communication interface 1030. For another example, the processor 1010 may store data to a memory or read data from a memory based on the communication interface 1030.

In some embodiments, the detail description of processor 1010 may refer to the aforementioned processor 210/260/276.

In some embodiments, the detail description of memory 1020 may refer to the aforementioned memory 208/258/278.

In some embodiments, the apparatus 1000 may comprise more modules.

In some embodiments, the apparatus 1000 may be applied as a BS or UE. And the apparatus 1000 may execute instructions to realize the steps executed by UE in Fig. 17B, Fig. 17C, Fig. 19B and Fig. 19C, or execute instructions to realize the steps executed by BS in Fig. 17B, Fig. 17C, Fig. 19B and Fig. 19C.

In some embodiments, the apparatus 1000 might be a chip or a chipset.

The processor mentioned in embodiments of this application may be a central processing unit (central processing unit, CPU) , the processor may further be another general-purpose processor, a digital signal processor (digital signal processor, DSP) , an ASIC, a FPGA, or another programmable logic device, a discrete gate, a transistor logic device, a discrete hardware component, or the like. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor or the like.

The memory mentioned in embodiments of this application may be a volatile memory or a non-volatile memory, or may include a volatile memory and a non-volatile memory. The non-volatile memory may be a ROM, a programmable read-only memory (programmable ROM, PROM) , an erasable programmable read-only memory (erasable PROM, EPROM) , an electrically erasable programmable read-only memory (electrically EPROM, EEPROM) , or a flash memory. The volatile memory may be a random access memory (RAM) . For example, the RAM may be used as an external cache. By way of example but not limitation, the RAM may include a plurality of forms in the following: a static random access memory (static RAM, SRAM) , a dynamic random access memory (dynamic RAM, DRAM) , a synchronous dynamic random access memory (synchronous DRAM, SDRAM) , a double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM) , an enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM) , a synchlink dynamic random access memory (synchlink DRAM, SLDRAM) , and a direct rambus random access memory (direct rambus RAM, DR RAM) .

It should be noted that when the processor is a general-purpose processor, a DSP, an ASIC, an FPGA, another programmable logic device, a discrete gate or a transistor logic device, or a discrete hardware component, the memory (storage module) may be integrated into the processor.

It should be further noted that the memory described in this specification is intended to include, but is not limited to, these memories and any other memory of a suitable type.

A person of ordinary skill in the art may be aware that, in combination with the examples described in embodiments disclosed in this specification, units and methods may be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed by hardware or software depends on particular applications and design constraints of the technical solutions. A person skilled in the art may use different methods to implement the described functions for each particular application, but it should not be considered that the implementation goes beyond the protection scope of this application.

It may be clearly understood by a person skilled in the art that, for the purpose of convenient and brief description, for a detailed working process of the foregoing apparatus and unit, refer to a corresponding process in the foregoing method embodiment. Details are not described herein again.

In the several embodiments provided in this application, the disclosed apparatuses and methods may be implemented in other manners. For example, the described apparatus embodiment is merely an example. For example, division into the units is merely logical function division and may be other division in an actual implementation. For example, a plurality of units or components may be combined or integrated into another system, or some features may be ignored or not performed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections may be implemented through some interfaces. The indirect couplings or communication connections between the apparatuses or units may be implemented in electronic forms, mechanical forms, or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units may be selected based on an actual requirement to implement the solutions provided in this application.

In addition, function units in embodiments of this application may be integrated into one unit, or each of the units may exist alone physically, or two or more units are integrated into one unit. All or some of foregoing embodiments may be implemented by using software, hardware, firmware, or any combination thereof. When the software is used to implement embodiments, all or a part of embodiments may be implemented in a form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the procedures or functions according to embodiments of this application are all or partially generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or another programmable apparatus. For example, the computer may be a personal computer, a server, a network device, or the like. The computer instructions may be stored in a computer-readable storage medium or may be transmitted from a computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired (for example, a coaxial cable, an optical fiber, or a digital subscriber line (DSL) ) or wireless (for example, infrared, radio, and microwave, or the like) manner. The computer-readable storage medium may be any usable medium accessible by the computer, or a data storage device, for example, a server or a data center, integrating one or more usable media. The usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, or a magnetic tape) , an optical medium (for example, a DVD) , a semiconductor medium (for example, a solid state disk (solid state disk, SSD) ) , or the like. For example, the usable medium may include but is not limited to any medium that can store program code, such as a USB flash drive, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disc.

The foregoing description is merely a specific implementation of this application, but is not intended to limit the protection scope of this application. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in this application shall fall within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims and the specification.

The various options and embodiments described herein may be combined in different permutations. Also, although the application has been described with reference to specific features and embodiments thereof, various modifications and combinations can be made thereto without departing from the application. The description and drawings above are, accordingly, to be regarded simply as an illustration of some embodiments of the application, and are contemplated to cover any and all modifications, variations, combinations or equivalents.

DEFINITIONS OF ACRONYMS & GLOSSARIES
LTE            long term evolution
NR             new radio
BS             base station
CSI            channel state information
CSI-RS         channel state information reference signal
DCI            downlink control information
DL             downlink
gNB            next generation (or 5g) base station
HARQ-ACK       hybrid automatic repeat request acknowledgement
MAC            medium access control
PDCCH          physical downlink control channel
PDSCH          physical downlink shared channel
PUCCH          physical uplink control channel
PUSCH          physical uplink shared channel
RB             resource block
RE             resource element
RRC            radio resource control
SR             scheduling request
SRS            sounding reference signal
SSB            synchronization signal block
UCI            uplink control information
UE             user equipment
UL             uplink

Claims

A communication method applied at a user equipment side, comprising:

receiving K sets of reference signal corresponding to K sub-channels of a downlink (DL) channel, K is a positive integer larger than 1; transmitting a channel state information (CSI) to a base station (BS) based on the K sets of reference signals;

wherein the CSI comprises:

a first information indicating the channel estimation for a first sub-channel among the K sub-channels, and

a second information indicating a relationship between channel estimation of the first sub-channel and channel estimation of one or more sub-channels within the K sub-channels other than the first sub-channel.
The method according to claim 1, wherein:

the K sub-channels are equally-sized.
The method according to claim 1 or 2, wherein:

the K sets of reference signals are respectively corresponding to the K sub-channels, and

each set of reference signals are transmitted on the corresponding sub-channel.
The method according to anyone of claim 1 to 3, wherein:

the first information comprises a first channel coefficient vector (h_i) of the first sub-channel;

the second information comprises a first transformation matrix (G_h) or a first transformation information indicating the first transformation matrix, and the first transformation matrix indicates a relationship between the first channel coefficient vector (h_i) and the channel coefficient vectors of one or more sub-channels within the K sub-channels other than the first channel coefficient vector (h_i) .
The method according to claim 3, wherein:

h_i+j=G_h ^jh_i, the first sub-channel is the i-th sub-channel in the K sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, i and j are integer, 1≤i+j≤K.
The method according to claim 4, wherein the first transformation information comprises one or more matrices determined by decomposing the first transformation matrix.
The method according to claim 6, wherein:

the one or more matrices comprise a first eigenvalue matrix (Ψ_h) and a first eigenvector matrix (Λ_h) ;

the first eigenvalue matrix (Ψ_h) and the first eigenvector matrix (Λ_h) are determined by performing Eigen-decomposition on the first transformation matrix (G_h) .
The method according to any one of claims 1-3, wherein:

the first information comprises a first low-dimension channel coefficient vector (c_i) corresponding to a first channel coefficient vector (h_i) of the first sub-channel;

the second information comprises a second transformation matrix (G_c) or a second transformation information indicating the second transformation matrix, and the second transformation matrix indicates a relationship between the first low-dimension channel coefficient vector (c_i) and low-dimension channel coefficient vectors corresponding to the channel coefficient vectors of one or more sub-channels within the K sub-channels other than the first low-dimension channel coefficient vector (c_i) .
The method according to claim 8, wherein:

c_i+j=G_c ^jc_i, the first sub-channel is the i-th sub-channel in the K sub-channels, c_i+j refers to the low-dimension channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, 1≤i+j≤K, i and j are integer.
The method according to claim 8, wherein:

the second transformation information comprises one or more matrices determined by decomposing the second matrix.
The method according to claim 10, wherein:

the one or more matrices comprises a second eigenvalue matrix (Ψ_c) and a second eigenvector matrix (Λ_c) ;

the second eigenvalue matrix (Ψ_c) and the second eigenvector matrix (Λ_c) is determined by performing Eigen-decomposition on the second transformation matrix (G_c) .
The method according to claim 8, wherein:

low-dimension channel coefficient vector of u-th sub-channel in the K sub-channels is determined by compressing channel coefficient vector of the u-th sub-channel using one or more low-dimension matrices of the common basis of the DL channel, u is integer and 1≤u≤K.
The method according to claim 12, wherein:

each of the one or more low-dimension matrices is using to compress channel coefficient vectors of one or more corresponding sub-channels in the K sub-channels.
The method according to claim 12, wherein the method further comprises:

receiving a compression information from the BS, wherein the compression information indicates the one or more low-dimension matrices of the common basis of the DL channel.
The method according to claim 13, wherein the compression information comprises the one or more low-dimension matrices, or inverse matrices or pseudoinverse matrices of the one or more low-dimension matrices.
The method according to any of claims 1 to 15, wherein the DL channel comprises M sub-channels, and M is an integer greater than or equal to K.
The method according to claim 16, wherein the M sub-channels are determined by dividing the DL channel based on one or more of the following dimensions: frequency domain, time domain, or space domain.
The method according to claim 16, wherein there are Q sub-channels between the nth sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels, Q and n are integer, 0≤Q≤M/K, 1≤n≤K-1.
The method according to any one of claims 1 to 18, wherein the method further comprises:

receiving a pattern information corresponding to the K sets of reference signals,

wherein the pattern information indicates at least one of the following information of each reference signal in the K sets of reference signals: index of frequency intervals, index of time intervals, index of antennas or antenna ports of the BS, index of antennas or antenna ports of the UE, values, antenna port, or transmit power; and

the receiving K sets of reference signals corresponding to K sub-channels of a DL channel comprising:

receiving the K sets of reference signals based on the pattern information.
The method according to any one of claims 1 to 19, wherein:

a first set of reference signals in the K sets of reference signals is the same as a second set of reference signals in the K sets of reference signals; or

a first set of reference signals in the K sets of reference signals is different from any other sets of reference signals in the K sets of reference signals.
A communication method applied at a base station side, comprising:

transmitting K sets of reference signals corresponding to K sub-channels of a downlink (DL) channel to a user equipment (UE) , K is a positive integer larger than 1;

receiving a channel state information (CSI) corresponding to the K sets of reference signals from the UE;

wherein, the CSI comprises:

a first information indicating the channel estimation for a first sub-channel among the K sub-channels, and

a second information indicating a relationship between channel estimation of the first sub-channel and channel estimation of one or more sub-channels within the K sub-channels other than the first sub-channel.
The method according to claim 21, wherein the method further comprising:

reconstructing the DL channel based on the CSI information.
The method according to claim 21 or 22, wherein:

the K sub-channels are equally-sized.
The method according to any one of claims 21 to 23, wherein:

the K sets of reference signals are respectively corresponding to the K sub-channels, and

each set of reference signals are transmitted on the corresponding sub-channel.
The method according to any one of claims 22 to 24, wherein:

the first information comprises a first channel coefficient vector (h_i) of the first sub-channel;

the second information comprises a first transformation matrix (G_h) or a first transformation information indicating the first transformation matrix, and the first transformation matrix indicates a between the first channel coefficient vector (h_i) and the channel coefficient vectors of one or more sub-channels within the K sub-channels other than the first channel coefficient vector (h_i) .
The method according to claim 25, wherein:

the reconstructing the DL channel based on the CSI, comprising:

determining channel coefficient vectors corresponding to the K sub-channels by h_i+j=G_h ^jh_i,

wherein the first sub-channel is the i-th sub-channel in the K sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, i and j are integer, 1≤i+j≤K.
The method according to claim 25, wherein the first transformation information comprises one or more matrices determined by decomposing the first transformation matrix.
The method according to claim 27, wherein:

the one or more matrices comprise a first eigenvalue matrix (Ψ_h) and a first eigenvector matrix (Λ_h) ;

the first eigenvalue matrix (Ψ_h) and the first eigenvector matrix (Λ_h) are determined by performing Eigen-decomposition on the first transformation matrix (G_h) .
The method according to claim 28, wherein:

the reconstructing the DL channel based on the CSI, comprising:

determining channel coefficient vectors corresponding to the K sub-channels by h_i+j=Ψ₁Λ₁ ^jΨ₁ ^-1h_i,

wherein the first sub-channel is the i-th sub-channel in the K sub-channels, h_i+j refers to the channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, i and j are integer, 1≤i+j≤K.
The method according to anyone of claims 22 to 24, wherein:

the first information comprises a first low-dimension channel coefficient vector (c_i) corresponding to a first channel coefficient vector (h_i) of the first sub-channel;

the second information comprises a second transformation matrix (G_c) or a second transformation information indicating the second transformation matrix, and the second transformation matrix indicates a relationship between the second low-dimension channel coefficient vector (c_i) and the low-dimension channel coefficient vectors of one or more sub-channels within the K sub-channels other than the second low-dimension channel coefficient vector (c_i) .
The method according to claim 30, wherein:

the reconstructing the DL channel based on the CSI, comprising:

determining channel coefficient vectors corresponding to the K sub-channels by h_i+j=UG_c ^jc_i, wherein, h_i+j refers to channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, U refers to a common basis of the DL channel, i and j are integer, 1≤i+j≤K.
The method according to claim 30, wherein the second transformation information comprises one or more matrices determined by decomposing the second transformation matrix.
The method according to claim 32, wherein:

the one or more matrices comprises a second eigenvalue matrix (Ψ_c) and a second eigenvector matrix (Λ_c) ;

the second eigenvalue matrix (Ψ_c) and the second eigenvector matrix (Λ_c) is determined by performing Eigen-decomposition on the second transformation matrix.
The method according to claim 33, wherein:

the reconstructing the DL channel based on the CSI, comprising:

determining channel coefficient vectors corresponding to the K sub-channels by h_i+j=UΨ_cΛ_c ^jΨ_c ^-1c_i, wherein, h_i+j refers to channel coefficient vector of the (i+j) -th sub-channel in the K sub-channels, U refers to a common basis of the DL channel, i and j are integer, 1≤i+j≤K.
The method according to any one of claims 30 to 34, wherein the method further comprises:

transmitting a compression information to the UE, wherein the compression information indicates one or more low-dimension matrices corresponding to the common basis of the DL channel, and each of the one or more low-dimension matrices is using for compressing channel coefficient vectors of one or more corresponding sub-channels in the K sub-channels.
The method according to claim 35, wherein the compression information comprises the one or more low-dimension matrices, or inverse matrices or pseudoinverse matrices of the one or more low-dimension matrices.
The method according to any one of claims 22 to 36, wherein the DL channel comprises M sub-channels, M is an integer greater than or equal to K.
The method according to claim 37, wherein the M sub-channels are determined by dividing the DL channel based on one or more of the following dimensions: frequency domain, time domain, or space domain.
The method according to claim 38, wherein there are Q sub-channels between the nth sub-channel in the K sub-channels and the (n+1) th sub-channel in the K sub-channels, Q and n are integer, 0≤Q≤M/K, 1≤n≤K-1.
The method according to any one of claims 21 to 39, wherein the method further comprises:

transmitting a pattern information corresponding to the K sets of reference signals to the UE,

wherein the pattern information indicates at least one of the following information of each reference signal in the K sets of reference signals: index of frequency intervals, index of time intervals, index of antennas or antenna ports of the BS, or index of antennas or antenna ports of the UE, values, antenna port, or transmit power.
The method according to any one of claims 21 to 40, wherein:

a first set of reference signals in the K sets of reference signals is the same as a second set of reference signals in the K sets of reference signals; or

a first set of reference signals in the K sets of reference signals is different from any other sets of reference signals in the K sets of reference signals.
An apparatus, wherein the apparatus comprises a processor, wherein the processor is configured to execute one or more instructions stored in a memory, to enable the apparatus to implement the method according to any one of claims 1 to 20 or claims 21 to 41.
The apparatus of claim 42, wherein the apparatus comprises the memory.
The apparatus of claim 42 or 43, wherein the apparatus comprises a communication interface, configured to input and/or output information.
An apparatus, wherein the apparatus comprises a function or unit to perform the method according to any one of claims 1 to 20 or perform the method according to any one of claims 21 to 41.
A communication system, comprising a transmitting apparatus and a receiving apparatus, wherein the transmitting apparatus performs the method according to any one of claims 1 to 20, and the receiving apparatus performs the method according to any one of claims 21 to 41.
A computer readable storage medium, comprising one or more instructions, wherein when the instructions are run on a computer, the computer performs the method according to any one of claims 1 to 20, or the method according to any one of claims 21 to 41.