WO2023286884A1 - Procédé et dispositif d'émission et de réception de signaux dans un système de communication sans fil - Google Patents

Procédé et dispositif d'émission et de réception de signaux dans un système de communication sans fil Download PDF

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Publication number
WO2023286884A1
WO2023286884A1 PCT/KR2021/008944 KR2021008944W WO2023286884A1 WO 2023286884 A1 WO2023286884 A1 WO 2023286884A1 KR 2021008944 W KR2021008944 W KR 2021008944W WO 2023286884 A1 WO2023286884 A1 WO 2023286884A1
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WIPO (PCT)
Prior art keywords
irs
information
index information
base station
terminal
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PCT/KR2021/008944
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English (en)
Korean (ko)
Inventor
오재기
박재용
하업성
김성진
Original Assignee
엘지전자 주식회사
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Priority to KR1020237038812A priority Critical patent/KR20240026441A/ko
Priority to PCT/KR2021/008944 priority patent/WO2023286884A1/fr
Publication of WO2023286884A1 publication Critical patent/WO2023286884A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station

Definitions

  • the following description relates to a wireless communication system, and relates to a method and apparatus for transmitting and receiving signals between a terminal and a base station in a wireless communication system.
  • a terminal and a base station may provide a method and apparatus for transmitting and receiving signals by controlling a radio channel environment through an intelligent reflect surface (IRS).
  • IIRS intelligent reflect surface
  • 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
  • MTC massive Machine Type Communications
  • the present disclosure may provide a method and apparatus for transmitting and receiving signals in a wireless communication system.
  • the present disclosure may provide a method for transmitting and receiving signals between a terminal and a base station using an intelligent reflector in a wireless communication system.
  • the present disclosure may provide a method for transmitting and receiving signals between a terminal and a base station based on a smart radio environment (SRE) in a wireless communication system.
  • SRE smart radio environment
  • the present disclosure may provide a method for controlling a passive intelligent reflector based on an artificial intelligence system in a wireless communication system.
  • the present disclosure may provide a method for transmitting and receiving signals between a terminal and a base station by controlling a passive intelligent reflector in a wireless communication system.
  • a method of operating a terminal in a wireless communication system transmitting first index information related to an intelligent reflect surface (IRS) control value to an IRS, a reference to which a first beamforming is applied from a base station Receiving a signal through an IRS, obtaining at least one of compensation value information and channel state information based on the received reference signal, first index information through at least one of the compensation value information and channel state information Updating to the second index information, transmitting the updated second index information to the IRS, and receiving a reference signal to which the second beamforming is applied from the base station through the IRS based on the updated second index information.
  • IRS intelligent reflect surface
  • a method for operating a base station in a wireless communication system transmitting a reference signal to which a first beamforming is applied to a terminal through an IRS, a first updated index information through an IRS 2 Receiving index information and transmitting the reference signal to which the second beamforming is applied to the terminal through the IRS, wherein the terminal transmits the first index information related to the IRS control value to the IRS, and the first beamforming Obtaining at least one of compensation value information and channel state information through the applied reference signal, updating the first index information to second index information through at least one of the compensation value information and channel state information, and Index information may be transmitted to the IRS.
  • the processor transmits first index information related to an IRS control value to the IRS through the transceiver,
  • the reference signal to which the first beamforming is applied is received from the base station through the transceiver through the IRS, at least one of compensation value information and channel state information is obtained based on the received reference signal, and compensation value information and channel state information are obtained.
  • Updating the first index information to the second index information through at least one of, transmitting the updated second index information to the IRS, and based on the updated second index information, a reference to which the second beamforming is applied from the base station.
  • a signal may be received through the IRS.
  • a base station of a wireless communication system includes a transceiver and a processor connected to the transceiver, and the processor transmits a reference signal to which a first beamforming is applied to a terminal through an IRS through the transceiver. and receives second index information updated from the first index information through the IRS through the transceiver, and transmits a reference signal to which the second beamforming is applied through the transceiver to the terminal through the IRS.
  • Transmits related first index information to the IRS obtains at least one of compensation value information and channel state information through a reference signal to which the first beamforming is applied, and uses at least one of the compensation value information and channel state information
  • the first index information may be updated with second index information, and the second index information may be transmitted to the IRS.
  • the at least one processor includes an IRS control value and Transmits related first index information to the IRS, receives a reference signal to which the first beamforming is applied from the base station through the IRS, and obtains at least one of compensation value information and channel state information based on the received reference signal, , update the first index information to the second index information through at least one of compensation value information and channel state information, transmit the updated second index information to the IRS, and base station based on the updated second index information
  • a reference signal to which the second beamforming is applied may be received through the IRS.
  • a non-transitory computer-readable medium storing at least one instruction, executable by a processor It includes at least one command, wherein the at least one command transmits first index information related to the IRS control value to the IRS, receives a reference signal to which the first beamforming is applied from the base station through the IRS, and receives the Obtaining at least one of compensation value information and channel state information based on the reference signal, updating the first index information to second index information through at least one of the compensation value information and the channel state information, and 2 index information may be transmitted to the IRS, and based on the updated second index information, a reference signal to which the second beamforming is applied may be received from the base station through the IRS.
  • the following items may be commonly applied to the above-described base station, terminal, device, and computer recording medium.
  • an IRS control value may be determined based on index information, and each phase value within an IRS element may be determined based on the determined IRS control value.
  • index information is information generated through a codebook based on a set of IRS direction vectors, and the index information may consist of a first factor based on an IRS azimuth and a second factor based on an IRS elevation angle.
  • a terminal receives a reference signal for initial value setting from a base station, and transmits channel state information and location information of the terminal obtained through the received reference signal for initial value setting to the base station.
  • Feedback and the terminal may receive artificial intelligence initial value information derived based on the location information of the terminal and the channel state information received by the base station from the base station.
  • the terminal may further receive candidate set information related to the index information from the base station.
  • the terminal may derive first index information based on the received artificial intelligence initial value information and candidate set information.
  • the base station may determine the first beamforming based on the location of the IRS when performing initial configuration with the IRS.
  • the IRS when the IRS updates the first index information to the second index information, the IRS transmits a radio environment change completion signal including the second index information to the base station, and the base station Second beamforming may be determined based on the 2 index information.
  • the terminal includes an artificial intelligence beam selector, and the artificial intelligence beam selector updates first index information to second index information through at least one of compensation value information and channel state information can do.
  • the artificial intelligence beam selector converts first index information into second index information based on any one of a multi-armed bandit (MAB) artificial intelligence (AI) learning model and a reinforcement learning learning model.
  • MAB multi-armed bandit
  • AI artificial intelligence
  • the artificial intelligence beam selector updates the first index information to the second index information based on the MAB AI learning model
  • the second index information is obtained through learning based on compensation value information.
  • Second index information may be acquired through learning based on state information and compensation value information.
  • the terminal includes an IRS performance measurer, and the IRS performance measurer may generate compensation value information using channel related information obtained through a reference signal.
  • Embodiments based on the present disclosure may provide a method for transmitting and receiving signals between a terminal and a base station using an intelligent reflector.
  • Embodiments based on the present disclosure may provide a method for transmitting and receiving signals between a terminal and a base station based on an intelligent radio channel environment.
  • Embodiments based on the present disclosure may provide a method for controlling a passive intelligent reflector based on an artificial intelligence system.
  • Embodiments based on the present disclosure may provide a method for transmitting and receiving signals between a terminal and a base station by controlling a passive intelligent reflector.
  • 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 is a diagram illustrating an example of a communication system applicable to the present disclosure.
  • FIG. 2 is a diagram illustrating an example of a wireless device applicable to the present disclosure.
  • FIG. 3 is a diagram illustrating another example of a wireless device applicable to the present disclosure.
  • FIG. 4 is a diagram illustrating an example of a portable device applicable to the present disclosure.
  • FIG. 5 is a diagram illustrating an example of a vehicle or autonomous vehicle applicable to the present disclosure.
  • AI Artificial Intelligence
  • FIG. 7 is a diagram showing an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.
  • FIG. 8 is a diagram showing an electromagnetic spectrum applicable to the present disclosure.
  • FIG. 9 is a diagram illustrating a radio channel environment according to an embodiment of the present disclosure.
  • FIG. 10 is a diagram illustrating an intelligent wireless environment according to an embodiment of the present disclosure.
  • FIG. 11 is a diagram illustrating an existing radio channel environment and an intelligent radio channel environment according to an embodiment of the present disclosure.
  • FIG. 12 is a diagram illustrating a method of performing optimization in an intelligent radio channel environment according to an embodiment of the present disclosure.
  • FIG. 13 is a diagram illustrating a confidence interval according to an embodiment of the present disclosure.
  • FIG. 14 is a diagram illustrating a method of performing optimization in a passive-based intelligent wireless channel environment according to an embodiment of the present disclosure.
  • 15 is a diagram illustrating a signal flow between a base station, a terminal, and an IRS to configure an optimized intelligent wireless environment (SRE) according to an embodiment of the present disclosure.
  • SRE optimized intelligent wireless environment
  • 16 is a diagram illustrating a signal flow between a base station, an IRS, and a terminal when an artificial intelligence beam selector is implemented in a terminal according to an embodiment of the present disclosure.
  • 17 may be a flowchart illustrating a method of setting an optimized intelligent wireless environment according to an embodiment of the present disclosure.
  • 18 is a diagram showing the structure of an SRE artificial intelligence system using reinforcement learning according to an embodiment of the present disclosure.
  • FIG. 19 may be a diagram in which components of an environment are further subdivided based on FIG. 18 .
  • 20 is a diagram illustrating use blocks of a terminal and a base station among environmental components in an artificial intelligence beam selector according to an embodiment of the present disclosure.
  • 21 is a diagram showing the structure of an artificial intelligence beam selector according to an embodiment of the present disclosure.
  • FIG. 22 is a diagram illustrating a method of implementing an artificial intelligence beam selector through MAB AI according to an embodiment of the present disclosure.
  • FIG. 23 is a diagram illustrating a method of implementing an artificial intelligence beam selector through reinforcement learning according to an embodiment of the present disclosure.
  • 24 is a diagram showing total regret using a greedy method according to an embodiment of the present disclosure.
  • FIG 25 shows the structure of an IRS performance measurer according to an embodiment of the present invention.
  • 26 is a diagram illustrating a method of setting an artificial intelligence initial value according to an embodiment of the present disclosure.
  • 27 may be a case where an artificial intelligence beam selector is implemented in a terminal according to an embodiment of the present disclosure.
  • FIG. 28 is a diagram showing the structure of an SRE artificial intelligence system including an artificial intelligence initial value setter according to an embodiment of the present disclosure.
  • 29 is a diagram showing an example of an artificial intelligence coding initial value setter applicable to the present disclosure.
  • FIG. 30 is a diagram illustrating a method of performing wireless environment optimization based on an artificial intelligence system according to an embodiment of 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.
  • 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 controller 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 portable 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 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.
  • Blocks 910 to 930/940a to 940d 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 920 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 930 may store control information and/or software codes required for operation/execution of the control unit 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 940c may be transmitted to an external device through the communication unit 610 and/or stored in the memory unit 630.
  • 6G (radio communications) systems are characterized by (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- It aims to lower energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities.
  • the vision of the 6G system can be four aspects such as “intelligent connectivity”, “deep connectivity”, “holographic connectivity”, and “ubiquitous connectivity”, and the 6G system can satisfy the requirements shown in Table 1 below. That is, Table 1 is a table showing the requirements of the 6G system.
  • the 6G system is enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), mMTC (massive machine type communications), AI integrated communication, tactile Internet (tactile internet), high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion and improved data security ( can have key factors such as enhanced data security.
  • eMBB enhanced mobile broadband
  • URLLC ultra-reliable low latency communications
  • mMTC massive machine type communications
  • AI integrated communication e.g., AI integrated communication
  • tactile Internet tactile internet
  • high throughput high network capacity
  • high energy efficiency high backhaul and access network congestion
  • improved data security can have key factors such as enhanced data security.
  • FIG. 7 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.
  • a 6G system is expected to have 50 times higher simultaneous wireless communication connectivity than a 5G wireless communication system.
  • URLLC a key feature of 5G, is expected to become a more mainstream technology by providing end-to-end latency of less than 1 ms in 6G communications.
  • the 6G system will have much better volume spectral efficiency, unlike the frequently used area spectral efficiency.
  • 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices in 6G systems may not need to be charged separately.
  • 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 machine-to-machine, 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
  • THz communication can be applied in 6G systems.
  • the data transmission rate can be increased by increasing the bandwidth. This can be done using sub-THz communication with wide bandwidth and applying advanced massive MIMO technology.
  • THz waves also known as sub-millimeter radiation
  • THz waves generally represent a frequency band between 0.1 THz and 10 THz with corresponding wavelengths in the range of 0.03 mm-3 mm.
  • the 100 GHz-300 GHz band range (sub THz band) is considered a major part of the THz band for cellular communications. Adding to the sub-THz band mmWave band will increase 6G cellular communications capacity.
  • 300 GHz-3 THz is in the far infrared (IR) frequency band.
  • the 300 GHz-3 THz band is part of the broad band, but is at the border of the wide band, just behind the RF band. Thus, this 300 GHz to 3 THz band exhibits similarities to RF.
  • THz communications include (i) widely available bandwidth to support very high data rates, and (ii) high path loss at high frequencies (highly directional antennas are indispensable).
  • the narrow beamwidth produced by the highly directional antenna reduces interference.
  • the small wavelength of the THz signal allows a much larger number of antenna elements to be incorporated into devices and BSs operating in this band. This enables advanced adaptive array technology to overcome range limitations.
  • the artificial intelligence system can be used to adjust the radio channel environment using the IRS, which will also be described later.
  • the current wireless communication technology can be controlled through end-point optimization that adapts to the channel environment (H). For example, when optimization is performed in a transmitter and a receiver, the transmitter and receiver adjust at least one of beamforming, power control, and adaptive modulation according to the channel environment (H) between the transmitter and the receiver to increase transmission efficiency.
  • H channel environment
  • the channel environment may be random, uncontrolled, and naturally fixed. That is, in the existing communication system, a method of controlling each end point to be optimized for the channel environment while the channel environment is fixed may be performed. Therefore, the transmitter and the receiver have no choice but to perform optimization to adapt to the channel and transmit/receive data through this optimization.
  • NLOS non-line of sight
  • 6G THz 6G THz
  • an intelligent reflector can be used as a factor capable of controlling a wireless channel like a transceiver.
  • a factor for a radio channel may be added as a factor used to optimize wireless communication transmission.
  • a factor for a radio channel may be added as a factor used to optimize wireless communication transmission.
  • the optimization process may be complicated.
  • the passive-based intelligent reflector overcomes the limitations of channel information acquisition Describe how to do it.
  • a new communication system e.g. 6G
  • MBRLLC Mobile Broadband Reliable Low Latency Communication
  • mURLLC Massive Ultra-Reliable, Low Latency communications
  • HCS Human-Centric Services
  • 3CLS Convergence of Communications, Computing, Control, Localization, and Sensing
  • relays are currently used to increase coverage of base station cells and support for shadow areas.
  • the method using a repeater may increase transmission efficiency, but may additionally generate interference signals for other users. Therefore, a limitation may occur in terms of overall communication resource efficiency.
  • the use of a relay requires high additional cost and energy, and it may not be easy to manage complex and mixed interference signals.
  • spectrum efficiency may be reduced by using a half duplex method, and space utilization and aesthetics may also be affected.
  • the wireless channel environment can be adjusted using an intelligent reflector (IRS).
  • IIRS intelligent reflector
  • the transmitter and receiver can perform optimization together to provide a solution that can overcome Shannon's channel capacity limit in a smart radio environment, which will be described later.
  • the corresponding value may have a dependency on the optimization of the transceiver, and thus complexity may increase.
  • the AO (Alternating Optimization) algorithm used for optimization may be repeatedly performed until convergence, and may impose a burden on all channels to be measured.
  • a method for performing optimization in an intelligent wireless environment with a passive-based intelligent reflector and an artificial intelligence system are described in consideration of the above points.
  • Table 2 may be terms in consideration of the following and above, and based on this, a method for performing optimization in an intelligent wireless environment with a passive-based intelligent reflector and an artificial intelligence system are described.
  • a radio channel environment H is naturally fixed and may be in a random state that cannot be controlled. Accordingly, the transmitter 910 and the receiver 920 can find an optimized transmission/reception method by adapting to the channel.
  • the transmitter 910 and the receiver 920 may measure a channel state through a signal (eg, a reference signal), and may be controlled to perform optimization based on the measured channel state.
  • a signal eg, a reference signal
  • Equation 1 may represent the capacity limit of Shannon. At this time, even if the transmission signal P is increased by applying precoding and processing in Equation 1, there may be a limit to increasing the channel capacity if the size of the channel
  • Equation 1 In a state where the radio channel environment is fixed, there may be a limit to increasing the channel capacity based on Equation 1.
  • an intelligent reflector IMS
  • multiple paths can be secured between the transmitter 910 and the receiver 920, and the aforementioned channel
  • FIG. 10 is a diagram illustrating an intelligent wireless environment according to an embodiment of the present disclosure.
  • may be a factor for optimization. More specifically, in FIG. 9 described above, optimization may be performed in the transmitter 910 and the receiver 920 based on “max ⁇ f(Tx, Rx) ⁇ ” as the end point optimization, which is as described above. . However, in FIG. 10 , optimization may be performed in the transmitter 1010 and the receiver 1020 based on “max ⁇ f(Tx, Rx, H) ⁇ ” as the end point optimization. That is, in an intelligent wireless environment, a channel
  • the existing radio channel environment may be P1.
  • the intelligent wireless channel environment may be P2.
  • the receiving end may receive the y signal.
  • the probability of P1 is fixed in the existing radio channel environment, and the receiving end (Decoder) can transmit feedback to the transmitting end through measurement of the transmitted signal.
  • the transmitting end may perform optimization to adapt to the radio channel environment through the feedback of the receiving end.
  • the receiving end may measure a channel quality indicator (CQI) of the transmission signal based on the reference signal transmitted by the transmitting end and provide feedback thereof.
  • the transmitting end may perform communication by adjusting a modulation coding scheme (MCS) based on the feedbacked information and providing information about the modulation coding scheme to the receiving end.
  • CQI channel quality indicator
  • MCS modulation coding scheme
  • the radio channel environment P2 is recognized and the radio channel environment can be changed through IRS control.
  • the receiving end may measure the received transmission signal and transmit a feedback thereof to the transmitting end. That is, the transmitter may perform optimization by receiving feedback information based on IRS control and feedback information of the receiver. At this time, the transmitter may change the radio channel environment by adjusting the IRS, and optimization may be performed in consideration of the radio channel environment and the transmitter.
  • FIG. 12 is a diagram illustrating a method of performing optimization in an intelligent radio channel environment according to an embodiment of the present disclosure.
  • an IRS 1220 may exist between a base station 1210 and a terminal 1230 in an intelligent radio channel environment.
  • a signal transmitted by the base station 1210 may have a path directly transmitted to the terminal 1230 and a path reflected by the IRS 1220 and transmitted. That is, in an intelligent radio channel environment, a radio channel (G) between the base station 1210 and the IRS 1220 and a radio channel between the IRS 1220 and the terminal 1230 ( ) and a direct radio channel between the base station 1210 and the terminal 1230 ( ) may exist.
  • G radio channel
  • a radio channel (G) between the base station 1210 and the IRS 1220 and a radio channel between the IRS 1220 and the terminal 1230 ( ) can be changed. Accordingly, optimization in an intelligent radio channel environment may be performed in consideration of the above-described radio channel environment.
  • the base station 1210 transmits a signal to terminal k 1230
  • the base station transmission beamforming vector for terminal k 1230 is , the signal transmitted to the terminal k (1230) is and receive noise can be
  • the signal received from the base station 1210 based on the environment in which the terminal k 1230 uses the IRS 1220 may be as shown in Equation 2 below, and each channel may be as shown in Table 3 below.
  • the signal-to-noise ratio (SNR) received by terminal k 1230 may be expressed as Equation 3 below.
  • transmit beamforming of terminal k 1230 in consideration of maximum-rate transmission in MIMO May be the same as Equation 5 below.
  • the IRS control value ⁇ can be determined by arithmetic.
  • an alternating optimization (AO) algorithm may be used to solve the aforementioned optimization problem.
  • the AO algorithm uses channel information ( , , G) may be a method of determining a trust region for each IRS element, and may be as shown in FIG. 13.
  • a binary decision is repeatedly performed until the value of the objective function converges. can be obtained.
  • the IRS may repeat the above-described operation to find an optimized value for each of the above-described IRS elements.
  • the AO (Alternating Optimization) algorithm needs to be repeated until convergence.
  • complexity and computational complexity may increase.
  • complexity and amount of calculation may increase according to the number M of antennas of the base station and the number N of IRS elements, and there may be a limit to calculating them.
  • measurement values of all channels including IRS may be required, and considering the above, there may be limitations in optimization.
  • FIG. 14 is a diagram illustrating a method of performing optimization in a passive-based intelligent wireless channel environment according to an embodiment of the present disclosure.
  • an IRS equipped with an active sensor may obtain channel information by sensing a signal transmitted from a base station.
  • a passive IRS without an active sensor may transmit a signal transmitted from a base station to a terminal based on a reflector structure, but may not perform sensing. Therefore, a passive IRS without an active sensor may be less expensive to install than an IRS with an active sensor, and based on this, the possibility of utilization may be high.
  • the passive-based IRS does not have an active sensor, it is necessary to supplement insufficient channel information with artificial intelligence. That is, channel information that can be measured by an active sensor needs to be acquired through artificial intelligence.
  • the optimized IRS control value may be derived based on artificial intelligence without being affected by the number M of base station antennas and the number N of IRS elements, which will be described later.
  • a channel G between the base station 1410 and the IRS 1420 may be a channel without position change in a line of sight (LoS) environment. Therefore, the base station 1410 performs transmit beamforming on channel G. can be recognized and managed in advance.
  • the artificial intelligence beam selector 1440 may select an optimized IRS control value ⁇ and transmit the selected information to an IRS controller 1450.
  • the artificial intelligence beam selector 1440 may be a component that selects an optimized beam based on the SRE artificial intelligence system.
  • the artificial intelligence beam selector 1440 may be implemented in the base station 1410. That is, the artificial intelligence beam selector 1440 operating based on the SRE artificial intelligence system may be implemented and operated in the base station 1410. As another example, the artificial intelligence beam selector 1440 may be implemented in association with the IRS controller 1450. As another example, the artificial intelligence beam selector 1440 may be implemented and operated in the terminal 1430. As another example, the artificial intelligence beam selector 1440 may be implemented through a cloud or a separate device separately from the base station 1410, and may not be limited to a specific form.
  • the artificial intelligence beam selector 1440 may be a configuration that performs optimized beam selection based on the SRE artificial intelligence system, and among the base station 1410, the IRS controller 1450, the terminal 1430, and a separate device/cloud It may be implemented based on at least one, and may not be limited to a specific form.
  • the IRS controller 1450 uses the corresponding value ⁇ as the IRS ( 1420) can be controlled.
  • the base station 1410 based on the IRS control value described above in the IRS 1420 can be transmitted.
  • the terminal 1430 may receive the reference signal transmitted by the base station 1410 through the IRS 1420. After that, the terminal 1430 may obtain a reward value (Reward) due to the IRS control value ⁇ based on the reference signal and measure channel information. At this time, the terminal 1430 provides the compensation value and measured channel information ( ) to the base station 1410.
  • the terminal 1430 provides indicator information (eg SNR, ) may be acquired and transmitted to the base station 1410.
  • the base station 1410 may learn the artificial intelligence system based on the feedback information transmitted from the terminal 1430.
  • the base station 1410 may perform learning through the artificial intelligence beam selector 1450.
  • the base station 1410 may transfer feedback information received from the terminal 1430 to perform learning.
  • the terminal 1430 may directly perform learning through a compensation value and channel information obtained through a reference signal.
  • the artificial intelligence beam selector 1450 uses state information as the control value of the current IRS 1420 and channel information. can be used.
  • the base station 1410 transmits beamforming optimized in a new wireless environment through the optimized IRS control value ⁇ .
  • can be computed and applied. may be calculated using artificial intelligence or a beam management method used in 5G NR may be applied, and may not be limited to a specific form.
  • 15 is a diagram illustrating a signal flow between a base station, a terminal, and an IRS to configure an optimized intelligent wireless environment (SRE) according to an embodiment of the present disclosure.
  • SRE optimized intelligent wireless environment
  • the base station 1510 performs transmit beamforming for channel G as described above.
  • the base station 1510 and the IRS 1520 when the base station 1510 and the IRS 1520 perform initial configuration, may be measured through a measuring device or a mobile device, and may be preset as a measurement value.
  • the base station 1510 and the IRS 1520 periodically use a low power/low cost sensor. can be measured, and based on the measured value can be updated periodically. For example, for Maximum Transmit Rate (MRT) Can be derived as shown in Equation 7 below.
  • MRT Maximum Transmit Rate
  • the base station 1510 and the IRS 1520 do not measure channel G, can support For example, may be determined as one of values supported based on beam management or beam sweeping used in a wireless communication system (eg NR).
  • the IRS 1520 may provide channel status information (CSI) feedback to the base station 1510, through which value can be determined. After that, the base station 1510 A reference signal applied with may be transmitted to the IRS 1520.
  • the control value of the IRS may be determined based on an artificial intelligence beam selector.
  • the artificial intelligence beam selector may be associated with the IRS controller, but may not be limited thereto.
  • the IRS 1520 may separately receive information related to the IRS control value, which will be described later.
  • the artificial intelligence beam selector is the phase change value for each element of the IRS.
  • the state information is the control value of the current IRS 1520 and channel information.
  • the IRS 1520 may be set to the phase value predicted by the artificial intelligence beam selector.
  • the base station 1510 is The reference signal to which is applied ( ) to the IRS 1520, and the terminal 1530 transmits a reference signal via the IRS 1520.
  • the terminal 1530 receives a reference signal It is possible to measure a reward value (reward) based on.
  • the terminal 1530 may have an IRS performance measurer, and a compensation value may be measured through the IRS performance measurer.
  • the compensation value is the signal-to-noise ratio of the IRS channel Mean Squared Error
  • the terminal 1530 may acquire channel state information by performing measurement on a reference signal. After that, the terminal 1530 may feed back the compensation value and channel state information.
  • the artificial intelligence beam selector may be learned through an IRS phase value, a reward value, and measured channel information. For example, learning may be repeated depending on whether a reward value or a predicted phase value converges. At this time, the artificial intelligence beam selector can remove repetition by using a model learned through initial transfer learning.
  • the IRS 1520 may transmit an environment change completion signal to the base station 1510 based on learning of the artificial intelligence beam selector. Thereafter, the base station 1510 transmits a reference signal to the terminal 1530 to obtain channel state information, determines and applies transmit beamforming optimized in the newly changed environment, and performs communication with the terminal 1530.
  • 16 is a diagram illustrating a signal flow between a base station, an IRS, and a terminal when an artificial intelligence beam selector is implemented in a terminal according to an embodiment of the present disclosure.
  • an artificial intelligence beam selector may be implemented in a terminal 1630. That is, as described above, the artificial intelligence beam selector may be implemented not only in the base station 1610, the IRS 1620, and a separate cloud/device but also in the terminal 1630.
  • an artificial intelligence beam selector implemented in the terminal 1630 may determine an IRS control value.
  • the terminal 1630 is an index indicating the elevation angle and azimuth angle of the beam direction of the IRS control based on the channel information measured through the artificial intelligence beam selector and the current IRS control value (or initial value). can decide At this time, the UE 1630 may deliver the determined index information to the IRS 1620.
  • the IRS 1620 receives the index information ( ), it is possible to determine the above-described phase value. After that, the base station 1610 The reference signal to which is applied ( ) to the IRS 1620, and the terminal 1630 transmits a reference signal via the IRS 1620. can receive After that, the terminal 1630 is a reference signal It is possible to measure a reward value (reward) based on.
  • the terminal 1630 may have an IRS performance measurer, and a compensation value may be measured through the IRS performance measurer.
  • the compensation value is the signal-to-noise ratio of the IRS channel Mean Squared Error However, other types of channel related information may also be included.
  • the terminal 1630 may acquire channel state information by performing measurement on a reference signal.
  • the artificial intelligence beam selector of the terminal 1630 may perform learning based on the compensation value and the channel state information. Here, learning may be repeated until convergence.
  • the artificial intelligence beam selector can update the IRS control value, and an index indicating the elevation angle and azimuth angle of the beam direction of the IRS control based on the compensation value, channel state information, and the current IRS control value. can be updated.
  • the terminal 1630 may transmit the updated index information to the IRS 1620.
  • the IRS transmits an environment change completion signal to the base station 1610 based on the IRS control value, and the base station 1610 may obtain channel state information by sending a reference signal to the terminal 1630. Through this, the base station 1610 can perform communication with the terminal 1630 by determining and applying transmit beamforming optimized in the newly changed environment.
  • 17 may be a flowchart illustrating a method of setting an optimized intelligent wireless environment according to an embodiment of the present disclosure.
  • the channel estimation step is a beamforming vector transmitted from the base station to the IRS based on the channel information G of the base station-IRS (BS-IRS).
  • BS-IRS base station-IRS
  • the base station and the IRS may perform initial configuration for channel G based on the location of the IRS.
  • the base station and the IRS transmit beamforming through artificial intelligence without channel measurement. can decide Also, as an example, channel G may be periodically checked based on the low power sensor, as described above. That is, the base station transmits beamforming to the IRS (transmit beamforming) can be recognized and managed in advance.
  • the terminal may receive a signal from the base station in order for the terminal to acquire the above-described compensation value and channel estimation information through the IRS performance measurer.
  • the base station may transmit the signal to the terminal by transferring the signal to the IRS, and beamforming may be essential for this.
  • the environment change step may be a step of finding and applying an optimal IRS control value using an artificial intelligence beam selector.
  • the base station transmits the beamforming vector pre-calculated through the above-described channel estimation step.
  • a reference signal applied with may be delivered to the IRS. For example, the signal between the base station and the IRS If it is sufficiently large even without applying , the signal can be transmitted without the above-described process.
  • the artificial intelligence beam selector operating based on the SRE artificial intelligence system controls the control value for each element of the passive intelligent judge board (IRS).
  • IRS passive intelligent judge board
  • the artificial intelligence beam selector is supervised learning, such as AO (alternating optimization) optimization or SDR (software defined radio) optimization Learning can be performed with the control value acquired through the algorithm.
  • the artificial intelligence beam selector may not exceed the performance of the algorithm used.
  • the artificial intelligence beam selector can quickly estimate a predicted value through a pre-learned learning model.
  • the artificial intelligence beam selector may obtain a prediction value through unsupervised reinforcement learning, obtain a corresponding reward value, and continuously update the learning model through learning. For example, the artificial intelligence beam selector may repeat learning until a compensation value or prediction value converges.
  • the artificial intelligence beam selector can reduce the number of iterations through transfer learning or by continuously updating the model.
  • the environment change step may end when the base station receives an environment change completion signal.
  • the base station transmits a reference signal to the terminal to optimize the environment in the new environment.
  • the base station transmit beamforming A reference signal may be generated by applying (S1740) and transmitted to the terminal through the IRS.
  • the terminal is the actual channel state information measured through the reference signal (effective channel state information) ⁇ G+ and the compensation value R may be transmitted to the artificial intelligence beam selector.
  • the artificial intelligence beam selector may be provided in a base station, an IRS, a terminal, or a cloud/device as described above.
  • the terminal may transmit an index value for controlling the IRS to the IRS by applying a compensation value and channel state information, as described above.
  • the terminal may transmit the compensation value and channel state information to the base station.
  • the artificial intelligence beam selector may perform learning based on the compensation value and channel state information received from the terminal (S1750).
  • learning may be continuously performed until convergence (S1756), and the convergence Based on the value, the base station may obtain environment change completion information.
  • the base station optimizes the given environment based on the received environment change completion information. It is possible to determine and perform communication.
  • the artificial intelligence beam selector can express a beam to be set in the form of a codebook and simplify an artificial intelligence model.
  • the direction vector function representing the array response vector in the reception direction can be expressed as in Equation 8 below.
  • N is the size of the array (antenna or IRS element), and w may be a phase difference between the antennas or IRS elements.
  • the reception response vector for the signal received by the IRS from the base station based on beamforming may be expressed as a direction vector function u( ⁇ ,N) as shown in Equation 9 below.
  • Equation 9 the azimuth of the IRS, is the elevation angle, Wow May be the number of horizontal and vertical IRS elements, respectively, may represent the Kronecker product.
  • the transmission response vector of the IRS It can also be expressed as a direction vector function u( ⁇ ,N), and can be expressed as Equation 10 below.
  • a transmission signal to which transmission beamforming is applied for the signal through the IRS can be expressed as in Equation 11 below.
  • Equation 11 is the pass gain of the BS-IRS channel, May be the pass gain of the IRS-UE channel.
  • Equation 12 since u( ⁇ ,N) is a function with a period of 2, It can be expressed as can be expressed as
  • the optimal beamforming vector v of the IRS that maximizes the received signal SNR is , and can be expressed as the Kronecker product of the direction vector function u( ⁇ ,N) of the azimuth and elevation angles.
  • the IRS control value may be managed in the form of an azimuth angle and an elevation angle, and control values according to each direction may be managed in a codebook.
  • codebook is a set of IRS direction vectors, respectively in the horizontal and vertical directions can have the size of
  • j ⁇ J is an index of a direction vector
  • J may be a total representable direction vector.
  • J is a value representing the number of beams used in the horizontal and vertical directions, respectively in the horizontal and vertical directions. may be expressed differently.
  • the beam set in the final artificial intelligence beam selector may be equal to Equation 14 based on Equation 13 below.
  • the artificial intelligence beam selector can express the beam as described above through the codebook, and through this, the artificial intelligence model can be simplified.
  • FIG. 18 is a diagram showing the structure of an SRE artificial intelligence system using reinforcement learning according to an embodiment of the present disclosure
  • FIG. 19 is a more subdivided environment component based on FIG. 18 It may be a single drawing.
  • the artificial intelligence beam selector based on the SRE artificial intelligence system may perform learning based on reinforcement learning.
  • reinforcement learning may consist of two inputs and one output. State information (state) and reward value (reward) can be used as inputs, and agent actions can be selected as outputs.
  • reinforcement learning may be multi-armed bandit (MAB), and in the case of MAB, state information may not be used, but is not limited thereto.
  • an action as an output may be an operation in which an IRS controller operates to select a beam providing an optimal communication environment to a terminal.
  • the artificial intelligence beam selector may obtain a reward value for an action and changed state information from the environment and use them for learning.
  • the artificial intelligence beam selector may repeat an operation of selecting an action again based on an input after learning.
  • the artificial intelligence beam selector may be a method of determining each state, reward, and action in consideration of environmental components.
  • the environment may include not only a channel environment through which signals are transmitted, but also an environment between a base station, a terminal, and an IRS for measuring compensation and channel conditions.
  • State is a factor received from the environment, and the horizontal and vertical indexes of the previously selected beam selector ( ) can be used.
  • the terminal measures channel information ( ) can be added to the State.
  • the channel information is a measured value or an indirect indicator (CQI, SNR, ), and the state may be as shown in Equation 15 below.
  • an action is to select an index of a codebook representing a beam direction vector of the IRS, and finally, phase shift values of each IRS element may be applied.
  • the action of the artificial intelligence beam selector may be as shown in Equation 16 below.
  • the action value is a value configured based on the codebook through the IRS direction vector generator, and what is actually selected in the selection artificial intelligence may be the index of the azimuth angle and elevation angle of the direction vector.
  • the reward value (Reward) may be a value measured by the terminal and a resultant value of a control value selected by the IRS. For example, if a block received from the terminal is added to the IRS, it can be directly received, but the cost of the IRS may increase. Accordingly, the IRS may obtain a compensation value from the base station based on information transmitted from the terminal to the base station, but may not be limited thereto. Also, as an example, the reward value (Reward) may be determined in consideration of other purposes of use.
  • the terminal may include an IRS performance measurer as described above. However, as an example, when the IRS performance measurer is not passed, the compensation value may be channel-related information measured by the UE based on the reference signal as shown in Equation 17.
  • FIG. 20 is a diagram illustrating use blocks of a terminal and a base station among environmental components in an artificial intelligence beam selector according to an embodiment of the present disclosure.
  • a base station may generate and transmit a reference signal through a radio channel.
  • the terminal may obtain a reference signal generated from the base station through the IRS and measure the reference signal to determine channel state information and compensation value information.
  • the channel state information may include information about a result of directly measuring a channel and indirect information related to a channel, and may not be limited to a specific form.
  • the environmental component may be considered not only the IRS but also the operation of the base station and the terminal, and is not limited to the above-described embodiment.
  • FIG. 21 is a diagram showing the structure of an artificial intelligence beam selector according to an embodiment of the present disclosure.
  • an artificial intelligence beam selector may be divided into a selection artificial intelligence and an IRS generator.
  • the artificial intelligence beam selector may derive a value such as Equation 18 below through state information and a reward value based on reinforcement learning, and derive the above-described action value based on this.
  • selection artificial intelligence can play a role of learning and predicting IRS control values to configure an optimal wireless environment.
  • selection artificial intelligence may be implemented through reinforcement learning or MAB as described above, but may not be limited thereto.
  • the IRS direction vector generator may generate a control value for each element based on the codebook.
  • selection artificial intelligence since selection artificial intelligence is learned in the form of an index of a codebook, the dimension of state information (state) and action (action) can be reduced. Therefore, it may be advantageous in terms of model size, complexity, and convergence speed compared to the learning method for each device. Based on the foregoing, it is possible to generate an optimized IRS control value without being greatly affected by the number M of base station antennas and the number N of IRS elements.
  • FIG. 22 is a diagram illustrating a method of implementing an artificial intelligence beam selector through MAB AI according to an embodiment of the present disclosure.
  • selection artificial intelligence (AI beam selector) using MAB AI using Thompson sampling can be largely divided into a learning unit and a selection unit.
  • the learning unit may update the Thomson sampling parameters ( ⁇ , ⁇ ) for the previous action i based on the reward value.
  • learning of ⁇ and ⁇ according to the compensation value may be as shown in Equation 19 below.
  • the selector may select an index of a beam direction having the largest value among sampled values by applying the accumulated Thomson sampling parameters ⁇ and ⁇ to a beta distribution.
  • ⁇ selected in FIG. 22 may be passed to the IRS direction vector generator.
  • the IRS direction vector generator is an index indicating the direction for the azimuth and elevation angles based on the above-described values ( ) can be used individually.
  • the equation for the beta distribution may be as shown in Equation 20 below.
  • FIG. 23 is a diagram illustrating a method of implementing an artificial intelligence beam selector through reinforcement learning according to an embodiment of the present disclosure.
  • selection artificial intelligence artificial intelligence beam selector
  • reinforcement learning may be composed of a reinforcement learning model and a beam direction adjuster that adjusts a beam direction with a selected action.
  • the selection artificial intelligence based on the above-described MAB AI
  • learning is performed based on reward values without state information, but the reinforcement learning model can perform learning through reward values and state information.
  • the MAB AI contrast state information is further used, the computation of the learning model may increase.
  • selection artificial intelligence directly directs the action to the beam direction ⁇ ) ⁇ for each azimuth and elevation direction ⁇ , ⁇ ⁇ .
  • selection artificial intelligence AI beam selector
  • reinforcement learning takes into account the case of having sufficient hardware specifications and direct beam direction ⁇ ⁇ may also be selected, and is not limited to the above-described embodiment.
  • the previous direction value index in the beam rudder The reinforcement learning model selected ⁇ , ⁇ ⁇ by applying , and finally It may be possible to generate and pass it to the intelligent reflective surface generator, and may not be limited to a specific form.
  • the reinforcement learning model has a learning part and a selection part, just like selection artificial intelligence (or artificial intelligence beam selector) using MAB AI, and can predict the next action at the same time as learning.
  • learning may be performed through Q-Learning based on Equation 21 below during reinforcement learning, and a specific action-value may be as shown in Equation 21 below.
  • the policy selected in Q-Learning is the current state. An action having the largest action-value in may be as shown in Equation 22 below.
  • an issue regarding Exploration and Exploitation control may occur in reinforcement learning.
  • Exploration may utilize a behavior sampled from multiple behaviors to obtain a better reward value.
  • Exploitation can utilize already recognized information based on repetitive actions.
  • proper control of Exploration and Exploitation may be required to achieve optimal performance, and an e-greedy method may be performed, which may be as shown in FIG. 24 .
  • e-greedy may be a method of executing Exploration with a predetermined probability. For example, referring to FIG. 24 , total registry can be improved by e-greedy compared to the greedy method that only exploits.
  • decaying e-greedy may be used as a method of approaching Total Regret logarithmically over time, but may not be limited to a specific form.
  • Equation 23 is an equation representing decaying e-greedy, where c is a constant, and
  • Exploration and Exploitation control can be further optimized using MAB (Multi Arm Bandit).
  • MAB Multi Arm Bandit
  • UMB Upper Confidence Bound
  • TS Thompson Sampling
  • Equation 24 below may be an expression for an action based on UCB, and Equation 15 may be Upper Confidence.
  • Upper Confidence is the number of actions It is set to be inversely proportional to , so that more opportunities are given to actions that are not selected. Based on the foregoing, the opportunity may be halved over time.
  • Thompson sampling is implemented through a beta distribution based on the above, and is simpler than UCB and can easily control Exploration and Exploitation.
  • FIG. 25 illustrates the structure of an IRS performance measurer according to an embodiment of the present disclosure.
  • the IRS performance measurer may perform at least one of standardization/normalization, batching, and weight applicator functions.
  • the IRS performance measurer can calculate SNR, channel gain, MSE, and spectral efficiency based on the reference signal from the base station, as well as measure energy charging using other monitoring systems.
  • each piece of measurement information may be an area of various values.
  • the standardization/normalization block may standardize or normalize values of various areas of measurement information in consideration of respective weights.
  • the batching block plays a role of accumulating such measurement information at regular intervals, and can also perform normalization for each accumulation.
  • the weight application block may express the final output value by applying a weight to each metric. For example, in a receiver in which spectral efficiency is important, a weight of spectral efficiency measurement may be set high.
  • the IRS performance measurer may generate a compensation value in the form of integrating measurement information after processing.
  • the IRS performance measurer may generate a compensation value by individually separating measurement information, and may not be limited to a specific embodiment.
  • 26 is a diagram illustrating a method of setting an artificial intelligence initial value according to an embodiment of the present disclosure.
  • an artificial intelligence initial value may be set based on an artificial intelligence beam selector operation.
  • the base station 2610 may transmit a reference signal to the terminal 2630.
  • the terminal 2630 may perform measurement using the reference signal and obtain channel state information between the base station 2610 and the terminal 2630 based on the measurement.
  • the terminal 2630 may transmit channel state information and location information of the terminal to the base station 2610.
  • the base station 2610 may set an AI initial value through an artificial intelligence initial value setter based on the received channel state information and location information of the terminal.
  • the artificial intelligence initial value setter may be implemented in a device other than the base station 2610 or a cloud.
  • the base station 2610 may transmit channel state information and location information received from the terminal 2630 to the artificial intelligence initial value setter, and obtain artificial intelligence initial value information from the artificial intelligence initial value setter.
  • the artificial intelligence initial value information is the initial value of the selection artificial intelligence used in the artificial intelligence beam selector (Thompson Sampling: , ) may be included.
  • the artificial intelligence initial value information is the above-described Thomson sampling parameter and state information ( ), and is not limited to the above-described embodiment.
  • the base station 2610 may transmit artificial intelligence initial value information and candidate set information to the IRS 2620.
  • candidate set information may be index set information of a defined beam direction codebook.
  • the candidate set information may be information about the codebook itself.
  • the codebook may be a set of beam direction vectors used differently from a precoding matrix codebook used in multi input multi output (MIMO). That is, a beam direction vector used for candidate sets may be a space of an action selected in an artificial intelligence model.
  • reinforcement learning or MAB AI can quickly perform convergence for learning and reduce the amount of computation.
  • the artificial intelligence beam selector may be connected to the IRS controller.
  • the IRS controller or AI server may initialize the artificial intelligence beam selector that operates by MAB AI (Thompson Sampling) or reinforcement learning through the above-mentioned information received. Subsequent operations may be the same as those of FIG. 15 described above.
  • the base station 2610 may transmit a reference signal to the terminal 2630 as described above, and acquire artificial intelligence initial value information through an artificial intelligence initial value setter through channel state information and location information of the terminal. Thereafter, the base station 2610 may operate as described above after reflecting the above-described artificial intelligence initial value information to the artificial intelligence beam selector.
  • FIG. 27 may be a case where an artificial intelligence beam selector is implemented in a terminal according to an embodiment of the present disclosure.
  • the base station 2710 may obtain channel state information and location information from the terminal 2730 through a reference signal as in FIG. can be the same
  • the base station may transmit the artificial intelligence initial value information and candidate set information described above in FIG. 26 to the terminal 2730 in which the artificial intelligence beam selector is implemented.
  • the terminal 2730 may apply the information received from the base station 2710 to the artificial intelligence beam selector, and subsequent operations may be the same as those of FIG. 16 described above.
  • FIG. 28 is a diagram showing the structure of an SRE artificial intelligence system including an artificial intelligence initial value setter according to an embodiment of the present disclosure.
  • the artificial intelligence initial value setter may set the initial weight value of the selected artificial intelligence in the artificial intelligence beam selector as in the above-described FIGS. 26 and 27.
  • the artificial intelligence initial value setter may update weights of models that have completed operations.
  • the updated weight value may be used as an initial value of MAB AI or an initial state of reinforcement learning, and may not be limited to a specific form.
  • the artificial intelligence initial value setter may be designed in the form of a predictor in the form of supervised learning.
  • the initial value can be set and transmitted to the artificial intelligence beam selector.
  • prediction may be performed in consideration of the channel environment (eg SNR) and location information of the terminal.
  • , BLER, , , location information and channel information are stored, and can be used as learning data during transfer learning of the AI initial value setter.
  • the artificial intelligence coding initial value setter may set an initial value in consideration of location information and a channel environment (signal-to-noise ratio, SNR) of a transmitter.
  • SNR signal-to-noise ratio
  • the artificial intelligence coding initial value setter may create candidate sets for the generation matrix in order of performance.
  • Candidate sets may be generated differently depending on the channel environment. These candidate sets can be defined in the form of codebooks.
  • the receiver may transmit a codebook set to be used by the transmitter in the form of an index. In addition, the receiver may directly transmit the codebook to be used by the transmitter.
  • the artificial intelligence coding initial value setter is an initial value as well as a candidate set. , can be set and transmitted to the receiver. At this time, it can be predicted in consideration of the channel environment and location information of the terminal. After communication ends, , , BLER, , , location information, and channel information can be stored and used as learning data when re-training the artificial intelligence coding initial value setter.
  • FIG. 30 is a diagram illustrating a method of performing wireless environment optimization based on an artificial intelligence system according to an embodiment of the present disclosure.
  • the terminal may transmit first index information related to the IRS control value to the IRS (S3010).
  • the IRS control value may be determined based on the first index information.
  • each phase value in the IRS element may be determined based on the IRS control value determined as described above, as described above.
  • the aforementioned first index information may be information generated through a codebook based on an IRS direction vector set.
  • the first index information may include a first factor based on the IRS azimuth and a second factor based on the IRS elevation, as described above.
  • the artificial intelligence beam selector may be included in the terminal.
  • the above-described first index information may be determined based on the artificial intelligence beam selector of the terminal, and the determined first index information may be transmitted to the IRS.
  • the terminal and the base station may set an initial value applied to the artificial intelligence beam selector. More specifically, the terminal may receive a reference signal for initial value setting from the base station. After that, the terminal can obtain channel state information through a reference signal for setting an initial value. The terminal may feed back the acquired channel state information and location information of the terminal to the base station.
  • the base station may include an artificial intelligence initial value setter. The base station may derive an artificial intelligence initial value based on the artificial intelligence initial value setter and transmit information about it to the terminal. Also, as an example, the terminal may further receive candidate set information related to the index information transmitted to the IRS, as described above. The terminal may derive the above-described first index information through learning based on the received artificial intelligence initial value information and candidate set information.
  • the terminal may receive the reference signal to which the first beamforming is applied from the base station through the IRS.
  • the base station Beamforming can be determined. That is, the information on the first beamforming may be information recognized and managed by the base station in advance.
  • the terminal may acquire at least one of compensation value information and channel state information through a reference signal transmitted by applying the first beamforming from the base station.
  • the terminal may update the first index information to the second index information through at least one of compensation value information and channel state information (S3040).
  • the terminal transmits the updated second index information to the IRS.
  • the terminal may include an artificial intelligence beam selector, as described above.
  • the artificial intelligence beam selector may update the above-described first index information to second index information through at least one of compensation value information and channel state information.
  • the artificial intelligence beam selector may update the first index information to the second index information based on any one of the above-described MAB AI learning model and the reinforcement learning model.
  • the artificial intelligence beam selector updates the first index information to the second index information based on the MAB AI learning model
  • the second index information may be obtained through learning based on compensation value information.
  • the artificial intelligence beam selector updates the first index information to the second index information based on the reinforcement learning learning model
  • state information is set based on the first index information
  • state information and compensation value information The second index information may be obtained through learning based on .
  • the above-described compensation value may be obtained through an IRS performance measurer.
  • the terminal includes an IRS performance measurer, and the IRS performance measurer may generate compensation value information using channel related information obtained through a reference signal, as described above.
  • the terminal may receive a reference signal to which the second beamforming is applied from the base station based on the updated second index information through the IRS (S3060).
  • the IRS converts the first index information into second index information.
  • the IRS may transmit a radio environment change completion signal including the second index information to the base station.
  • the base station may determine the above-described second beamforming based on the second index information and apply it to reference signal transmission.
  • 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

La présente invention peut fournir un procédé de fonctionnement de terminal dans un système de communication sans fil. Le procédé de fonctionnement de terminal peut comprendre les étapes consistant à : transmettre des premières informations d'indice associées à une valeur de commande d'IRS à un IRS ; recevoir, d'une station de base, un premier signal de référence en forme de faisceau par le biais de l'IRS ; acquérir des informations de valeur de compensation et/ou des informations d'état de canal d'après le signal de référence reçu ; mettre à jour les premières informations d'index en secondes informations d'index au moyen des informations de valeur de compensation et/ou des informations d'état de canal ; transmettre les secondes informations d'index mises à jour à l'IRS ; et recevoir, de la station de base, un second signal de référence en forme de faisceau par le biais de l'IRS d'après les secondes informations d'index mises à jour.
PCT/KR2021/008944 2021-07-13 2021-07-13 Procédé et dispositif d'émission et de réception de signaux dans un système de communication sans fil WO2023286884A1 (fr)

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