WO2023120781A1 - Appareil et procédé de transmission de signal dans un système de communication sans fil - Google Patents

Appareil et procédé de transmission de signal dans un système de communication sans fil Download PDF

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Publication number
WO2023120781A1
WO2023120781A1 PCT/KR2021/019800 KR2021019800W WO2023120781A1 WO 2023120781 A1 WO2023120781 A1 WO 2023120781A1 KR 2021019800 W KR2021019800 W KR 2021019800W WO 2023120781 A1 WO2023120781 A1 WO 2023120781A1
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Prior art keywords
meta
reference signal
learning
information
base station
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PCT/KR2021/019800
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English (en)
Korean (ko)
Inventor
이경호
이상림
정익주
김영준
조민석
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엘지전자 주식회사
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Priority to PCT/KR2021/019800 priority Critical patent/WO2023120781A1/fr
Publication of WO2023120781A1 publication Critical patent/WO2023120781A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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

Definitions

  • the following description relates to a wireless communication system, and relates to an apparatus and method for signal transmission in a wireless communication system.
  • a wireless access system is widely deployed to provide various types of communication services such as voice and data.
  • a wireless access system is a multiple access system capable of supporting communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.).
  • Examples of the multiple access system include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, and a single carrier frequency (SC-FDMA) system. division multiple access) system.
  • CDMA code division multiple access
  • FDMA frequency division multiple access
  • TDMA time division multiple access
  • OFDMA orthogonal frequency division multiple access
  • SC-FDMA single carrier frequency division multiple access
  • eMBB enhanced mobile broadband
  • RAT radio access technology
  • a communication system considering reliability and latency-sensitive services/UE (user equipment) as well as mMTC (massive machine type communications) providing various services anytime and anywhere by connecting multiple devices and objects has been proposed. .
  • Various technical configurations for this have been proposed.
  • the present disclosure may provide an apparatus and method for signal transmission in a wireless communication system.
  • the present disclosure may provide a signal transmission method and apparatus for meta learning in a wireless communication system.
  • the present disclosure may provide a method and apparatus for transmitting a reference signal based on meta-learning in a wireless communication system.
  • the present disclosure provides a method for operating a meta-learning area in a wireless communication system.
  • the present disclosure provides a signal transmission method and apparatus for meta-learning based on a meta-learning region in a frequency band such as mmWave and THz.
  • a method of operating a terminal in a wireless communication system includes the step of the terminal requesting meta-learning model information from a base station in a first meta-learning area, the base station performing meta-learning related to the terminal (meta learning) if the terminal has model information, receiving the meta-learning model information from the base station, if the base station does not have meta-learning model information related to the terminal, the terminal meta-learning from the base station Receiving setting information and learning, by the terminal, a meta-learning model based on the meta-running setting information.
  • the meta-running setting information includes reference signal related information, wherein the reference signal related information is DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal) , PRS (positioning reference signal), SSB (synchronization signal block, SSB), SRS (sounding reference signal), meta-learning reference signal (meta-learning reference signal), and at least one resource information and weight of a reference signal for a specific purpose value, wherein the reference signal related information is based on a frequency band.
  • DM-RS demodulation-reference signal
  • PTRS phase-tracking reference signal
  • CSI-RS channel status information-reference signal
  • PRS positioning reference signal
  • SSB synchronization signal block, SSB
  • SRS sounding reference signal
  • meta-learning reference signal meta-learning reference signal
  • at least one resource information and weight of a reference signal for a specific purpose value wherein the reference signal related information is based on a frequency band.
  • the terminal learns a parameter related to a meta-learning model for a reference signal received from the base station or a reference signal transmitted to the base station can do.
  • meta model list related information related to the first meta learning area may be received from the base station.
  • the terminal may report a meta-learning model learning result to the base station.
  • the terminal may receive a request for information related to a meta learning area list from the base station.
  • the terminal may transmit meta-running region list-related information to the base station.
  • the meta-learning area list related information may include a meta-learning result of the first meta-learning area.
  • the first meta-learning area may be hierarchically configured.
  • a terminal in a wireless communication system, includes a transceiver and a processor connected to the transceiver.
  • the processor controls the transceiver to request meta-learning model information from the base station in a first meta-learning area, and when the base station has meta-learning model information related to the terminal, the The transceiver controls to receive the meta-learning model information from the base station, and when the base station does not have meta-learning model information related to the terminal, controls the transceiver to receive meta-running setting information from the base station, and the meta A meta-learning model is learned based on the running setting information.
  • the meta-running setting information includes reference signal related information, wherein the reference signal related information is DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal) , PRS (positioning reference signal), SSB (synchronization signal block, SSB), SRS (sounding reference signal), meta-learning reference signal (meta-learning reference signal), and at least one resource information and weight of a reference signal for a specific purpose value, wherein the reference signal related information is based on a frequency band.
  • DM-RS demodulation-reference signal
  • PTRS phase-tracking reference signal
  • CSI-RS channel status information-reference signal
  • PRS positioning reference signal
  • SSB synchronization signal block, SSB
  • SRS sounding reference signal
  • meta-learning reference signal meta-learning reference signal
  • at least one resource information and weight of a reference signal for a specific purpose value wherein the reference signal related information is based on a frequency band.
  • the processor learns a parameter related to a meta learning learning model for a reference signal received from the base station or a reference signal transmitted to the base station based on the received meta learning model or a learning result of the meta learning model can do.
  • the terminal and the base station define a meta model list
  • the terminal may receive meta model list related information related to the first meta-learning area from the base station.
  • the processor may control the transceiver to report a meta-learning model learning result to the base station.
  • the processor may control the transceiver to receive a request for information related to a meta learning area list from the base station, and control the transceiver to transmit meta learning area list related information to the base station.
  • the meta-learning area list related information includes a meta-running learning result of the first meta-running area.
  • the first meta-learning area may be hierarchically configured.
  • a communication device includes at least one processor and at least one computer memory coupled to the at least one processor and storing instructions that direct operations as executed by the at least one processor.
  • the processor controls the communication device to request meta-learning model information from the base station in a first meta-learning area, and when the base station has meta-learning model information related to the terminal, Control to receive the meta-learning model information from the base station, and if the base station does not have meta-learning model information related to the terminal, control to receive meta-running setting information from the base station, based on the meta-running setting information so that the meta-learning model can be controlled to learn.
  • the meta-running setting information includes reference signal related information, wherein the reference signal related information is DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal) , PRS (positioning reference signal), SSB (synchronization signal block, SSB), SRS (sounding reference signal), meta-learning reference signal (meta-learning reference signal), and at least one resource information and weight of a reference signal for a specific purpose value, wherein the reference signal related information is based on a frequency band.
  • DM-RS demodulation-reference signal
  • PTRS phase-tracking reference signal
  • CSI-RS channel status information-reference signal
  • PRS positioning reference signal
  • SSB synchronization signal block, SSB
  • SRS sounding reference signal
  • meta-learning reference signal meta-learning reference signal
  • at least one resource information and weight of a reference signal for a specific purpose value wherein the reference signal related information is based on a frequency band.
  • a non-transitory computer-readable medium storing at least one instruction (instructions) includes the at least one instruction executable by a processor.
  • the at least one command instructs the computer-readable medium to request meta-learning model information from a base station in a first meta-learning area, and the base station receives a meta-learning model related to the terminal information, instruct to receive the meta-learning model information from the base station, and if the base station does not have meta-running model information related to the terminal, control to receive meta-running setting information from the base station, and Instructs to learn a meta-learning model based on running setting information.
  • the meta-running setting information includes reference signal related information, wherein the reference signal related information is DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal) , PRS (positioning reference signal), SSB (synchronization signal block, SSB), SRS (sounding reference signal), meta-learning reference signal (meta-learning reference signal), and at least one resource information and weight of a reference signal for a specific purpose value, wherein the reference signal related information is based on a frequency band.
  • DM-RS demodulation-reference signal
  • PTRS phase-tracking reference signal
  • CSI-RS channel status information-reference signal
  • PRS positioning reference signal
  • SSB synchronization signal block, SSB
  • SRS sounding reference signal
  • meta-learning reference signal meta-learning reference signal
  • at least one resource information and weight of a reference signal for a specific purpose value wherein the reference signal related information is based on a frequency band.
  • a method of operating a base station in a wireless communication system includes receiving a request for meta-learning model information from a terminal in a first meta-learning area by the base station, meta-learning related to the terminal by the base station Transmitting the meta-learning model information to the terminal if it has meta-learning model information, and transmitting meta-learning setting information to the terminal if the base station does not have meta-learning model information related to the terminal includes
  • a meta-learning model is learned based on the meta-running setting information, and the meta-running setting information includes reference signal-related information, wherein the reference signal-related information is DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal), PRS (positioning reference signal), SSB (synchronization signal block, SSB), SRS (sounding reference signal), meta-learning reference signal and at least one resource information and a weight value among reference signals for a specific purpose, wherein the reference signal-related information is based
  • a base station in a wireless communication system, includes a transceiver and a processor connected to the transceiver.
  • the processor controls the transceiver to receive a request for meta-learning model information from a terminal in a first meta-learning area, and when the base station has meta-learning model information related to the terminal, The transceiver controls to transmit meta-learning model information from the base station to the terminal, and when the base station does not have meta-learning model information related to the terminal, the transceiver controls to transmit meta-running setting information to the terminal do.
  • a meta-learning model is learned based on the meta-learning setting information.
  • the meta-running setting information includes reference signal related information, wherein the reference signal related information is DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal) , PRS (positioning reference signal), SSB (synchronization signal block, SSB), SRS (sounding reference signal), meta-learning reference signal (meta-learning reference signal), and at least one resource information and weight of a reference signal for a specific purpose value, and the reference signal related information is based on a frequency band.
  • the reference signal related information is DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal) , PRS (positioning reference signal), SSB (synchronization signal block, SSB), SRS (sounding reference signal), meta-learning reference signal (meta-learning reference signal), and at least one resource information and weight of a reference signal for a specific purpose value, and the reference signal related information is based
  • a terminal and a base station may transmit and receive reference signals based on meta-learning.
  • a terminal and a base station can efficiently use radio resources for reference signals.
  • a terminal and a base station can learn about a reference signal transmitted and received based on learning data for a reference signal different from the reference signal transmitted and received.
  • a terminal and a base station may perform meta-learning on a reference signal based on a small amount of data set.
  • the device may transmit and receive reference signals by using meta-learning for each frequency band such as mmWave and THz.
  • a device may transmit/receive a reference signal based on a meta-running region.
  • Effects obtainable in the embodiments of the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned are technical fields to which the technical configuration of the present disclosure is applied from the description of the following embodiments of the present disclosure. can be clearly derived and understood by those skilled in the art. That is, unintended effects according to implementing the configuration described in the present disclosure may also be derived by those skilled in the art from the embodiments of the present disclosure.
  • FIG. 1 shows an example of a communication system applicable to the present disclosure.
  • FIG. 2 shows an example of a wireless device applicable to the present disclosure.
  • FIG. 3 illustrates another example of a wireless device applicable to the present disclosure.
  • FIG. 4 shows an example of a portable device applicable to the present disclosure.
  • FIG. 5 illustrates an example of a vehicle or autonomous vehicle applicable to the present disclosure.
  • AI Artificial Intelligence
  • FIG. 7 illustrates a method of processing a transmission signal applicable to the present disclosure.
  • FIG 8 shows the structure of a perceptron included in an artificial neural network applicable to the present disclosure.
  • FIG 9 illustrates an artificial neural network structure applicable to the present disclosure.
  • FIG. 10 shows a deep neural network applicable to the present disclosure.
  • FIG. 11 shows a convolutional neural network applicable to the present disclosure.
  • FIG. 12 illustrates a filter operation of a convolutional neural network applicable to the present disclosure.
  • FIG. 13 illustrates a neural network structure in which a circular loop applicable to the present disclosure exists.
  • FIG. 14 illustrates an operating structure of a recurrent neural network applicable to the present disclosure.
  • 15a to 15c are diagrams for explaining meta-learning applicable to the present disclosure.
  • 16 illustrates an example of a meta-learning area applicable to the present disclosure.
  • 17 illustrates an example of a meta-learning area applicable to the present disclosure.
  • 19 illustrates an example of an operation procedure of a terminal and a base station applicable to the present disclosure.
  • FIG. 22 illustrates an example of a meta parameter update procedure in a meta learning domain applicable to the present disclosure.
  • each component or feature may be considered optional unless explicitly stated otherwise.
  • Each component or feature may be implemented in a form not combined with other components or features.
  • an embodiment of the present disclosure may be configured by combining some elements and/or features. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.
  • a base station has meaning as a terminal node of a network that directly communicates with a mobile station.
  • a specific operation described as being performed by a base station in this document may be performed by an upper node of the base station in some cases.
  • the 'base station' is a term such as a fixed station, Node B, eNode B, gNode B, ng-eNB, advanced base station (ABS), or access point. can be replaced by
  • a terminal includes a user equipment (UE), a mobile station (MS), a subscriber station (SS), a mobile subscriber station (MSS), It may be replaced with terms such as mobile terminal or advanced mobile station (AMS).
  • UE user equipment
  • MS mobile station
  • SS subscriber station
  • MSS mobile subscriber station
  • AMS advanced mobile station
  • the transmitting end refers to a fixed and/or mobile node providing data service or voice service
  • the receiving end refers to a fixed and/or mobile node receiving data service or voice service. Therefore, in the case of uplink, the mobile station can be a transmitter and the base station can be a receiver. Similarly, in the case of downlink, the mobile station may be a receiving end and the base station may be a transmitting end.
  • Embodiments of the present disclosure are wireless access systems, such as an IEEE 802.xx system, a 3rd Generation Partnership Project (3GPP) system, a 3GPP Long Term Evolution (LTE) system, a 3GPP 5G (5th generation) NR (New Radio) system, and a 3GPP2 system. It may be supported by at least one disclosed standard document, and in particular, the embodiments of the present disclosure are supported by 3GPP technical specification (TS) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents It can be.
  • 3GPP technical specification TS 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents It can be.
  • embodiments of the present disclosure may be applied to other wireless access systems, and are not limited to the above-described systems.
  • it may also be applicable to a system applied after the 3GPP 5G NR system, and is not limited to a specific system.
  • CDMA code division multiple access
  • FDMA frequency division multiple access
  • TDMA time division multiple access
  • OFDMA orthogonal frequency division multiple access
  • SC-FDMA single carrier frequency division multiple access
  • LTE is 3GPP TS 36.xxx Release 8 or later
  • LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A
  • xxx Release 13 may be referred to as LTE-A pro.
  • 3GPP NR may mean technology after TS 38.xxx Release 15.
  • 3GPP 6G may mean technology after TS Release 17 and/or Release 18.
  • "xxx" means a standard document detail number.
  • LTE/NR/6G may be collectively referred to as a 3GPP system.
  • FIG. 1 is a diagram illustrating an example of a communication system applied to the present disclosure.
  • a communication system 100 applied to the present disclosure includes a wireless device, a base station, and a network.
  • the wireless device means a device that performs communication using a radio access technology (eg, 5G NR, LTE), and may be referred to as a communication/wireless/5G device.
  • the wireless device includes a robot 100a, a vehicle 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, and a home appliance. appliance) 100e, Internet of Thing (IoT) device 100f, and artificial intelligence (AI) device/server 100g.
  • a radio access technology eg, 5G NR, LTE
  • XR extended reality
  • IoT Internet of Thing
  • AI artificial intelligence
  • the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, and the like.
  • the vehicles 100b-1 and 100b-2 may include an unmanned aerial vehicle (UAV) (eg, a drone).
  • UAV unmanned aerial vehicle
  • the XR device 100c includes augmented reality (AR)/virtual reality (VR)/mixed reality (MR) devices, and includes a head-mounted device (HMD), a head-up display (HUD) installed in a vehicle, a television, It may be implemented in the form of smart phones, computers, wearable devices, home appliances, digital signage, vehicles, robots, and the like.
  • the mobile device 100d may include a smart phone, a smart pad, a wearable device (eg, a smart watch, a smart glass), a computer (eg, a laptop computer), and the like.
  • the home appliance 100e may include a TV, a refrigerator, a washing machine, and the like.
  • the IoT device 100f may include a sensor, a smart meter, and the like.
  • the base station 120 and the network 130 may also be implemented as a wireless device, and a specific wireless device 120a may operate as a base station/network node to other wireless devices.
  • the wireless devices 100a to 100f may be connected to the network 130 through the base station 120 .
  • AI technology may be applied to the wireless devices 100a to 100f, and the wireless devices 100a to 100f may be connected to the AI server 100g through the network 130.
  • the network 130 may be configured using a 3G network, a 4G (eg LTE) network, or a 5G (eg NR) network.
  • the wireless devices 100a to 100f may communicate with each other through the base station 120/network 130, but communicate directly without going through the base station 120/network 130 (e.g., sidelink communication). You may.
  • the vehicles 100b-1 and 100b-2 may perform direct communication (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication).
  • the IoT device 100f eg, sensor
  • the IoT device 100f may directly communicate with other IoT devices (eg, sensor) or other wireless devices 100a to 100f.
  • Wireless communication/connection 150a, 150b, and 150c may be performed between the wireless devices 100a to 100f/base station 120 and the base station 120/base station 120.
  • wireless communication/connection includes various types of uplink/downlink communication 150a, sidelink communication 150b (or D2D communication), and inter-base station communication 150c (eg relay, integrated access backhaul (IAB)). This can be done through radio access technology (eg 5G NR).
  • radio access technology eg 5G NR
  • a wireless device and a base station/wireless device, and a base station can transmit/receive radio signals to each other.
  • the wireless communication/connections 150a, 150b, and 150c may transmit/receive signals through various physical channels.
  • various configuration information setting processes for transmitting / receiving radio signals various signal processing processes (eg, channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.) At least a part of a resource allocation process may be performed.
  • FIG. 2 is a diagram illustrating an example of a wireless device applicable to the present disclosure.
  • a first wireless device 200a and a second wireless device 200b may transmit and receive radio signals through various wireless access technologies (eg, LTE and NR).
  • ⁇ the first wireless device 200a, the second wireless device 200b ⁇ denotes the ⁇ wireless device 100x and the base station 120 ⁇ of FIG. 1 and/or the ⁇ wireless device 100x and the wireless device 100x.
  • ⁇ can correspond.
  • the first wireless device 200a includes one or more processors 202a and one or more memories 204a, and may further include one or more transceivers 206a and/or one or more antennas 208a.
  • the processor 202a controls the memory 204a and/or the transceiver 206a and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein.
  • the processor 202a may process information in the memory 204a to generate first information/signal, and transmit a radio signal including the first information/signal through the transceiver 206a.
  • the processor 202a may receive a radio signal including the second information/signal through the transceiver 206a and store information obtained from signal processing of the second information/signal in the memory 204a.
  • the memory 204a may be connected to the processor 202a and may store various information related to the operation of the processor 202a.
  • memory 204a may perform some or all of the processes controlled by processor 202a, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them.
  • the processor 202a and the memory 204a may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • the transceiver 206a may be coupled to the processor 202a and may transmit and/or receive wireless signals through one or more antennas 208a.
  • the transceiver 206a may include a transmitter and/or a receiver.
  • the transceiver 206a may be used interchangeably with a radio frequency (RF) unit.
  • RF radio frequency
  • a wireless device may mean a communication modem/circuit/chip.
  • the second wireless device 200b includes one or more processors 202b, one or more memories 204b, and may further include one or more transceivers 206b and/or one or more antennas 208b.
  • the processor 202b controls the memory 204b and/or the transceiver 206b and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein.
  • the processor 202b may process information in the memory 204b to generate third information/signal, and transmit a radio signal including the third information/signal through the transceiver 206b.
  • the processor 202b may receive a radio signal including the fourth information/signal through the transceiver 206b and store information obtained from signal processing of the fourth information/signal in the memory 204b.
  • the memory 204b may be connected to the processor 202b and may store various information related to the operation of the processor 202b.
  • the memory 204b may perform some or all of the processes controlled by the processor 202b, or instructions for performing the descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein. It may store software codes including them.
  • the processor 202b and the memory 204b may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR).
  • the transceiver 206b may be coupled to the processor 202b and may transmit and/or receive wireless signals through one or more antennas 208b.
  • the transceiver 206b may include a transmitter and/or a receiver.
  • the transceiver 206b may be used interchangeably with an RF unit.
  • a wireless device may mean a communication modem/circuit/chip.
  • one or more protocol layers may be implemented by one or more processors 202a, 202b.
  • the one or more processors 202a and 202b may include one or more layers (eg, PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource) control) and functional layers such as service data adaptation protocol (SDAP).
  • One or more processors 202a, 202b may generate one or more protocol data units (PDUs) and/or one or more service data units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and/or operational flow charts disclosed herein.
  • PDUs protocol data units
  • SDUs service data units
  • processors 202a, 202b may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and/or operational flow diagrams disclosed herein.
  • One or more processors 202a, 202b generate PDUs, SDUs, messages, control information, data or signals (eg, baseband signals) containing information according to the functions, procedures, proposals and/or methods disclosed herein , may be provided to one or more transceivers 206a and 206b.
  • One or more processors 202a, 202b may receive signals (eg, baseband signals) from one or more transceivers 206a, 206b, and descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein PDUs, SDUs, messages, control information, data or information can be obtained according to these.
  • signals eg, baseband signals
  • One or more processors 202a, 202b may be referred to as a controller, microcontroller, microprocessor or microcomputer.
  • One or more processors 202a, 202b may be implemented by hardware, firmware, software, or a combination thereof.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • firmware or software may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, and the like.
  • Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods and/or operational flow charts disclosed in this document may be included in one or more processors 202a or 202b or stored in one or more memories 204a or 204b. It can be driven by the above processors 202a and 202b.
  • the descriptions, functions, procedures, suggestions, methods and/or operational flow charts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and/or sets of instructions.
  • One or more memories 204a, 204b may be coupled to one or more processors 202a, 202b and may store various types of data, signals, messages, information, programs, codes, instructions and/or instructions.
  • One or more memories 204a, 204b may include read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), flash memory, hard drive, registers, cache memory, computer readable storage media, and/or It may consist of a combination of these.
  • One or more memories 204a, 204b may be located internally and/or externally to one or more processors 202a, 202b.
  • one or more memories 204a, 204b may be connected to one or more processors 202a, 202b through various technologies such as wired or wireless connections.
  • One or more transceivers 206a, 206b may transmit user data, control information, radio signals/channels, etc. referred to in the methods and/or operational flow charts of this document to one or more other devices.
  • One or more transceivers 206a, 206b may receive user data, control information, radio signals/channels, etc. referred to in descriptions, functions, procedures, proposals, methods and/or operational flow charts, etc. disclosed herein from one or more other devices. there is.
  • one or more transceivers 206a and 206b may be connected to one or more processors 202a and 202b and transmit and receive radio signals.
  • one or more processors 202a, 202b may control one or more transceivers 206a, 206b to transmit user data, control information, or radio signals to one or more other devices.
  • one or more processors 202a, 202b may control one or more transceivers 206a, 206b to receive user data, control information, or radio signals from one or more other devices.
  • one or more transceivers 206a, 206b may be coupled to one or more antennas 208a, 208b, and one or more transceivers 206a, 206b may be connected to one or more antennas 208a, 208b to achieve the descriptions, functions disclosed in this document.
  • one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports).
  • One or more transceivers (206a, 206b) in order to process the received user data, control information, radio signal / channel, etc. using one or more processors (202a, 202b), the received radio signal / channel, etc. in the RF band signal It can be converted into a baseband signal.
  • One or more transceivers 206a and 206b may convert user data, control information, and radio signals/channels processed by one or more processors 202a and 202b from baseband signals to RF band signals.
  • one or more transceivers 206a, 206b may include (analog) oscillators and/or filters.
  • FIG. 3 is a diagram illustrating another example of a wireless device applied to the present disclosure.
  • a wireless device 300 corresponds to the wireless devices 200a and 200b of FIG. 2, and includes various elements, components, units/units, and/or modules. ) can be configured.
  • the wireless device 300 may include a communication unit 310, a control unit 320, a memory unit 330, and an additional element 340.
  • the communication unit may include communication circuitry 312 and transceiver(s) 314 .
  • communication circuitry 312 may include one or more processors 202a, 202b of FIG. 2 and/or one or more memories 204a, 204b.
  • transceiver(s) 314 may include one or more transceivers 206a, 206b of FIG.
  • the control unit 320 is electrically connected to the communication unit 310, the memory unit 330, and the additional element 340 and controls overall operations of the wireless device. For example, the control unit 320 may control electrical/mechanical operations of the wireless device based on programs/codes/commands/information stored in the memory unit 330. In addition, the control unit 320 transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310 through a wireless/wired interface, or transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310. Information received through a wireless/wired interface from other communication devices) may be stored in the memory unit 330 .
  • the additional element 340 may be configured in various ways according to the type of wireless device.
  • the additional element 340 may include at least one of a power unit/battery, an input/output unit, a driving unit, and a computing unit.
  • the wireless device 300 may be a robot (FIG. 1, 100a), a vehicle (FIG. 1, 100b-1, 100b-2), an XR device (FIG. 1, 100c), a mobile device (FIG. 1, 100d) ), home appliances (FIG. 1, 100e), IoT devices (FIG.
  • Wireless devices can be mobile or used in a fixed location depending on the use-case/service.
  • various elements, components, units/units, and/or modules in the wireless device 300 may be entirely interconnected through a wired interface or at least partially connected wirelessly through the communication unit 310 .
  • the control unit 320 and the communication unit 310 are connected by wire, and the control unit 320 and the first units (eg, 130 and 140) are connected wirelessly through the communication unit 310.
  • each element, component, unit/unit, and/or module within wireless device 300 may further include one or more elements.
  • the control unit 320 may be composed of one or more processor sets.
  • control unit 320 may include a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphic processing processor, a memory control processor, and the like.
  • memory unit 330 may include RAM, dynamic RAM (DRAM), ROM, flash memory, volatile memory, non-volatile memory, and/or combinations thereof. can be configured.
  • FIG. 4 is a diagram illustrating an example of a portable device applied to the present disclosure.
  • a portable device may include a smart phone, a smart pad, a wearable device (eg, smart watch, smart glasses), and a portable computer (eg, a laptop computer).
  • a mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).
  • MS mobile station
  • UT user terminal
  • MSS mobile subscriber station
  • SS subscriber station
  • AMS advanced mobile station
  • WT wireless terminal
  • a portable device 400 includes an antenna unit 408, a communication unit 410, a control unit 420, a memory unit 430, a power supply unit 440a, an interface unit 440b, and an input/output unit 440c. ) may be included.
  • the antenna unit 408 may be configured as part of the communication unit 410 .
  • Blocks 410 to 430/440a to 440c respectively correspond to blocks 310 to 330/340 of FIG. 3 .
  • the communication unit 410 may transmit/receive signals (eg, data, control signals, etc.) with other wireless devices and base stations.
  • the controller 420 may perform various operations by controlling components of the portable device 400 .
  • the controller 420 may include an application processor (AP).
  • the memory unit 430 may store data/parameters/programs/codes/commands necessary for driving the portable device 400 . Also, the memory unit 430 may store input/output data/information.
  • the power supply unit 440a supplies power to the portable device 400 and may include a wired/wireless charging circuit, a battery, and the like.
  • the interface unit 440b may support connection between the mobile device 400 and other external devices.
  • the interface unit 440b may include various ports (eg, audio input/output ports and video input/output ports) for connection with external devices.
  • the input/output unit 440c may receive or output image information/signal, audio information/signal, data, and/or information input from a user.
  • the input/output unit 440c may include a camera, a microphone, a user input unit, a display unit 440d, a speaker, and/or a haptic module.
  • the input/output unit 440c acquires information/signals (eg, touch, text, voice, image, video) input from the user, and the acquired information/signals are stored in the memory unit 430.
  • the communication unit 410 may convert the information/signal stored in the memory into a wireless signal, and directly transmit the converted wireless signal to another wireless device or to a base station.
  • the communication unit 410 may receive a radio signal from another wireless device or base station and then restore the received radio signal to original information/signal. After the restored information/signal is stored in the memory unit 430, it may be output in various forms (eg, text, voice, image, video, or haptic) through the input/output unit 440c.
  • FIG. 5 is a diagram illustrating an example of a vehicle or autonomous vehicle to which the present disclosure applies.
  • a vehicle or an autonomous vehicle may be implemented as a mobile robot, vehicle, train, manned/unmanned aerial vehicle (AV), ship, etc., and is not limited to a vehicle type.
  • AV unmanned aerial vehicle
  • a vehicle or autonomous vehicle 500 includes an antenna unit 508, a communication unit 510, a control unit 520, a driving unit 540a, a power supply unit 540b, a sensor unit 540c, and an autonomous driving unit.
  • a portion 540d may be included.
  • the antenna unit 550 may be configured as a part of the communication unit 510 .
  • Blocks 510/530/540a to 540d respectively correspond to blocks 410/430/440 of FIG. 4 .
  • the communication unit 510 may transmit/receive signals (eg, data, control signals, etc.) with external devices such as other vehicles, base stations (eg, base stations, roadside base units, etc.), servers, and the like.
  • the controller 520 may perform various operations by controlling elements of the vehicle or autonomous vehicle 500 .
  • the controller 520 may include an electronic control unit (ECU).
  • ECU electronice control unit
  • AI devices include TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc. It may be implemented as a device or a movable device.
  • the AI device 600 includes a communication unit 610, a control unit 620, a memory unit 630, an input/output unit 640a/640b, a running processor unit 640c, and a sensor unit 640d.
  • a communication unit 610 can include Blocks 610 to 630/640a to 640d may respectively correspond to blocks 310 to 330/340 of FIG. 3 .
  • the communication unit 610 communicates wired and wireless signals (eg, sensor information, user data) with external devices such as other AI devices (eg, FIG. 1, 100x, 120, and 140) or AI servers (Fig. input, learning model, control signal, etc.) can be transmitted and received. To this end, the communication unit 610 may transmit information in the memory unit 630 to an external device or transmit a signal received from the external device to the memory unit 630 .
  • external devices eg, sensor information, user data
  • AI devices eg, FIG. 1, 100x, 120, and 140
  • AI servers Fig. input, learning model, control signal, etc.
  • the controller 620 may determine at least one executable operation of the AI device 600 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. And, the controller 620 may perform the determined operation by controlling components of the AI device 600 . For example, the control unit 620 may request, retrieve, receive, or utilize data from the learning processor unit 640c or the memory unit 630, and may perform a predicted operation among at least one feasible operation or one determined to be desirable. Components of the AI device 600 may be controlled to execute an operation. In addition, the control unit 620 collects history information including user feedback on the operation contents or operation of the AI device 600 and stores it in the memory unit 630 or the running processor unit 640c, or the AI server ( 1, 140) can be transmitted to an external device. The collected history information can be used to update the learning model.
  • the memory unit 630 may store data supporting various functions of the AI device 600 .
  • the memory unit 630 may store data obtained from the input unit 640a, data obtained from the communication unit 610, output data of the learning processor unit 640c, and data obtained from the sensing unit 640.
  • the memory unit 630 may store control information and/or software codes required for operation/execution of the controller 620 .
  • the input unit 640a may obtain various types of data from the outside of the AI device 600.
  • the input unit 620 may obtain learning data for model learning and input data to which the learning model is to be applied.
  • the input unit 640a may include a camera, a microphone, and/or a user input unit.
  • the output unit 640b may generate an output related to sight, hearing, or touch.
  • the output unit 640b may include a display unit, a speaker, and/or a haptic module.
  • the sensing unit 640 may obtain at least one of internal information of the AI device 600, surrounding environment information of the AI device 600, and user information by using various sensors.
  • the sensing unit 640 may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. there is.
  • the learning processor unit 640c may learn a model composed of an artificial neural network using learning data.
  • the running processor unit 640c may perform AI processing together with the running processor unit of the AI server (FIG. 1, 140).
  • the learning processor unit 640c may process information received from an external device through the communication unit 610 and/or information stored in the memory unit 630 .
  • the output value of the learning processor unit 640c may be transmitted to an external device through the communication unit 610 and/or stored in the memory unit 630.
  • the transmitted signal may be processed by a signal processing circuit.
  • the signal processing circuit 700 may include a scrambler 710, a modulator 720, a layer mapper 730, a precoder 740, a resource mapper 750, and a signal generator 760.
  • the operation/function of FIG. 7 may be performed by the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 .
  • blocks 710 to 760 may be implemented in the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 .
  • blocks 710 to 760 may be implemented in the processors 202a and 202b of FIG. 2 .
  • blocks 710 to 750 may be implemented in the processors 202a and 202b of FIG. 2 and block 760 may be implemented in the transceivers 206a and 206b of FIG. 2 , and are not limited to the above-described embodiment.
  • the codeword may be converted into a radio signal through the signal processing circuit 700 of FIG. 7 .
  • a codeword is an encoded bit sequence of an information block.
  • Information blocks may include transport blocks (eg, UL-SCH transport blocks, DL-SCH transport blocks).
  • Radio signals may be transmitted through various physical channels (eg, PUSCH, PDSCH).
  • the codeword may be converted into a scrambled bit sequence by the scrambler 710.
  • a scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of a wireless device.
  • the scrambled bit sequence may be modulated into a modulation symbol sequence by modulator 720.
  • the modulation method may include pi/2-binary phase shift keying (pi/2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), and the like.
  • the complex modulation symbol sequence may be mapped to one or more transport layers by the layer mapper 730. Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by the precoder 740 (precoding).
  • the output z of the precoder 740 can be obtained by multiplying the output y of the layer mapper 730 by the N*M precoding matrix W.
  • N is the number of antenna ports and M is the number of transport layers.
  • the precoder 740 may perform precoding after transform precoding (eg, discrete fourier transform (DFT)) on complex modulation symbols. Also, the precoder 740 may perform precoding without performing transform precoding.
  • transform precoding eg, discrete fourier transform (DFT)
  • the resource mapper 750 may map modulation symbols of each antenna port to time-frequency resources.
  • the time-frequency resource may include a plurality of symbols (eg, CP-OFDMA symbols and DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain.
  • the signal generator 760 generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to other devices through each antenna.
  • CP cyclic prefix
  • DAC digital-to-analog converter
  • the signal processing process for the received signal in the wireless device may be configured in reverse to the signal processing process 710 to 760 of FIG. 7 .
  • a wireless device eg, 200a and 200b of FIG. 2
  • the received radio signal may be converted into a baseband signal through a signal restorer.
  • the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast fourier transform (FFT) module.
  • ADC analog-to-digital converter
  • FFT fast fourier transform
  • the baseband signal may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a de-scramble process.
  • a signal processing circuit for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.
  • AI The most important and newly introduced technology for the 6G system is AI.
  • AI was not involved in the 4G system.
  • 5G systems will support partial or very limited AI.
  • the 6G system will be AI-enabled for full automation.
  • Advances in machine learning will create more intelligent networks for real-time communication in 6G.
  • Introducing AI in communications can simplify and enhance real-time data transmission.
  • AI can use a plethora of analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.
  • AI can also play an important role in M2M, machine-to-human and human-to-machine communications.
  • AI can be a rapid communication in the brain computer interface (BCI).
  • BCI brain computer interface
  • AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.
  • AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanism, It may include AI-based resource scheduling and allocation.
  • MIMO multiple input multiple output
  • Machine learning may be used for channel estimation and channel tracking, and may be used for power allocation, interference cancellation, and the like in a downlink (DL) physical layer. Machine learning can also be used for antenna selection, power control, symbol detection, and the like in a MIMO system.
  • DL downlink
  • AI algorithms based on deep learning require a lot of training data to optimize training parameters.
  • a lot of training data is used offline. This is because static training on training data in a specific channel environment may cause a contradiction between dynamic characteristics and diversity of a radio channel.
  • Machine learning refers to a set of actions that train a machine to create a machine that can do tasks that humans can or cannot do.
  • Machine learning requires data and a running model.
  • data learning methods can be largely classified into three types: supervised learning, unsupervised learning, and reinforcement learning.
  • Neural network training is aimed at minimizing errors in the output.
  • Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error. ) to update the weight of each node in the neural network.
  • Supervised learning uses training data in which correct answers are labeled in the learning data, and unsupervised learning may not have correct answers labeled in the learning data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and an error may be calculated by comparing the output (category) of the neural network and the label of the training data. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation.
  • a reverse direction ie, from the output layer to the input layer
  • the amount of change in the connection weight of each updated node may be determined according to a learning rate.
  • the neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch).
  • the learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate is used in the early stages of neural network learning to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and a low learning rate can be used in the late stage to increase accuracy.
  • the learning method may vary depending on the characteristics of the data. For example, in a case where the purpose of the receiver is to accurately predict data transmitted by the transmitter in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.
  • the learning model corresponds to the human brain, and the most basic linear model can be considered. ) is called
  • the neural network cord used as a learning method is largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent boltzmann machine (RNN). and this learning model can be applied.
  • DNN deep neural networks
  • CNN convolutional deep neural networks
  • RNN recurrent boltzmann machine
  • FIG. 8 shows the structure of a perceptron included in an artificial neural network applicable to the present disclosure.
  • 9 shows an artificial neural network structure applicable to the present disclosure.
  • an artificial intelligence system may be applied in a 6G system.
  • the artificial intelligence system may operate based on a learning model corresponding to the human brain, as described above.
  • a paradigm of machine learning using a neural network structure having a high complexity such as an artificial neural network as a learning model may be referred to as deep learning.
  • the neural network cord used in the learning method is largely a deep neural network (DNN), a convolutional deep neural network (CNN), and a recurrent neural network (RNN).
  • DNN deep neural network
  • CNN convolutional deep neural network
  • RNN recurrent neural network
  • the artificial neural network may be composed of several perceptrons.
  • the huge artificial neural network structure extends the simplified perceptron structure shown in FIG. 8, and the input vector can be applied to different multi-dimensional perceptrons.
  • an input value or an output value is referred to as a node.
  • the perceptron structure shown in FIG. 8 can be described as being composed of a total of three layers based on input values and output values.
  • An artificial neural network with H number of (d + 1) dimensional perceptrons between the 1 st layer and the 2 nd layer and K number of (H + 1) dimensional perceptrons between the 2 nd layer and the 3 rd layer can be expressed as shown in FIG. can
  • the layer where the input vector is located is called the input layer
  • the layer where the final output value is located is called the output layer
  • all layers located between the input layer and the output layer are called hidden layers.
  • the artificial neural network illustrated in FIG. 9 can be understood as a total of two layers since the number of actual artificial neural network layers is counted excluding the input layer.
  • the artificial neural network is composed of two-dimensionally connected perceptrons of basic blocks.
  • the above-described input layer, hidden layer, and output layer can be jointly applied to various artificial neural network structures such as CNN and RNN, which will be described later, as well as multi-layer perceptrons.
  • CNN neural network
  • RNN multi-layer perceptrons
  • DNN deep neural network
  • FIG. 10 shows a deep neural network applicable to the present disclosure.
  • the deep neural network may be a multi-layer perceptron composed of 8 hidden layers + 8 output layers.
  • the multilayer perceptron structure can be expressed as a fully-connected neural network.
  • a fully-connected neural network there is no connection relationship between nodes located on the same layer, and a connection relationship may exist only between nodes located on adjacent layers.
  • DNN has a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, so it can be usefully applied to identify the correlation characteristics between inputs and outputs.
  • the correlation characteristic may mean a joint probability of input and output.
  • 11 shows a convolutional neural network applicable to the present disclosure.
  • 12 illustrates a filter operation of a convolutional neural network applicable to the present disclosure.
  • various artificial neural network structures different from the aforementioned DNN can be formed depending on how a plurality of perceptrons are connected to each other.
  • nodes located inside one layer are arranged in a one-dimensional vertical direction.
  • the nodes are two-dimensionally arranged with w nodes horizontally and h nodes vertically. (convolutional neural network structure in FIG. 11).
  • a weight is added for each connection in the connection process from one input node to the hidden layer, a total of h ⁇ w weights should be considered. Since there are h ⁇ w nodes in the input layer, a total of h 2 w 2 weights may be required between two adjacent layers.
  • the convolutional neural network of FIG. 11 has a problem in that the number of weights increases exponentially according to the number of connections, so instead of considering all mode connections between adjacent layers, it can be assumed that there is a filter with a small size.
  • a weighted sum and an activation function operation may be performed on a portion where filters overlap.
  • one filter has weights corresponding to the number of filters, and learning of weights can be performed so that a specific feature on an image can be extracted as a factor and output.
  • a 3 ⁇ 3 filter is applied to a 3 ⁇ 3 area at the top left of the input layer, and an output value obtained by performing a weighted sum and an activation function operation on a corresponding node may be stored in z 22 .
  • the above-described filter is moved by a certain distance horizontally and vertically while scanning the input layer, and the weighted sum and activation function calculations are performed, and the output value can be placed at the position of the current filter.
  • the deep neural network of this structure is called a convolutional neural network (CNN), and the result of the convolution operation
  • the hidden layer may be called a convolutional layer.
  • a neural network including a plurality of convolutional layers may be referred to as a deep convolutional neural network (DCNN).
  • the number of weights may be reduced by calculating a weighted sum including only nodes located in a region covered by the filter in the node where the current filter is located. This allows one filter to be used to focus on features for a local area. Accordingly, CNN can be effectively applied to image data processing in which a physical distance in a 2D area is an important criterion. Meanwhile, in the CNN, a plurality of filters may be applied immediately before the convolution layer, and a plurality of output results may be generated through a convolution operation of each filter.
  • a structure in which this method is applied to an artificial neural network can be referred to as a recurrent neural network structure.
  • 13 illustrates a neural network structure in which a circular loop applicable to the present disclosure exists.
  • 14 illustrates an operating structure of a recurrent neural network applicable to the present disclosure.
  • a recurrent neural network is an element ⁇ x 1 (t) , x 2 (t) , . , x d (t) ⁇ into the fully connected neural network, the immediately preceding point in time t-1 is the hidden vector ⁇ z 1 (t-1) , z 2 (t-1) , . . . , z H (t-1) ⁇ together to apply a weighted sum and an activation function.
  • the reason why the hidden vector is transmitted to the next time point in this way is that information in the input vector at previous time points is regarded as being accumulated in the hidden vector of the current time point.
  • the recurrent neural network may operate in a predetermined sequence of views with respect to an input data sequence.
  • the input vector at time point 1 ⁇ x 1 (t) , x 2 (t) , . . . , x d (t) ⁇ is input to the recurrent neural network ⁇ z 1 (1) , z 2 (1) , . . . , z H (1) ⁇ is the input vector ⁇ x 1 (2) , x 2 (2) , . , x d (2) ⁇ , the vector ⁇ z 1 (2) , z 2 (2) , . . . of the hidden layer through the weighted sum and activation function , z H (2) ⁇ is determined.
  • This process is at time point 2, time point 3, . . . , iteratively performed until time point T.
  • a deep recurrent neural network a recurrent neural network
  • Recurrent neural networks are designed to be usefully applied to sequence data (eg, natural language processing).
  • neural network core used as a learning method, in addition to DNN, CNN, and RNN, restricted Boltzmann machine (RBM), deep belief networks (DBN), and deep Q-Network It includes various deep learning techniques such as computer vision, voice recognition, natural language processing, and voice/signal processing.
  • RBM restricted Boltzmann machine
  • DNN deep belief networks
  • Q-Network It includes various deep learning techniques such as computer vision, voice recognition, natural language processing, and voice/signal processing.
  • AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver, not a traditional communication framework, in fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling ( scheduling) and allocation.
  • a device When a device receives and measures a signal passing through a wireless channel based on a pre-trained neural network of a wireless communication system, performance may deteriorate. This is because the number of propagation environment characteristics is very large, but the supervised learning data that can be obtained from each characteristic is small. That is, the probability that a certain propagation environment appears has a long-tail distribution.
  • the device uses a previously trained neural network offline, the device requires a neural network with a very large model size and a lot of data to train it. However, it is not easy to collect these data from communication channels with long-tail distribution. Even if the terminal moves, experiences a new channel, collects data, and fine-tunes the previously trained neural network offline again, it is difficult for the terminal to sufficiently reflect the new change. It is time consuming for the device to adapt to a new channel based on the weights of a large model without sufficient data. Accordingly, when a device communicates based on a neural network, communication quality that is sensitive to speed and delay may be degraded.
  • Neural network training should reflect the effect of the channel between the sender and the receiver.
  • a reference signal or pilot signal may be used as training data to reflect channel effects.
  • a device may perform transmission and reception training for a channel by receiving data based on a demodulation reference signal (DM-RS) for reception.
  • DM-RS demodulation reference signal
  • When a device transmits many DM-RS signals to learn a channel effect there is a problem of using a lot of radio resources. Accordingly, there is a problem in that resources required for data transmission are reduced and spectral efficiency is reduced. Therefore, there is a need for a neural network-requiring technique in which a device can maximize reference signal radio resources and quickly learn in a propagation environment having a long-tail distribution. Accordingly, the present disclosure proposes a communication system, communication procedure, and signaling method based on online meta-learning.
  • Meta-learning uses pre-trained neural networks based on several tasks. Meta-learning is learning that allows a device to perform inference such as regression and classification well with respect to a new task using such a pre-trained neural network. That is, meta-learning is a method of improving learning and estimation performance for a new task, and is a learn-to-learn method.
  • the model weight of the neural network pre-trained through the task is a meta parameter It is called.
  • the model weights of the previously trained neural network are not limited to the above terms. Meta parameters in this disclosure Learning is called meta-training.
  • learning model weights of a neural network trained in advance is not limited to the above-described terms, and may be referred to as other terms.
  • meta parameters when the device experiences a new task model parameters suitable for the new task from Re-learning and inferring based on the re-learning by the device is defined as adaptation.
  • 15a to 15c are diagrams for explaining meta-learning applicable to the present disclosure.
  • 15A shows an example for explaining meta-learning.
  • riding a bicycle as a new task ( 1, 1502a).
  • a new task of riding a bicycle can be easily performed.
  • task 1 (1504a) riding a horse
  • task 2 (1504b) riding a surfboard
  • task 3 (1504c) riding an electric bicycle
  • the new task of riding a skateboard ( 2, 1502b) and a new task riding a motorized quickboard ( 3, 1502c) can be easily adapted.
  • the device collects data from the task distribution p(D), and meta-parameters based on the neural network model f to learn Devices are required for new tasks based on meta-parameters. can be obtained.
  • device is Adaptation can be performed based on.
  • Each task T may be defined as in Equation 1 below.
  • T ⁇ L( ⁇ , D), ⁇ (x 1 ), ⁇ (x t+1
  • Equation 1 L means a loss function.
  • D stands for the related data set.
  • x t , y t ) means the conditional transition probability of task data.
  • H denotes the temporal length of this task. Metaparameters in metalearning In the target function and task distribution for learning , the data set can be expressed as in Equation 2 below.
  • the data set may consist of multiple task data sets. Also, one task may have a training set (D tr ) and a test set (D ts ).
  • task 1 (1504a), task 2 (1504b), and task 3 (1504c) each have a data set. That is, each task has a training set and a test set. Individual task data is gathered to become a meta-training set.
  • the device performs new tasks 1502a, 1502b, and 1502c. can be obtained. Adaptation to the new task may be performed by maximizing the conditional probability value. As a specific example, the device is an optimal meta parameter And adaptation can be performed by maximizing the conditional probability that best describes the meta test data. Adaptation can be expressed as Equation 3 below.
  • Meta-learning algorithms for obtaining can be classified into three types: model (black box)-based methods, optimization-based methods, and non-parametric methods. These methods have the following in common. First, these methods perform generalization on data obtained from the distribution of several tasks. Second, these methods sample one task from the meta task data set and repeatedly perform learning using related task data D tr and D ts .
  • a model-based method is another model or neural network that well describes a particular sampled task i. using is a way to obtain
  • the optimization-based method is not based on a model that best describes task i, but is a method of obtaining an optimal meta-parameter based on gradient information of the current model.
  • the non-parametric method of task i It is a way to consider a model that explains the features of .
  • meta-learning When the data set has a long-tailed distribution, meta-learning has the greatest effect and performance improvement.
  • a data set with a long tail distribution has many classes in terms of classification, and the data size within each class is very small. Meta-learning shows good performance even on small data sets.
  • meta-learning can be used for few-shot learning. The device can perform meta-learning based on only a few images and then show excellent performance in learning to identify new images.
  • the device may transmit a phase-tracking reference signal (PTRS) having a specific pattern to correct a phase error in a high frequency band.
  • PTRS phase-tracking reference signal
  • DM-RS demodulation reference signal
  • the device tracks time synchronization of a channel, tracks a beam, and finds channel quality information such as RI (rank indicator) / PMI (precoding matrix indicator) / CQI (channel quality indicator), a specific pattern
  • CSI-RS channel state information-reference signal having can be transmitted.
  • the device may transmit a sounding reference signal (SRS) having a specific pattern p for channel sounding.
  • the terminal may transmit a positioning reference signal (PRS) for measuring a location.
  • SRS sounding reference signal
  • PRS positioning reference signal
  • the device may transmit various types of reference signals according to the purpose, and is not limited to the above-described embodiments.
  • This disclosure defines transmission, reception and measurement of reference signals as a task for a device to use radio resources most efficiently.
  • the present disclosure proposes a meta-learning area for quickly and accurately performing communication in mmWave and THz bands.
  • the present disclosure proposes a technique for operating a meta-learning area.
  • the meta-learning region is an optimal meta-parameter for performing a task related to a reference signal based on frequency. It may be a specific area that shares .
  • the meta-learning domain is a meta-parameter It may be a region that shares the meta-learning data set D meta_train that can explain with maximum likelihood.
  • the base station provides the terminal with meta-learning model parameters in the meta-learning area. can be transmitted to the terminal. In addition, the base station can train the terminal through transmission of a reference signal corresponding to D meta_train .
  • An input value of the meta-learning area is information related to the location of the terminal. In the present disclosure, this information is defined as a UE context. Parameters constituting the terminal context may include global position values, velocity and acceleration values obtained from sensors, and the like.
  • 16 illustrates an example of a meta-learning area applicable to the present disclosure.
  • the meta-learning domain technique is a divide-and-conquer technique for tasks related to long-tail distributions.
  • the meta-learning domain technique is a technique of dividing and conquering a task related to a reference signal of a high frequency band.
  • the long-tail distribution of a high-frequency propagation environment is based on topography and channels originating from features. For example, not only buildings and terrain in a static state, but also moving objects, antenna placement, and movement of terminals affect the propagation environment. The geographical distribution in which a specific terminal is located has the greatest effect. Accordingly, meta-learning for tasks related to reference signals can be configured locally.
  • the distribution of reference signal tasks may not change dramatically within a particular terrain. For example, when a terminal moves, the probability that the location of the terminal changes from a dense urban area with many large buildings to a rural area is higher than a probability that the location of the terminal is changed from a dense city with many buildings to a typical urban area. are more likely to change to In addition, when the terminal is located in the out-of-town area, the probability that the terminal continues to be located in the out-of-town area is higher than the probability that it is not. In addition, a terminal with high mobility (eg, a vehicle) has a higher probability of moving along a path along the direction of the road than a probability of not moving. Therefore, meta parameters may not change significantly within a specific area.
  • a terminal with high mobility eg, a vehicle
  • the meta-learning area can be hierarchically configured. The complexity and performance of system operation may have a trade-off relationship. Within the meta-learning domain, devices can share meta-parameters. Referring to FIG. 16 , each cell configures a meta-learning area that shares meta-parameters. The meta-learning area is hierarchically organized. The meta-learning area can be subdivided according to the complexity of the system. In addition, the meta-learning area can be expanded to a larger area depending on the complexity of the system. Referring to FIG. 16, cell A (cell A, 1602), cell B (1604), cell C (1606), and cell D are meta-running areas that share meta parameters. Cell A (1602) and cell B (1604) ) contains a more subdivided meta-learning area.
  • cell 1 1702 and cell 2 1704 each have a hierarchical meta-running area.
  • the meta-learning domain can be subdivided based on the complexity of the system.
  • the subdivided meta-learning domains can be integrated into one meta-learning domain based on the complexity of the system. For example, if the difference between meta parameters between meta-learning areas is large, the meta-learning area may be further subdivided. As another example, when the meta parameter difference between meta-learning areas is small, the meta-learning areas may be merged into one.
  • some of the meta-running regions of cell 1 may be combined to form cell 2. Conversely, a part of the meta-running region of cell 2 may be subdivided like cell 1.
  • the meta-learning area may be configured based on the characteristics of the beam prediction task.
  • the meta-learning area may be configured as an area in which characteristics of beam prediction tasks are common.
  • Base stations may also have different beam propagation environments due to reasons such as roads and densely populated areas. Accordingly, base stations may be separated from each other so that meta learning parameters may be operated.
  • a terminal moving on the road is highly likely to be a moving object such as a car.
  • a beam propagation probability distribution of such a terminal may have a different shape from that of a terminal in a dense area with many low buildings.
  • meta-learning parameters can be operated hierarchically by subdividing the entire area. In a typical city center, the size of the buildings is very high, so radio waves may arrive by reflection in many cases. In this case, meta-learning parameters may exist separately.
  • each meta-learning parameter can be operated.
  • Meta learning parameters may exist for each frequency such as below 6 GHz, mmWave, and THz bands for all reference signals such as time synchronization, CSI-RS for reporting channel status, DMRS, PTRS, and PRS, respectively, as follows.
  • the base station may directly download the meta model for each meta area. If there is a list of meta models agreed upon in advance with the terminal, the base station may transmit information on the meta model assigned to each meta area to the terminal without downloading the meta model.
  • the base station may transmit a reference signal set (RS set) for meta training when the terminal does not have a meta model.
  • the base station can simultaneously transmit all possible reference signal combinations for each frequency, such as below 6 GHz, mmWave, and THz bands.
  • the reference signal sets include DMRS, PTRS, CSI-RS, PRS, synchronization signal block (SSB), SRS, cell-specific reference signal (CRS) and meta learning reference signal (meta learning RS). At least one of them may be included.
  • the reference signal set includes a DMRS with data, a DMRS without data, a PTRS with data, a PTRS without data, a CSI-RS for beam measurement, and a CSI-RS for link quality measurement.
  • RS CSI-RS for time-frequency synchronization measurement
  • PRS for location measurement
  • SSB for synchronization
  • SRS transmitted by the terminal CRS and meta-running RS transmitted on a specific port for meta-learning purposes, and for other purposes It may include at least one of RS.
  • the base station may transmit optimal learning weights for the reference signal task in the meta-learning area to the terminal. For example, the base station may inform the terminal of the optimal learning weight for the reference signal task in the meta-learning area for each frequency band, such as below 6 GHz, mmWave, and THz bands. As another example, the base station may inform the terminal of the optimal learning weight for the reference signal task in the meta-learning region in the optimal frequency band.
  • the base station may inform the terminal of the optimal learning weight for the reference signal task in the meta-learning region in the optimal frequency band.
  • the terminal may signal transmission and stop of the reference signal for each meta-running area to the base station.
  • the terminal may request to stop transmitting the reference signal.
  • step S1901 the terminal informs the base station about the terminal model for each frequency band. can request
  • the base station is the terminal meta model If not, the base station may inform the terminal of meta training start.
  • the base station and the terminal may train a model based on the transmission of the meta training reference signal set.
  • the base station is the terminal meta model If you have, the base station may be transmitted to the terminal or a model identifier may be transmitted. The base station may directly download the meta model for each meta area. If there is a list of meta models agreed upon in advance with the terminal, the base station may transmit information on the meta model assigned to each meta area to the terminal without downloading the meta model.
  • a terminal or a base station may perform adaptation.
  • meta-learning parameters at a specific time t The base station or terminal with is based on the meta learning adaptation algorithm can induce The base station or the terminal may perform the RS task based on the adaptation algorithm.
  • the terminal is a meta-learning parameter It can be obtained by downloading from the base station or obtained by performing meta-training.
  • the UE may infer a reference signal task based on a UE context and a specific RS for task.
  • the base station may consider a reference signal unrelated to a currently configured beam as another task.
  • the base station may consider a beam based on a reference signal unrelated to a currently available beam based on DMRS, PRS, etc. as another task. Since beams may be received through similar paths due to characteristics of high-frequency radio waves, a reference signal unrelated to a set beam may contribute to meta-learning.
  • the terminal or the base station may update meta parameters by considering weights for each reference signal type and frequency band not directly related to a specific reference signal task. The weight is a parameter that adjusts the contribution in the meta-training of a task.
  • the base station 22 illustrates an example of a meta parameter update procedure in a meta learning domain applicable to the present disclosure.
  • the base station can maintain an optimal meta parameter value.
  • the base station may manage a frequency of a meta-running region that best describes an optimal meta-parameter and a meta-running set and a weight according to each reference signal task.
  • the terminal may measure an optimal meta parameter value for each meta learning area and report it to the base station.
  • step S2201 the base station sends the terminal a meta learning parameter for a meta domain list for each frequency band. can request
  • step S2203 the terminal performs optimal meta-learning for each meta area.
  • the terminal may search for optimal meta-learning parameters for all regions of the meta-region list for each given frequency band.
  • the terminal may report the searched optimal meta-learning parameter and artificial intelligence model identifier to the base station together.
  • step S2205 the base station base station based on the report received from the terminal of the meta-learning area can be updated.
  • the terminal may perform a task of beam prediction based on mmWave CSI-RS and a task of location estimation based on below 6 GHz PRS. These terminals may move from suburban areas to densely populated areas. In this case, the terminal may obtain a metamodel optimized for each of the task of beam prediction based on the mmWave CSI-RS and the task of location estimation based on the below 6 GHz PRS from the base station.
  • the base station provides the terminal with the meta-learning parameter with the best performance for each task. can be requested to explore. Accordingly, the base station can update the optimal meta-learning parameter.
  • the UE has the optimal meta-parameter of the task for below 6GHz PRS.
  • the best meta-learning parameters can be learned based on several weight candidates of the task for the mmWave CSI-RS task and the below 6GHz PRS to search for . This search may be performed based on a given list of meta regions.
  • the meta-learning technique of the present disclosure solves the problem of an artificial intelligence model based on existing offline learning that does not adaptively cope with the complexity of mmWave and 6GHz propagation environments and requires a large number of data sets. That is, the meta-learning technique of the present disclosure is a learning technique capable of adaptively coping with a long-tail distribution channel environment.
  • mmWave and 6 GHz environments are highly dependent on geographic features, such as static terrain and the motion of dynamic objects along the propagation path. Accordingly, the mmWave and 6GHz environments may change rapidly according to the movement of the terminal. In this situation, the meta-learning area allows the base station to quickly process tasks related to reference signals with different frequency bands through the interaction between the base station and the terminal.
  • the device can improve the performance of the wireless communication system by maintaining an optimal meta-learning parameter within the meta-learning domain.
  • This disclosure discloses a procedure in which a base station and a terminal can provide a meta training set that can best describe a meta model even if the base station and the terminal have different meta models.
  • the device can significantly save radio resources based on the meta-training set for each frequency band.
  • the terminal may request meta-learning model information from the base station in the first meta-learning area.
  • the first meta-learning area may be hierarchically configured. Also, the first meta-learning area may be subdivided as described above or combined with other meta-learning areas.
  • step S2303 if the base station has meta-learning model information related to the terminal, the terminal may receive meta-learning model information from the base station.
  • the terminal may receive meta-running setting information from the base station.
  • the meta-running setting information includes reference signal related information, wherein the reference signal related information is DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal) , PRS (positioning reference signal), SSB (synchronization signal block, SSB), SRS (sounding reference signal), meta-learning reference signal (meta-learning reference signal), and at least one resource information and weight of a reference signal for a specific purpose may contain values.
  • reference signal related information may include a configuration in which reference signals are combined based on each purpose and frequency. The reference signal related information may be based on a frequency band.
  • the terminal may learn a meta-learning model based on the meta-learning setting information.
  • the terminal learns a parameter related to a meta-learning model for a reference signal received from the base station or a reference signal transmitted to the base station based on the received meta-learning model or a learning result of the meta-learning model can
  • Reference signal sets may be combined according to frequency bands and purposes. That is, the terminal may perform adaptation.
  • the terminal and the base station define a meta model list
  • information related to the meta model list related to the first meta-learning area may be received from the base station.
  • the terminal may report the meta-learning model learning result to the base station.
  • the base station may adjust the meta-learning area based on the meta-learning model learning result received from the terminal.
  • meta-learning areas having similar weights or optimal meta-parameters may be merged into one meta-learning area based on meta-learning results for a plurality of meta-learning areas received by the base station from a plurality of terminals.
  • the base station may subdivide the meta-learning area when the meta-parameters or weights received from the plurality of terminals are different based on the meta-learning learning result for one meta-learning area received from the plurality of terminals. .
  • the terminal may receive a request for information related to a meta learning area list from the base station.
  • the terminal may transmit meta-running region list-related information to the base station.
  • the meta-learning area list related information may include a meta-running learning result of the first meta-running area.
  • the base station may receive a request for meta-learning model information from the terminal in the first meta-learning area.
  • step S2403 if the base station has meta-learning model information related to the terminal, it may transmit the meta-learning model information to the terminal.
  • the base station may transmit meta-running setting information to the terminal.
  • a meta-learning model may be learned based on the meta-learning setting information.
  • the meta-training setting information includes reference signal related information, and the reference signal related information includes DM-RS (demodulation-reference signal), PTRS (phase-tracking reference signal), CSI-RS (channel status information-reference signal), At least one resource information and weight value of a positioning reference signal (PRS), a synchronization signal block (SSB), a sounding reference signal (SRS), a meta-learning reference signal, and a reference signal for a specific purpose can include
  • the reference signal related information may be based on a frequency band.
  • examples of the proposed schemes described above may also be included as one of the implementation methods of the present disclosure, and thus may be regarded as a kind of proposed schemes.
  • the proposed schemes described above may be implemented independently, but may also be implemented in a combination (or merged) form of some proposed rooms*.
  • Information on whether the proposed methods are applied may be defined so that the base station informs the terminal through a predefined signal (eg, a physical layer signal or a higher layer signal). .
  • Embodiments of the present disclosure may be applied to various wireless access systems.
  • various wireless access systems there is a 3rd Generation Partnership Project (3GPP) or 3GPP2 system.
  • 3GPP 3rd Generation Partnership Project
  • 3GPP2 3rd Generation Partnership Project2
  • Embodiments of the present disclosure may be applied not only to the various wireless access systems, but also to all technical fields to which the various wireless access systems are applied. Furthermore, the proposed method can be applied to mmWave and THz communication systems using ultra-high frequency bands.
  • embodiments of the present disclosure may be applied to various applications such as free-running vehicles and drones.

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Abstract

Un mode de réalisation de la présente divulgation concerne un procédé de fonctionnement de terminal, et le procédé de fonctionnement de terminal dans un système de communication sans fil comprend les étapes dans lesquelles un terminal : demande des informations de modèle de méta-apprentissage en provenance d'une station de base dans une première zone de méta-apprentissage ; reçoit les informations de modèle de méta-apprentissage en provenance de la station de base si la station de base possède les informations de modèle de méta-apprentissage associées au terminal ; reçoit des informations de configuration de méta-apprentissage en provenance de la station de base si la station de base ne possède pas les informations de modèle de méta-apprentissage associées au terminal ; et entraîne un modèle de méta-apprentissage sur la base des informations de configuration de méta-apprentissage. Les informations de configuration de méta-apprentissage comprennent des informations relatives au signal de référence, et les informations relatives au signal de référence comprennent des informations de ressource et une valeur de pondération d'au moins un signal parmi un signal de référence de démodulation (DM-RS), un signal de référence de suivi de phase (PTRS), un signal de référence d'informations d'état de canal (CSI-RS), un signal de référence de positionnement (PRS), un bloc de signal de synchronisation (SSB), un signal de référence de sondage (SRS), un signal de référence de méta-apprentissage et un signal de référence pour un usage spécifique. Les informations relatives au signal de référence sont basées sur une bande de fréquence.
PCT/KR2021/019800 2021-12-24 2021-12-24 Appareil et procédé de transmission de signal dans un système de communication sans fil WO2023120781A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200015048A (ko) * 2018-08-02 2020-02-12 삼성전자주식회사 메타-학습에 기반하여 기계학습의 모델을 선정하는 방법 및 장치
KR102117448B1 (ko) * 2012-04-06 2020-06-01 삼성전자 주식회사 Massive MIMO 전송을 지원하는 이동 통신 시스템에서 채널 송수신 방법 및 장치
KR20210135022A (ko) * 2020-05-04 2021-11-12 한국전자통신연구원 통신 시스템에서의 빔포밍 방법 및 장치

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102117448B1 (ko) * 2012-04-06 2020-06-01 삼성전자 주식회사 Massive MIMO 전송을 지원하는 이동 통신 시스템에서 채널 송수신 방법 및 장치
KR20200015048A (ko) * 2018-08-02 2020-02-12 삼성전자주식회사 메타-학습에 기반하여 기계학습의 모델을 선정하는 방법 및 장치
KR20210135022A (ko) * 2020-05-04 2021-11-12 한국전자통신연구원 통신 시스템에서의 빔포밍 방법 및 장치

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
YANG RUMING; ZHANG ZHENGMING; YANG LUXI: "Meta-Learning for mmWave Beam Prediction Using Sub-6 GHz Channels", 2021 13TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP), 20 October 2021 (2021-10-20), pages 1 - 5, XP034032712, DOI: 10.1109/WCSP52459.2021.9613525 *
YANG YUWEN; GAO FEIFEI; ZHONG ZHIMENG; AI BO; ALKHATEEB AHMED: "Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO Systems", IEEE TRANSACTIONS ON COMMUNICATIONS, vol. 68, no. 12, 24 August 2020 (2020-08-24), PISCATAWAY, NJ. USA. , pages 7485 - 7497, XP011825759, ISSN: 0090-6778, DOI: 10.1109/TCOMM.2020.3019077 *

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