WO2022260189A1

WO2022260189A1 - Method and device for transmitting and receiving signal in wireless communication system

Info

Publication number: WO2022260189A1
Application number: PCT/KR2021/007065
Authority: WO
Inventors: 오재기; 김성진; 박재용; 하업성
Original assignee: 엘지전자 주식회사
Priority date: 2021-06-07
Filing date: 2021-06-07
Publication date: 2022-12-15
Also published as: KR20240017776A

Abstract

The present disclosure may provide an operation method of a base station in a wireless communication system. An example of the present disclosure may comprise the steps of: transmitting a first reference signal to an intelligent reflecting surface; receiving, from the IRS, channel information between a base station and the IRS and channel information between the IRS and a terminal, the channel information being measured on the basis of the first reference signal; determining beamforming on the basis of the channel information between the base station and the IRS; deriving an IRS control value on the basis of the channel information between the base station and the IRS and the channel information between the IRS and the terminal; and applying the determined beamforming to transmit a second reference signal to the terminal through the IRS.

Description

Method and apparatus for transmitting and receiving signals in a wireless communication system

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.

In particular, 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).

A wireless access system is widely deployed to provide various types of communication services such as voice and data. In general, 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.

In particular, as many communication devices require large communication capacity, an enhanced mobile broadband (eMBB) communication technology compared to existing radio access technology (RAT) has been proposed. In addition, communication systems considering reliability and latency sensitive services/UEs as well as massive Machine Type Communications (MTC) providing various services anytime and anywhere by connecting multiple devices and objects have been proposed. Various technical configurations for this have been proposed.

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 an intelligent radio channel environment in a wireless communication system.

The present disclosure may provide a method for controlling an 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 an intelligent reflector in a wireless communication system.

The technical objects to be achieved in the present disclosure are not limited to the above-mentioned matters, and other technical problems not mentioned above are common knowledge in the art to which the technical configuration of the present disclosure is applied from the embodiments of the present disclosure to be described below. can be considered by those who have

As an example of the present disclosure, a method for operating a base station in a wireless communication system may be provided. As an example of the present disclosure, transmitting a first reference signal to an intelligent reflector, receiving channel information between the base station and the IRS and channel information between the IRS and the terminal measured based on the first reference signal from the IRS, and Determining beamforming based on inter-IRS channel information, deriving an IRS control value based on channel information between the base station and the IRS and channel information between the IRS and the terminal, and applying the determined beamforming to convert the second reference signal to the IRS It may include transmitting to the terminal through.

In addition, as an example of the present disclosure, a method of operating a terminal in a wireless communication system includes transmitting a first reference signal to an IRS and receiving a second reference signal to which beamforming is applied from a base station through the IRS, , IRS is adjusted based on the IRS control value, channel information between the IRS and the terminal is measured based on the first reference signal, and the base station receives channel information between the base station and the IRS and channel information between the IRS and the terminal from the IRS, Beamforming may be determined based on channel information between the base station and the IRS, and an IRS control value may be derived based on channel information between the base station and the IRS and channel information between the IRS and the terminal.

In addition, as an example of the present disclosure, a base station of a wireless communication system includes a transceiver and a processor connected to the transceiver, the processor transmits a first reference signal to an IRS through the transceiver, and transmits a first reference signal from the IRS through the transceiver. Channel information between the base station and the IRS and channel information between the IRS and the terminal measured based on the reference signal are received, beamforming is determined based on the channel information between the base station and the IRS, and channel information between the base station and the IRS and between the IRS and the terminal are determined. An IRS control value may be derived based on the channel information, and the second reference signal may be transmitted to the terminal through the IRS by applying beamforming determined through the transceiver.

In addition, as an example of the present disclosure, in a terminal of a wireless communication system, it may include a transceiver and a processor connected to the transceiver. At this time, the processor transmits the first reference signal to the IRS through the transceiver and receives the second reference signal to which beamforming is applied through the transceiver through the IRS, the IRS is adjusted based on the IRS control value, and the first reference signal Channel information between the IRS and the terminal is measured based on the signal, the base station receives channel information between the base station and the IRS and channel information between the IRS and the terminal from the IRS, and determines beamforming based on the channel information between the base station and the IRS, An IRS control value may be derived based on channel information between the base station and the IRS and channel information between the IRS and the terminal.

In addition, as an example of the present disclosure, in an apparatus including at least one memory and at least one processor functionally connected to the at least one memory, the at least one processor is controlled by the IRS through a transceiver. 1 Transmits a reference signal, receives channel information between the base station and the IRS and channel information between the IRS and the terminal measured based on the first reference signal from the IRS through the transceiver, and performs beamforming based on the channel information between the base station and the IRS. determine, derive an IRS control value based on the channel information between the base station and the IRS and the channel information between the IRS and the terminal, and apply the beamforming determined through the transceiver to transmit the second reference signal to the terminal through the IRS.

In addition, as an example of the present disclosure, in a non-transitory computer-readable medium storing at least one instruction, at least one executable by a processor Includes a command of, at least one command, the device transmits the first reference signal to the IRS through the transceiver, channel information between the base station and the IRS measured based on the first reference signal from the IRS through the transceiver and the IRS and Receiving channel information between terminals, determining beamforming based on channel information between the base station and the IRS, deriving an IRS control value based on channel information between the base station and the IRS and channel information between the IRS and the terminal, and through the transceiver The second reference signal may be transmitted to the terminal through the IRS by applying the determined beamforming.

The following items may be commonly applied to the above-described base station, terminal, device, and computer recording medium.

As an example of the present disclosure, the base station may derive an IRS control value through an artificial intelligence system and transmit the derived IRS control value to the IRS to control the phase value of each element in the IRS.

In addition, as an example of the present disclosure, the artificial intelligence system may derive an IRS control value by further considering channel information formed by the base station, the IRS, and the terminal.

In addition, as an example of the present disclosure, the IRS may include an active sensor, and based on the active sensor, measurement of the first reference signal may be performed to estimate channel information between the base station and the IRS.

In addition, as an example of the present disclosure, the terminal may transmit a third reference signal to the IRS, and channel information between the IRS and the terminal may be measured based on the third reference signal.

Also, as an example of the present disclosure, each element in the IRS may be used as either an active sensor or a reflective element.

In addition, as an example of the present disclosure, the active sensor may be provided in the IRS independently of each element in the IRS.

In addition, as an example of the present disclosure, when the terminal receives the second reference signal from the base station through the IRS, compensation value information for the IRS control value is generated based on the second reference signal, and the generated compensation value information can be transmitted to the base station.

In addition, as an example of the present disclosure, the base station updates the IRS control value through the artificial intelligence system based on the compensation value received from the terminal, updates the beamforming based on the updated IRS control value, and updates the updated beamforming Based on, the third reference signal may be transmitted to the terminal through the IRS.

Also, as an example of the present disclosure, compensation value information may be configured in the form of channel related information.

The above-described aspects of the present disclosure are only some of the preferred embodiments of the present disclosure, and various embodiments in which the technical features of the present disclosure are reflected are the detailed descriptions of the present disclosure to be detailed below by those of ordinary skill in the art. It can be derived and understood based on the description.

The following effects may be obtained by embodiments based on the present disclosure.

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.

In embodiments based on the present disclosure, a method for controlling an intelligent reflector based on an artificial intelligence system may be provided.

Embodiments based on the present disclosure may provide a method for transmitting and receiving signals between a terminal and a base station by controlling an 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.

The accompanying drawings are provided to aid understanding of the present disclosure, and may provide embodiments of the present disclosure together with a detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and features disclosed in each drawing may be combined with each other to form a new embodiment. Reference numerals in each drawing may mean structural elements.

1 is a diagram illustrating an example of a communication system applicable to the present disclosure.

2 is a diagram illustrating an example of a wireless device applicable to the present disclosure.

3 is a diagram illustrating another example of a wireless device applicable to the present disclosure.

4 is a diagram illustrating an example of a portable device applicable to the present disclosure.

5 is a diagram illustrating an example of a vehicle or autonomous vehicle applicable to the present disclosure.

6 is a diagram showing an example of AI (Artificial Intelligence) applicable to the present disclosure.

7 is a diagram showing an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.

8 is a diagram showing an electromagnetic spectrum applicable to the present disclosure.

9 is a diagram illustrating a radio channel environment according to an embodiment of the present disclosure.

10 is a diagram illustrating an intelligent wireless environment according to an embodiment of the present disclosure.

11 is a diagram illustrating an existing radio channel environment and an intelligent radio channel environment according to an embodiment of the present disclosure.

12 is a diagram illustrating a method of performing optimization in an intelligent radio channel environment according to an embodiment of the present disclosure.

13 is a diagram illustrating a confidence interval according to an embodiment of the present disclosure.

14 is a diagram illustrating a method of performing optimization in an intelligent radio channel environment according to an embodiment of the present disclosure.

15 is a diagram illustrating signal flows of a base station, an IRS, and a terminal to configure an intelligent wireless environment according to an embodiment of the present disclosure.

16 is a flowchart illustrating a method of performing optimization in an intelligent radio channel environment according to an embodiment of the present disclosure.

17 is a diagram illustrating a method of performing reinforcement learning based on an artificial intelligence system in an intelligent wireless channel environment according to an embodiment of the present disclosure.

18 is a diagram showing total regret based on reinforcement learning according to an embodiment of the present disclosure.

19 is a diagram showing the structure of an artificial intelligence system operating by selecting a model based on channel information obtained through an active sensor according to an embodiment of the present disclosure.

20 illustrates reinforcement learning using DDPG according to an embodiment of the present disclosure.

21 is a diagram illustrating an IRS according to an embodiment of the present disclosure.

22 shows the structure of an IRS performance measurer according to an embodiment of the present invention.

23 is a diagram illustrating a method of performing optimization based on an artificial intelligence system according to an embodiment of the present disclosure.

The following embodiments are those that combine elements and features of the present disclosure in a predetermined form. 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. In addition, 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.

In the description of the drawings, procedures or steps that may obscure the gist of the present disclosure have not been described, and procedures or steps that can be understood by those skilled in the art have not been described.

Throughout the specification, when a part is said to "comprising" or "including" a certain element, it means that it may further include other elements, not excluding other elements, unless otherwise stated. do. In addition, terms such as “… unit”, “… unit”, and “module” described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, "a or an", "one", "the" and similar related words in the context of describing the present disclosure (particularly in the context of the claims below) Unless indicated or otherwise clearly contradicted by context, both the singular and the plural can be used.

Embodiments of the present disclosure in this specification have been described with a focus on a data transmission/reception relationship between a base station and a mobile station. Here, 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.

That is, in a network composed of a plurality of network nodes including a base station, various operations performed for communication with a mobile station may be performed by the base station or network nodes other than the base station. At this time, 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

In addition, in the embodiments of the present disclosure, 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).

In addition, the transmitting end refers to a fixed and/or mobile node providing data service or voice service, and 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.

In addition, embodiments of the present disclosure may be applied to other wireless access systems, and are not limited to the above-described systems. For example, it may also be applicable to a system applied after the 3GPP 5G NR system, and is not limited to a specific system.

That is, obvious steps or parts not described in the embodiments of the present disclosure may be described with reference to the above documents. In addition, all terms disclosed in this document can be explained by the standard document.

Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description set forth below in conjunction with the accompanying drawings is intended to describe exemplary embodiments of the present disclosure, and is not intended to represent the only embodiments in which the technical configurations of the present disclosure may be practiced.

In addition, specific terms used in the embodiments of the present disclosure are provided to aid understanding of the present disclosure, and the use of these specific terms may be changed in other forms without departing from the technical spirit of the present disclosure.

The following technologies include code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), orthogonal frequency division multiple access (OFDMA), single carrier frequency division multiple access (SC-FDMA), and the like. It can be applied to various wireless access systems.

In the following, in order to clarify the following description, the description is based on the 3GPP communication system (e.g. (eg, LTE, NR, etc.), but the technical spirit of the present invention is not limited thereto. LTE is 3GPP TS 36.xxx Release 8 or later In detail, LTE technology after 3GPP TS 36.xxx Release 10 is referred to as LTE-A, and LTE technology after 3GPP TS 36.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.

For background art, terms, abbreviations, etc. used in the present disclosure, reference may be made to matters described in standard documents published prior to the present invention. As an example, 36.xxx and 38.xxx standard documents may be referred to.

본 개시에 적용 가능한 통신 시스템Communication systems applicable to the present disclosure

Although not limited thereto, various descriptions, functions, procedures, proposals, methods and / or operational flowcharts of the present disclosure disclosed in this document may be applied to various fields requiring wireless communication / connection (eg, 5G) between devices. have.

Hereinafter, it will be exemplified in more detail with reference to the drawings. In the following drawings/description, the same reference numerals may represent the same or corresponding hardware blocks, software blocks or functional blocks unless otherwise specified.

1 is a diagram illustrating an example of a communication system applied to the present disclosure.

Referring to FIG. 1 , a communication system 100 applied to the present disclosure includes a wireless device, a base station, and a network. Here, 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. Although not limited thereto, 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. For example, 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. Here, the vehicles 100b-1 and 100b-2 may include an unmanned aerial vehicle (UAV) (eg, a drone). 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. For example, 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. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication). In addition, the IoT device 100f (eg, sensor) may directly communicate with other IoT devices (eg, sensor) or other wireless devices 100a to 100f.

Referring to FIG. 2 , 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). Here, {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. For example, 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. In addition, 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. For example, 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. Here, 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. In the present disclosure, 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. For example, 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. In addition, 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. For example, 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. Here, 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. In the present disclosure, a wireless device may mean a communication modem/circuit/chip.

Hereinafter, hardware elements of the

wireless devices

200a and 200b will be described in more detail. Although not limited to this, one or more protocol layers may be implemented by one or

more processors

202a, 202b. For example, 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. can create One or

more 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.

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. For example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), or one or more field programmable gate arrays (FPGAs). may be included in one or

more processors

202a and 202b. The descriptions, functions, procedures, proposals, methods and/or operational flowcharts disclosed in this document 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. In addition, 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. have. For example, one or

more transceivers

206a and 206b may be connected to one or

more processors

202a and 202b and transmit and receive radio signals. For example, 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. In addition, 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. In addition, 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. , procedures, proposals, methods and / or operation flowcharts, etc. can be set to transmit and receive user data, control information, radio signals / channels, etc. 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. To this end, one or

more transceivers

206a, 206b may include (analog) oscillators and/or filters.

본 개시에 적용 가능한 무선 기기 구조Wireless device structure applicable to the present disclosure

3 is a diagram illustrating another example of a wireless device applied to the present disclosure.

Referring to FIG. 3, 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. For example, 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 . For example, communication circuitry 312 may include one or

more processors

202a, 202b of FIG. 2 and/or one or

more memories

204a, 204b. For example, transceiver(s) 314 may include one or

more transceivers

206a, 206b of FIG. 2 and/or one or

more antennas

208a, 208b. The control unit 320 is electrically connected to the communication unit 310, the memory unit 330, and the additional element 340 and controls overall operations of the wireless device. For example, the control unit 320 may control electrical/mechanical operations of the wireless device based on programs/codes/commands/information stored in the memory unit 330. In addition, the control unit 320 transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310 through a wireless/wired interface, or transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310. Information received through a wireless/wired interface from other communication devices) may be stored in the memory unit 330 .

The additional element 340 may be configured in various ways according to the type of wireless device. For example, 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. Although not limited thereto, 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. 1, 100f), digital broadcasting terminals, hologram devices, public safety devices, MTC devices, medical devices, fintech devices (or financial devices), security devices, climate/ It may be implemented in the form of an environment device, an AI server/device (FIG. 1, 140), a base station (FIG. 1, 120), a network node, and the like. Wireless devices can be mobile or used in a fixed location depending on the use-case/service.

In FIG. 3 , 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 . For example, in the wireless device 300, 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. can be connected Additionally, each element, component, unit/unit, and/or module within wireless device 300 may further include one or more elements. For example, the control unit 320 may be composed of one or more processor sets. For example, the 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. As another example, the memory unit 330 may include RAM, dynamic RAM (DRAM), ROM, flash memory, volatile memory, non-volatile memory, and/or combinations thereof. can be configured.

본 개시가 적용 가능한 휴대 기기Mobile device to which the present disclosure is applicable

4 is a diagram illustrating an example of a portable device applied to the present disclosure.

4 illustrates 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).

Referring to FIG. 4 , a portable device 400 includes an antenna unit 408, a communication unit 410, a control unit 420, a memory unit 430, a power supply unit 440a, an interface unit 440b, and an input/output unit 440c. ) may be included. The antenna unit 408 may be configured as part of the communication unit 410 . Blocks 410 to 430/440a to 440c respectively correspond to blocks 310 to 330/340 of FIG. 3 .

The communication unit 410 may transmit/receive signals (eg, data, control signals, etc.) with other wireless devices and base stations. The controller 420 may perform various operations by controlling components of the portable device 400 . The controller 420 may include an application processor (AP). The memory unit 430 may store data/parameters/programs/codes/commands necessary for driving the portable device 400 . Also, the memory unit 430 may store input/output data/information. The power supply unit 440a supplies power to the portable device 400 and may include a wired/wireless charging circuit, a battery, and the like. The interface unit 440b may support connection between the mobile device 400 and other external devices. The interface unit 440b may include various ports (eg, audio input/output ports and video input/output ports) for connection with external devices. The input/output unit 440c may receive or output image information/signal, audio information/signal, data, and/or information input from a user. The input/output unit 440c may include a camera, a microphone, a user input unit, a display unit 440d, a speaker, and/or a haptic module.

For example, in the case of data communication, 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. can be stored 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. In addition, 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.

본 개시가 적용 가능한 무선 기기 종류Types of wireless devices to which this disclosure is applicable

5 is a diagram illustrating an example of a vehicle or autonomous vehicle to which the present disclosure applies.

5 illustrates a vehicle or autonomous vehicle to which the present disclosure is applied. 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.

Referring to FIG. 5 , 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).

6 is a diagram illustrating an example of an AI device applied to the present disclosure. As an example, 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.

Referring to FIG. 6, 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. can include 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 .

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 . For example, 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. Also, 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. For example, 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. have.

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 . In addition, 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 통신 시스템 6G communication system

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.

At this time, 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.

7 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.

Referring to FIG. 7 , 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. At this time, 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.

6G 시스템의 핵심 구현 기술Core implementation technology of 6G system

- 인공 지능(artificial Intelligence, AI)- Artificial Intelligence (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. However, 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.

Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in machine-to-machine, machine-to-human and human-to-machine communications. In addition, AI can be a rapid communication in the brain computer interface (BCI). 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.

Recently, there have been attempts to integrate AI with wireless communication systems, but these are focused on the application layer, network layer, and especially deep learning, wireless resource management and allocation. come. However, such research is gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission are appearing in the physical layer. 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.

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.

However, the application of DNN for transmission in the physical layer may have the following problems.

AI algorithms based on deep learning require a lot of training data to optimize training parameters. However, due to limitations in acquiring data in a specific channel environment as training data, 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.

In addition, current deep learning mainly targets real signals. However, the signals of the physical layer of wireless communication are complex signals. In order to match the characteristics of a wireless communication signal, further research is needed on a neural network that detects a complex domain signal.

Hereinafter, machine learning will be described in more detail.

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. In machine learning, 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. 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.

THz(Terahertz) 통신Terahertz (THz) communication

THz communication can be applied in 6G systems. For example, 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.

8 is a diagram showing an electromagnetic spectrum applicable to the present disclosure. As an example, referring to FIG. 8 , THz waves, also known as sub-millimeter radiation, 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. Among the defined THz bands, 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-3 THz band exhibits similarities to RF.

The main characteristics of 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.

In the following, a method of controlling a wireless channel environment using an intelligent reflect surface (IRS) having an active sensor will be described. Here, the artificial intelligence system can be used to adjust the radio channel environment using the IRS, which will also be described later.

For example, 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. can

At this time, 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. At this time, in an environment of NLOS (non-line of sight) in a shaded area or an environment where signal loss is high and multipath is difficult to exist, such as 6G THz, Shannon's capacity limit is overcome only by optimizing the endpoint. It can be difficult to do, and it can be difficult to expect the desired throughput.

Considering the above points, communication can be performed based on a smart radio environment in a new communication system. At this time, in an intelligent wireless environment, an intelligent reflector (IRS) can be used as a factor capable of controlling a wireless channel like a transceiver.

That is, awareness of a radio channel may be added as a factor used to optimize radio communication transmission. Through this, it is possible to reset the channel as an unsolvable problem in the existing communication system or to overcome the channel capacity limitation of Shannon. However, in an intelligent wireless environment, it is necessary to optimize by simultaneously considering the measurement of channels added by the intelligent reflector (IRS) and the intelligent reflector (IRS) as a transceiver, and accordingly, the optimization process may be complicated.

For example, there may be limitations in controlling the IRS using an alternating optimization (AO) algorithm applied in an intelligent wireless environment together with limitations of current wireless communication technology. At this time, in order to solve the above-mentioned limitations, it is possible to supplement the limitations in acquiring channel information by utilizing an active sensor in an intelligent reflector. Also, as an example, the aforementioned limitations can be overcome by optimizing a radio channel environment using artificial intelligence.

More specifically, in a conventional communication system, it is possible to operate in a manner approaching the limit of Shannon's channel capacity through control of a transmitter and a receiver in a fixed wireless channel environment. However, in a poor NLOS environment such as a shadow area, transmission and reception may be almost impossible due to limitations in channel capacity. For example, in the NLOS channel environment, the transmitter can improve the limit of the channel capacity by increasing the power, but the size of noise and interference can also increase accordingly. At this time, in an environment where signal loss is high and multipath is difficult to exist, such as in a 6G THz environment, there may be limitations in overcoming the channel capacity limit of Shannon only by optimizing the transmitter and receiver.

Here, as an example, in 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) and 3CLS (Convergence of Communications, Computing, Control, Localization, and Sensing) needs to be satisfied, and communication based on an intelligent wireless environment may be required for this purpose.

In addition, as an example, many relays are currently used to increase coverage of base station cells and support for shadow areas. However, 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. In addition, the use of a relay requires high additional cost and energy, and it may not be easy to manage complex and mixed interference signals. In addition, as an example, spectrum efficiency may be reduced by using a half duplex method, and space utilization and aesthetics may also be affected.

On the other hand, in an intelligent wireless environment, the wireless channel environment can be adjusted using an intelligent reflector (IRS). At the same time, 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.

However, in addition to the conventional channel between the base station and the terminal, there is a need to consider the channel between the base station-intelligent reflector (IRS) and the intelligent reflector (IRS)-terminal. In addition, in the past, it is sufficient to optimize only the transceiver according to the environment, but in the smart radio environment, there is a need to control the intelligent reflector (IRS) as well.

In addition, the value may have a dependency on the optimization of the transceiver, and thus complexity may increase. Here, 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. In the following, a method for performing optimization in an intelligent wireless environment with an intelligent reflector having an active sensor and an artificial intelligence system will be described in consideration of the above points.

In addition, as an example, Table 2 may be terms in consideration of the following and above, and based on this, a method of performing optimization in an intelligent wireless environment with an intelligent reflector having an active sensor and an artificial intelligence system are described.

9 is a diagram illustrating a radio channel environment according to an embodiment of the present disclosure. Referring to FIG. 9 , in an existing communication system, 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. However, as described above, there may be limitations in data transmission in a case where signal loss is high and multipath application is difficult, such as in a terahertz environment, and in an NLOS environment, such as in a shadow area. As an example, Equation 1 below 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 |H| is small.

In a state where the radio channel environment is fixed, there may be a limit to increasing the channel capacity based on Equation 1. At this time, if an intelligent reflector (IRS) is used, multiple paths can be secured between the transmitter 910 and the receiver 920, and the aforementioned channel |H| can be increased. That is, in an intelligent wireless environment, the wireless channel environment can be an adjustable factor based on the intelligent current reflector, and through this, the channel capacity can be increased.

As an example, FIG. 10 is a diagram illustrating an intelligent wireless environment according to an embodiment of the present disclosure. Referring to FIG. 10 , in an intelligent radio channel environment, a radio channel |H| 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 |H| may be used as a factor for optimization based on an intelligent reflector.

11 is a diagram illustrating an existing radio channel environment and an intelligent radio channel environment according to an embodiment of the present disclosure. For example, referring to FIG. 11(a), the existing radio channel environment may be P1. Also, referring to FIG. 11(b), the intelligent wireless channel environment may be P2. In this case, when the x signal is transmitted through the radio channel at the transmitting end in FIGS. 11(a) and 11(b), the receiving end may receive the y signal. At this time, 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. As a more specific example, 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.

On the other hand, referring to FIG. 11(b), in the intelligent radio channel environment, the radio channel environment P2 is recognized and the radio channel environment can be changed through IRS control. At the same time, 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.

More specifically, 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. Referring to FIG. 12 , an IRS 1220 may exist between a base station 1210 and a terminal 1230 in an intelligent radio channel environment. For example, 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. Here, based on the control of the IRS 1220, 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.

More specifically, when 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 At this time, 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.

Here, the signal-to-noise ratio (SNR) received by terminal k 1230 may be expressed as Equation 3 below.

Therefore, when configuring an intelligent radio environment (SRE) for optimizing the received SNR, it may be a case of setting IRS control and transmit beamforming as shown in Equation 4 below.

At this time, transmit beamforming of terminal k 1230 in consideration of maximum-rate transmission in MIMO

May be the same as Equation 5 below.

here,

May be the maximum transmission power in the IRS,

and to the formulas that optimize Φ

Substituting , optimization may be as shown in Equation 6 below.

At this time, if the IRS control value Φ is determined,

can be determined by arithmetic. Here, an alternating optimization (AO) algorithm may be used to solve the aforementioned optimization problem. For example, the AO algorithm uses channel information (

) may be used to determine a trust region for each IRS element, and may be the same as in FIG. 13. In addition, a binary decision is repeatedly performed until the value of the objective function converges.

can be obtained. Here, if the upper bound of the convergence value is the Ideal IRS,

can be At this time, as an example, in FIG. 12, the IRS may repeat the above-described operation to find an optimized value for each of the above-described IRS elements.

Here, the AO (Alternating Optimization) algorithm needs to be repeated until convergence. In addition, since each optimization value must be derived for each IRS element, complexity and computational complexity may increase. In this case, the 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 limitations in calculating them. In addition, when optimizing an Alternating Optimization (AO) algorithm, measurement values of all channels including IRS may be required, and considering the above, there may be limitations in optimization.

As described above, when channel optimization is performed based on IRS in an intelligent radio channel environment, the amount of computation may increase exponentially according to the repeatability of an alternating optimization (AO) algorithm and IRS elements. Also, for example, in an intelligent wireless channel environment, there is a need to measure all channel information including IRS, and thus complexity may increase.

In the following, a method of performing optimization based on an IRS with an active sensor will be described in consideration of the above points. At this time, the burden of channel information acquisition can be reduced through the IRS equipped with an active sensor. Also, as an example, channel information that is insufficient compared to total channel information may be supplemented based on an artificial intelligence system. Through this, even when the number M of antennas and the number N of IRS elements of the base station increase, complexity in generating an optimized IRS control value can be reduced.

More specifically, referring to FIG. 14, the IRS 1420 having an active sensor is a channel between the base station 1410 and the IRS 1420 through the active sensor (

) and the channel between the terminal 1430 and the IRS 1420 (

) can be measured. At this time, the base station 1410 is measured

Based on, the reference signal may be transferred to the IRS 1420. Here, the artificial intelligence system agent described above

,

and serial channel information through BS-IRS-UE

The optimized IRS control value Φ can be obtained by using as state information. After that, the artificial intelligence system agent can perform learning of the artificial intelligence system using the reward due to the IRS control value Φ and the measured BS-IRS-UE channel information. In addition, as an example, the artificial intelligence system agent indirectly indicates the reward due to the IRS control value Φ and the measured BS-IRS-UE channel information (

), learning may be performed, and may not be limited to a specific form.

At this time, as an example, the artificial intelligence system agent may operate based on the IRS controller. As another example, the artificial intelligence system agent may be connected to the IRS in the form of a cloud, obtain the above-described information, and perform learning. As another example, the artificial intelligence system agent may be located in the base station 1410, acquire the above-described information through feedback, and then perform learning. As another example, the artificial intelligence system agent may be located in the terminal 1430. At this time, the terminal 1430 may perform channel measurement in consideration of the relationship with the IRS 1420 or may perform learning by receiving the above-described information from the base station 1410, and is not limited to a specific form.

That is, the artificial intelligence system agent may not be limited to a specific subject, may be implemented in various forms, and is not limited to the above-described embodiment. Also, as an example, the artificial intelligence system agent may perform learning based on artificial intelligence, and through this, an optimized IRS control value Φ may be obtained. Here, the artificial intelligence system agent may be a name for convenience of explanation, and may not be limited to a specific name. For example, the artificial intelligence system agent may acquire the above-described IRS control value Φ by performing learning based on at least one of supervised learning, unsupervised learning, and reinforcement learning based on the above-described artificial intelligence, and in a specific form. Not limited. However, in the following, for convenience of explanation, it is described as an artificial intelligence system agent, but may not be limited thereto.

For example, the initial setting of the optimized IRS control value Φ based on the artificial intelligence system agent may be completed. That is, the base station 1410, the IRS 1420, and the terminal 1430 may complete the configuration based on the learning of the above-described IRS control value Φ during the initial connection process. After that, the base station performs transmit beamforming optimized in the above-described radio channel environment.

can be computed and applied. For example,

can be computed through an artificial intelligence system. As another example,

may be determined based on a beam management method, and may not be limited to a specific form.

As a more specific example, FIG. 15 is a diagram illustrating signal flows of a base station, an IRS, and a terminal to set an intelligent wireless environment according to an embodiment of the present disclosure.

Referring to FIG. 15, the base station 1510 sends a reference signal to an intelligent reflector (IRS) 1520.

can transmit. At the same time, the terminal 1530 transmits the reference signal to the IRS 1520.

can transmit. At this time, for example, the reference signal

and reference signal

may be an orthogonal relationship, through which mutual interference may be reduced. Here, the IRS 1520 may include an active sensor, through which the reference signal may be sensed. At this time, the IRS 1520 receives the reference signal and uses the BS-IRS measurement channel

and UE-IRS measurement channel

information can be obtained. After that, the IRS 1520 is a BS-IRS measurement channel

Information may be forwarded to the base station 1510. base station 1510 is

Beamforming of signals transmitted to the IRS 1520 based on information

It can be calculated, applied to the reference signal, and transmitted to the IRS 1520.

Here, for the MRT

May be the same as Equation 7 below.

In this case, for example,

may be supported in the form of beam sweeping. The IRS 1520 may provide CSI feedback to allow the base station 1510 to select a specific beam.

Also, as an example, the artificial intelligence system agent

information and

The control value Φ may be acquired by performing learning based on the information. In other words, the artificial intelligence system agent measures the channel information

Wow

Based on the phase change value for each element of the intelligent judge board (IRS)

can predict At this time, as an example, the artificial intelligence system agent measures the channel for the BS-IRS-UE

A phase change value for each element of the IRS may be predicted by further considering the information, and the above-described embodiment is not limited.

After that, the phase change value predicted by the artificial intelligence system agent

may be delivered to the IRS controller. The IRS controller may control the IRS 1520 based on the phase change value.

After that, the base station 1510

The reference signal to which is applied (

) to the IRS 1520, the terminal 1530 transmits the above-described reference signal via the IRS 1520.

can receive At this time, the terminal 1530 receives the reference signal

Based on , the reward value (Reward) can be measured. For example, the compensation value is the signal-to-noise ratio of the IRS channel ?SNR?_IRS or the mean square error

can be used However, this is just one example and other compensation values may be applied.

After that, the artificial intelligence system agent may perform an update based on the IRS phase shift value, reward value, and measured channel information. At this time, as an example, the artificial intelligence system agent may derive a final convergence value by performing learning according to the convergence of the reward value or the predicted phase change value. Also, as an example, the artificial intelligence system agent may use a model learned through initial transfer learning, thereby removing repetition. However, it may not be limited to a specific form. After that, an environment change completion signal may be transmitted to the base station 1510 based on the artificial intelligence system agent. The base station 1510 may communicate with the terminal 1530 by transmitting a reference signal to the terminal 1530, acquiring channel state information, determining and applying beamforming optimized in a changed environment.

16 is a flowchart illustrating a method of performing optimization in an intelligent radio channel environment according to an embodiment of the present disclosure. Referring to FIG. 16, optimization of a radio channel environment can be divided into a channel estimation step, an environment change step, and an environment adaptation step.

More specifically, in the channel estimation step, the channel information of the BS-IRS is identified, and the beamforming vector transmitted from the base station to the IRS

It may be a step of calculating , and simultaneously obtaining channel information between the UE and the IRS through a reference signal from the terminal. To this end, the base station and the terminal may transmit each reference signal to the IRS. (S1610) At this time, the IRS uses an active sensor to obtain channel information for the BS-IRS

and channel information for UE-IRS

(S1630) Then, the IRS channel information for the BS-IRS

to the base station, and the base station transmits beamforming

Can be calculated. (S1630) Through the above, the channel estimation step can be completed.

After that, the information obtained in the channel estimation step may be used as a reference value for IRS control in the environment change step. For example, in order for the IRS performance measurer of the UE to properly operate, the signal of the base station must first be transmitted to the IRS, and thus beamforming in the IRS direction may be essential. More specifically, in the environment change step, the artificial intelligence system agent can derive the optimal IRS control value. For example, the artificial intelligence system agent provides channel information for BS-IRS

and channel information for UE-IRS

It is possible to obtain the control value Φ of the IRS through. (S1640)

As another example, the artificial intelligence system agent measures the channel for the BS-IRS-UE

The IRS control value Φ can be obtained by further considering the information, and is not limited to the above-described embodiment. Then, the aforementioned IRS control value Φ may be applied to the IRS (S1650). After that, the base station performs the calculated transmission beamforming.

The reference signal may be transmitted to the terminal based on (S1660). The terminal may obtain a compensation value through measurement through the reference signal to which beamforming is applied, and transmit it to the artificial intelligence system agent. After that, the artificial intelligence system agent may perform learning based on the reward value. (S1670) At this time, for example, when the artificial intelligence system agent performs learning, in supervisor learning, the pre-learned model Through this, it is possible to quickly estimate the predicted value. However, it may not be easy to update the learning model.

On the other hand, reinforcement learning, which is unsupervised learning, can continue learning the model by executing the predicted value and obtaining a corresponding reward value. At this time, it may be repeated until the compensation value or prediction value converges. However, as an example, the number of iterations may be reduced through transfer learning or updating a learning model. For example, the artificial intelligence system agent may perform learning based on the above-described supervised learning or unsupervised learning, and may not be limited to a specific form. Thereafter, the base station may acquire an environment change completion signal based on the above-described update (S1680). Through this, the environment change step may be completed.

After that, in the environment adaptation step, the base station transmits a reference signal to the terminal to optimize the new environment.

It may be a step to find . For example, the base station sends a reference signal to the terminal, and the terminal transmits the measured effective channel state information

can be fed back to the base station. The base station optimizes a given environment based on the received feedback information.

may be determined and applied. (S1690) After that, the base station may communicate with the terminal. Here, as an example, beamforming of the base station may be applied as it is to the beam management method of the existing system, and may not be limited to a specific form.

As an example, FIG. 17 is a diagram illustrating a method of performing reinforcement learning based on an artificial intelligence system in an intelligent wireless channel environment according to an embodiment of the present disclosure. As described above, the artificial intelligence system agent may perform learning based on the reward value obtained by the terminal. Here, the agent of the artificial intelligence system may perform learning based on reinforcement learning. For example, reinforcement learning may consist of two inputs and one output.

More specifically, the two inputs may be state information and a compensation value. In this case, the state information may be a factor obtained based on a radio channel environment. As an example, the state information is the BS-IRS estimated channel (acquired in the channel estimation step)

) and the UE-IRS estimated channel (

) can be set based on.

At this time, since the estimated channel is for distinguishing the current state in the artificial intelligence system and does not have to be a complete channel, the burden of channel restoration can be reduced. As another example, in order to reflect changes in the radio channel environment for the control value for the IRS to the status information, the status information includes BS-IRS-UE channel information (

), and may not be limited to a specific form. At this time, the BS-IRS-UE channel information may be measured information. As another example, the BS-IRS-UE channel information is an indirect indicator representing a channel through the IRS (

), and is not limited to a specific form. Based on the above, state information may be set as shown in Equation 8 below.

At this time, the action may be defined as selecting phase shift values of each element of the IRS, and may be as shown in Equation 9 below.

At this time, as an example, the phase shift values may be displayed as continuous phase values according to the usability of the device and may be as shown in Equation 10. As another example, the phase shift values may be expressed by protonating with predetermined bits, and may not be limited to a specific form. Alternatively, it may be expressed by being quantized with a predetermined bit.

As another example, the phase shift value may be set in the form of a codebook to reduce computation. As an example, a case where the size of the codebook is |P| may be considered. At this time, the action represents an index pointing to the codebook, and the IRS controller corresponds to the codebook.

can be assigned. Here, a steering vector according to an angle may be set, and an action may be as shown in Equation 11 below.

In this case, the reward value (Reward) may be a value transmitted from the terminal to the artificial intelligence system. The compensation value may be the result of the control value selected by the IRS, as described above. For example, when a block for receiving a compensation value from the terminal is added to the IRS, the IRS may directly receive the compensation value. However, considering the cost of the IRS, the IRS may receive a compensation value from the terminal via the base station.

As another example, if the AI system agent described above is located in the terminal, the terminal may directly deliver the above-described compensation value to the AI system agent. As another example, if the above-described artificial intelligence system agent is located in the base station, the terminal transmits a compensation value to the base station, and the transmitted compensation value may be applied to the artificial intelligence system agent, and is not limited to a specific embodiment.

At this time, as an example, the reward value (Reward) may be a value processed by the terminal. The UE may add an IRS performance measurer to the UE in order to utilize the compensation value in various ways. A compensation value based on the above may be as shown in Equation 12 below.

Also, as an example, an issue regarding Exploration and Exploitation control may occur in reinforcement learning. At this time, Exploration may utilize a behavior sampled from multiple behaviors to obtain a better reward value. On the other hand, Exploitation can utilize already recognized information based on repetitive actions. At this time, as an example, in reinforcement learning, 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. 18 . At this time, e-greedy may be a method of executing Exploration with a predetermined probability. For example, referring to FIG. 18 , Total Regret can be improved by e-greedy compared to the greedy method that only exploits, as shown in 17 . In addition, 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.

As an example, Equation 13 below is an equation representing decaying e-greedy, where c is a constant, and |A| is the size of the action space,

can be reduced with time as e-greedy over time and inversely proportional to the square of the minimum Regret.

Also, for example, Exploration and Exploitation control can be further optimized using MAB (Multi Arm Bandit). For example, Upper Confidence Bound (UCB) or Thompson Sampling (TS) may be applied, and may not be limited to a specific form.

In this case, Equation 14 below may be an expression for an action based on UCB, and Equation 15 may be Upper Confidence. At this time, 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.

For example, Thompson sampling is implemented through a beta distribution based on the above, and is simpler than UCB and can easily control Exploration and Exploitation.

Also, as an example, FIG. 19 is a diagram illustrating a structure of an artificial intelligence system that operates by selecting a model based on channel information obtained through an active sensor according to an embodiment of the present disclosure. For example, referring to FIG. 19 , less state information may be used compared to FIG. 17 described above. Accordingly, the calculation speed and convergence speed may be fast. For example, the artificial intelligence system agent obtains channel information through an active sensor.

,

Choose a suitable model based on the weight of the model

You can initialize the weights you want to use. Here, the operation of the artificial intelligence system agent may be the same as that of FIG. 17, and the state information may be as shown in Equation 16 below.

That is, in order to make a simple model, the IRS-related channel estimate (

,

) and the phase shift value Φ of the element are the angles of the steering vector, respectively.

can be treated as

may also be replaced by SNR. Based on the above, the wireless channel setting is completed, and the selected and learned model is the reference compensation value (

) or more, the learned weight

can be updated with the model weight selector.

In addition, as an example, in an intelligent wireless channel environment, an artificial intelligence system may apply various algorithms during reinforcement learning, but may require a model capable of processing a continuous space or a large discrete space, stable, and having a fast convergence speed. have. For example, DDPG (Deep Deterministic Policy Gradient) has the advantages of both Policy Gradient and DQN. Therefore, DDPG is stable and can be used in continuous space. In addition, as an example, since a deterministic policy is used, convergence is possible quickly and it may be a relatively lightweight algorithm.

As an example, FIG. 20 illustrates reinforcement learning using DDPG according to an embodiment of the present disclosure. Referring to FIG. 20, the characteristics of both the Actor-Critic algorithm and the DQN algorithm may be included. Q (Value Action Function) and behavior can be predicted through Actor network and Critic network, respectively. At this time, the Critic network can be learned in the direction of reducing TD-Error (Temporal Difference) using Experience Replay Memory. In addition, the learned Critic Network can be used for Policy Gradient calculation and learning. For example, since the target policy is deterministic, the Bellman equation can be expressed as Equation 17 below through μ instead of π.

At this time, State and Action are respectively

Wow

, and E indicates that it is extracted to the environment.

is an action-value function for policy μ, γ is a discount factor, and r may be a reward value. In addition, the weight of the Critic Network

, and when an action policy with various probability distributions is referred to as β, the loss of the Critic Network may be as shown in

Equations

18 and 19 below.

At this time, for example, in the above-described

Equations

18 and 19, the expected value is Transitions stored in Relay Buffer R (

) can be expressed by minibatching and taking the average value.

In addition,

becomes TD (Temporal Difference), and the Critic Network can be learned in the direction of reducing TD Error. At this time, the Actor Network may update the policy in the direction of maximizing the expected return, and may be as shown in Equation 21 below.

In addition, the expected value may be approximated using a Relay Buffer as shown in Equation 22 below.

At this time, DDPG may be easy to add Exploration as an off-policy model. For example, the Exploration policy may be generated by adding a noise distribution, and Equation 23 below may be considered.

At this time, for example, the variance of the normal distribution

The exploration rate can be adjusted using , and it can be adjusted adaptively using the aforementioned decaying e-greedy or UCB.

Also, as an example, FIG. 21 is a diagram illustrating an IRS according to an embodiment of the present disclosure. As described above, the IRS may include an active sensor. At this time, referring to FIG. 21 (a), the active sensor and the reflective element may be commonly used in the IRS. For example, an active sensor 2110 may operate at a specific location in the IRS. Here, in order to show the operating characteristics of the IRS, the structure diagram of FIG. 21 is illustrated with an antenna and a phase shifter, but may not be limited thereto. For example, the IRS may be in the form of a PCB in which a copper plate is controlled by a diode or a varactor. As another example, the form of the IRS Meta Surface may also be supported, and may not be limited to a specific form. At this time, each element of the IRS may be selectable as a switch for use as a reflector and active sensor. More specifically, when an element of the IRS is used as a reflector, a switch in the corresponding element may disconnect the RF chain and be connected to a phase shifter. In this case, the phase shifter may be controlled by the phase value controlled by the IRS controller. On the other hand, when a corresponding element is used as an active sensor, switches in the corresponding element may be connected in an RF chain. At this time, the corresponding element demodulates the reference signal from the base station and the terminal through the baseband unit, and each channel information described above.

,

can be extracted.

As another example, FIG. 21 (b) may be an IRS structure using an independent active sensor. For example, the active sensor 2120 may be separately separated from the reflector. Therefore, differently from FIG. 21(a), the path of the active sensor and the path of the reflector controlled by the IRS controller can be distinguished. For example, the IRS performance measurer used by the terminal measures the performance of the control value set by the artificial intelligence system applied to the intelligent radio channel environment based on the reference signal transmitted from the base station and transmits it to the IRS. have. At this time, it may play a role of generating and processing a reward value (Reward) in consideration of performance learning for the control value set by the artificial intelligence system, as described above.

As another example, FIG. 22 illustrates the structure of an IRS performance measurer according to an embodiment of the present disclosure. For example, the IRS performance measurer may perform at least one of standardization/normalization, batching, and weight applicator functions. At this time, 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. Here, each piece of measurement information may be an area of various values.

Accordingly, the standardization/normalization block may standardize or normalize values of various areas of measurement information in consideration of respective weights. In addition, the batching block plays a role of accumulating such measurement information at regular intervals, and can also perform normalization for each accumulation. Also, as an example, 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. At this time, the IRS performance measurer may generate a compensation value in the form of integrating measurement information after processing. As another example, the IRS performance measurer may generate a compensation value by individually separating measurement information, and may not be limited to a specific embodiment.

Referring to FIG. 23, a method of operating a base station in a wireless communication system may be provided. For example, the artificial intelligence system may be located in a base station, but may not be limited thereto. For example, as described above, the artificial intelligence system may be located in a separate cloud or terminal.

Referring to FIG. 23, the base station may transmit the first reference signal to the IRS (S2310). At this time, the IRS may include an active sensor. At this time, as an example, each of the IRS elements may be used as any one of the above-described active sensor or reflective element, as described above. As another example, the active sensor may be provided in the IRS independently of each element in the IRS, and is not limited to a specific embodiment.

Next, the base station may receive channel information between the base station and the IRS and channel information between the IRS and the terminal measured based on the first reference signal from the IRS (S2320). (S2330) In addition, since the artificial intelligence system is located in the base station, the base station can derive an IRS control value based on channel information between the base station and the IRS and channel information between the IRS and the terminal. ( S2340) Next, the base station may transmit the second reference signal to the terminal through the IRS by applying the determined beamforming. (S2350) At this time, as an example, the base station derives the IRS control value through the artificial intelligence system and derives it. It is possible to control the phase value of each element in the IRS by transmitting the IRS control value to the IRS, as described above. In addition, for example, when the IRS control value is derived based on the artificial intelligence system, the artificial intelligence system may derive the IRS control value by further considering channel information formed by the base station, the IRS, and the terminal, which is as described above. same. In addition, the active sensor of the IRS can measure each channel information based on the above-described first reference signal transmitted from the base station and the third reference signal transmitted from the terminal, as described above.

Also, as an example, when the base station transmits the second reference signal to the signal beamformed through the IRS based on the derived control value, the terminal generates a compensation value for the IRS control value based on the second reference signal. can At this time, the artificial intelligence system may perform learning based on the above-described compensation value, through which the IRS control value may be updated.

Also, as an example, the base station may update beamforming based on the updated IRS control value and transmit the third reference signal to the terminal through the IRS based on the updated beamforming. Here, the compensation value information may be configured in the form of channel related information, but may not be limited to a specific form.

It is obvious that examples of the proposed schemes described above may also be included as one of the implementation methods of the present disclosure, and thus may be regarded as a kind of proposed schemes. In addition, the above-described proposed schemes may be implemented independently, but may also be implemented in a combination (or merged) form of some proposed schemes. Information on whether the proposed methods are applied (or information on the rules of the proposed methods) may be defined so that the base station informs the terminal through a predefined signal (eg, a physical layer signal or a higher layer signal). have.

The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential characteristics described in the present disclosure. Accordingly, the above detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of the present disclosure should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent range of the present disclosure are included in the scope of the present disclosure. In addition, claims that do not have an explicit citation relationship in the claims may be combined to form an embodiment or may be included as new claims by amendment after filing.

Embodiments of the present disclosure may be applied to various wireless access systems. As an example of various wireless access systems, there is a 3rd Generation Partnership Project (3GPP) or 3GPP2 system.

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.

Additionally, embodiments of the present disclosure may be applied to various applications such as free-running vehicles and drones.

Claims

In the method of operating a base station in a wireless communication system,

Transmitting a first reference signal to an intelligent reflect surface (IRS);

Receiving channel information between the base station and the IRS and channel information between the IRS and the terminal measured based on the first reference signal from the IRS;

Determining beamforming based on the channel information between the base station and the IRS;

Deriving an IRS control value based on the channel information between the base station and the IRS and the channel information between the IRS and the terminal; and

and transmitting a second reference signal to the terminal through the IRS by applying the determined beamforming.
According to claim 1,

wherein the base station derives the IRS control value through an artificial intelligence system, and transmits the derived IRS control value to the IRS to control a phase value for each element in the IRS.
According to claim 2,

The artificial intelligence system further considers channel information formed by the base station, the IRS, and the terminal to derive the IRS control value.
According to claim 2,

The IRS includes an active sensor, and based on the active sensor, measurement of the first reference signal is performed to estimate the channel information between the base station and the IRS.
According to claim 4,

The terminal transmits a third reference signal to the IRS, and the channel information between the IRS and the terminal is measured based on the third reference signal.
According to claim 4,

Wherein each of the elements in the IRS is used as one of the active sensor or the reflective element.
According to claim 4,

The active sensor is provided in the IRS independently of each element in the IRS, the base station operating method.
According to claim 1,

When the terminal receives the second reference signal from the base station through the IRS, generating compensation value information for the IRS control value based on the second reference signal,

Transmitting the generated compensation value information to the base station, a base station operating method.
According to claim 8,

The base station updates the IRS control value through the artificial intelligence system based on the compensation value received from the terminal,

Updating the beamforming based on the updated IRS control value;

Transmitting a third reference signal to the terminal through the IRS based on the updated beamforming, the base station operating method.
According to claim 8,

The compensation value information is configured in the form of channel-related information, the base station operating method.
In a terminal operating method in a wireless communication system,

Transmitting a first reference signal to the IRS; and

Receiving a second reference signal to which beamforming is applied from a base station through the IRS; Including,

The IRS is adjusted based on an IRS control value,

Based on the first reference signal, channel information between the IRS and the terminal is measured,

The base station receives channel information between the base station and the IRS and channel information between the IRS and the terminal from the IRS, and determines the beamforming based on the channel information between the base station and the IRS,

The terminal operation method of deriving the IRS control value based on the channel information between the base station and the IRS and the channel information between the IRS and the terminal.
In a base station of a wireless communication system,

transceiver; and

A processor connected to the transceiver;

the processor,

Transmitting a first reference signal to an IRS through the transceiver;

Receiving channel information between the base station and the IRS and channel information between the IRS and the terminal measured based on the first reference signal from the IRS through the transceiver;

Determining beamforming based on the channel information between the base station and the IRS;

An IRS control value is derived based on the channel information between the base station and the IRS and the channel information between the IRS and the terminal, and

The base station transmits the second reference signal to the terminal through the IRS by applying the determined beamforming through the transceiver.
In the terminal of the wireless communication system,

transceiver; and

A processor connected to the transceiver;

the processor,

Transmitting a first reference signal to an IRS through the transceiver, and

Receiving a second reference signal to which beamforming is applied through the transceiver through the IRS,

The IRS is adjusted based on an IRS control value,

Based on the first reference signal, channel information between the IRS and the terminal is measured,

The base station receives channel information between the base station and the IRS and channel information between the IRS and the terminal from the IRS, and determines the beamforming based on the channel information between the base station and the IRS,

Deriving the IRS control value based on the channel information between the base station and the IRS and the channel information between the IRS and the terminal.
An apparatus comprising at least one memory and at least one processor functionally connected to the at least one memory, comprising:

The at least one processor is the device,

Transmitting a first reference signal to an IRS through the transceiver;

Receiving channel information between the base station and the IRS and channel information between the IRS and the terminal measured based on the first reference signal from the IRS through the transceiver;

Determining beamforming based on the channel information between the base station and the IRS;

An IRS control value is derived based on the channel information between the base station and the IRS and the channel information between the IRS and the terminal, and

Apparatus for transmitting a second reference signal to the terminal through the IRS by applying the determined beamforming through the transceiver.
In a non-transitory computer-readable medium storing at least one instruction,

comprising the at least one instruction executable by a processor;

The at least one command,

The device transmits a first reference signal to an IRS through the transceiver,

Receiving channel information between the base station and the IRS and channel information between the IRS and the terminal measured based on the first reference signal from the IRS through the transceiver,

Determining beamforming based on the channel information between the base station and the IRS;

An IRS control value is derived based on the channel information between the base station and the IRS and the channel information between the IRS and the terminal, and

The computer readable medium for transmitting the second reference signal to the terminal through the IRS by applying the determined beamforming through the transceiver.