WO2020073342A1

WO2020073342A1 - Non-linear precoding procedure

Info

Publication number: WO2020073342A1
Application number: PCT/CN2018/110151
Authority: WO
Inventors: Nuan SONG
Original assignee: Nokia Shanghai Bell Co Ltd; Nokia Solutions and Networks Oy
Current assignee: Nokia Shanghai Bell Co Ltd; Nokia Solutions and Networks Oy
Priority date: 2018-10-12
Filing date: 2018-10-12
Publication date: 2020-04-16
Anticipated expiration: 2021-04-12
Also published as: CN112823479A; EP3834296A4; EP3834296A1; CN112823479B

Abstract

There are methods, devices and computer readable mediums for a unified non-linear precoding procedure. The method(400) comprises transmitting, to a network device, channel information of about a channel between a network device and the terminal device; receiving, from the network device, an indication of a receiving mode for the terminal device to decode precoded data and a precoded reference signal, the receiving mode being determined based on a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and being determined based on a parameter set determined based on the channel information by the network device and decoding the precoded data based on the precoded reference signal and the indication.

Description

NON-LINEAR PRECODING PROCEDURE

TECHNICAL FIELD

Embodiments of the present disclosure generally relate to the field of telecommunication, and in particular, to methods, devices and computer readable storage media for a non-linear precoding procedure.

BACKGROUND

As one of the advanced transmission schemes in NR MIMO, non-linear precoding shows promising advantages to achieve a significantly enhanced system performance and support more users than linear precoding. With full CSI at the transmitter side, “Dirty-Paper” Coding (DPC) technique that relies on a pre-subtraction of the non-causally known interference can achieve the maximum sum rate of the system and provide the maximum diversity order. There are generally two types of non-linear precoding techniques, namely Tomlinson-Harashima Precoding (THP) and Vector Perturbation (VP) . They are simplified and efficient versions of DPC, which are less computationally demanding and thus more attractive for practical implementation.

SUMMARY

In general, example embodiments of the present disclosure provide methods, devices and computer readable storage media for a non-linear precoding procedure.

In a first aspect, there is provided method implemented at a network device. The method comprises determining, at the network device, a parameter set based on channel information about a channel between the network device and a terminal device; determining, based on the parameter set, a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode comprising one of THP and VP;determining, based on the precoding mode, a receiving mode for the terminal device to decode the precoded data and the precoded reference signal and transmitting an indication of the receiving mode to the terminal device.

In a second aspect, there is provided method implemented at a terminal device. The method comprises transmitting, to a network device, channel information of about a channel between a network device and the terminal device; receiving, from the network device, an indication of a receiving mode for the terminal device to decode precoded data and a precoded reference signal, the receiving mode being determined based on a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode comprising one of THP and VP and being determined based on a parameter set determined based on the channel information by the network device and decoding the precoded data based on the precoded reference signal and the indication.

In an third aspect, there is provided a terminal device. The device comprises at least one processor; and at least one memory including computer program codes. The at least one memory and the computer program codes are configured to, with the at least one processor, cause the device at least to perform the method according to the first aspect.

In a fourth aspect, there is provided a network device. The device comprises at least one processor; and at least one memory including computer program codes. The at least one memory and the computer program codes are configured to, with the at least one processor, cause the device at least to perform the method according to the second aspect.

In an fifth aspect, there is provided an apparatus comprising means to perform the steps of the method according to the first aspect.

In a sixth aspect, there is provided an apparatus comprising means to perform the steps of the method according to the second aspect.

In a seventh aspect, there is provided a computer readable medium having a computer program stored thereon which, when executed by at least one processor of a device, causes the device to carry out the method according to the first aspect.

In an eighth aspect, there is provided a computer readable medium having a computer program stored thereon which, when executed by at least one processor of a device, causes the device to carry out the method according to the second aspect.

It is to be understood that the summary section is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become easily comprehensible through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

Through the more detailed description of some example embodiments of the present disclosure in the accompanying drawings, the above and other objects, features and advantages of the present disclosure will become more apparent, wherein:

FIG. 1 shows an example communication system 100 in which example embodiments of the present disclosure can be implemented;

FIG. 2 shows a diagram of an example process 200 for a non-linear precoding procedure according to some example embodiments of the present disclosure;

FIG. 3A and 3B show diagrams of cell throughput according to some example embodiments of the present disclosure, respectively;

FIG. 4 shows a flowchart of an example method 400 for a NLP procedure according to some example embodiments of the present disclosure;

FIG. 5 shows a flowchart of an example method 500 for a NLP procedure according to some example embodiments of the present disclosure; and

FIG. 6 is a simplified block diagram of a device that is suitable for implementing example embodiments of the present disclosure.

Throughout the drawings, the same or similar reference numerals represent the same or similar element.

DETAILED DESCRIPTION

Principle of the present disclosure will now be described with reference to some example embodiments. It is to be understood that these embodiments are described only for the purpose of illustration and help those skilled in the art to understand and implement the present disclosure, without suggesting any limitation as to the scope of the disclosure. The disclosure described herein can be implemented in various manners other than the ones described below.

In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure belongs.

As used herein, the term “communication network” refers to a network that follows any suitable communication standards or protocols such as long term evolution (LTE) , LTE-Advanced (LTE-A) and 5G NR, and employs any suitable communication technologies, including, for example, Multiple-Input Multiple-Output (MIMO) , OFDM, time division multiplexing (TDM) , frequency division multiplexing (FDM) , code division multiplexing (CDM) , Bluetooth, ZigBee, machine type communication (MTC) , eMBB, mMTC and uRLLC technologies. For the purpose of discussion, in some embodiments, the LTE network, the LTE-A network, the 5G NR network or any combination thereof is taken as an example of the communication network.

As used herein, the term “network device” refers to any suitable device at a network side of a communication network. The network device may include any suitable device in an access network of the communication network, for example, including a base station (BS) , a relay, an access point (AP) , a node B (NodeB or NB) , an evolved NodeB (eNodeB or eNB) , a gigabit NodeB (gNB) , a Remote Radio Module (RRU) , a radio header (RH) , a remote radio head (RRH) , a low power node such as a femto, a pico, and the like. For the purpose of discussion, in some embodiments, the eNB is taken as an example of the network device.

The network device may also include any suitable device in a core network, for example, including multi-standard radio (MSR) radio equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs) , Multi-cell/multicast Coordination Entities (MCEs) , Mobile Switching Centers (MSCs) and MMEs, Operation and Management (O&M) nodes, Operation Support System (OSS) nodes, Self-Organization Network (SON) nodes, positioning nodes, such as Enhanced Serving Mobile Location Centers (E-SMLCs) , and/or Mobile Data Terminals (MDTs) .

As used herein, the term “terminal device” refers to a device capable of, configured for, arranged for, and/or operable for communications with a network device or a further terminal device in a communication network. The communications may involve transmitting and/or receiving wireless signals using electromagnetic signals, radio waves, infrared signals, and/or other types of signals suitable for conveying information over air. In some embodiments, the terminal device may be configured to transmit and/or receive information without direct human interaction. For example, the terminal device may transmit information to the network device on predetermined schedules, when triggered by an internal or external event, or in response to requests from the network side.

Examples of the terminal device include, but are not limited to, user equipment (UE) such as smart phones, wireless-enabled tablet computers, laptop-embedded equipment (LEE) , laptop-mounted equipment (LME) , and/or wireless customer-premises equipment (CPE) . For the purpose of discussion, in the following, some embodiments will be described with reference to UEs as examples of the terminal devices, and the terms “terminal device” and “user equipment” (UE) may be used interchangeably in the context of the present disclosure.

As used herein, the term “cell” refers to an area covered by radio signals transmitted by a network device. The terminal device within the cell may be served by the network device and access the communication network via the network device.

As used herein, the term “circuitry” may refer to one or more or all of the following:

(a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry) and

(b) combinations of hardware circuits and software, such as (as applicable) : (i) a combination of analog and/or digital hardware circuit (s) with software/firmware and (ii) any portions of hardware processor (s) with software (including digital signal processor (s) ) , software, and memory (ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions) and

(c) hardware circuit (s) and or processor (s) , such as a microprocessor (s) or a portion of a microprocessor (s) , that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.

This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and/or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobile device or a similar integrated circuit in server, a cellular network device, or other computing or network device.

As used herein, the singular forms “a” , “an” , and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. The term “includes” and its variants are to be read as open terms that mean “includes, but is not limited to” . The term “based on” is to be read as “based at least in part on” . The term “one embodiment” and “an embodiment” are to be read as “at least one embodiment” . The term “another embodiment” is to be read as “at least one other embodiment” . Other definitions, explicit and implicit, may be included below.

FIG. 1 illustrates a communication network 100 in which embodiments of the present disclosure can be implemented. Communication network 100 comprises network devices (e.g., gNBs) 110 and terminal devices (e.g., UEs) 120-1, ..., 120-K and 120’-1, ..., 120’-k (hereinafter collectively referred to as terminal devices 120 or UEs 120) in communication therewith. The gNB 100 and the UEs 120-1, ..., 120-K and 120’-1, ..., 120’-k may communicate with each other via a channel 130 between the gNB 100 and the UEs 120-1, ..., 120-K and 120’-1, ..., 120’-K.

It is to be understood that the numbers of network devices and terminal devices are shown only for the purpose of illustration without suggesting any limitation. The communication network 100 may include any suitable numbers of network devices and terminal devices. The communication between the network device 110 and the terminal device 120 may utilize any suitable technology that already exists or will be developed in the future.

As one of the advanced transmission schemes in NR MIMO, a Non-Linear Precoding (NLP) shows promising advantages to achieve a significantly enhanced system performance and support more users than linear precoding. There are several types of non-linear precoding techniques, for example, THP and VP.

Most existing non-linear precoding procedures may only support THP based precoding. THP successively handles the interference at the transmitter side by applying modulo operations in the feedback loop and avoids transmit power enhancement. Another scheme is VP, where the signal to be transmitted to all the receivers is jointly perturbed by another vector to minimize transmit power from the extended constellation. The VP scheme is able to further enhance the performance with a little higher complexity, as compared to the THP.

In general, for the THP scheme, the signal at the transmitter undergoes the feedback loop and the modulo operation to suppress the interference among layers as well as to reduce the transmit power. It is then precoded with the feedforward filter and transmitted to the UE. At the UE side, when UE has multiple antennas, the receive combining is required to map the received signal from the antennas to the layers. Each UE should be able to estimate the effective channel via the proper DMRS and the weighting coefficients for the data demodulation. For the VP scheme, the signal is perturbed, precoded, and scaled with a power normalization factor

at the transmitter. Accordingly, UE should also estimate the effective channel via the specific DMRS to obtain the receive combining. But different from THP, if a UE performs precoding using the VP scheme, the UE should scale the data stream by the weighting

that is related to the power normalization factor at the transmitter and is the same for all UEs.

For both schemes, modulo operation is required at the receiver side to recover the desired signal. However, to support both THP and VP based schemes and enhance the system performance, one key technical challenge is that different transmit procedures of THP and VP lead to different receive procedures at the UE.

Some approaches attempt to support both THP and VP. For example, it is proposed that the spatial multiplexing DMRS undergoes non-linear precoding together with data. In this way, the spatial multiplexing DMRS can be transmitted in the same resource to reduce the overhead. However, the gNB and the UE require a DMRS corrector and a perturbation vector adder, respectively, which imposes a high implementation complexity at both ends. Furthermore, in this approach, it is not considered that how the receive combining is designed when UE has multiple antennas.

Further, regarding the existing VP algorithms for MU MIMO systems, such as Geometric Mean Decomposition (GMD) , receive combining is obtained by the GMD and the perturbed vector is optimized conditioned on the receive combiner. Such schemes impose difficulties for the UE to calculate the receive combiner according to the estimation of the effective channel via DMRS. Accordingly the reception scheme is not applicable to THP, as the unified procedure requires the same receive processing for both THP and VP. Furthermore, the proposed algorithm will lead to a large performance degradation if the number of receive antennas is greater than the number of ports at transmitter. This is because the limitation of pure ZF algorithms.

As shown in FIG. 1, a Non-Linear Precoding (NLP) procedure may be performed at gNB 110. The precoding procedure may be performed based on a THP procoder 11 l-1 or a VP precoder 111-2. It should be understood that gNB 110 may determine the NLP mode and precode the data streams for multiple users by the corresponding precoder. Although, the gNB 110, in the example of FIG. 1, includes NLP precoders 111-1 and 111-2, thoses skilled in the art would understand that the gNB 110 may only include one of the NLP precoders 111-1 and 111-2. After undergoing a NLP procedure, the data streams and corresponding reference signals (for example, Demodulation Reference Signal (DMRS) ) may be transmitted to the corresponding UEs via the channel 130.

As shown in FIG. 1, there are K UEs in the system and each UE has

antennas () . There are M _T antennas and N _T ports at the gNB and in total

data streams, where the gNB transmits r _k streams to the UE k. For the THP based NLP procedure (the NLP procedure performed by the precoder 111-1) , the matrix

is the feedforward filter and the matrix

corresponds to the feedback filter for interference preprocessing. The data

undergoes the regular THP based NLP procedure while the DMRS

goes through the feedback loop and the feedforward filter. At the UE (UEs 120-1, ..., 120-K) side, the receive processing mainly includes a linear combiner

a weighting process

and a modulo operation Mod (.) before the demodulation and decoding. Each UE measures its effective channel via DMRS and obtains the receive combiner W _k as well as the weights for the data stream D _k.

Further, for the VP based NLP procedure (i.e. the NLP procedure performed by the precoder 111-2) , the data is perturbed by the vector τl, where τ is a real number and l is a r-dimensional complex vector a + ib with a and b being integers. The DMRS of the VP scheme d does not undergo the vector perturbation, but is precoded by P together with data and scaled by the power normalization factor

At the UE side UEs 120’-1, ..., 120’-K, each UE also measures its effective channel via DMRS and recovers receive combining weights W _k as well as the power normalization factor

for data modulation. The channel

is the beamformed CSI, where the beamforming (e.g., eigen beamformer) maps N _T ports to M _T antenna elements and

is the total number of receive antennas from all UEs.

Due to the possibility of performing different NLP procedures at gNB, the corresponding decoding procedures have to be performed at UEs. As described above, most existing non-linear precoding procedures may only support THP based precoding. Therefore, incorporating VP in the system to allow a further performance enhancement with a minimized impact on the transceiver implementation will be discussed in the present disclosure.

Principle and implementations of the present disclosure will be described in detail below with reference to FIG. 2, which shows process 200 according to example embodiments of the present disclosure. For the purpose of discussion, the process 200 will be described with reference to FIG. 1. The process 200 may involve a random access procedure.

As shown in FIG. 2, at 210, the gNB 110 receives channel information from the UE 120. The channel information may be considered as Channel State Information (CSI) , which characterizes the channel between the gNB 110 and the UE 120. At 220, the gNB 110 determines parameter set based on the received channel information, to design the procoding scheme, i.e. the recoding mode for precoding data and a reference signal for the the UE 120. As mentioned above, the precoding mode indicating a non-linear precoding scheme used by the network device.

As an option, receive combining matrix W may be considered to be independent of the precoding design. In some example embodiments, the gNB 110 may obtain effective CSI from the channel information. In this case, the gNB 110 may determine a matrix for characterizing the channel from the effective CSI, the matrix being associated with the number of ports N _T of the gNB 110, the number of antennas M _T of the gNB 110, the number of antennas

of the UE 120 and a combining matrix W of the UE 120 and determine the parameter set based on this matrix.

In some example embodiments, if the parameter set is associated with THP, the parameter set may comprise at least one of a precoding matrix P and a feedback matrix B ^-1.

In some example embodiments, if the parameter set is associated with VP, the parameter set may comprise at least one of a precoding matrix P, a power normalization factor

and a perturbed vector l.

For example, the total receive combining matrix W is block diagonal, which may written in Equation (1) as below:

where blkdiag {·} represents constructing the block diagonal matrix.

For the VP procoding scheme, assuming Maximum Ratio Combining (MRC) receiver is applied and the receive combiner for each UE can be obtained in Equation (2) as below:

W _k = H _k (2)

The VP precoder (for example, the precoder 111-2 of FIG. 1) is then designed based on the channel

or the whole channel

Considering Zero-Forcing (ZF) based VP scheme and the precoding matrix P can be obtained by Equation (3) as below:

For this case, the key point is to choose the optimal l that is used to perturb the data vector. The vector l is chosen to minimize the following cost function in Equation (4) as below:

where ||·|| represents the Euclidean norm. This is a r-dimensional integer-lattice least squares problem and can be efficiently solved by Lenstra-Lenstra-Lovász (LLL) algorithm on the columns of P.

The transmitted signal should be normalized by the factor

with Equation (5) as below:γ = ||P (s+ τl) || ² (5)

For the THP procoding scheme, similarly to VP, the MRC receiver can be applied to each UE based on Equation (2) and the THP precoding is design according to the channel

By calculating an LQ decomposition on the channel

Equation (6) may be obtained as below:

where L is a lower triangular matrix and Q is a unitary matrix. The feedforward and feedback filters for the THP algorithm can be obtained in Equations (7) - (9) as below:

P = Q ^H (7)

B = DL (8)

and

D = diag {L ^-1 (1, 1) , ..., L ^-1 (r, r) } (9)

respectively, where L (i, i) is the i-th diagonal element of the matrix L.

As another option, receive combining matrix W may be considered to be designed jointly with the precoding design. In some example embodiments, the gNB 110 may obtain full CSI from the channel information. In general, the full CSI may refer to CSI at the antenna level for both the base station and the terminal.

In this case, the gNB 110 may determine a matrix for characterizing the channel from the full CSI, the matrix being associated with the number of ports N _T of the gNB 110, the number of antennas M _T of the gNB 110 and the number of antennas

of the UE 120 and determine the parameter set based on this matrix.

In some example embodiments, if the parameter set is associated with THP, the parameter set may comprise at least one of a precoding matrix P and a feedback matrix B ^-1 and a combining matrix W for the UE 120.

a perturbed vector l and a combining matrix W for the UE 120.

In the case of the receive combining is designed jointly with NLP, for the VP procoding scheme, according to the ZF criterion, the precoding and combining matrices should follow Equation (10) as below:

W ^HHP = I (10)

where I denotes the identity matrix, and the VP precoding design is to choose the optimal perturbed vector that minimizes the cost function in Equation (4) with the obtained P.

To achieve the matrix decomposition and satisfy the ZF criterion in Equation (10) , it is proposed to calculate the receive combining matrix W via a certain matrix decomposition such as GMD by Equation (11) as follow:

W ^HHF = L (11)

where L is a lower triangular matrix.

In this case, the channel matrix H in Equation (11) may be updated by successive decomposing based on at least one of Geometric Mean Decomposition (GMD) , Singular Value Decomposition (SVD) , or Generalized Triangular Decomposition (GTD) . The Equation (11) may be reformulated into Equation (12) as below:

W ^HH (FL ^-1) = I (12)

Equation (12) satisfies the ZF criterion in Equation (10) . The above mentioned algorithm for VP precoding scheme may be named as Successive Decomposition VP (SD-VP) .

For example, taken the GMD as an example to achieve the successive matrix decomposition of H in Equation (11) . The related matrices can be written in Equation (13) as follow:

wh ere

correspond to the receive combining, beamformed channel, feedforward filter, and equivalent lower-triangular channel for users from k to K. For the 1 ^st UE (for example, UE 120’-1 of FIG. 1) , the receive beamforming and feedforward filter can be obtained by applying GMD algorithm to construct the lower triangular matrix

where W ₁ and F ₁ contain orthogonal columns. Additionally, to make sure that 1 ^st UE does not interfere with the rest scheduled UEs, i.e.,

projecting

by eliminating the effect of F1 as

and obtain another lower-triangular equivalent channel

which can be similarly solved by GMD. The total lower-triangular matrix L in Equation (13) can be constructed by calculating Ξ ₁ as

Then the further decomposition of the matrix

can be carried out in the same manner for the 2 ^nd UE to K ^th UE (for example, UE 120’-K of FIG. 1) . Besides GMD, SVD and GTD can also be similarly applied.

Then the precoding matrix P can be defined by Equation (14) as below:

P = FL ^-1 (14)

The transmitted signal should be normalized by the factor

with Equation (5) .

In the case of the receive combining is designed jointly with NLP, for the THP procoding scheme, applying the block diagonal GMD based THP, i.e., by constructing the channel

into a lower triangular structure in Equation (15) as below:

W ^HHP = L (15)

The block diagonal GMD-THP algorithm can be implemented recursively to obtain W _k, P _k, and L _k.

Back refer to FIG. 2, at 220, the gNB 110 also determine a receiving mode for the UE 120 to decode the precoded data and the precoded reference signal based on the precoding mode.

In some example embodiments, the gNB 110 may perform a non-linear process for the data and a linear process for the reference signal and then precode the processed data and the reference signal based on the precoding mode. For example, as shown in FIG. 1, for the THP based NLP procedure, the data undergoes the regular THP based NLP procedure while the DMRS goes through the feedback loop and the feedforward filter, while for the VP based NLP procedure, the data is perturbed by the vector and the DMRS does not undergo the vector perturbation.

As shown in FIG. 2, at 230, the gNB 110 transmits an indication of the receiving mode to the UE 120.

At 240, after receiving the indication of a receiving mode, the UE 120 decodes the precoded data based on the precoded reference signal and the indication. The precoded reference signal, which generated by gNB 110, is considered to be a unified indication for indicating the unified decoding mode to the UE 120.

In some example embodiments, the UE 120 may determine a weight associated with the precoding mode by decoding the precoded reference signal based on the indication. For the THP precoding scheme, the weight may comprise a weighting process

For the VP precoding scheme, the weight may comprise power normalization factor

In some example embodiments, if full CSI is obtained by the gNB 110, the UE 120 may also determine a combing matrix W for the UE 120 based on the the precoded reference signal.

As mentioned above, the reference signal (for example, DMRS) is only linearly precoded and undergoes the transmit processing that is different from the data. In the case of THP procedure, the DMRS goes through the feedback loop B ^-1 as well as the feedforward filter P, which represents the equivalent channel to be measured at UE by Equation (16) as below:

H _e = HPB ^-1 (16)

In the case of VP procedure, the DMRS is only precoded by the ZF precoder P as shown in Equation (14) and the measured equivalent channel via this DMRS in Equation (17) as follow:

Regardless of the precoding types (i.e., THP or VP) , UE receives the unified non-linear precoding mode indication and carries out the same estimation and reception procedure to demodulate data.

For VP precoding procedure, the received DMRS can be written in Equation (18) as follow:

where n is the Additive White Gaussian Noise (AWGN) with covariance σ ².

Accordingly the DMRS after the receive combiner can be expressed in Equation (18) as:

In a case of receive combining matrix W is independent of the precoding design, the receive combining matrix W can be calculated by Equation (2) using the beamformed channel H. Plugging the precoding matrix P from Equation (3) into Equation (19) and obtain Equation (20) as:

At each stream, the equivalent received DMRS is represented in Equation (21) :

where

is the AWGN after receive combining. The received data stream should also be scaled back by the power normalization factor, which can be estimated directly from Equation (21) via DMRS. The weight of each stream is calculated by the inverse of the estimated power scaling factor.

In a case of the receive combining W is designed jointly with NLP, reformulating the received DMRS in Equation (18) as Equation (22) as below:

At each stream, the equivalent received DMRS is represented in Equation (23) :

where w _j is the j-th column of the receive combining matrix W. Thus, v _j (denoted by

) and recover the receive combining matrix by

as well as the power scaling factor by

will be simply estimated.

For THP precoding procedure, if receive combining matrix Wis independent of the precoding design, the MRC receiver is applied and the LQ decomposition in THP is carried out on the effective channel

which can be represent in Equation (24) as:

where diag {·} denotes constructing a diagonal matrix.

The DMRS received after MRC combiner is written as below:

The received DMRS for each data stream is denoted by Equation (26) :

The weights for the data stream at each UE can be computed by taking the inverse of the estimated coefficient, i.e.,

In a case of the receive combining W is designed jointly with NLP, assuming GMD-THP is applied for this case, according to Equation (11) with P = F , the received DMRS before receive combining is calculated by Equation (27) :

r _d = HPB ^-1d + n = W. diag {L (1, 1), ..., L (r, r) } d + n (27)

The received DMRS for each data stream is denoted by Equation (28) :

(28)

The receive combiner for each UE is thus obtained by the normalized estimate

i.e.,

The weighting of the stream should be the inverse of L (j, j) , i.e.,

from Equation (28) .

According to the embodiments of the present disclosure, the transceiver implementation is simplified when the gNB intends to perform the procoding procedure including both THP and VP schemes. By including the VP precoding functionality, the system performance may be enhanced with marginal changing on the non-linear precoding procedure. The UE does not have to know the type of non-linear precoding methods, i.e., UE transparent, which simplifies the procedure and addition signalling to UE.

Further, according to the embodiments of the present disclosure, the gNB is allowed to be dynamically switched between the non-linear pecoding schemes, in order to improve the system and UE performance.

The cell throughput performances of the proposed non-linear precoding procedure for both THP and VP algorithms are evaluated in both cases, namely, the receive combining W is designed to be independent of NLP and the receive combining W is designed jointly with NLP cases. The detailed simulation parameters can be found in Table 1.

Table 1: Simulation setup

FIG. 3A and 3B exhibit the Cumulative Distribution function (CDF) of the cell throughput in the above two cases. FIG. 3A shows one case with M _T = 32, N _T = 8, K = 8 , while FIG. 3B shows the other case with M _T = 64, N _T = 16, K = 16. It can be observed that the proposed VP based algorithms (i.e., curves 330 and 340 in FIG. 3A and curves 330’and 340’in FIG. 3B. ) for both cases outperform their THP counterparts (i.e., curves 310 and 320 in FIG. 3A and curves 310’and 320’in FIG. 3B. ) . The proposed “SD-VP” scheme in the case that the receive combining W is designed jointly with NLP (i.e., curves 340 in FIG. 3A and 340’in FIG. 3B) , shows a performance enhancement as compared to the VP method in the case that the receive combining W is designed to be independent of NLP (i.e., curves 330 in FIG. 3A and 330’in FIG. 3B) .

More details of the example embodiments in accordance with the present disclosure will be described with reference to FIGs. 4-5.

FIG. 4 shows a flowchart of an example method 300 for NLP procedure according to some example embodiments of the present disclosure. The method 400 can be implemented at the gNB 110 as shown in FIG. 1. For the purpose of discussion, the method 400 will be described with reference to FIG. 1.

At 410, the network device (gNB) 110 determines a parameter set based on channel information about a channel between the network device and a terminal device.

In some example embodiments, the network device 110 may obtain effective Channel State Information CSI from the channel information. The network device 110 may further determine a first matrix for characterizing the channel from the effective CSI, the first matrix being associated with the number of antennas of the network device, the number of ports of the network device, the number of antennas of the terminal device and a combining matrix of the terminal device and determine the parameter set based on the first matrix.

In some example embodiments, the parameter set is associated with THP and comprises at least one of a precoding matrix; and a feedback matrix.

In some example embodiments, the parameter set is associated with VP and comprises at least one of a precoding matrix; a power normalization factor; and a perturbed vector.

In some example embodiments, the network device 110 may obtain full Channel State Information CSI from the channel information. The network device 110 may further determine a second matrix for characterizing the channel from the full CSI, the second matrix being associated with the number of antennas of the network device, the number of ports of the network device, and the number of antennas of the terminal device and determine the parameter set based on the second matrix.

In some example embodiments, the parameter set is associated with THP and comprises at least one of a precoding matrix; a feedback matrix and a combining matrix for the terminal device.

In some example embodiments, the parameter set is associated with VP and comprises at least one of a precoding matrix; a power normalization factor; a perturbed vector and a combining matrix for the terminal device.

In some example embodiments, if the parameter set is associated with VP, the network device 110 may update the second matrix by successive decomposing the second matrix based on at least one of Geometric Mean Decomposition, GMD; Singular Value Decomposition, SVD; and Generalized Triangular Decomposition, GTD, and determine the parameter set based on the updated second matrix

At 420, the network device 110 determines, based on the parameter set, a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device.

At 430, the network device 110 determines, based on the precoding mode, a receiving mode for the terminal device to decode the precoded data and the precoded reference signal.

At 440, the network device 110 transmits an indication of the receiving mode to the terminal device.

In some example embodiments, the network device 110 may further perform a non-linear process for the data and a linear process for the reference signal and precode the processed data and the reference signal based on the precoding mode.

FIG. 5 shows a flowchart of an example method 500 for NLP procedure according to some example embodiments of the present disclosure. The method 500 can be implemented at the UE 120 as shown in FIG. 1. For the purpose of discussion, the method 500 will be described with reference to FIG. 1.

At 510, the terminal device (UE) 120 transmits, to a network device 110, channel information of about a channel between a network device and the terminal device.

At 520, the terminal device 120 receives, from the network device 110, an indication of a receiving mode for the terminal device to decode precoded data and a precoded reference signal, the receiving mode being determined based on a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and being determined based on a parameter set determined based on the channel information by the network device.

At 530, the terminal device 120 decodes the precoded data based on the precoded reference signal and the indication.

In some example embodiments, the terminal device 120 may determine a weight associated with the precoding mode by decoding the precoded reference signal based on the indication; and decode the precoded data based on the weight.

In some example embodiments, if full CSI is obtained by the network device 110, the terminal device 120 may determine a combing matrix for the terminal device based on the precoded reference signal.

In some example embodiments, an apparatus capable of performing the method 400 (for example, the gNB 110) may comprise means for performing the respective steps of the method 400. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module.

In some example embodiments, the apparatus comprises: means for determining, at the network device, a parameter set based on channel information about a channel between the network device and a terminal device; means for determining, based on the parameter set, a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device; means for determining, based on the precoding mode, a receiving mode for the terminal device to decode the precoded data and the precoded reference signal; and means for transmitting an indication of the receiving mode to the terminal device.

In some example embodiments, the means for determining the parameter set may comprises means for obtaining effective Channel State Information CSI from the channel information; means for determining a first matrix for characterizing the channel from the effective CSI, the first matrix being associated with the number of antennas of the network device, the number of ports of the network device, the number of antennas of the terminal device and a combining matrix of the terminal device; and means for determining the parameter set based on the first matrix.

In some example embodiments, the means for determining the parameter set may comprises means for obtaining full Channel State Information CSI from the channel information; means for determining a second matrix for characterizing the channel from the full CSI, the second matrix being associated with the number of antennas of the network device, the number of ports of the network device and the number of antennas of the terminal device; and means for determining the parameter set based on the second matrix.

In some example embodiments, the parameter set is associated with VP, the means for determining the parameter set may comprises means for updating the second matrix by successive decomposing the second matrix based on at least one of Geometric Mean Decomposition, GMD; Singular Value Decomposition, SVD; and Generalized Triangular Decomposition, GTD, and means for determining the parameter set based on the updated second matrix.

In some example embodiments, the apparatus may further comprise means for performing a non-linear process for the data and a linear process for the reference signal and means for precoding the processed data and the reference signal based on the precoding mode.

In some example embodiments, an apparatus capable of performing the method 500 (for example, the UE 120) may comprise means for performing the respective steps of the method 500. The means may be implemented in any suitable form. For example, the means may be implemented in a circuitry or software module.

In some example embodiments, the apparatus comprises: means for transmitting, to a network device, channel information of about a channel between a network device and the terminal device; means for receiving, from the network device, an indication of a receiving mode for the terminal device to decode precoded data and a precoded reference signal, the receiving mode being determined based on a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and being determined based on a parameter set determined based on the channel information by the network device; and means for decoding the precoded data based on the precoded reference signal and the indication.

In some example embodiments, the means for decoding may comprise means for determining a weight associated with the precoding mode by decoding the precoded reference signal based on the indication; and means for decoding the precoded data based on the weight.

In some example embodiments, if full Channel State Information CSI is obtained by the network device, the means for decoding may comprise means for determining a combing matrix for the terminal device based on the precoded reference signal.

FIG. 6 is a simplified block diagram of a device 600 that is suitable for implementing example embodiments of the present disclosure. The device 600 can be considered as a further example implementation of gNB 110 as shown in FIG. 1. Accordingly, the device 600 can be implemented at or as at least a part of UE 120.

As shown, the device 600 includes a processor 610, a memory 620 coupled to the processor 610, a suitable transmitter (TX) and receiver (RX) 640 coupled to the processor 610, and a communication interface coupled to the TX/RX 640. The memory 610 stores at least a part of a program 630. The TX/RX 640 is for bidirectional communications. The TX/RX 640 has at least one antenna to facilitate communication, though in practice an Access Node mentioned in this application may have several ones. The communication interface may represent any interface that is necessary for communication with other network elements, such as X2 interface for bidirectional communications between eNBs, S1 interface for communication between a Mobility Management Entity (MME) /Serving Gateway (S-GW) and the eNB, Un interface for communication between the eNB and a relay node (RN) , or Uu interface for communication between the eNB and a terminal device.

The program 630 is assumed to include program instructions that, when executed by the associated processor 610, enable the device 600 to operate in accordance with the example embodiments of the present disclosure, as discussed herein with reference to Figs. 2 to 5. The example embodiments herein may be implemented by computer software executable by the processor 610 of the device 600, or by hardware, or by a combination of software and hardware. The processor 610 may be configured to implement various example embodiments of the present disclosure. Furthermore, a combination of the processor 610 and memory 610 may form processing means 650 adapted to implement various example embodiments of the present disclosure.

The memory 610 may be of any type suitable to the local technical network and may be implemented using any suitable data storage technology, such as a non-transitory computer readable storage medium, semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory and removable memory, as non-limiting examples. While only one memory 610 is shown in the device 600, there may be several physically distinct memory modules in the device 600. The processor 610 may be of any type suitable to the local technical network, and may include one or more of general purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs) and processors based on multicore processor architecture, as non-limiting examples. The device 600 may have multiple processors, such as an application specific integrated circuit chip that is slaved in time to a clock which synchronizes the main processor.

Generally, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present disclosure are illustrated and described as block diagrams, flowcharts, or using some other pictorial representation, it will be appreciated that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computer-executable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor, to carry out the process or method as described above with reference to any of Figs. 2 to 5. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented. The program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present disclosure, the computer program codes or related data may be carried by any suitable carrier to enable the device, apparatus or processor to perform various processes and operations as described above. Examples of the carrier include a signal, computer readable media.

The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM) , a read-only memory (ROM) , an erasable programmable read-only memory (EPROM or Flash memory) , an optical fiber, a portable compact disc read-only memory (CD-ROM) , an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the present disclosure, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments separately or in any suitable sub-combination.

Although the present disclosure has been described in language specific to structural features and/or methodological acts, it is to be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

A method implemented at a network device, comprising:

determining, at the network device, a parameter set based on channel information about a channel between the network device and a terminal device;

determining, based on the parameter set, a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device;

determining, based on the precoding mode, a receiving mode for the terminal device to decode the precoded data and the precoded reference signal; and

transmitting an indication of the receiving mode to the terminal device.
The method of Claim 1, wherein determining the parameter set comprises:

obtaining effective Channel State Information CSI from the channel information;

determining a first matrix for characterizing the channel from the effective CSI, the first matrix being associated with at least one of:

the number of antennas of the network device;

the number of ports of the network device;

the number of antennas of the terminal device; and

a combining matrix of the terminal device; and

determining the parameter set based on the first matrix.
The method of Claim 2, wherein the parameter set is associated with Tomlinson-Harashima Precoding THP and comprises at least one of:

a precoding matrix; and

a feedback matrix.
The method of Claim 2, wherein the parameter set being associated with Vector Perturbation VP and comprises at least one of:

a precoding matrix;

a power normalization factor; and

a perturbed vector.
The method of Claim 1, wherein determining the parameter set comprises:

obtaining full Channel State Information CSI from the channel information;

determining a second matrix for characterizing the channel from the full CSI, the second matrix being associated with at least one of:

the number of antennas of the network device;

the number of ports of the network device; and

the number of antennas of the terminal device; and

determining the parameter set based on the second matrix.
The method of Claim 5, wherein the parameter set is associated with Tomlinson-Harashima Precoding THP and comprises at least one of:

a precoding matrix;

a feedback matrix; and

a combining matrix for the terminal device.
The method of Claim 5, wherein the parameter set is associated with Vector Perturbation VP and comprises at least one of:

a precoding matrix;

a power normalization factor;

a perturbed vector; and

a combining matrix for the terminal device.
The method of Claim 5, wherein the parameter set is associated with Vector Perturbation VP and determining the parameter set comprises:

updating the second matrix by successive decomposing the second matrix based on at least one of:

Geometric Mean Decomposition, GMD

Singular Value Decomposition, SVD; and

Generalized Triangular Decomposition, GTD, and

determining the parameter set based on the updated second matrix.
The method of Claim 1, further comprising:

performing a non-linear process for the data and a linear process for the reference signal;

precoding the processed data and the reference signal based on the precoding mode.
A method implemented at a terminal device, comprising:

transmitting, to a network device, channel information of about a channel between the network device and the terminal device;

receiving, from the network device, an indication of a receiving mode for the terminal device to decode precoded data and a precoded reference signal, the receiving mode being determined based on a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device; and

decoding the precoded data based on the precoded reference signal and the receiving mode.
The method of Claim 10, wherein decoding the precoded data comprising:

determining a weight associated with the precoding mode by decoding the precoded reference signal based on the receiving mode; and

decoding the precoded data based on the weight.
The method of Claim 10, further comprising:

determining a combing matrix for the terminal device based on the precoded reference signal.
A network device, comprising:

at least one processor; and

at least one memory including computer program codes;

the at least one memory and the computer program codes are configured to, with the at least one processor, cause the terminal device at least to:

determine, at the network device, a parameter set based on channel information about a channel between the network device and a terminal device;

determine, based on the parameter set, a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device;

determine, based on the precoding mode, a receiving mode for the terminal device to decode the precoded data and the precoded reference signal; and

transmit an indication of the receiving mode to the terminal device.
The network device of Claim 13, wherein the network device is caused to determine the parameter set by:

obtaining effective Channel State Information CSI from the channel information;

determining a first matrix for characterizing the channel from the effective CSI, the first matrix being associated with at least one of:

the number of antennas of the network device;

the number of ports of the network device;

the number of antennas of the terminal device; and

a combining matrix of the terminal device; and

determining the parameter set based on the first matrix.
The network device of Claim 14, wherein the parameter set is associated with Tomlinson-Harashima PrecodingTHP and comprises at least one of:

a precoding matrix; and

a feedback matrix.
The network device of Claim 14, wherein the parameter set being associated with Vector Perturbation VP and comprises at least one of:

a precoding matrix;

a power normalization factor; and

a perturbed vector.
The network device of Claim 13, wherein the network device is caused to determine the parameter set by:

obtaining full Channel State Information CSI from the channel information;

determining a second matrix for characterizing the channel from the full CSI, the second matrix being associated with at least one of:

the number of antennas of the network device;

the number of ports of the network device; and

the number of antennas of the terminal device; and

determining the parameter set based on the second matrix.
The network device of Claim 17, wherein the parameter set is associated with Tomlinson-Harashima Precoding THP and comprises at least one of:

a precoding matrix;

a feedback matrix; and

a combining matrix for the terminal device.
The network device of Claim 17, wherein the parameter set is associated with Vector Perturbation VP and comprises at least one of:

a precoding matrix;

a power normalization factor;

a perturbed vector; and

a combining matrix for the terminal device.
The network device of Claim 17, wherein the parameter set is associated with Vector Perturbation VP and determining the parameter set comprises:

updating the second matrix by successive decomposing the second matrix based on at least one of:

Geometric Mean Decomposition, GMD

Singular Value Decomposition, SVD; and

Generalized Triangular Decomposition, GTD, and

determining the parameter set based on the updated second matrix.
The network device of Claim 13, wherein the network device is further caused to:

perform a non-linear process for the data and a linear process for the reference signal;

precode the processed data and the reference signal based on the precoding mode.
A terminal device, comprising:

at least one processor; and

at least one memory including computer program codes;

the at least one memory and the computer program codes are configured to, with the at least one processor, cause the network device at least to:

transmit, to a network device, channel information of about a channel between a network device and the terminal device; and

receive, from the network device, an indication of a receiving mode for the terminal device to decode precoded data and a precoded reference signal, the receiving mode being determined based on a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and being determined based on a parameter set determined based on the channel information by the network device; and

decode the precoded data based on the precoded reference signal and the indication.
The terminal device of Claim 22, wherein the terminal device is caused to decode the precoded data by:

determining a weight associated with the precoding mode by decoding the precoded reference signal based on the indication; and

decoding the precoded data based on the weight.
The terminal device of Claim 22, wherein full Channel State Information CSI being obtained by the network device and the terminal device is further caused to:

determinin, a combing matrix for the terminal device based on the precoded reference signal.
An apparatus for a non-linear precoding procedure, comprising:

means for determining, at the network device, a parameter set based on channel information about a channel between the network device and a terminal device;

means for determining, based on the parameter set, a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device;

means for determining, based on the precoding mode, a receiving mode for the terminal device to decode the precoded data and the precoded reference signal; and

means for transmitting an indication of the receiving mode to the terminal device.
An apparatus for a non-linear precoding procedure, comprising:

means for transmitting, to a network device, channel information of about a channel between a network device and the terminal device; and

means for receiving, from the network device, an indication of a receiving mode for the terminal device to decode precoded data and a precoded reference signal, the receiving mode being determined based on a precoding mode for precoding data and a reference signal for the terminal device, the precoding mode indicating a non-linear precoding scheme used by the network device and being determined based on a parameter set determined based on the channel information by the network device; and

means for decoding the precoded data based on the precoded reference signal and the indication.
A non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the method of any of claims 1-9.
A non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the method of any of claims 10-12.