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WO2024167265A1 - Procédé et dispositif d'estimation de canal à l'aide d'un autocodeur dans un système de communication - Google Patents

Procédé et dispositif d'estimation de canal à l'aide d'un autocodeur dans un système de communication Download PDF

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Publication number
WO2024167265A1
WO2024167265A1 PCT/KR2024/001711 KR2024001711W WO2024167265A1 WO 2024167265 A1 WO2024167265 A1 WO 2024167265A1 KR 2024001711 W KR2024001711 W KR 2024001711W WO 2024167265 A1 WO2024167265 A1 WO 2024167265A1
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WIPO (PCT)
Prior art keywords
base station
encoder
cfi
information
rsrp
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Ceased
Application number
PCT/KR2024/001711
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English (en)
Korean (ko)
Inventor
이정수
한진백
홍의현
서영길
최완
김범준
권정현
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hyundai Motor Co
SNU R&DB Foundation
Kia Corp
Original Assignee
Hyundai Motor Co
Seoul National University R&DB Foundation
Kia Corp
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Publication date
Application filed by Hyundai Motor Co, Seoul National University R&DB Foundation, Kia Corp filed Critical Hyundai Motor Co
Priority to CN202480011724.8A priority Critical patent/CN120660294A/zh
Publication of WO2024167265A1 publication Critical patent/WO2024167265A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0023—Systems modifying transmission characteristics according to link quality, e.g. power backoff characterised by the signalling
    • H04L1/0026—Transmission of channel quality indication
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/04—Protocols for data compression, e.g. ROHC

Definitions

  • the present disclosure relates to improved communication technologies, and more particularly to technologies for channel estimation.
  • Communication networks are being developed to provide improved communication services compared to existing communication networks (e.g., LTE (long term evolution), LTE-A (advanced), etc.).
  • a 5G communication network e.g., NR (new radio) communication network
  • NR new radio
  • a 5G communication network can support not only a frequency band below 6 GHz but also a frequency band above 6 GHz. That is, the 5G communication network can support FR1 band and/or FR2 band.
  • a 5G communication network can support various communication services and scenarios compared to an LTE communication network.
  • usage scenarios of a 5G communication network can include eMBB (enhanced Mobile BroadBand), URLLC (Ultra Reliable Low Latency Communication), mMTC (massive Machine Type Communication), etc.
  • 6G communication networks can support various communication services and scenarios compared to 5G communication networks.
  • 6G communication networks can satisfy requirements of ultra-performance, ultra-bandwidth, ultra-space, ultra-precision, ultra-intelligence, and/or ultra-reliability.
  • 6G communication networks can support various and wide frequency bands and can be applied to various usage scenarios (e.g., terrestrial communication, non-terrestrial communication, sidelink communication, etc.).
  • Codebook-based CSI feedback may mean a method in which a base station periodically or aperiodically transmits a downlink Channel State Information-Reference Signal (CSI-RS) to a UE, and the UE transmits CSI to the base station.
  • the CSI may include a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), and a Rank Indication (RI).
  • CQI Channel Quality Indicator
  • PMI Precoding Matrix Indicator
  • RI Rank Indication
  • the base station can estimate CSI using a predefined codebook based on the CQI, PMI, RI, etc. reported by the UE.
  • 3GPP is discussing an artificial intelligence/machine learning (AI/ML) method for the physical layer as a study item (SI) of Release 18.
  • AI/ML artificial intelligence/machine learning
  • SI study item
  • the AI/ML method learns output data for input data to extract specific patterns or parameters, and can use these to predict output data for arbitrary input data.
  • 3GPP discussions are underway to apply the AI/ML method to technologies such as CSI feedback, beam management, and positioning accuracy enhancements.
  • the purpose of the present disclosure to solve the above problems is to provide a method and device for CSI feedback using AI/ML in a communication system.
  • a method of a user equipment may include: receiving a channel feature indicator (CFI) transmission period and a latent variable dimension (LVD) of an encoder to perform online learning from a base station; determining a first encoder to perform online learning based on the CFI transmission period and the LVD; receiving a first reference signal (RS) from the base station; generating a CFI by compressing the received first RS through the first encoder; and transmitting the first CFI to the base station based on the CFI transmission period.
  • CFI channel feature indicator
  • LLD latent variable dimension
  • the above first CFI can be determined as the product of the number of nodes in the latent space of the first encoder and the LVD.
  • the above LVD may be the number of bits required to express each latent variable value included in the latent space of the first encoder.
  • the above CFI transmission period can be determined based on at least one of the network size or channel coherent time of the first encoder.
  • the above LVD may be determined based on at least one of the channel resolution or latency requirement of the base station.
  • the method may further include: receiving re-learning related information of the first encoder from the base station; resetting the first encoder based on the re-learning related information; receiving a second RS from the base station; generating a second CFI by compressing the received second RS with the reset first encoder; and transmitting the second CFI to the base station.
  • the above relearning related information may include at least one of relearning instruction information of the encoder, drop-out node information of the encoder, or identifier information of the first encoder.
  • the identifier information of the above first encoder may further include the number of dropouts.
  • the method may further include: receiving learning termination instruction information of the first encoder from the base station; updating the first encoder based on information learned by the first encoder immediately before receiving the learning termination instruction information; receiving a third RS from the base station; generating a third CFI by compressing the third RS using the updated first encoder; and transmitting the third CFI to the base station.
  • the method may further include: receiving learning expiration information including optimal encoder information from the base station; updating the first encoder based on the optimal encoder information from the base station; setting the updated first encoder as a channel estimation encoder; receiving a fourth RS from the base station; generating a fourth CFI by compressing the fourth RS using the updated first encoder; and transmitting the fourth CFI to the base station.
  • the above optimal encoder information may include an encoder identifier and information on the number of drop-outs of the encoder.
  • a method of a base station may include: determining a channel feature indicator (CFI) transmission period and a latent variable dimension (LVD) of an encoder to perform online learning with user equipment (UE); transmitting the CFI transmission period and the LVD to the UE; receiving a first CFI from the UE based on the CFI transmission period; and restoring a reception RS using the received first CFI.
  • CFI channel feature indicator
  • LDD latent variable dimension
  • the method may include: obtaining a first reference signal received power (RSRP) value of the restored reception RS; checking whether the first RSRP is saturated; checking whether the first RSRP is equal to or greater than a preset threshold value when the first RSRP is saturated; and transmitting learning completion information of the encoder to the UE when the first RSRP is equal to or greater than the preset threshold value.
  • RSRP reference signal received power
  • the above first CFI can be determined as the product of the number of nodes in the latent space of the encoder that performs the online learning and the LVD.
  • the first RSRP value is equal to or within a predetermined range of RSRP values obtained from a CFI previously received from the UE, the first RSRP may be determined to be saturated.
  • the method may further include a step of generating re-learning related information of the encoder when the first RSRP is not saturated; and a step of transmitting the re-learning related information of the encoder to the UE; and a step of performing an encoder re-learning procedure with the UE.
  • the above relearning related information may include at least one of relearning instruction information of the encoder, drop-out node information of the encoder, or identifier information of the encoder.
  • the identifier information of the above encoder may further include the number of dropouts.
  • the method may further include: checking the number of dropouts corresponding to an RSRP having a highest RSRP value among learned RSRP values when relearning is not completed within a predetermined time; and transmitting encoder identifier information including the number of dropouts and learning expiration information to the UE.
  • the above CFI transmission period can be determined based on at least one of the network size or channel coherence time of the encoder performing the online learning.
  • the above LVD may be determined based on at least one of the channel resolution or latency requirement of the base station.
  • the method may further include: transmitting a second RS to the UE after the learning is completed; receiving a second CFI corresponding to the second RS from the UE; obtaining a second RSRP value of the received RS from the second CFI; and estimating a downlink channel to the UE using the second RSRP.
  • a user equipment comprises a transceiver configured to transmit and receive signals with a base station; and at least one processor, wherein the at least one processor:
  • the method may cause an encoder to perform online learning from a base station, receive a channel feature indicator (CFI) transmission period and a latent variable dimension (LVD); determine a first encoder to perform online learning based on the CFI transmission period and the LVD; receive a first reference signal (RS) from the base station; generate a CFI by compressing the received first RS through the first encoder; and transmit the first CFI to the base station based on the CFI transmission period.
  • CFI channel feature indicator
  • LLD latent variable dimension
  • the above first CFI can be determined as the product of the number of nodes in the latent space of the determined encoder and the LVD.
  • the CFI transmission period may be determined based on at least one of a network size or a channel coherent time of the first encoder, and the LVD may be determined based on at least one of a channel resolution or a latency requirement of the base station.
  • the first encoder relearning related information may be received from the base station; the first encoder to be reset based on the relearning related information; the second RS to be received from the base station; the second CFI to be generated by compressing the received second RS with the reset first encoder; and the second CFI to be transmitted to the base station.
  • the above relearning related information includes at least one of relearning instruction information of the encoder, drop-out node information of the encoder, or identifier information of the encoder, and the identifier information of the first encoder may further include the number of drop-outs.
  • the UE when CSI feedback using an autoencoder is performed, since the UE compresses the received CSI-RS and reports it to the base station, there is an advantage of lower overhead than in the case of codebook-based CSI feedback.
  • the autoencoder can be adaptively learned.
  • the learning of the autoencoder can be performed quickly.
  • Figure 1 is a conceptual diagram illustrating a first embodiment of a communication system.
  • FIG. 2 is a block diagram illustrating a first embodiment of a communication node constituting a communication system.
  • FIG. 3 is a block diagram illustrating a first embodiment of communication nodes performing communication.
  • FIG. 4a is a block diagram illustrating a first embodiment of a transmission path.
  • FIG. 4b is a block diagram illustrating a first embodiment of a receiving path.
  • Figure 5 is a conceptual diagram illustrating a first embodiment of a system frame in a communication system.
  • Figure 6 is a conceptual diagram illustrating a first embodiment of a subframe in a communication system.
  • Figure 7 is a conceptual diagram illustrating a first embodiment of a slot in a communication system.
  • Figure 8 is a conceptual diagram illustrating a first embodiment of time-frequency resources in a communication system.
  • Figure 9 is a conceptual diagram explaining the offline learning procedure of an autoencoder and the operation of a UE to acquire an autoencoder.
  • Figure 10 is a conceptual diagram explaining the encoder and decoder configuration of an autoencoder and a channel estimation scenario using an autoencoder.
  • Figure 11 is a conceptual diagram illustrating a dropout encoder model when dropout is instructed at a specific node of the encoder.
  • Figure 12 is a flowchart of operations between a base station and a UE during CFI feedback for online training.
  • Figure 13 is a flowchart explaining the procedure for conducting online training of an autoencoder at a base station.
  • Figure 14 is a flowchart explaining a case where saturation of RSRP values is determined at a base station.
  • Figure 15 is a flowchart illustrating the procedure for terminating online training of an autoencoder at a base station.
  • Figure 16 is a conceptual diagram explaining a case where the entire operation of the present disclosure is combined and operated.
  • first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are only used to distinguish one component from another.
  • first component could be referred to as the second component, and similarly, the second component could also be referred to as the first component.
  • the term "and/or" can mean a combination of a plurality of related listed items or any one of a plurality of related listed items.
  • At least one of A and B can mean “at least one of A or B” or “at least one of combinations of one or more of A and B.” Additionally, in the present disclosure, “at least one of A and B” can mean “at least one of A or B” or “at least one of combinations of one or more of A and B.”
  • (re)transmitting can mean “transmitting”, “retransmitting”, or “transmitting and retransmitting”
  • (re)setting can mean “setting”, “resetting”, or “setting and resetting”
  • (re)connecting can mean “connecting”, “reconnecting”, or “connecting and reconnecting”
  • (re)connecting can mean “connecting”, “reconnecting”, or “connecting and reconnecting”.
  • a second communication node corresponding thereto can perform a method (e.g., receiving or transmitting a signal) corresponding to the method performed by the first communication node. That is, if an operation of a UE (user equipment) is described, a base station corresponding thereto can perform an operation corresponding to the operation of the UE. Conversely, if an operation of a base station is described, a UE corresponding thereto can perform an operation corresponding to the operation of the base station.
  • the base station may be referred to as a NodeB, an evolved NodeB, a gNodeB (next generation node B), a gNB, a device, an apparatus, a node, a communication node, a BTS (base transceiver station), an RRH (radio remote head), a TRP (transmission reception point), a RU (radio unit), an RSU (road side unit), a radio transceiver, an access point, an access node, etc.
  • a NodeB an evolved NodeB
  • a gNodeB next generation node B
  • a gNB next generation node B
  • a device an apparatus, a node, a communication node, a BTS (base transceiver station), an RRH (radio remote head), a TRP (transmission reception point), a RU (radio unit), an RSU (road side unit), a radio transceiver, an access point, an access node, etc.
  • the UE may be referred to as a terminal, a device, an apparatus, a node, a communication node, an end node, an access terminal, a mobile terminal, a station, a subscriber station, a mobile station, a portable subscriber station, an OBU (on-broad unit), etc.
  • a terminal a device, an apparatus, a node, a communication node, an end node, an access terminal, a mobile terminal, a station, a subscriber station, a mobile station, a portable subscriber station, an OBU (on-broad unit), etc.
  • OBU on-broad unit
  • signaling may be at least one of upper layer signaling, MAC signaling, or PHY (physical) signaling.
  • a message used for upper layer signaling may be referred to as an "upper layer message” or an "upper layer signaling message”.
  • a message used for MAC signaling may be referred to as a "MAC message” or a "MAC signaling message”.
  • a message used for PHY signaling may be referred to as a "PHY message” or a "PHY signaling message”.
  • Upper layer signaling may refer to a transmission and reception operation of system information (e.g., a master information block (MIB), a system information block (SIB)) and/or an RRC message.
  • MIB master information block
  • SIB system information block
  • MAC signaling may refer to a transmission and reception operation of a MAC control element (CE).
  • PHY signaling may refer to a transmission and reception operation of control information (e.g., downlink control information (DCI), uplink control information (UCI), sidelink control information (SCI)).
  • DCI downlink control information
  • UCI uplink control information
  • SCI sidelink control information
  • an operation e.g., a transmission operation
  • setting information for the operation e.g., an information element, a parameter
  • information instructing performance of the operation are signaled.
  • An information element e.g., a parameter
  • a signal and/or a channel may mean a signal, a channel, or "a signal and a channel,” and a signal may be used to mean “a signal and/or a channel.”
  • the communication network to which the embodiment is applied is not limited to what is described below, and the embodiment can be applied to various communication networks (e.g., 4G communication network, 5G communication network, and/or 6G communication network).
  • the communication network can be used in the same meaning as the communication system.
  • Figure 1 is a conceptual diagram illustrating a first embodiment of a communication system.
  • the communication system (100) may include a plurality of communication nodes (110-1, 110-2, 110-3, 120-1, 120-2, 130-1, 130-2, 130-3, 130-4, 130-5, 130-6).
  • the communication system (100) may further include a core network (e.g., a serving-gateway (S-GW), a packet data network (PDN)-gateway (P-GW), a mobility management entity (MME)).
  • a core network e.g., a serving-gateway (S-GW), a packet data network (PDN)-gateway (P-GW), a mobility management entity (MME)
  • the core network may include an access and mobility management function (AMF), a user plane function (UPF), a session management function (SMF), etc.
  • AMF access and mobility management function
  • UPF user plane function
  • SMF session management function
  • a plurality of communication nodes (110 to 130) can support a communication protocol specified in a 3rd generation partnership project (3GPP) standard (e.g., LTE communication protocol, LTE-A communication protocol, NR communication protocol, etc.).
  • the plurality of communication nodes (110 to 130) may support CDMA (code division multiple access) technology, WCDMA (wideband CDMA) technology, TDMA (time division multiple access) technology, FDMA (frequency division multiple access) technology, OFDM (orthogonal frequency division multiplexing) technology, Filtered OFDM technology, CP (cyclic prefix)-OFDM technology, DFT-s-OFDM (discrete Fourier transform-spread-OFDM) technology, OFDMA (orthogonal frequency division multiple access) technology, SC (single carrier)-FDMA technology, NOMA (non-orthogonal multiple access) technology, GFDM (generalized frequency division multiplexing) technology, FBMC (filter bank multi-carrier) technology, UFMC (universal
  • FIG. 2 is a block diagram illustrating a first embodiment of a communication node constituting a communication system.
  • a communication node (200) may include at least one processor (210), a memory (220), and a transceiver device (230) that is connected to a network and performs communication.
  • the communication node (200) may further include an input interface device (240), an output interface device (250), a storage device (260), etc.
  • Each component included in the communication node (200) may be connected by a bus (270) and communicate with each other.
  • the processor (210) can execute a program command stored in at least one of the memory (220) and the storage device (260).
  • the processor (210) may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present disclosure are performed.
  • Each of the memory (220) and the storage device (260) may be configured with at least one of a volatile storage medium and a nonvolatile storage medium.
  • the memory (220) may be configured with at least one of a read only memory (ROM) and a random access memory (RAM).
  • the communication system (100) may include a plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) and a plurality of terminals (130-1, 130-2, 130-3, 130-4, 130-5, 130-6).
  • Each of the first base station (110-1), the second base station (110-2), and the third base station (110-3) may form a macro cell.
  • Each of the fourth base station (120-1) and the fifth base station (120-2) may form a small cell.
  • the fourth base station (120-1), the third terminal (130-3), and the fourth terminal (130-4) may be within the cell coverage of the first base station (110-1).
  • the second terminal (130-2), the fourth terminal (130-4), and the fifth terminal (130-5) may be within the cell coverage of the second base station (110-2).
  • the fifth base station (120-2), the fourth terminal (130-4), the fifth terminal (130-5), and the sixth terminal (130-6) may be within the cell coverage of the third base station (110-3).
  • the first terminal (130-1) may be within the cell coverage of the fourth base station (120-1).
  • the sixth terminal (130-6) may be within the cell coverage of the fifth base station (120-2).
  • each of the plurality of base stations may be referred to as a NodeB (NB), an evolved NodeB (eNB), a gNB, an advanced base station (ABS), a high reliability-base station (HR-BS), a base transceiver station (BTS), a radio base station, a radio transceiver, an access point, an access node, a radio access station (RAS), a mobile multihop relay-base station (MMR-BS), a relay station (RS), an advanced relay station (ARS), a high reliability-relay station (HR-RS), a home NodeB (HNB), a home eNodeB (HeNB), a road side unit (RSU), a radio remote head (RRH), a transmission point (TP), a transmission and reception point (TRP), etc.
  • NB NodeB
  • eNB evolved NodeB
  • gNB an advanced base station
  • HR-BS high reliability-base station
  • BTS base transceiver station
  • RAS mobile multihop
  • Each of the plurality of terminals may be referred to as a user equipment (UE), terminal equipment (TE), advanced mobile station (AMS), high reliability-mobile station (HR-MS), terminal, access terminal, mobile terminal, station, subscriber station, mobile station, portable subscriber station, node, device, OBU (on board unit), etc.
  • UE user equipment
  • TE terminal equipment
  • AMS advanced mobile station
  • HR-MS high reliability-mobile station
  • OBU on board unit
  • each of the plurality of base stations may operate in a different frequency band or may operate in the same frequency band.
  • Each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) may be connected to each other via an ideal backhaul link or a non-ideal backhaul link, and may exchange information with each other via the ideal backhaul link or the non-ideal backhaul link.
  • Each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) may be connected to a core network via an ideal backhaul link or a non-ideal backhaul link.
  • Each of the plurality of base stations can transmit a signal received from the core network to the corresponding terminal (130-1, 130-2, 130-3, 130-4, 130-5, 130-6), and can transmit a signal received from the corresponding terminal (130-1, 130-2, 130-3, 130-4, 130-5, 130-6) to the core network.
  • each of the plurality of base stations can support MIMO transmission (e.g., single user (SU)-MIMO, multi user (MU)-MIMO, massive MIMO, etc.), coordinated multipoint (CoMP) transmission, carrier aggregation (CA) transmission, transmission in an unlicensed band, sidelink communication (e.g., device to device communication (D2D), proximity services (ProSe)), Internet of Things (IoT) communication, dual connectivity (DC), etc.
  • MIMO transmission e.g., single user (SU)-MIMO, multi user (MU)-MIMO, massive MIMO, etc.
  • CoMP coordinated multipoint
  • CA carrier aggregation
  • sidelink communication e.g., device to device communication (D2D), proximity services (ProSe)
  • IoT Internet of Things
  • DC dual connectivity
  • each of the plurality of terminals can perform an operation corresponding to the base station (110-1, 110-2, 110-3, 120-1, 120-2) and an operation supported by the base station (110-1, 110-2, 110-3, 120-1, 120-2).
  • the second base station (110-2) can transmit a signal to the fourth terminal (130-4) based on the SU-MIMO scheme
  • the fourth terminal (130-4) can receive a signal from the second base station (110-2) by the SU-MIMO scheme.
  • the second base station (110-2) can transmit signals to the fourth terminal (130-4) and the fifth terminal (130-5) based on the MU-MIMO method, and each of the fourth terminal (130-4) and the fifth terminal (130-5) can receive signals from the second base station (110-2) by the MU-MIMO method.
  • Each of the first base station (110-1), the second base station (110-2), and the third base station (110-3) can transmit a signal to the fourth terminal (130-4) based on the CoMP scheme, and the fourth terminal (130-4) can receive a signal from the first base station (110-1), the second base station (110-2), and the third base station (110-3) based on the CoMP scheme.
  • Each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) can transmit and receive a signal with terminals (130-1, 130-2, 130-3, 130-4, 130-5, 130-6) within its cell coverage based on the CA scheme.
  • Each of the first base station (110-1), the second base station (110-2), and the third base station (110-3) can control sidelink communication between the fourth terminal (130-4) and the fifth terminal (130-5), and each of the fourth terminal (130-4) and the fifth terminal (130-5) can perform sidelink communication under the control of the second base station (110-2) and the third base station (110-3).
  • communication nodes performing communication in a communication network can be configured as follows.
  • the communication node illustrated in Fig. 3 may be a specific embodiment of the communication node illustrated in Fig. 2.
  • FIG. 3 is a block diagram illustrating a first embodiment of communication nodes performing communication.
  • each of the first communication node (300a) and the second communication node (300b) may be a base station or a UE.
  • the first communication node (300a) may transmit a signal to the second communication node (300b).
  • the transmission processor (311) included in the first communication node (300a) may receive data (e.g., a data unit) from a data source (310).
  • the transmission processor (311) may receive control information from the controller (316).
  • the control information may include at least one of system information, RRC configuration information (e.g., information configured by RRC signaling), MAC control information (e.g., MAC CE), or PHY control information (e.g., DCI, SCI).
  • the transmitting processor (311) can perform a processing operation (e.g., an encoding operation, a symbol mapping operation, etc.) on data to generate data symbol(s).
  • the transmitting processor (311) can perform a processing operation (e.g., an encoding operation, a symbol mapping operation, etc.) on control information to generate control symbol(s).
  • the transmitting processor (311) can generate synchronization/reference symbol(s) for a synchronization signal and/or a reference signal.
  • the Tx MIMO processor (312) can perform spatial processing operations (e.g., a precoding operation) on data symbol(s), control symbol(s), and/or synchronization/reference symbol(s).
  • An output (e.g., a symbol stream) of the Tx MIMO processor (312) can be provided to modulators (MODs) included in the transceivers (313a to 313t).
  • the modulators (MODs) can perform processing operations on the symbol streams to generate modulation symbols and perform additional processing operations (e.g., an analog conversion operation, an amplification operation, a filtering operation, an upconversion operation) on the modulation symbols to generate signals.
  • the signals generated by the modulators (MODs) of the transceivers (313a to 313t) can be transmitted via the antennas (314a to 314t).
  • the signals transmitted by the first communication node (300a) may be received by the antennas (364a to 364r) of the second communication node (300b).
  • the signals received by the antennas (364a to 364r) may be provided to the demodulators (DEMODs) included in the transceivers (363a to 363r).
  • the demodulator (DEMOD) may perform a processing operation (e.g., a filtering operation, an amplification operation, a down-conversion operation, a digital conversion operation) on the signal to obtain samples.
  • the demodulator (DEMOD) may perform an additional processing operation on the samples to obtain symbols.
  • the MIMO detector (362) may perform a MIMO detection operation on the symbols.
  • the receiving processor (361) may perform a processing operation (e.g., a deinterleaving operation, a decoding operation) on the symbols.
  • the output of the receiving processor (361) may be provided to a data sink (360) and a controller (366).
  • data may be provided to the data sink (360) and control information may be provided to the controller (366).
  • the second communication node (300b) can transmit a signal to the first communication node (300a).
  • the transmitting processor (368) included in the second communication node (300b) can receive data (e.g., data units) from a data source (367) and perform a processing operation on the data to generate data symbol(s).
  • the transmitting processor (368) can receive control information from the controller (366) and perform a processing operation on the control information to generate control symbol(s).
  • the transmitting processor (368) can perform a processing operation on a reference signal to generate reference symbol(s).
  • the Tx MIMO processor (369) can perform spatial processing operations (e.g., precoding operations) on data symbol(s), control symbol(s), and/or reference symbol(s).
  • An output (e.g., a symbol stream) of the Tx MIMO processor (369) can be provided to modulators (MODs) included in the transceivers (363a to 363t).
  • the modulators (MODs) can perform processing operations on the symbol streams to generate modulation symbols and can perform additional processing operations (e.g., an analog conversion operation, an amplification operation, a filtering operation, an upconversion operation) on the modulation symbols to generate signals.
  • the signals generated by the modulators (MODs) of the transceivers (363a to 363t) can be transmitted via the antennas (364a to 364t).
  • the signals transmitted by the second communication node (300b) may be received by the antennas (314a to 314r) of the first communication node (300a).
  • the signals received by the antennas (314a to 314r) may be provided to the demodulators (DEMODs) included in the transceivers (313a to 313r).
  • the demodulator (DEMOD) may perform a processing operation (e.g., a filtering operation, an amplification operation, a down-conversion operation, a digital conversion operation) on the signal to obtain samples.
  • the demodulator (DEMOD) may perform an additional processing operation on the samples to obtain symbols.
  • the MIMO detector (320) may perform a MIMO detection operation on the symbols.
  • the receiving processor (319) may perform a processing operation (e.g., a deinterleaving operation, a decoding operation) on the symbols.
  • the output of the receiving processor (319) may be provided to a data sink (318) and a controller (316).
  • data may be provided to the data sink (318) and control information may be provided to the controller (316).
  • Memories (315 and 365) can store data, control information, and/or program code.
  • Scheduler (317) can perform scheduling operations for communications.
  • Processors (311, 312, 319, 361, 368, 369) and controllers (316, 366) illustrated in FIG. 3 may be processors (210) illustrated in FIG. 2 and may be used to perform the methods described in the present disclosure.
  • FIG. 4a is a block diagram illustrating a first embodiment of a transmission path
  • FIG. 4b is a block diagram illustrating a first embodiment of a reception path.
  • a transmission path (410) may be implemented in a communication node that transmits a signal
  • a reception path (420) may be implemented in a communication node that receives a signal.
  • the transmission path (410) may include a channel coding and modulation block (411), an S-to-P (serial-to-parallel) block (512), an N IFFT (Inverse Fast Fourier Transform) block (413), a P-to-S (parallel-to-serial) block (414), a CP (cyclic prefix) addition block (415), and an UC (up-converter) (UC) (416).
  • the receiving path (420) may include a DC (down-converter) (421), a CP removal block (422), an S-to-P block (423), an N FFT block (424), a P-to-S block (425), and a channel decoding and demodulation block (426).
  • N may be a natural number.
  • information bits may be input to a channel coding and modulation block (411).
  • the channel coding and modulation block (411) may perform a coding operation (e.g., a low-density parity check (LDPC) coding operation, a polar coding operation, etc.) and a modulation operation (e.g., a quadrature phase shift keying (QPSK), a quadrature amplitude modulation (QAM), etc.) on the information bits.
  • a coding operation e.g., a low-density parity check (LDPC) coding operation, a polar coding operation, etc.
  • a modulation operation e.g., a quadrature phase shift keying (QPSK), a quadrature amplitude modulation (QAM), etc.
  • QPSK quadrature phase shift keying
  • QAM quadrature amplitude modulation
  • the S-to-P block (412) can convert modulation symbols in the frequency domain into parallel symbol streams to generate N parallel symbol streams.
  • N can be an IFFT size or an FFT size.
  • the N IFFT block (413) can perform an IFFT operation on the N parallel symbol streams to generate signals in the time domain.
  • the P-to-S block (414) can convert the output (e.g., parallel signals) of the N IFFT block (413) into a serial signal to generate a serial signal.
  • the CP addition block (415) can insert a CP into a signal.
  • the UC (416) can up-convert the frequency of the output of the CP addition block (415) to an RF (radio frequency) frequency. Additionally, the output of the CP addition block (415) can be filtered at baseband before up-conversion.
  • a signal transmitted from the transmission path (410) may be input to the reception path (420).
  • An operation in the reception path (420) may be an inverse operation of the operation in the transmission path (410).
  • the DC (421) may down-convert the frequency of the received signal to a frequency of the baseband.
  • the CP removal block (422) may remove a CP from the signal.
  • An output of the CP removal block (422) may be a serial signal.
  • the S-to-P block (423) may convert the serial signal into parallel signals.
  • the N FFT block (424) may perform an FFT algorithm to generate N parallel signals.
  • the P-to-S block (425) may convert the parallel signals into a sequence of modulation symbols.
  • the channel decoding and demodulation block (426) may perform a demodulation operation on the modulation symbols and perform a decoding operation on the result of the demodulation operation to restore data.
  • FIGS. 4A and 4B Discrete Fourier Transform (DFT) and Inverse DFT (IDFT) can be used instead of FFT and IFFT.
  • DFT Discrete Fourier Transform
  • IDFT Inverse DFT
  • Each of the blocks (e.g., components) in FIGS. 4A and 4B can be implemented by at least one of hardware, software, or firmware.
  • some of the blocks in FIGS. 4A and 4B can be implemented by software, and the remaining blocks can be implemented by hardware or a “combination of hardware and software.”
  • one block can be subdivided into multiple blocks, multiple blocks can be integrated into one block, some blocks can be omitted, and blocks supporting other functions can be added.
  • Figure 5 is a conceptual diagram illustrating a first embodiment of a system frame in a communication system.
  • time resources in a communication system can be divided into frame units.
  • system frames can be set sequentially in the time domain of the communication system.
  • the length of a system frame can be 10 ms (milliseconds).
  • a system frame number (SFN) can be set from #0 to #1023.
  • 1024 system frames can be repeated in the time domain of the communication system.
  • the SFN of a system frame after system frame #1023 can be #0.
  • a system frame may include two half frames.
  • a half frame may be 5 ms long.
  • a half frame located at the beginning of the system frame may be referred to as "half frame #0", and a half frame located at the end of the system frame may be referred to as "half frame #1”.
  • a system frame may include 10 subframes.
  • a subframe may be 1 ms long. The 10 subframes within a system frame may be referred to as "subframe #0-9".
  • Figure 6 is a conceptual diagram illustrating a first embodiment of a subframe in a communication system.
  • one subframe may include n slots, where n may be a natural number. Accordingly, one subframe may be composed of one or more slots.
  • Figure 7 is a conceptual diagram illustrating a first embodiment of a slot in a communication system.
  • one slot may include one or more symbols.
  • One slot illustrated in FIG. 7 may include 14 symbols.
  • the length of a slot may vary depending on the number of symbols included in the slot and the length of the symbols. Alternatively, the length of a slot may vary depending on the numerology.
  • a numerology applied to a physical signal and a channel can be variable.
  • the numerology can be variable to meet various technical requirements of the communication system.
  • the numerology can include a subcarrier spacing and a CP length (or a CP type).
  • Table 1 may be a first embodiment of a method for configuring a numerology for a CP-OFDM-based communication system. At least some of the numerologies in Table 1 may be supported depending on a frequency band in which the communication system operates. In addition, the communication system may additionally support numerology(s) not listed in Table 1.
  • the slot length can be 1 ms. In this case, one system frame can contain 10 slots.
  • the slot length can be 0.5 ms. In this case, one system frame can contain 20 slots.
  • the length of a slot can be 0.25 ms.
  • one system frame can contain 40 slots.
  • the length of a slot can be 0.125 ms.
  • one system frame can contain 80 slots.
  • the length of a slot can be 0.0625 ms.
  • one system frame can contain 160 slots.
  • a symbol may be configured as a downlink (DL) symbol, a flexible (FL) symbol, or an uplink (UL) symbol.
  • DL slot A slot composed of only DL symbols may be referred to as a "DL slot”
  • FL slot a slot composed of only FL symbols
  • UL slot a slot composed of only UL symbols
  • the slot format can be semi-statically set by higher layer signaling (e.g., RRC signaling).
  • Information indicating the semi-static slot format can be included in system information, and the semi-static slot format can be set cell-specifically.
  • the semi-static slot format can be additionally set for each terminal by terminal-specific higher layer signaling (e.g., RRC signaling).
  • a flexible symbol of a slot format set cell-specifically can be overridden with a downlink symbol or an uplink symbol by terminal-specific higher layer signaling.
  • the slot format can be dynamically indicated by physical layer signaling (e.g., a slot format indicator (SFI) included in DCI).
  • SFI slot format indicator
  • a semi-statically set slot format can be overridden by a dynamically indicated slot format. For example, a flexible symbol set semi-statically can be overridden with a downlink symbol or an uplink symbol by the SFI.
  • the reference signal may be a channel state information-reference signal (CSI-RS), a sounding reference signal (SRS), a demodulation-reference signal (DM-RS), a phase tracking-reference signal (PT-RS), etc.
  • the channel may be a physical broadcast channel (PBCH), a physical downlink control channel (PDCCH), a physical downlink shared channel (PDSCH), a physical uplink control channel (PUCCH), a physical uplink shared channel (PUSCH), a physical sidelink control channel (PSCCH), a physical sidelink shared channel (PSSCH), etc.
  • a control channel may mean a PDCCH, a PUCCH, or a PSCCH
  • a data channel may mean a PDSCH, a PUSCH, or a PSSCH.
  • Figure 8 is a conceptual diagram illustrating a first embodiment of time-frequency resources in a communication system.
  • a resource composed of one symbol (e.g., OFDM symbol) in the time domain and one subcarrier in the frequency domain may be defined as a "RE (resource element)".
  • REG resource element group
  • a REG may include K REs.
  • a REG may be used as a basic unit of resource allocation in the frequency domain.
  • K may be a natural number.
  • K may be 12.
  • N may be a natural number.
  • N may be 14.
  • the N OFDM symbols may be used as a basic unit of resource allocation in the time domain.
  • RB may mean CRB (common RB).
  • RB may mean PRB or VRB (virtual RB).
  • CRB may mean RB constituting a set of consecutive RBs (e.g., common RB grid) based on a reference frequency (e.g., point A).
  • Carriers and/or bandwidth portions may be arranged on the common RB grid. That is, the carrier and/or bandwidth portions may be composed of CRB(s).
  • RBs or CRBs constituting the bandwidth portions may be referred to as PRBs, and a CRB index within the bandwidth portion may be appropriately converted to a PRB index.
  • Downlink data can be transmitted via PDSCH.
  • the base station can transmit configuration information (e.g., scheduling information) of the PDSCH to the terminal via PDCCH.
  • the terminal can obtain the configuration information of the PDSCH by receiving the PDCCH (e.g., downlink control information (DCI)).
  • the configuration information of the PDSCH can include an MCS (modulation coding scheme) used for transmitting and receiving the PDSCH, time resource information of the PDSCH, frequency resource information of the PDSCH, feedback resource information for the PDSCH, etc.
  • the PDSCH may refer to a radio resource through which downlink data is transmitted and received. Alternatively, the PDSCH may refer to the downlink data itself.
  • the PDCCH may refer to a radio resource through which downlink control information (e.g., DCI) is transmitted and received. Alternatively, the PDCCH may refer to the downlink control information itself.
  • the terminal can perform a monitoring operation for the PDCCH in order to receive the PDSCH transmitted from the base station.
  • the base station can inform the terminal of the configuration information for the monitoring operation of the PDCCH using a higher layer message (e.g., an RRC (radio resource control) message).
  • the configuration information for the monitoring operation of the PDCCH can include CORESET (control resource set) information and search space information.
  • the CORESET information may include PDCCH DMRS (demodulation reference signal) information, PDCCH precoding information, PDCCH occasion information, etc.
  • the PDCCH DMRS may be a DMRS used to demodulate the PDCCH.
  • the PDCCH occasion may be a region in which a PDCCH can exist. That is, the PDCCH occasion may be a region in which DCI can be transmitted.
  • the PDCCH occasion may be referred to as a PDCCH candidate.
  • the PDCCH occasion information may include time resource information and frequency resource information of the PDCCH occasion.
  • the length of the PDCCH occasion in the time domain may be indicated in symbol units.
  • the size of the PDCCH occasion in the frequency domain may be indicated in RB units (for example, in PRB (physical resource block) units or CRB (common resource block) units).
  • the search space information may include a CORESET ID (identifier) associated with the search space, a period of PDCCH monitoring, and/or an offset. Each of the period and offset of PDCCH monitoring may be indicated in slot units.
  • the search space information may further include an index of a symbol at which a PDCCH monitoring operation starts.
  • a base station can set a BWP (bandwidth part) for downlink communication.
  • the BWP can be set differently for each terminal.
  • the base station can inform the terminal of the configuration information of the BWP using upper layer signaling.
  • the upper layer signaling can mean "transmission operation of system information" and/or "transmission operation of RRC (radio resource control) message.”
  • the number of BWPs set for one terminal can be 1 or more.
  • the terminal can receive configuration information of the BWP from the base station, and can identify the BWP(s) set by the base station based on the configuration information of the BWP.
  • the base station can activate one or more BWPs among the plurality of BWPs.
  • the base station can transmit the configuration information of the activated BWP(s) to the terminal using at least one of upper layer signaling, MAC (medium access control) CE (control element), or DCI.
  • the base station can perform downlink communication using the activated BWP(s).
  • the terminal can identify the activated BWP(s) by receiving configuration information of the activated BWP(s) from the base station, and perform a downlink reception operation in the activated BWP(s).
  • Codebook-based CSI feedback may mean a method in which a base station periodically or aperiodically transmits a downlink Channel State Information-Reference Signal (CSI-RS) to a UE, and the UE transmits CSI to the base station.
  • the CSI may include a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), and a Rank Indication (RI).
  • CQI Channel Quality Indicator
  • PMI Precoding Matrix Indicator
  • RI Rank Indication
  • the base station can estimate CSI using a predefined codebook based on the CQI, PMI, RI, etc. reported by the UE.
  • 6G communication systems are expected to utilize unlicensed bands of millimeter waves or terahertz bands to achieve ultra-high capacity/ultra-wideband/ultra-low latency communication services.
  • a frequency band that is tens of times wider than the FR2 band used in 5G NR will be used. Therefore, a CSI feedback process with higher accuracy and higher overhead may be required.
  • 3GPP is discussing an artificial intelligence/machine learning (AI/ML) method for the physical layer as a study item (SI) of Release 18.
  • AI/ML artificial intelligence/machine learning
  • SI study item
  • the AI/ML method learns output data for input data to extract specific patterns or parameters, and can use these to predict output data for arbitrary input data.
  • 3GPP discussions are underway to apply the AI/ML method to technologies such as CSI feedback, beam management, and positioning accuracy enhancements.
  • AI/ML for improved CSI feedback in the Rel-18 RAN1 #109-e meeting, in order to support CSI feedback using AI/ML, the AI/ML network is configured as a two-sided model consisting of the CSI feedback part and the CSI reconstruction part, and it was agreed that the base station and UE each use either the CSI feedback part or the CSI reconstruction part to perform channel estimation.
  • the AI/ML method being discussed for CSI feedback can be composed of a model training stage and a model inference stage.
  • the model training stage can be a stage that uses learning data as input to learn an AI model and/or ML model and extracts parameters, etc.
  • the model inference stage can be a stage that uses the parameters obtained in the training stage of the AI model and/or ML model to obtain an inference value (output) using the input data to be inferred.
  • the model training stage is generally a computationally intensive stage with many repetitions and can be performed for a long time because it requires repetitive operations.
  • the performance of model inference can be determined depending on the accuracy of the parameters learned in the model training stage.
  • the AI model and/or ML model training step is a step in which learning is performed using input data to obtain parameters to be used for channel estimation, and the model inference step can be interpreted as a step in which channel estimation is performed in an actual channel environment using the parameters obtained in the AI model and/or ML model training step.
  • 3GPP assumed online training and offline training methods as AI/ML model training procedures to support AI/ML.
  • Online training is a method of creating new learning data in real time and learning AI/ML models through the learning data
  • offline training is a method of learning AI/ML models through already collected data.
  • the base station or the server conducting the training can use previously collected data to train the AI/ML model in advance, so channel estimation is possible within the channel coherent time.
  • offline training applies parameters learned through previously collected data to the AI/ML model for AI/ML model training, accurate channel estimation may not be possible.
  • one way to solve the problem of channel estimation and feedback methods using AI/ML techniques is to consider a method of combining online and offline training. For example, it can be a procedure for training an AI/ML model to derive parameters that can achieve a certain level of accurate channel estimation through offline training. Therefore, offline training can be a coarse training procedure. Afterwards, a procedure for fine tuning the AI/ML model through online training can be considered to reflect the real-time channel.
  • the channel estimation procedure and method for performing offline and online training together for AI models and/or ML models have not yet been agreed upon in 3GPP.
  • the parameters obtained through offline training may be overfitted to previously collected data, and even if online training is performed in real time, a situation may occur in which parameters satisfying a certain level of accurate channel estimation for the AI model and/or ML model cannot be obtained.
  • the present disclosure described below will describe a method for providing a CSI feedback procedure that reflects the characteristics of an offline-trained AI/ML model in channel estimation using an AI/ML method.
  • the base station (or server) considers an environment in which online training is additionally performed on the AI/ML model to ensure a certain level of accurate channel estimation in a situation in which offline training is performed on the AI/ML model using collected data.
  • a channel estimation technique that includes a process for determining the period and dimension of a feedback signal that the UE should report to the base station in the CSI feedback process and an online training-based relearning process utilizing dropout to solve the overfitting problem.
  • the present disclosure described below considers a situation in which a downlink channel is estimated using an AI/ML model.
  • the present disclosure is not limited to the case of estimating only a downlink channel. It can also be applied to the case of estimating an uplink channel in a manner identical or similar to the method described in the present disclosure.
  • the case of downlink channel estimation will be described for convenience of explanation.
  • parameters are defined for AI/ML-based CSI feedback according to the present disclosure instead of at least one of CSI, e.g., CQI, PMI and RI, or CSI reported by a UE to a base station in a codebook-based CSI feedback process.
  • CSI e.g., CQI, PMI and RI
  • CSI reported by a UE to a base station in a codebook-based CSI feedback process e.g., CQI, PMI and RI
  • CSI reported by a UE to a base station in a codebook-based CSI feedback process e.g., CQI, PMI and RI
  • CSI reported by a UE to a base station in a codebook-based CSI feedback process e.g., CQI, PMI and RI
  • CSI reported by a UE to a base station in a codebook-based CSI feedback process e.g., CQI, PMI and RI
  • the base station has a decoder and the UE has an encoder.
  • the base station and the UE may each have both an encoder and a decoder.
  • the autoencoder described in the present disclosure may refer to a network that compresses input data into a signal having a small dimension and then restores the compressed data back to its original data form.
  • Such an autoencoder may be composed of an encoder, which is a part that compresses data, and a decoder that restores the compressed data.
  • the base station can transmit the CSI-RS to the UE over a wireless channel. Accordingly, the UE can receive the CSI-RS from the base station over the wireless channel. Since the CSI-RS received by the UE is received over the wireless channel, the CSI-RS received by the UE may be degraded (or distorted) due to the influence of the wireless channel. In this way, the CSI-RS received by the UE over the wireless channel will be referred to as 'received CSI-RS'.
  • the UE can compress the received CSI-RS using an encoder. Then, the UE can transmit the compressed received CSI-RS to the base station. In addition, the UE can transmit the compressed received CSI-RS and additional information to the base station, if necessary.
  • the base station can receive the compressed received CSI-RS or the compressed received CSI-RS and additional information from the UE. Then, the base station can restore the received CSI-RS by decoding the compressed received CSI-RS through a decoder. Since the base station knows the CSI-RS transmitted by the base station, the downlink channel can be estimated using the restored received CSI-RS and the CSI-RS transmitted by the base station. Then, the base station can train the autoencoder using the estimated channel information.
  • the UE during the learning process of the autoencoder, the UE only needs to generate compressed received CSI-RS through the encoder and report it to the base station in the channel estimation procedure. Therefore, the uplink overhead of the UE according to the present disclosure has an advantage of being much lower than the overhead of transmitting codebook-based CSI feedback according to the current standard specification of 5G NR.
  • an autoencoder can be trained through offline training using previously collected data.
  • the offline training can be performed at a base station or a specific server.
  • the offline training of the autoencoder can also be performed at the server.
  • the server can provide the trained autoencoder to the base station in advance.
  • the UE can receive the offline trained autoencoder from the base station or the server.
  • Figure 9 is a conceptual diagram explaining the offline learning procedure of an autoencoder and the operation of a UE to acquire an autoencoder.
  • FIG. 9 is an exemplary drawing assuming that the base station (910) has an autoencoder (911) and that the base station (910) performs offline learning of the autoencoder (911).
  • a server not the base station (910), may perform offline learning of the autoencoder and provide the offline-learned autoencoder (911) to the base station (910).
  • the database (930) can store data collected for offline learning of the autoencoder (911). Since the present disclosure describes an autoencoder (911) for estimating a downlink channel, the collected data can be channel information.
  • the channel information can be channel information obtained through simulation.
  • the channel information can be various channel information actually measured between each of the base stations and the UEs from each of the plurality of base stations.
  • the collected data stored in the database (930) can be data preprocessed in a form for learning the autoencoder according to the present disclosure.
  • the base station (910) may have an autoencoder (911) according to the present disclosure.
  • the autoencoder (911) may be composed of an encoder (9111) and a decoder (9112) as described above.
  • the decoder of the autoencoder will be described as a decoder (9112) and the encoder of the autoencoder will be described as an encoder (9111).
  • the base station (910) may receive data collected from a database (930) and perform offline training of the autoencoder (911). Therefore, the base station (910) may perform offline training of the autoencoder using the collected data.
  • the autoencoder may be an autoencoder having a compression and restoration technique.
  • the encoder (9111) can encode collected data received from the database (930).
  • the encoded data can be provided to the decoder (9112) (step S941). Accordingly, the decoder (9112) can receive compressed data from the encoder (9111) (step S941).
  • the decoder (9112) can decode and restore the compressed data. And the decoder (9112) can obtain channel information using the restored information. The decoder (9112) can generate control information for adjusting the encoder (9111) and the decoder (9112) based on the obtained channel information. The control information for adjusting the encoder (9111) and the decoder (9112) will be described in more detail with reference to the drawings described below. And the decoder (9112) can transmit the control information to the encoder (9111) (S942). Therefore, the encoder (9111) can receive the control information from the decoder (9112) (S942).
  • the encoder (9111) can be trained by applying the received control information.
  • the encoder (9111) can re-encode the collected data based on the encoding method to which the control information is applied.
  • the subsequent procedure can be repeated in the same manner as the operation described above. This repetition can be repeated until the decoded information in the decoder (9112) reaches a preset level.
  • the encoding data generated by the encoding operation of the encoder (9111) is provided to the decoder (9112), and the decoder (9112) can decode the encoded data to check whether the channel estimation is performed properly. If the channel estimation is not performed properly, in other words, if the channel estimation is not performed within a desired error range, the encoder (9111) and/or the decoder (9112) can generate control information for controlling the operations of the encoder (9111) and the decoder (9112), and apply the control information to the encoder (9111) and/or the decoder (9112).
  • the encoder (9111) and/or the decoder (9112) to which the control information for controlling the operations of the encoder (9111) and the decoder (9112) is applied can repeat the offline training procedure while reducing the error through steps S9141 and S942.
  • the base station (910) can transmit the autoencoder (911) to be used for channel estimation or the encoder (9111) constituting the autoencoder to the UE (920).
  • the base station (910) can transmit the autoencoder (911) to be used for channel estimation or the encoder (9111) constituting the autoencoder to the UE (920).
  • the encoder (9111) constituting the autoencoder is transmitted to the UE (920). It should be noted that since the encoder (9111) in the UE (920) and the encoder (9111) in the base station (920) are the same in Fig. 9, the same reference numeral is used.
  • the base station (910) may include part or all of the configuration of the communication node (200) described above in FIG. 2. If offline learning of the autoencoder (911) is performed in a specific server and the base station (910) receives the autoencoder (911) from the server, the base station (910) may further include an interface for communicating with the server in addition to the configuration of FIG. 2.
  • the memory (220) of the base station (910) may store the autoencoder (911).
  • the processor (210) of the base station (910) may be an entity that performs the operation and/or control of the autoencoder (911) described below.
  • the UE (920) may receive and store the offline-learned autoencoder (911) or the encoder (9111) of the offline-learned autoencoder (9111) from the base station as described above. As another example, the UE (920) may have stored the offline-learned encoder (9111) in advance. As another example, the UE (920) may receive the offline-learned autoencoder (911) or the encoder (9111) of the offline-learned encoder (9111) from the server described above.
  • the UE (920) can update the encoder (9111) based on the version information or the offline learning date information of the offline-learned encoder (9111). For example, if the UE (920) has stored the offline-learned encoder (9111) in advance, the UE (920) can receive (in advance) the version information or the offline learning date information of the autoencoder (911) that the base station (910) intends to use from the base station (910).
  • the UE (920) can use the stored encoder (9111) as is.
  • the version information of the autoencoder (911) received from the base station (910) and the version information of the encoder (9111) stored in the UE (920) are different (even if the version of the autoencoder (911) of the base station (910) is low)
  • the UE (910) can receive and update the encoder (9111) from the base station (910).
  • the configuration of the UE (920) in FIG. 9 exemplifies a configuration including only the encoder (9111) of the autoencoder (911).
  • the UE (920) may include part or all of the configuration of the communication node (200) described above in FIG. 2. Additionally, the UE (910) may further include an interface and/or sensor(s) for the convenience of the user.
  • the encoder (9111) of the autoencoder (911) may be stored in the memory (220) of the UE (910).
  • the processor (210) of the UE (920) may perform control for the operation of the autoencoder (911) described in the present disclosure.
  • the offline learning of the autoencoder (911), or in other words, the procedure in which the encoder (9111) and the decoder (9112) learn through offline training may be referred to as 'primary learning' or 'pre-learning' or 'offline learning'.
  • the base station (910) can learn the autoencoder (911) offline by utilizing the data collected in advance, and the offline learned encoder (9111) can be transmitted to the UE (920). In this way, the first learning or pre-learning or offline learning can be learned through coarse training for channel estimation.
  • Figure 10 is a conceptual diagram explaining the encoder and decoder configuration of an autoencoder and a channel estimation scenario using an autoencoder.
  • an encoder (9111) of an autoencoder (910), a wireless channel (1001), and a decoder (9112) of the autoencoder (9111) are illustrated. Therefore, the encoder (9111) and the decoder (9112) may be an encoder and a decoder in which offline learning is performed, respectively.
  • the encoder (9111) may be carried by the UE (920), and the decoder (9112) may be carried by the base station (910).
  • the autoencoder (911) is used for downlink channel estimation.
  • the encoder (9111) may be carried by the base station (910), and the decoder (9112) may be carried by the UE (920).
  • the base station (910) and the UE (920) may each have both an offline-learned encoder (9111) and an offline-learned decoder (9112).
  • the encoder (9111) may include an input layer (1010), a hidden layer (1020), and an output layer (1030).
  • the hidden layer (1020) is composed of one layer.
  • the hidden layer (1020) may be composed of two or more layers.
  • the processing time of the encoder (9111) increases, while more accurate channel estimation may be possible.
  • the processing time of the encoder (9111) decreases, while the accuracy of channel estimation may deteriorate.
  • the input layer (1010) of the encoder (9111) may be composed of multiple input nodes.
  • FIG. 10 illustrates a case where the input layer (1010) of the encoder (9111) is composed of six nodes.
  • Each node constituting the input layer (1010) of the encoder (9111) in FIG. 10 may also be referred to as a neuron.
  • one black dot in FIG. 10 may correspond to one node or one neuron.
  • Each node of the input layer (1010) can receive input data.
  • the data may be referred to as an input variable.
  • the input variables provided to the input layer (1010) may be information for channel estimation.
  • the input variables may be the received CSI-RS described above.
  • Each node constituting the input layer (1010) may be connected to nodes of the hidden layer (1020). If the hidden layer (1020) has two or more layers, each node constituting the input layer (1010) may be connected to each node of the first hidden layer. When each node constituting the input layer (1010) is connected to nodes of the hidden layer (1020), the connections between the nodes may have features based on learned weights.
  • the hidden layer (1020) may be composed of multiple layers. If the hidden layer (1020) is composed of multiple layers, each hidden layer may also be composed of multiple nodes. For example, if the hidden layer (1020) is composed of two layers, each node of the second hidden layer may be connected to each node of the first hidden layer. Additionally, each node of the second hidden layer may be connected to each node of the output layer (1030).
  • each of the nodes constituting the hidden layer (1020) can be connected to the input nodes of the input layer (1010).
  • the hidden layer (1020) is illustrated as consisting of four nodes.
  • the number of nodes in the hidden layer (1020) is not limited to the form illustrated in Fig. 10.
  • the hidden layer (1020) may be composed of five or more nodes, or three or fewer nodes.
  • each of the nodes constituting the hidden layer (1020) may be connected to the nodes of the output layer (1030). Based on this configuration, each of the nodes constituting the hidden layer (1020) may provide a connection between the nodes of the input layer (1010) and the nodes of the output layer (1030). At this time, each of the nodes of the hidden layer (1020) may transmit information calculated based on weighted sum information learned offline and/or learned online when transmitting input variables from the nodes of the input layer (1010) to the nodes of the output layer (1030).
  • the output layer (1030) of the encoder (9111) can output latent variables based on information received from each node of the hidden layer (1020).
  • the latent variable can mean a value output from nodes existing in the latent space, which is the output layer (1030) of the encoder (9111).
  • the M value can be set to 2. Since the number of latent space nodes of the output layer (1030), or the M value, is less than the number of input variable nodes of the input layer (1010), the encoding operation of the encoder (9111) according to the present disclosure can be understood as a compression operation.
  • the base station (910) can transmit a reference signal (RS) for channel estimation, such as a CSI-RS or a CSI-RS for online learning of an autoencoder, to the UE (920). Accordingly, the UE (920) can receive the CSI-RS transmitted by the base station (910). At this time, since the CSI-RS is transmitted through a wireless channel (1001), degradation or distortion may occur due to the wireless channel (1001). Accordingly, the UE (920) can compress the received CSI-RS using an encoder (9111).
  • RS reference signal
  • the encoder (9111) can transmit the received CSI-RS provided to the input layer (1010) to the hidden layer (1020) and the output layer (1030) based on the offline learning information.
  • the received CSI-RS is calculated based on the offline learned weighted sum information, and can be compressed and output through each node of the input layer (1010), each node of the hidden layer (1020), and each node of the output layer (1030).
  • the encoder (9111) can compress the received CSI-RS based on the offline learning.
  • the compressed CSI-RS can be latent variable values, which are output data of each node of the output layer (1030).
  • the UE (920) can generate a channel feature indicator (CFI) using the latent variable values.
  • CFI channel feature indicator
  • N can be determined as the product of the number of nodes (M) in the latent space and the latent variable dimension (LVD).
  • the number of nodes (M) in the latent space can be determined as the number of output nodes of the output layer (1030) in the case of the encoder (9111).
  • the LVD will be examined in more detail below.
  • the UE (920) can transmit the CFI to the base station (910) through the wireless channel (1230). Accordingly, the base station (910) can receive the CFI transmitted by the UE (920) through the wireless channel (1001) from the UE (920). The UE (920) can transmit the CFI to the base station (910) when online learning of the autoencoder is required and/or when estimating the channel using the autoencoder.
  • the base station (910) can transmit CSI-RS periodically. In addition, the base station (910) can transmit additional CSI-RS if necessary. Therefore, the UE (920) can receive CSI-RS periodically. In addition, the UE (920) can further receive additional CSI-RS. When channel estimation is required from the base station (910), the UE (920) can transmit CFI to the base station (910) instead of CSI feedback.
  • the base station (910) may inform the UE (920) of the CFI reporting cycle, and the UE (920) may report the CFI to the base station (910) based on the CFI reporting cycle.
  • the base station (910) may transmit the CSI-RS based on the CFI reporting cycle, or may transmit the CSI-RS at a preset cycle.
  • the CFI reporting cycle will be described in more detail below.
  • the base station (910) can use the CFI received from the UE (920) as an input to the decoder (9112) to restore the received CSI-RS from the CFI.
  • the received CSI-RS can be a CSI-RS that has been degraded or distorted through the wireless channel (1001) from the CSI-RS transmitted by the base station (910). Therefore, the CSI-RS restored by the base station (910) using the CFI value received from the UE (920) as an input to the decoder (9112) can theoretically be the same information as the received CSI-RS received by the UE (920).
  • the operation of the decoder (9112) may correspond to the reverse process of the encoding procedure of the encoder (9111).
  • the decoder (9112) may include an input layer (1040), a hidden layer (1050), and an output layer (1060).
  • the input layer (1040) of the decoder (9112) may be configured with the same number of nodes as the output layer (1030) of the encoder (9111) to process the CFI value. Therefore, the nodes of the input layer (1040) of the decoder (9112) may be an input latent space and may be configured with two nodes identical to the latent space of the output layer (1030) of the encoder (9111).
  • the hidden layer (1050) of the decoder (9112) can be composed of the same number of nodes as the hidden layer (1020) of the encoder (9111).
  • the hidden layer (1050) of the decoder (9112) can be composed of four nodes.
  • the output layer (1060) of the decoder (9112) can be composed of six nodes, which is the same as the input layer (1010) of the encoder (9111).
  • the base station (910) can restore the received CSI-RS from the CFI reported by the UE (920). Then, the base station (910) can obtain a reference signal received power (RSRP) value from the restored received CSI-RS.
  • RSRP reference signal received power
  • the base station (910) can receive CFI values from the UE (920) two or more times. Then, the base station (910) can obtain RSRP values from each of the received CFI values. The base station can compare the obtained RSRP values to determine whether the RSRP value is saturated.
  • saturation can mean a case where the RSRP value does not change by a certain level or more for a certain period of time or a certain number of times.
  • the base station (910) can check whether the RSRP value has been acquired two or more times before the current time point (t) at which the RSRP value was acquired. Let the time point at which the current RSRP value was acquired be t, and let the previous times at which the RSRP values were acquired be t-1 and t-2, respectively. Then, if the RSRP(t-2) value acquired by the base station (910) at time point t-2, the RSRP(t-1) acquired at time point t-1, and the RSRP(t) acquired at time point t have the same value or are within a preset range, the RSRP value can be determined to be saturated.
  • the base station (910) can determine that the RSRP value is saturated.
  • the base station (910) can determine that the RSRP values are not saturated.
  • the base station (910) can complete online learning and check whether the saturated RSRP value satisfies the desired level of RSRP.
  • the desired level of RSRP can be determined based on the distance between the base station (910) and the UE (920), the channel environment, etc.
  • the base station (910) obtains a desired RSRP value during online learning of the autoencoder.
  • the base station (910) may receive location information of the UE from the UE (920) and determine a desired RSRP value based on the distance between the base station (910) and the UE (920).
  • the UE (920) may feed back CSI based on the 3GPP standard during online learning of the autoencoder or immediately before online learning. Accordingly, the base station (910) may determine a desired RSRP value based on the CSI information fed back from the UE (920). In another way.
  • the base station (910) may determine (or estimate) a desired RSRP value from the transmission power intensity of a signal for which the UE (920) reports a CFI value and the intensity information of a signal that received the received CFI value. In this case, the UE (920) may transmit the transmission power intensity for which the CFI value is reported as additional information.
  • the learning saturation of AI/ML models can be determined by how similar the restored input data is to the actual input data based on the output data of the learning network.
  • saturation can be determined by the restoration accuracy.
  • the base station completes the learning of the encoder (9111) and decoder (9112) and can use the parameters obtained through online training for channel estimation, which is the model inference step.
  • the base station (910) may determine that the parameters learned through offline training are overfitted to previously collected data. If the base station (910) determines that the parameters learned through offline training are overfitted to previously collected data, the base station (910) may instruct the UE (920) to drop out specific node(s) of the encoder (9111) to resolve this.
  • dropout may mean a technique of partially omitting a neural network to solve an overfitting problem.
  • dropout may be applied by setting a probability that a node will be dropped for a node to which dropout is to be applied. Then, a case where dropout of an encoder node is applied will be examined with reference to the attached drawing.
  • Figure 11 is a conceptual diagram illustrating a dropout encoder model when dropout is instructed at a specific node of the encoder.
  • the encoder (9111) illustrated in Fig. 11 may be an encoder stored in the UE (920). In other words, it may be an encoder received from a server or base station (910) based on what was described above in Fig. 9. However, Fig. 11 illustrates a case where a specific node is dropped out.
  • the input layer (1110) of the encoder (9111) illustrated in FIG. 11 has the same number of nodes as the input layer (1010) of the encoder (9111) illustrated in FIG. 10
  • the hidden layer (1120) of the encoder (9111) illustrated in FIG. 11 has the same number of nodes as the hidden layer (1020) of the encoder (9111) illustrated in FIG. 10
  • the output layer (1130) of the encoder (9111) illustrated in FIG. 11 has the same number of nodes as the output layer (1030) of the encoder (9111) illustrated in FIG. 10.
  • connection between the input layer (1010) and the hidden layer (1020) of the encoder (9111) illustrated in Fig. 10 and the connection between the input layer (1110) and the hidden layer (1120) of the encoder (9111) illustrated in Fig. 11 may have different forms.
  • each node of the input layer (1110) illustrated in Fig. 11 may have no connection relationship with the second node (1121) of the hidden layer (1120).
  • the connection between the hidden layer (1120) and the output layer (1130) may also have a different form than in Fig. 10.
  • the second node (1121) of the hidden layer (1120) illustrated in Fig. 11 is not connected to any node of the output layer (1130).
  • the second node (1121) indicated by a white dot in the hidden layer (1120) in Fig. 11 may not be connected to the input layer (1110) and the output layer (1130) according to the dropout instruction.
  • the connections between the remaining nodes are the same as the form illustrated in FIG. 10 above.
  • the dropout of a specific node can be caused by setting the weight to '0 (zero)', thereby causing the corresponding node to be dropped out.
  • the connection from the input layer (1110) to the second node (1121) of the hidden layer (1120) does not provide any value since the weight is '0'.
  • the second node (1121) of the hidden layer (1120) can apply the weight '0' to the nodes of the output layer (1130) so that it does not provide any value.
  • the base station transmits CSI-RS to the UE, and the UE generates CFI based on the received CSI-RS and reports it to the base station.
  • the procedures (1) to (7) exemplified above will be examined in more detail below.
  • the procedures exemplified above are procedures for learning an autoencoder, and can be operations for learning fine tuning for downlink channel estimation through online learning for an autoencoder that has undergone offline learning.
  • a UE that receives an offline-learned autoencoder from a base station or an offline-learned autoencoder from a server can perform CFI feedback.
  • the base station may have all offline-learned autoencoders or decoders of offline-learned autoencoders.
  • the UE may have all offline-learned autoencoders or encoders of offline-learned autoencoders. Since offline learning of autoencoders has already been described above, a redundant description will be omitted.
  • a base station transmits a CSI-RS to a UE, and the UE can generate a CFI that compresses the received CSI-RS. Then, the UE considers a situation in which online training is performed through a process of transferring the generated CFI to the base station through a CSI feedback process.
  • the learning of the autoencoder may mean the learning of the encoder (9111) as exemplified in FIG. 9, the learning of the decoder (9112), or the learning of both the encoder (9111) and the decoder (9112).
  • learning and training may mean a procedure in which learning of an autoencoder, an encoder, or a decoder is performed through online training.
  • Figure 12 is a flowchart of operations between a base station and a UE during CFI feedback for online training.
  • the UE (920) and the base station (910) will be examined based on what was described above in FIG. 9. Therefore, it should be noted that the reference numerals of the UE (920) and the base station (910) use the same reference numerals as described in FIG. 9.
  • the encoder (9111) of the autoencoder may have the configuration of the encoder described in FIG. 10
  • the decoder (9112) of the autoencoder may also have the configuration of the decoder described in FIG. 10. Therefore, the encoder (9111) and the decoder (9112) may be in an offline-learned state.
  • the UE (920) may perform negotiation with the base station (910) for online training of the autoencoder.
  • the negotiation for online training of the autoencoder may be initiated by the base station (910) instructing the UE (920) to perform online training of the autoencoder or by the UE (920) requesting the base station to perform online training of the autoencoder.
  • the base station (910) and the UE (920) must have the same autoencoder. If the UE (920) does not have an autoencoder that requires online training, the base station (910) may provide the UE (920) with all of the autoencoders that require online training or the encoders of the autoencoder that the UE (920) should train.
  • the autoencoder may have two or more autoencoders with different network sizes as described below. Each of the autoencoders may be selected according to the required situation, and online training may be performed on at least one of the autoencoders.
  • the base station (910) may provide the UE (920) with the necessary information for autoencoder training.
  • the base station (910) may provide the UE (920) with the table information and/or the online training start time described below.
  • the UE (920) may provide its location information to the base station (910) at the request of the base station (910) or voluntarily.
  • the base station (910) can transmit a reference signal, for example, a CSI-RS, to the UE (920).
  • the UE (920) can report an RSRP value, which is reception power information measured by the CSI-RS transmitted by the base station (910), to the base station (910).
  • the procedure of the base station (910) transmitting the CSI-RS to the UE (920) and the UE (920) reporting the CSI to the base station (910) can be performed based on a CSI feedback procedure based on the current 3GPP standard.
  • the negotiation procedure for online training of the autoencoder at step S1200 may require other procedures, but since it is not possible to explain all of them, the description of other procedures or operations will be omitted.
  • the base station (910) can determine the CFI transmission period.
  • the base station (910) can determine the CFI transmission period based on the network size and/or the channel coherence time of the autoencoder (911) as factors for determining the CFI transmission period.
  • additional factors may be considered when determining the CFI transmission period. In the present disclosure, only the above two contents will be examined.
  • the base station (910) can determine the transmission period of the CFI according to the network size of the autoencoder (911).
  • the network of the autoencoder (911) can mean a network of the autoencoder (911) learned by the base station (910) or the server through offline training.
  • the network size (NS) of the autoencoder (911) can be determined as the sum of the network width and the network height constituting the encoder (9111) of the autoencoder (911) or the decoder (9112) of the autoencoder.
  • the network width can be defined as the number of nodes forming one layer in the encoder (9111) illustrated in Fig. 10. Since the decoder (9112) has a configuration that performs the reverse operation of the encoder (9111), the network width can also be defined as the number of nodes forming one layer in the decoder (9112).
  • the network height may mean the number of layers that constitute the encoder (9111).
  • the encoder (9111) is illustrated as having a hidden layer (1020) composed of one layer.
  • the hidden layer (1020) may be composed of two or more layers. Therefore, the network height may vary depending on the number of hidden layers (1020).
  • the width of the network can mean the sum of the number of nodes forming the input layer, the number of nodes forming the hidden layer, and the number of nodes forming the output layer.
  • the input layer (1010), the hidden layer (1020), and the output layer (1030) are each composed of one layer.
  • the number of nodes constituting the input layer (1010) is 6
  • the number of nodes constituting the hidden layer (1020) is 4
  • the number of offline-learned autoencoders can be plural.
  • each of the offline-learned autoencoders can have a different network size.
  • each of the offline-learned autoencoders can be included in both the base station (910) and the UE (920). In this way, the CFI transmission period according to the network size (NS) of each of the autoencoders having different sizes can be (pre)-configured to have a different period according to the network size.
  • NS #1 first network size
  • NS #2 second network size
  • NS #3 third network size
  • the CFI periods can be (pre)-configured to different values, as shown in Table 2 below.
  • Each of the CFI transmission cycles (CFI_P #1, CFI_P #2, and CFI_P #3) illustrated in Table 2 can be a cycle with a different time interval.
  • the time of each of the CFI transmission cycles (CFI_P #1, CFI_P #2, and CFI_P #3) can be set as CFI_P #1 ⁇ CFI_P #2 ⁇ CFI_P #3.
  • the base station (910) may consider various factors when determining the network size of the autoencoder. For example, the base station (910) may determine the network size of the autoencoder according to the available power and/or channel environment of the UE (920). Here, determining the network size of the autoencoder may be understood as selecting the autoencoder. In other words, when the base station (910) selects the first autoencoder, the network size (NS #1) corresponding to the first autoencoder is selected, when the second autoencoder is selected, the network size (NS #2) corresponding to the second autoencoder is selected, and when the third autoencoder is selected, the network size (NS #3) corresponding to the third autoencoder is selected.
  • the base station (910) determines the network size of the autoencoder based on the available power of the UE (920).
  • the base station (910) can determine the network size of the autoencoder that will perform online learning with the UE (920). If the available power reported in advance by the UE (920) is low, in other words, if the remaining battery power of the UE (920) is below a preset threshold, the base station (910) can select an autoencoder with a small network size. Conversely, if the remaining battery power of the UE (920) is above a preset threshold, the base station (910) can select an autoencoder with a large network size.
  • a large network size of the autoencoder means that the UE (920) must perform operations on many layers and many nodes when performing encoding.
  • the base station (910) can determine the network size of the autoencoder based on the available power of the UE (920).
  • the CFI period can be determined as exemplified in Table 2. If the online learning of the autoencoder with a small network size is determined, the CFI period can be shortened, and if the online learning of the autoencoder with a large network size is determined, the CFI period can be lengthened.
  • the network size of the autoencoder is determined based on the channel environment between the base station (910) and the UE (920), as follows.
  • the base station (910) may have previously checked the change rate of the channel environment between the UE (920) and the base station (910) in the preceding step S1200.
  • the base station (910) may determine an autoencoder to perform online learning based on the change rate of the channel environment. For example, if the change rate of the channel environment is fast, the base station (910) may select an autoencoder with a small network size. On the other hand, if the change rate of the channel environment is slow or there is little change in the channel environment, the base station (910) may select an autoencoder with a large network size. As described above, if the network size is large, the UE (920) has a long encoding time.
  • the base station (910) may select an autoencoder with a small network size to cope with the fast channel change.
  • the base station (910) may select an autoencoder with a large network size.
  • an autoencoder may also be selected by simultaneously considering both the available power of the UE (920) and the rate of change in the channel environment between the UE (920) and the base station (910).
  • the base station (910) can receive the CFI from the UE (920) during the online training process and input it into the decoder of the autoencoder for decoding. At this time, if the CFI reporting cycle is too fast, in other words, the UE (920) may not be able to compress the CSI-RS to report the CFI. In addition, even if the UE (920) reports the CFI to the base station (910), if the CFI is reported before the decoding procedure is completed in the decoder of the base station (910), the received CFI may not be properly processed.
  • the base station (910) can provide the table information of Table 2 to the UE (920) in advance.
  • the base station (910) can provide the table information of Table 2 to the UE (920) in advance.
  • the base station (910) may transmit the information to the UE (920) through various signaling.
  • the mapping information such as the table in Table 2 transmitted to the UE (920) may be transmitted using a System Information Block (SIB), included in an RRC Reconfiguration message, or included in a MAC-CE message.
  • SIB System Information Block
  • mapping information such as the table in Table 2 may be transmitted to the UE (920) via DCI.
  • mapping information such as the table in Table 2 may be transmitted to the UE (920) using the second message (Massage 2, Msg2) of the 4-step RACH procedure or message B (Massage B, MsgB) of the 2-step RACH procedure.
  • an RRC signaling message is newly defined to transmit mapping information such as the table in Table 2, the newly defined RRC signaling message may be transmitted to the UE (920).
  • the channel coherent time may mean a time during which there is no change in channel gain.
  • the channel coherent time is generally due to the mobility of the UE, but may also be affected by the surrounding channel environment in addition to the mobility of the UE.
  • the shorter the channel coherent time the faster the UE (920) must report the CFI to the base station (910).
  • the effective time during which the UE (920) can report the CFI becomes shorter, and thus the number of times the CFI is reported also decreases. If the number of times the CFI is reported decreases, the CFI that can be used for online training decreases, and thus sufficient learning may not be performed. If the online learning of the autoencoder is insufficient, the parameters with low accuracy of the autoencoder are obtained, and accurate channel estimation cannot be performed.
  • the CFI reporting cycle can be determined based on the channel coherence time (CT), and an example of the CFI reporting cycle based on the channel coherence time (CT) is as shown in Table 3 below.
  • the time values of the first channel coherent time (CT #1), the second channel coherent time (CT #2), and the third channel coherent time (CT #3) illustrated in Table 3 can assume that CT #1 has the shortest time value, CT #2 has a longer time value than CT #1, and CT #3 has the longest time value.
  • CT #1 has the shortest time value
  • CT #2 has a longer time value than CT #1
  • CT #3 has the longest time value.
  • the opposite case is also possible.
  • the CFI transmission periods (CFI_P #1, CFI_P #2, CFI_P #3) can become longer as CFI_P #1 ⁇ CFI_P #2 ⁇ CFI_P #3.
  • the CFI transmission period (CFI_P #1) is the shortest for the first coherent time (CT #1)
  • the CFI transmission period (CFI_P #3) is the longest for the third coherent time (CT #3). This means that as the channel coherent time becomes shorter, the CFI transmission period can be set shorter to obtain more inputs to the decoder of the base station (910).
  • the channel coherent values are divided into three categories, but in actual implementation, only two types can be configured, or four or more types of channel coherent values can be configured.
  • the types (number of types) of channel coherent values can be preset, or the base station (910) can flexibly determine them according to the channel environment.
  • the base station (910) can provide the table information of Table 3 to the UE (920) in advance.
  • the base station (910) can provide the table information of Table 3 to the UE (920) in advance.
  • the base station (910) may transmit the information to the UE (920) through various signaling.
  • the mapping information such as the table in Table 3 transmitted to the UE (920) may be transmitted using SIB, included in an RRC reconfiguration message, or included in a MAC-CE message.
  • mapping information such as the table in Table 3 may be transmitted to the UE (920) via DCI.
  • mapping information such as the table in Table 3 may be transmitted to the UE (920) using the second message (Massage 2, Msg2) of the 4-step RACH procedure or message B (Massage B, MsgB) of the 2-step RACH procedure.
  • an RRC signaling message is newly defined to transmit mapping information such as the table in Table 3, the newly defined RRC signaling message may be transmitted to the UE (920).
  • section a which performs step S1210, the operation of determining the CFI transmission period based on the size of the autoencoder is described, and in section b, the operation of determining the CFI transmission period based on the channel coherent time is described.
  • the CFI transmission period may be determined by combining the method of using the network size (Table 2) and the method of using the channel coherent time (Table 3).
  • the base station (910) may determine the CFI transmission period by considering the network size described in Table 2 and the channel coherent time described in Table 3 together.
  • a new table considering the contents of Tables 2 and 3 may be defined. Since the new table considering Tables 2 and 3 may take various forms, specific examples are not provided in this disclosure.
  • the base station (910) may determine the transmission period of the CFI by considering the channel coherent time, and then determine the network size of the autoencoder between the base station (910) and the UE (920) based on the determined CFI period as described in Table 2.
  • the base station (910) may request information of the UE (920) in advance at step S1200 and receive the corresponding information from the UE (920).
  • the information that the base station (910) requests from the UE (920) may be information on available power of the UE (920) as described in Table 2 above.
  • the base station (910) may request the UE (920) through a UE Information Request message.
  • the UE (920) may generate a UE Information Response message in response thereto and send it back to the base station (910) including the information requested by the base station (910).
  • the base station (910) may request this from the UE (920) and receive the information from the UE (920). At this time, the UE (920) may use any one of uplink control information (UCI), the first message (Msg1) in the 4-step RACH procedure, or message A (MsgA) in the 2-step RACH procedure as the information requested by the base station (910).
  • UCI uplink control information
  • Msg1 the first message
  • MsgA message A
  • the RRC signaling message can be used.
  • the base station (910) can determine the CFI transmission cycle in step S1210 through one or a combination of two or more of the methods described above.
  • the base station (910) can determine the latent variable dimension (LVD) of the autoencoder to perform online learning.
  • the latent variable dimension (LVD) of the autoencoder can be determined based on the channel resolution or the latency requirement. In addition to the channel resolution and latency requirement described in this disclosure, additional factors may be considered when determining the LVD of the autoencoder. However, this disclosure will only examine cases where the above two requirements are considered when determining the LVD.
  • the channel resolution may be the number of downlink channels that the base station (910) intends to distinguish.
  • the channel resolution may be determined based on the number of downlink beams. Accordingly, the channel resolution may have a higher channel resolution as the base station forms a narrower beam or intends to perform more precise channel estimation.
  • LVD may mean a dimension required to express one latent variable value existing in a latent space. Additionally, LVD may be interpreted as the number of bits required to express one latent variable value. Therefore, the higher the LVD value, the more accurately the latent variable can be expressed. Accordingly, the mapping between the channel resolution (CR) and the latent variable dimension (LVD) may be exemplified as in Table 4 below.
  • the channel resolution (CR) is assumed such that the first channel resolution (CR #1) has the smallest channel resolution, the second channel resolution (CR #2) is higher than the first channel resolution (CR #1) and lower than the third channel resolution (CR #3), and the third channel resolution (CR #3) has the highest channel resolution. Then, the channel resolutions can have a relationship such as CR #1 ⁇ CR #2 ⁇ CR #3.
  • the LVD values in Table 4 can also have the relationship LVD #1 ⁇ LVD #2 ⁇ LVD #3.
  • Table 4 exemplifies a case where three channel resolutions are distinguished, but this is only one embodiment, and only two channel resolution values may be used, or four or more channel resolution values may be used.
  • the number of channel resolutions according to the present disclosure may be preset, or the base station (910) may adaptively change it according to the channel environment.
  • the mapping information between the channel resolution and LVD as exemplified in Table 4 can be transmitted in advance by the base station (910) to the UE (920).
  • the base station (910) can transmit in advance the mapping information between the channel resolution and LVD as exemplified in Table 4 to the UE (920).
  • the base station (910) can transmit the mapping information between the channel resolution and LVD to the UE (920) through various signaling.
  • mapping information such as Table 4 transmitted to UE (920) may be transmitted using a System Information Block (SIB), included in an RRC Reconfiguration message, or included in a MAC-CE message.
  • SIB System Information Block
  • mapping information such as Table 4 can be transmitted to the UE (920) via DCI.
  • mapping information such as Table 4 may be transmitted to the UE (920) using the second message (Massage 2, Msg2) of the 4-step RACH procedure or message B (Massage B, MsgB) of the 2-step RACH procedure.
  • an RRC signaling message may be transmitted to the UE (920) using the newly defined RRC signaling message.
  • LVD refers to the dimension required to express one latent variable value existing in the latent space, and can also be interpreted as the number of bits required to express the latent variable value.
  • the amount (number of bits) of CFI that the UE (920) reports to the base station (910) through the CSI feedback increases.
  • the time required for online training of the autoencoder may increase.
  • the latency requirement may be a delay requirement due to the characteristics of the UE (920) itself, or a delay requirement according to the type of service provided to the UE (920).
  • the UE (920) is a specific node in a time-sensitive network
  • a delay requirement may exist in the time-sensitive network.
  • the UE (920) may have a delay requirement according to the characteristics of the time-sensitive network node.
  • a URLLC (Ultra Reliable Low Latency Communication) service when a URLLC (Ultra Reliable Low Latency Communication) service is provided to a UE (920), it may have a delay requirement required according to the characteristics of the service. In this way, there may be a delay requirement based on the characteristics of the network (or characteristics of the UE) in which the UE (920) operates and a delay requirement in the service.
  • URLLC Ultra Reliable Low Latency Communication
  • LVD can be determined based on such delay requirements, and the mapping between delay requirements and LVD can be exemplified as shown in Table 5 below.
  • each of the delay requirements can have the relationship LR #1 ⁇ LR #2 ⁇ LR #3.
  • the LVD values corresponding to the delay requirements can also have the relationship LVD #1 ⁇ LVD #2 ⁇ LVD #3.
  • the shorter the delay requirement the smaller the latent variable dimension (LVD)
  • the longer the delay requirement the larger the latent variable dimension (LVD).
  • Table 5 exemplifies a case where three delay requirements are distinguished, but this is only one embodiment, and it may be configured with only two delay requirements, or it may be configured with four or more delay requirements.
  • the number of these delay requirements and channel resolutions may be preset, and the base station (910) may adaptively change them according to the channel environment.
  • mapping information between the delay requirement and LVD as exemplified in Table 5 can be transmitted in advance from the base station (910) to the UE (920).
  • the base station (910) can transmit in advance the mapping information between the delay requirement and LVD as exemplified in Table 5 to the UE (920).
  • the mapping information between the delay requirement and LVD can be transmitted from the base station (910) to the UE (920) through various signaling.
  • mapping information such as Table 5 transmitted to UE (920) may be transmitted using SIB, included in an RRC Reconfiguration message, or included in a MAC-CE message.
  • mapping information such as Table 5 may be transmitted to the UE (920) via DCI.
  • mapping information such as Table 5 may be transmitted to the UE (920) using the second message (Massage 2, Msg2) of the 4-step RACH procedure or message B (Massage B, MsgB) of the 2-step RACH procedure.
  • an RRC signaling message may be transmitted to the UE (920) using the newly defined RRC signaling message.
  • section a which performs step S1220, the operation of determining LVD based on channel resolution is described, and in section b, the operation of determining LVD based on delay requirements is described.
  • the base station (910) may request the UE (920) for information necessary for LVD determination in step S1200 described above, and may receive information necessary for latent variable dimension determination from the UE (920).
  • UE (920) can use either uplink control information (UCI) or UE assistance information.
  • UCI uplink control information
  • UE assistance information UE assistance information
  • the UE (920) may report this in advance to the base station (910) using the first message (Msg1) of the 4-step RACH procedure or message A (MsgA) of the 2-step RACH procedure.
  • the UE (920) may provide UE information using the newly defined RRC signaling message.
  • the base station (910) may request UE information from the UE (920) and receive the information from the UE (920). At this time, the base station (910) may transmit a UE Information Request message to the UE (920), and the UE (920) may include the information requested in the UE Information Request message in a UE Information response message and report it to the base station (910).
  • step S1230 the base station (910) can transmit the CFI transmission period information determined in step S1210 and the latent variable dimension information determined in step S1220 to the UE (920).
  • the base station (910) can transmit a CFI transmission period to the UE (920).
  • the CFI transmission period may be determined by considering only the network size of the autoencoder as described in Table 2 above, may be determined by considering only the channel coherent time as described in Table 3, or may be determined by considering both the autoencoder and the channel coherent.
  • the base station (910) may transmit latent variable dimension (LVD) information to the UE (920).
  • the LVD may be determined by considering only the channel resolution as described in Table 4 above, or by considering only the delay requirements as described in Table 5, or by considering both the channel resolution and the delay requirements together.
  • the base station (910) considers the size of the autoencoder and the channel coherent time together when determining the CFI period. In addition, in the embodiment of the present disclosure, it is assumed that the base station (910) considers the channel resolution and delay requirements together when determining the LVD.
  • the base station (910) may determine the CFI transmission period and LVD by additionally considering various factors such as frequency resource utilization, the number of UEs connected to the base station (910), RSRP of each UE, available power of each UE, etc. in addition to the above-mentioned factors in determining the CFI determination and the LVD determination.
  • the base station (910) may already have a large number of frequency resources being used for communication. Therefore, the amount of frequency resources that can be used by the base station (910) may not be large.
  • the base station (910) may set a CFI value with a long period and set a low LVD in order to train the autoencoder while providing a communication service without interference between all frequency bands or UEs.
  • a shorter CFI transmission period and a higher LVD can be determined to increase the reliability of the CFI signal as the RSRP of the UE that wants to communicate with the base station (910) is lower.
  • the base station (910) can determine the network size based on the available power of each UE reported by the UEs, and determine the CFI transmission period and LVD based on the determined network size.
  • the base station (910) can use various forms of signaling when transmitting CFI transmission cycle and LVD information to the UE (920).
  • Such signaling can use signaling methods as described in Tables 2 to 5 above.
  • the UE (920) can receive CFI transmission cycle and LVD information from the base station (910).
  • step S1240 the UE (920) can determine an encoder to learn based on the CFI transmission period and LVD information received from the base station (910).
  • the UE (920) can receive two or more autoencoders (or encoders of autoencoders) that have been learned offline from the base station (910) or the server.
  • the UE (910) can receive autoencoders having two or more network sizes. Therefore, in step S1230, the autoencoder to perform online learning can be determined based on the CFI transmission period information and LVD information received from the base station (910).
  • the CFI transmission period can be determined based on the network size and channel coherent time as described above. Therefore, the UE (920) can know the network size of the autoencoder from the CFI transmission period information. In addition, when the LVD information is determined, the number of nodes of the output layer (1030) of the encoder (9111) as illustrated in FIG. 10 can be determined. Based on the above information, the UE (920) can select an autoencoder to perform online learning.
  • the base station (910) can transmit a CSI-RS for online training (or learning) of the autoencoder to the UE (920).
  • the CSI-RS may be a CSI-RS defined separately for online training of the autoencoder, or may be a CSI-RS transmitted periodically. Therefore, in step S1250, the UE (920) can receive a CSI-RS from the base station (910).
  • the UE (920) can use the received CSI-RS as input data of the encoder (9111) of the autoencoder (910) for online training.
  • the CSI-RS received by the UE (920) can be the received CSI-RS described above in FIG. 10.
  • the UE (920) can generate a CFI by using the received CSI-RS as an input of the encoder (9111).
  • the CFI can be determined as in the mathematical expression 1 described above, and if expressed again, it can be expressed as in the mathematical expression 2 below.
  • CFI can be generated based on the number of nodes (M) of the latent space of the autoencoder and the LVD information indicated in step S1230.
  • the number of nodes (M) of the latent space can be determined based on the decision of the autoencoder.
  • the number of nodes (M) of the latent space of the autoencoder can be determined accordingly.
  • the number of nodes (M) of the latent space is 2
  • the LVD is expressed as the number of bits representing each node constituting each latent space. Therefore, the CFI expressed as mathematical expression 2 can be expressed as the product of 2 and LVD in the case of Fig. 10.
  • step S1270 the UE (920) can transmit the CFI to the base station (910) based on the CFI transmission cycle.
  • the CFI transmission cycle may be the CFI transmission cycle that the base station (910) transmitted to the UE (920) in step S1230. Therefore, the base station (910) can receive the CFI from the UE (920) based on the CFI transmission cycle in step S1270.
  • the base station (910) can receive the CFI from the UE (920).
  • the CFI transmitted by the UE (920) to the base station (910) can be reported in various forms.
  • the CFI since the CFI is a report on CSI-RS, it can be transmitted to the base station (910) through a measurement report message of the CSI reporting procedure, which is a CSI-RS feedback procedure according to the current 3GPP standard.
  • the CFI can be transmitted to the base station (910) via the newly defined RRC signaling.
  • a UE must generate CSI expressed in RI, CQI, and PMI values based on CSI-RS received from a base station, and perform Type 1 codebook-based CSI feedback or Type 2 codebook-based CSI feedback.
  • the method according to the present disclosure can generate CFI using an encoder capable of expressing the received CSI-RS itself in a low dimension, and feed it back to the base station. Therefore, the CFI feedback procedure according to the present disclosure can be defined as a new feedback procedure for AI/ML-based CSI feedback.
  • the UE (920) transmits to the base station (910) at step S1270 it can be one method that can map the latent variable to the CFI.
  • the mapping relationship between the latent variable and the CFI can be determined in various ways.
  • the present disclosure described below will describe a procedure for learning an autoencoder through online training and a procedure for relearning the autoencoder in channel estimation using AI/ML.
  • the autoencoder for online training may be learned in advance through offline training.
  • the base station may have the entire autoencoder or a part of the autoencoder (for example, a decoder in the case of downlink channel estimation) in advance.
  • the UE may also have the entire autoencoder or a part of the autoencoder (for example, an encoder in the case of downlink channel estimation) in advance. Since the case where the base station and/or the UE have the entire or a part of the autoencoder and the configuration thereof have been described above in FIG. 9, the same description will be omitted.
  • the base station and the UE when the base station and the UE have autoencoders, the base station and the UE can perform fine-tuning of the autoencoder through online training.
  • the procedure for obtaining information for this fine-tuning procedure is described in Section [A].
  • section [B] a procedure for learning an autoencoder through online training for fine-tuning at a base station using the acquired information and a procedure for retraining the autoencoder will be described. Therefore, the operation described in section [B] may be an operation based on the information acquired in section [A]. However, the operation described in section [B] may also utilize information acquired through a CSI reporting procedure according to the current 3GPP standard. In the following description, a procedure for learning and retraining an autoencoder through online training will be described based on the description in section [A].
  • Figure 13 is a flowchart explaining the procedure for conducting online training of an autoencoder at a base station.
  • the base station (910) can receive a CFI from the UE (920) and input the received CFI into the decoder of the autoencoder that the base station (910) has.
  • the received CFI can be a value that the UE (920) reported to the base station (910) in step S1270 in the procedure of FIG. 12 above.
  • the base station (910) can obtain the received CSI-RS of the UE (920) by decoding the received CFI using the decoder. Therefore, the base station (910) can obtain the RSRP value from the received CSI-RS.
  • the base station (910) can determine whether the obtained RSRP is saturated.
  • the RSRP saturation can mean a phenomenon in which the RSRP does not increase even though the base station continues online training using the decoder.
  • the base station (910) can use information in the table 7 below to determine RSRP saturation status.
  • COUNTSAT saturated count: COUNTSAT, Maximum saturation count: MAXCOUNT_SAT
  • COUNTSAT MAXCOUNT_SAT Saturation is determined and training (or learning) is stopped.
  • the conditions for determining whether learning progresses can use the saturation count (COUNTSAT) value and the maximum saturation count (MAXCOUNT_SAT) value. If the saturation count value is less than the maximum count value, training or learning can continue, and if the saturation count value is equal to the maximum count value, it can be determined to be saturated and training or learning can be stopped. This will be examined in more detail with reference to the attached Figure 14.
  • Figure 14 is a flowchart explaining a case where saturation of RSRP values is determined at a base station.
  • step S1400 the base station (910) can set the saturation count (COUNTSAT) value to '0'. Then, in step S1402, the base station (910) can restore the reception CSI-RS by using the received CFI information as an input to the decoder of the base station (910) as described above. Then, the base station (910) can obtain the RSRP value by using the restored reception CSI-RS.
  • COUNTSAT saturation count
  • the base station (910) can compare the acquired RSRP value with the previous RSRP value. If the received CFI is the first reception, the acquired RSRP value may be the first RSRP value. Therefore, if there is no previous RSRP (if the first CFI value is acquired) or it is not the same as the previous RSRP value, the base station (910) can proceed to S1400.
  • the base station (910) can compare the RSRP value obtained through decoding with the RSRP value obtained in the previous training, and check whether the two values are the same in step S1404.
  • the base station (910) can proceed to step S1406 and increase the saturation count value by 1.
  • the base station (910) can proceed to step S1400.
  • a repetition count limit value can be set. Since the method of using a repetition count limit value to prevent infinite repetition is a widely known method, further description will be omitted.
  • step S1606 After increasing the saturation count value by 1 in step S1606, the base station (910) can proceed to step S1408.
  • the base station (910) can compare whether the saturation count value is equal to the maximum saturation count value (MACCOUNT_SAT). If the saturation count value is equal to the maximum saturation count value, the base station (910) can determine that RSRP is saturated and terminate the routine of FIG. 14.
  • the base station (910) may proceed to step S1402 and repeat the training according to the present disclosure.
  • the maximum saturation count value can be actively set by the base station (910) based on various factors such as the channel environment between the base station (910) and the UE (920) or the mobility of the UE (920). For example, in an environment where the channel changes rapidly or the mobility of the UE (920) is large, the base station (910) can set the maximum count value to a small value in order to quickly progress the learning speed. On the other hand, in a case where there is little channel change and the mobility of the UE (920) is not large, the base station (910) can set the maximum count value to a large value in order to more accurately learn.
  • the base station (910) can determine whether RSRP is saturated based on the above-described steps. If RSRP is saturated, the base station can proceed to step S1510.
  • a method for a base station (910) to determine whether RSRP is saturated based on CFI reported by a UE (920) is exemplified, but the UE (920) may also determine whether RSRP is saturated. This is because when the UE (920) generates CFI based on the received CSI-RS and the CFI generates the same value, it may determine that RSRP is saturated. As another example, the UE (920) may directly measure the RSRP of the received CSI-RS and determine that RSRP is saturated.
  • the base station (910) can provide maximum saturation count (MAXCOUNT_SAT) value information to the UE (920).
  • the maximum saturation count value information can be provided to the UE (920) through various forms of signaling.
  • the base station (910) may provide the maximum saturation count value information to the UE (920) via SIB, DCI, MAC-CE, the second message (Msg2) of the 4-step RACH procedure, message B (MsgB) of the 2-step RACH procedure, or an RRC Reconfiguration message.
  • the base station (910) may also transmit information about the maximum count value to the UE (920) using the new RRC signaling message.
  • the UE (920) can notify the base station (910) if RSRP is saturated. At this time, various forms of signaling can be used as a method for the UE (920) to notify the base station (910) of the RSRP saturation state.
  • the UE (920) may notify the base station (910) of an RSRP saturation state using either UCI or UE Assistance Information.
  • the UE (920) may notify the base station (910) of the RSRP saturation state using either the first message (Msg1) of the 4-step RACH procedure or message A (Msg A) of the 2-step RACH procedure.
  • the UE (920) can indicate RSRP saturation to the base station (910) using the newly defined RRC signaling message.
  • a base station (910) when a base station (910) requests RSRP saturation status information from a UE (920) using a UE Information Request message, the UE (920) may inform the base station (910) of the RSRP saturation status information using a UE Information Response message.
  • the base station (910) or UE (920) determines the RSRP saturation state has been described. In the following description, for convenience of explanation, it is assumed that the base station (910) determines the RSRP saturation state.
  • step S1300 If RSRP saturation occurs by performing step S1300, the base station (910) can proceed to step S1310.
  • the base station (910) can check whether the RSRP value is at a satisfactory level.
  • a method for checking whether the level is satisfactory can be done by comparing the RSRP threshold (RSRP TH ) with a saturated RSRP value.
  • the RSRP th parameter can mean a certain level of RSRP value set by the base station (910) or the UE (920), and can be set through various criteria such as the accuracy of channel estimation desired by the base station (910) and/or the UE (920) or a hardware problem of the UE (920).
  • the UE (920) can report the RSRP or CSI directly measured from the CSI-RS received by the base station (910) in step S1200 described above in FIG. 12. Therefore, the base station (910) can determine the RSRP threshold based on the RSRP or CSI reported in step S1200.
  • the base station (910) and/or the UE (920) may set a high RSRP threshold and perform re-learning for the desired performance.
  • the base station (910) and/or the UE (920) may set a low RSRP threshold to reduce the number of re-learnings.
  • the learning results based on the RSRP threshold in this way can be exemplified as shown in Table 8 below.
  • RSRP Threshold RSRP th
  • the base station (910) can terminate learning if the RSRP value is equal to or greater than the RSRP threshold (RSRP th ). On the other hand, if the RSRP value is lower than the RSRP threshold (RSRP th ), the base station (910) can determine that re-learning is necessary.
  • the base station (910) determines whether RSRP is satisfied and decides whether to relearn is exemplified.
  • the UE (920) may also determine whether RSRP is satisfied.
  • the entity that determines whether to relearn the autoencoder may be the base station (910) or the UE (920).
  • the base station (910) When the base station (910) is the entity that determines whether to relearn the autoencoder, the base station (910) can determine whether to relearn by determining whether RSRP is satisfied. At this time, the base station (910) can determine relearning based on the comparison result of the RSRP value reported by the UE (920) and the RSRP threshold value.
  • the UE (920) can compare the RSRP measured by itself with the RSRP threshold value to determine retraining. At this time, if the RSRP threshold value is determined by the base station (910), the RSRP threshold value can be provided to the UE (920) in advance by the base station (910).
  • the method for the base station (910) to provide RSRP threshold information to the UE (920) can utilize various signaling methods.
  • RSRP threshold information transmitted to UE (920) may be transmitted using SIB, included in an RRC Reconfiguration message, or included in a MAC-CE message.
  • RSRP threshold information can be transmitted to the UE (920) via DCI.
  • the RSRP threshold information may be transmitted to the UE (920) using the second message (Massage 2, Msg2) of the 4-step RACH procedure or message B (Massage B, MsgB) of the 2-step RACH procedure.
  • an RRC signaling message may be transmitted to the UE (920) using the newly defined RRC signaling message.
  • the UE (920) is the entity that determines whether autoencoder re-learning is necessary, if it determines that autoencoder re-learning is necessary, it can notify the base station (910) that autoencoder re-learning is necessary.
  • the UE (920) can use various forms of signaling messages to notify the base station (910) of information for notifying that autoencoder re-learning is necessary.
  • the UE (920) may transmit information to the base station (910) to indicate the need for autoencoder retraining using either uplink control information (UCI), the first message (Msg1) in the 4-step RACH procedure, or message A (MsgA) in the 2-step RACH procedure.
  • UCI uplink control information
  • Msg1 the first message
  • MsgA message A
  • the UE (920) may transmit information to the base station (910) to notify that autoencoder retraining is necessary using UE Assistance Information.
  • the RRC signaling message can be used.
  • the base station (910) determines the entity that determines whether re-learning of the autoencoder is necessary in advance as the UE (920) at step S1200 of FIG. 12 or a separate step, the base station (910) can determine this to the UE (920). In this way, notifying that the entity that determines whether re-learning of the autoencoder is necessary is the UE (920) can be understood as the same as requesting whether re-learning of the autoencoder is necessary to the UE (920). Accordingly, in this case, the base station (910) can request re-learning determination information from the UE (920) using a UE Information Request message. Then, the UE (920) can determine whether re-learning of the autoencoder is necessary as described above, and report information on whether or not to re-learn the autoencoder to the base station (910) using a UE Information Response message.
  • the base station (910) may proceed to step S1320. If the desired RSRP is satisfied, that is, if the saturated RSRP is greater than or equal to the RSRP threshold, the base station (910) may proceed to step S1320.
  • Step S1320 is a state in which re-learning of the autoencoder is required because the saturated RSRP does not satisfy the desired RSRP, i.e., the threshold RSRP value.
  • the base station (910) can generate re-learning related information.
  • step S1330 the base station (910) can transmit the generated autoencoder re-learning related information to the UE (920).
  • An example of information related to autoencoder retraining generated in step S1320 and transmitted to UE (920) in step S1330 may be information as shown in Table 9 below.
  • the transmitted information may include encoder re-training information indicating encoder retraining, encoder reconfiguration information, and an encoder identifier.
  • the encoder identifier (ID) exemplified in Table 9 may be information for informing the UE (920) of which encoder the current base station (910) is using to perform online learning.
  • the UE (920) can determine which encoder the base station (910) indicates through the encoder identifier received from the base station (910).
  • the encoder re-training parameters illustrated in Table 9 may mean information that the decoder learned through the CFI transmitted by the current UE (920) does not satisfy a certain level of RSRP and thus begins re-training. Accordingly, the encoder re-training parameters may indicate that the training procedure of the encoder and decoder having the autoencoder identifier is performed again.
  • the encoder reset parameters exemplified in Table 9 may include dropout node information as described above in FIG. 11.
  • the dropout node information may include node information to which dropout is to be applied among the nodes of each layer constituting the encoder (9111) stored in the UE (920) and dropout probability information of the corresponding node.
  • a layer to which dropout is to be applied may be a node of the hidden layer (1120).
  • the hidden layer (1120) is composed of one layer or two or more layers, all nodes of a specific hidden layer (1120) must not be dropped out. This is because if all nodes of a specific hidden layer (1120) are dropped out, the encoding operation is not performed.
  • the present disclosure may define the minimum value of the nodes constituting a specific layer and the maximum value of the nodes constituting a specific layer.
  • the hidden layer (1120) is composed of two layers, a first hidden layer connected to the input layer (1110) and a second hidden layer connected to the output layer (1130), and let's assume that the minimum and maximum values of the nodes constituting the first and second hidden layers are the same.
  • the number of nodes that the first hidden layer can be configured by dropout can have the following relationship, and the second hidden layer can also have the same relationship.
  • the minimum value of the nodes that make up the first hidden layer ⁇ the number of nodes in the first hidden layer to be made by the drop probability ⁇ the maximum value of the nodes that make up the first hidden layer
  • Dropout node information can be determined by the above relationship.
  • the last "n" in the parameter of the encoder identifier can be the number to which dropout is applied. If dropout is not applied, it consists of only the encoder identifier, and if it is an encoder to which dropout is applied once, the number of dropouts can be specified as "n", such as "Encoder_ID_1". Therefore, the number of "n” can increase depending on the number of dropouts.
  • the method of transmitting the encoder reset information may not be set in step S1320, but may be set in advance.
  • the base station (910) may transmit in advance information about the order of nodes to be dropped for nodes existing in all layers required for additional encoder re-learning.
  • the base station (910) may transmit to the UE (920) information about the order of nodes to be dropped for nodes in each layer. The transmission of such information may be transmitted to the UE (920) in advance in step S1200 described above in FIG. 12.
  • the base station (910) transmits drop-out node information to the UE (920) in advance, and if re-learning is required, the base station (910) can transmit only the encoder re-training and encoder identifier to the UE (920). Then, the UE (920) can confirm that the encoder needs to be reset from the encoder re-training parameters, and can drop out the corresponding nodes based on the drop-out node information promised in advance.
  • the base station (910) can notify the UE (920) that the first drop out is indicated by transmitting the encoder identifier set to “Encoder_ID_1” as described above.
  • the base station (920) transmits autoencoder retraining related information, such as Table 9, to the UE (910)
  • various forms of signaling can be used.
  • the autoencoder retraining related information, such as Table 9, transmitted to the UE (920) can be transmitted using SIB, included in an RRC Reconfiguration message, or included in a MAC-CE message.
  • information related to autoencoder retraining can be transmitted to the UE (920) via DCI.
  • information related to autoencoder retraining such as Table 9, may be transmitted to the UE (920) using the second message (Massage 2, Msg2) of the 4-step RACH procedure or message B (Massage B, MsgB) of the 2-step RACH procedure.
  • the newly defined RRC signaling message may be transmitted to the UE (920).
  • the UE (920) can receive autoencoder retraining related information based on the method described above.
  • the UE (920) may reset the autoencoder based on the information related to autoencoder retraining.
  • what the UE (920) resets may be the encoder of the autoencoder. In other words, it may be an operation of setting the dropout of a specific layer of the encoder described in FIG. 11.
  • the UE (920) can transmit Encoder Reconfiguration Complete information to the base station (910) through the encoder dropout.
  • the Encoder Reconfiguration Complete information can be information indicating that the dropout for the encoder of a specific autoencoder is complete. Therefore, it can include the identifier information of the autoencoder.
  • the UE (920) can report to the base station (910) which autoencoder has been reset and the number of times the dropout has been performed. Through this, the base station (910) can confirm whether it is the autoencoder that it has instructed to the UE (920) and whether the number of times the dropout has been instructed is the same.
  • the autoencoder reported by the UE (920) can be specifically an encoder.
  • the input and output to the node can have their weights set to "0". In other words, both the input and output can be set to "0".
  • the autoencoder reset completion message can be transmitted to the base station (910) through various signaling.
  • the UE (920) may transmit an autoencoder reset complete message to the base station (910) using either UCI or UE assistance information.
  • the UE (920) may report the autoencoder reconfiguration completion message to the base station (910) in advance using the first message (Msg1) of the 4-step RACH procedure or message A (MsgA) of the 2-step RACH procedure.
  • the UE (920) may provide UE information using the newly defined RRC signaling message.
  • the base station (910) may request transmission of an autoencoder reconfiguration complete message to the UE (920) by transmitting a UE Information Request message.
  • the UE (920) may report the autoencoder reconfiguration complete message to the base station (910) by including it in a UE Information response message.
  • the base station (910) can receive an autoencoder reset completion message.
  • the base station (910) may perform an autoencoder retraining procedure based on receiving an autoencoder re-reset completion message.
  • the autoencoder retraining procedure may be a procedure that starts again from the CSI-RS transmission step described above in FIG. 12.
  • the time for learning the autoencoder through online training between the base station (910) and the UE (920) may continue to increase. For example, when the desired RSRP is not satisfied, the learning time of the autoencoder may continue to increase.
  • traffic occurs between the base station (910) and the UE (920), which may increase the congestion of the wireless channel and the frequency usage.
  • the UE (920) is generally powered by a battery, the battery consumption of the UE (920) also increases. Therefore, even when the desired RSRP is not satisfied, it may be desirable to stop the learning or relearning procedure at an appropriate time.
  • Figure 15 is a flowchart illustrating the procedure for terminating online training of an autoencoder at a base station.
  • the base station and the UE are the base station (910) and the UE (920) described in Figs. 9 to 13. Therefore, it should be noted that the reference numerals of the UE (920) and the base station (910) use the same reference numerals as described in Fig. 9.
  • the base station (910) can start online training of the autoencoder.
  • Starting online training of the autoencoder can be a procedure in which initial online training is performed based on the procedure described above in FIGS. 12 and 13.
  • the base station (910) can obtain an RSRP value in the online training procedure of the autoencoder and store the obtained RSRP and the corresponding autoencoder information.
  • the RSRP value can be obtained based on the CFI value reported by the UE (920) as described above.
  • step S1504 the base station (910) can check whether the acquired RSRP satisfies the desired RSRP value.
  • step S1504 can be a procedure for checking whether it is greater than or equal to the RSRP threshold value described in FIG. 12 above. If the acquired RSRP value satisfies the desired RSRP value, the base station (910) can terminate the online training of the autoencoder.
  • the base station (910) can proceed to step S1506 to check whether the online training forced termination condition is satisfied.
  • the online training forced termination condition can be defined with various values.
  • the OnlineTrainingTimeMax value may be set by the base station (910), or may be set by the UE (920) based on its battery capacity or hardware requirements. If the UE (920) sets the OnlineTrainingTimeMax value, the UE (920) may transmit the OnlineTrainingTimeMax value to the base station (910) when the online learning of the autoencoder is initiated, or before the online learning of the autoencoder is initiated, so that the base station (910) may check the time expiration.
  • the base station (910) may transmit to the UE (920) a set of possible OnlineTrainingTimeMax values according to the situation of the UE (920), and the UE (920) may select one value from the set and transmit it to the base station (910).
  • the OnlineTrainingTimeMax value can be set based on various criteria, such as the channel environment between the base station (910) and the UE (920) or the mobility of the UE (920). For example, in an environment where the channel changes rapidly or when the mobility of the UE (920) is large, a small OnlineTrainingTimeMax value can be set to quickly respond to changes in the channel by accelerating the learning speed for more accurate channel estimation.
  • the UE (920) may use various forms of signaling when transmitting the OnlineTrainingTimeMax value set by the UE (920) to the base station (910). For example, the UE (920) may transmit the OnlineTrainingTimeMax value set by using UCI and UE assistance information to the base station (910). As another example, the UE (920) may transmit the OnlineTrainingTimeMax value set by using the first message (Msg1) of the 4-step RACH procedure or the message A (MsgA) of the 2-step RACH procedure to the base station (910). As another example, the UE may use a newly defined RRC signaling message when a new RRC signaling message is defined to transmit the set OnlineTrainingTimeMax value to the base station (910).
  • Msg1 the first message
  • MsgA message A
  • the UE may use a newly defined RRC signaling message when a new RRC signaling message is defined to transmit the set OnlineTrainingTimeMax value to the base station (910).
  • the base station (910) requests the UE (920) to report information on the maximum time value that can be allocated to online training as the OnlineTrainingTimeMax value through a UE information request message
  • the UE (910) may also reply to the base station (910) through a UE information response message with information on the maximum time value that it can allocate to online training.
  • the base station (910) can check whether the online training forced termination condition is met in step S1506. In other words, the base station (910) can check whether the online training forced condition is met by checking whether the timer set to the OnlineTrainingTimeMax value has expired.
  • the base station (910) can proceed to step S1508, and if the condition for forced termination of online training is not met, the base station (910) can proceed to step S1512.
  • the base station (910) can determine the training model with the highest RSRP value among the autoencoder models learned through online training so far as a model for downlink channel estimation.
  • the base station (910) can transmit the determined channel estimation model to the UE (920).
  • the channel estimation model information can be encoder identifier information.
  • identifier information for indicating a specific autoencoder among multiple autoencoders and encoder identifier information including the number of dropouts indicating which dropout was performed as described above can be transmitted to the UE (920).
  • the training expiration information exemplified in Table 10 above may be information that the base station (910) notifies (or instructs) the UE (920) that the online training of the autoencoder has ended.
  • the encoder identifier may indicate a specific autoencoder (or an encoder of the autoencoder) among multiple autoencoders, and the last number "3" may indicate a case where three dropouts have been performed. Through this, the optimal encoder can be selected among the encoders that have performed online training up to now.
  • the training expiration information and the encoder information as in Table 10 may be transmitted from the base station (910) to the UE (920) using various forms of signaling as described above.
  • the training expiration information and the encoder information as in Table 10 may be transmitted from the base station (910) to the UE (920) using any one of the SIB, MAC-CE, or RRC Reconfiguration message.
  • the training expiration information and the encoder information as in Table 10 may be transmitted from the base station (910) to the UE (920) via DCI.
  • the training expiration information and the encoder information as in Table 10 may be transmitted from the base station (910) to the UE (920) using the second message (Msg2) of the 4-step RACH procedure or the message B (MsgB) of the 2-step RACH procedure.
  • the training expiration information and encoder information may be transmitted from the base station (910) to the UE (920) using the newly defined RRC signaling message.
  • the base station (910) may proceed to step S1512 and perform an online re-learning procedure.
  • the online re-learning procedure may include steps S1250 to S1270 described above in FIG. 12, and may be a procedure of steps S1300 to S1350 described in FIG. 13.
  • the base station (910) can store the RSRP value obtained in the corresponding round and the corresponding model information and, so to speak, the number of dropouts information whenever online retraining is completed. Using this, the base station (910) can identify the RSRP with the highest value among the RSRPs obtained through online training of the autoencoder in step S1508, and can obtain the identifier of the autoencoder with the highest RSRP value and the number of dropouts information in step S1510.
  • Fig. 13 it is simply explained in the form of termination of online learning, but the base station (910) can notify the UE (920) of termination of online learning.
  • the base station (910) can notify the UE (920) of termination of online learning.
  • Table 11 information for the base station (910) to notify the UE (920) of termination of online learning is exemplified, it can be exemplified as in Table 11 below.
  • the base station (910) can transmit encoder training completion information to the UE (920).
  • the encoder training completion information can mean that the encoder at the current trained time can be used. Accordingly, when the UE (920) receives the encoder training completion information, it can update the encoder to the encoder in the online training state performed immediately before receiving the encoder training completion information.
  • the base station (910) can also store the encoder and decoder of the autoencoder to maintain the same state based on the completion of the encoder training.
  • the encoder training completion information can be transmitted from the base station (910) to the UE (920) via various forms of signaling.
  • the encoder training completion information as in Table 11 can be transmitted from the base station (910) to the UE (920) using any one of the SIB, MAC-CE, or RRC Reconfiguration message.
  • the encoder training completion information as in Table 11 can be transmitted from the base station (910) to the UE (920) via DCI.
  • the encoder training completion information as in Table 11 can be transmitted from the base station (910) to the UE (920) using the second message (Msg2) of the 4-step RACH procedure or the message B (MsgB) of the 2-step RACH procedure.
  • Msg2 the second message
  • MsgB message B
  • the encoder training completion information may be transmitted from the base station (910) to the UE (920) using the newly defined RRC signaling message.
  • the UE (920) and the base station (910) can perform model inference using the acquired autoencoder model when estimating a downlink channel transmitted from the base station (910) using the parameters acquired during the current online training process.
  • the model inference procedure can be a procedure similar to the online training process, which is a step for performing actual channel estimation.
  • the base station (910) can transmit the CSI-RS, and the UE (920) can perform a procedure for obtaining CFI through an encoder for the received CSI-RS and then reporting the obtained CFI to the base station (910). Then, the base station (910) can obtain the received CSI-RS through a decoder and obtain an RSRP value, thereby determining it as downlink CSI.
  • Figure 16 is a conceptual diagram explaining a case where the entire operation of the present disclosure is combined and operated.
  • Step S1610 is an operation performed at the base station (910), and step S1611 may be an operation corresponding to step S1210 described above in FIG. 12.
  • the base station (910) may determine a CFI transmission period based on the network size and channel coherent time.
  • step S1612 may be an operation corresponding to step S1220 described above in FIG. 12.
  • the base station (910) may determine LVD based on channel resolution and delay requirements.
  • Step S1620 may be an operation corresponding to steps S1230 and S1250 described above in FIG. 12.
  • the base station (910) may transmit a CFI transmission period, LVD to the UE (920), and transmit a CSI-RS to the UE (920).
  • Step S1630 may be an operation corresponding to steps S1240, S1260, and S1270 described above in FIG. 12.
  • the UE (920) may determine a CFI dimension, compress the received CSI-RS through an encoder, and report the CFI to the base station (910).
  • Step S1640 may be an operation corresponding to steps S1300 and S1310 described above in FIG. 13.
  • the base station (910) may stop learning when RSRP is saturated and compare the saturated RSRP value with the RSRP threshold value.
  • step S1640 If the saturated RSRP value in step S1640 is greater than or equal to the RSRP threshold value, step S1650 may be performed, and if the saturated RSRP value in step S1640 is less than the RSRP value, step S1660 may be performed.
  • Step S1650 can perform the operation described in section [D].
  • the base station (910) can transmit learning completion information to the UE (920) after learning is completed.
  • Step S1660 may correspond to steps S1320 and S1330 described in FIG. 13 described above. Therefore, in step S1660, the base station (910) may transmit relearning information, encoder reset information, and encoder identifier information to the UE (920).
  • step S1670 may correspond to steps S1340 and S1350 in FIG. 13 described above. Therefore, in step S1670, the UE (920) may reset the encoder based on the relearning information, the encoder reset information, and the encoder identifier information, and then report the encoder reset completion information to the base station (910).
  • step S1670 the UE (920) can report encoder reset completion information to the base station (910) after resetting the encoder. If learning is not completed within the allocated time in step S1670, step S1680 can be performed.
  • Step S1680 may be a case where the learning described in the [C] section and FIG. 15 above is not completed within the allocated time. Therefore, in step S1680, the base station (910) may transmit learning completion information and the most optimal encoder identifier to the UE (920).
  • Figure 16 exemplifies a case where all the procedures described in the present disclosure are configured as a single procedure. However, some of the procedures in Figure 16 may be omitted or modified. Some of these modified examples have been described in the drawings described above. Since the present disclosure cannot cover all forms of modifications, it should be noted that various forms of modifications and combinations may be possible based on the contents described in the present disclosure.
  • the operation of the method according to the present disclosure can be implemented as a computer-readable program or code on a computer-readable recording medium.
  • the computer-readable recording medium includes all types of recording devices that store information that can be read by a computer system.
  • the computer-readable recording medium can be distributed over network-connected computer systems so that the computer-readable program or code can be stored and executed in a distributed manner.
  • the computer-readable recording medium may include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc.
  • the program instructions may include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by the computer using an interpreter, etc.
  • a block or device corresponds to a method step or a feature of a method step.
  • aspects described in the context of a method may also be described as a feature of a corresponding block or item or a corresponding device.
  • Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, a programmable computer or an electronic circuit. In some embodiments, at least one or more of the most significant method steps may be performed by such a device.
  • a programmable logic device e.g., a field-programmable gate array
  • a field-programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described in this disclosure. In general, the methods are preferably performed by some hardware device.

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Abstract

Selon la présente divulgation, un procédé d'un UE peut comprendre les étapes consistant à : recevoir d'une station de base une période de transmission de CFI et un LVD d'un codeur permettant d'effectuer un entraînement en ligne ; sur la base de la période de transmission de CFI et du LVD, déterminer le codeur permettant d'effectuer l'entraînement en ligne ; recevoir un premier RS provenant de la station de base ; générer des CFI en comprimant le premier RS reçu au moyen du codeur déterminé ; et, sur la base de la période de CFI, transmettre les premières CFI à la station de base.
PCT/KR2024/001711 2023-02-08 2024-02-06 Procédé et dispositif d'estimation de canal à l'aide d'un autocodeur dans un système de communication Ceased WO2024167265A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022212253A1 (fr) * 2021-03-30 2022-10-06 Idac Holdings, Inc. Détermination, à base de modèles, d'informations renvoyées concernant l'état d'un canal
US20220383118A1 (en) * 2021-05-28 2022-12-01 DeepSig Inc. Generating variable communication channel responses using machine learning networks
WO2022257157A1 (fr) * 2021-06-12 2022-12-15 Huawei Technologies Co.,Ltd. Adaptation de liaison activée par intelligence artificielle
US20220405602A1 (en) * 2021-06-21 2022-12-22 Qualcomm Incorporated Channel feature extraction via model-based neural networks
WO2023274926A1 (fr) * 2021-06-28 2023-01-05 Telefonaktiebolaget Lm Ericsson (Publ) Combinaison de techniques propriétaires et normalisées pour une rétroaction d'informations d'état de canal (csi)

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022212253A1 (fr) * 2021-03-30 2022-10-06 Idac Holdings, Inc. Détermination, à base de modèles, d'informations renvoyées concernant l'état d'un canal
US20220383118A1 (en) * 2021-05-28 2022-12-01 DeepSig Inc. Generating variable communication channel responses using machine learning networks
WO2022257157A1 (fr) * 2021-06-12 2022-12-15 Huawei Technologies Co.,Ltd. Adaptation de liaison activée par intelligence artificielle
US20220405602A1 (en) * 2021-06-21 2022-12-22 Qualcomm Incorporated Channel feature extraction via model-based neural networks
WO2023274926A1 (fr) * 2021-06-28 2023-01-05 Telefonaktiebolaget Lm Ericsson (Publ) Combinaison de techniques propriétaires et normalisées pour une rétroaction d'informations d'état de canal (csi)

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