WO2023277218A1

WO2023277218A1 - Method and device for transmitting signal through reflecting plate in wireless communication system

Info

Publication number: WO2023277218A1
Application number: PCT/KR2021/008274
Authority: WO
Inventors: 김성진; 박재용; 오재기; 안병규
Original assignee: 엘지전자 주식회사
Priority date: 2021-06-30
Filing date: 2021-06-30
Publication date: 2023-01-05
Also published as: KR20240024775A

Abstract

The present disclosure relates to a method for operating a base station in a wireless communication system, and the method may comprise the steps of: transmitting initialization information to a quantum Thompson sampling device; receiving first best combination information from the quantum Thompson sampling device; transmitting group configuration information to each of a plurality of terminals on the basis of the first best combination information; transmitting a signal to each of the plurality of terminals through a reflecting plate selected by the each of the plurality of terminals; receiving feedback information from the each of the plurality of terminals; calculating group feedback information on the basis of the feedback information for the each of the plurality of terminals, and transmitting the calculated group feedback information to the quantum Thompson sampling device; and receiving second best combination information from the quantum Thompson sampling device.

Description

Method and apparatus for transmitting a signal through a reflector 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 using a reflector in the wireless communication system.

In particular, a method and apparatus for mapping a reflector and a terminal through quantum Thompson sampling in a wireless communication system may be provided.

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 relates to a method and apparatus for transmitting and receiving signals between a terminal and a base station using a reflector in a wireless communication system.

The present disclosure may provide a method of mapping a reflector and a terminal in an environment in which a plurality of reflectors and a plurality of terminals coexist.

The present disclosure may provide a method for mapping a reflector and a terminal based on quantum Thomson sampling.

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, in a method of operating a base station in a wireless communication system, transmitting initialization information to a Quantum Thomson sampling device, receiving first best combination information from a Quantum Thomson sampling device, and in the first best combination information Transmitting group configuration information to each of a plurality of terminals, transmitting a signal to each of a plurality of terminals through a reflector selected from each of a plurality of terminals, and receiving feedback information from each of a plurality of terminals. , calculating group feedback information based on feedback information for each of a plurality of terminals, transmitting the group feedback information to a Quantum Thomson sampling device, and receiving second best combination information from the Quantum Thomson sampling device.

In addition, as an example of the present disclosure, in a method of operating a terminal in a wireless communication system, receiving group configuration information from a base station, checking a corresponding reflector based on the received group configuration information, and receiving information from the base station through the reflector Receiving a signal, and generating feedback information based on the received signal and transmitting the generated feedback information to the base station, wherein the base station transmits initialization information to the Quantum Thomson sampling device and then receives first best combination information from the Quantum Thomson sampling device. Receive, transmit group configuration information to each of a plurality of terminals based on the first best combination information, calculate group feedback information based on the feedback information for each of a plurality of terminals, and transmit to the Quantum Thomson sampling device and receive second best combination information from the Quantum Thomson sampling device.

In addition, as an example of the present disclosure, in a base station of a wireless communication system, including a transceiver and a processor connected to the transceiver, the processor transmits initialization information to the Quantum Thomson sampling device through the transceiver, Quantum Thomson sampling through the transceiver Receives first best combination information from a device, transmits group configuration information to each of a plurality of terminals based on the first best combination information through a transceiver, and transmits group configuration information to each of a plurality of terminals through a transceiver and through a reflector selected from each of the plurality of terminals. Transmits a signal to each terminal, receives feedback information from each of a plurality of terminals through a transceiver, calculates group feedback information based on the feedback information for each of a plurality of terminals through a transceiver, Quantum Thomson sampling device and receive second best combination information from the Quantum Thomson sampling device through the transceiver.

In addition, as an example of the present disclosure, in a terminal of a wireless communication system, including a transceiver and a processor connected to the transceiver, the processor receives group configuration information from the base station through the transceiver, and based on the received group configuration information Check the corresponding reflector, receive a signal from the base station through the reflector through the transceiver, and generate feedback information based on the signal received through the transceiver and transmit it to the base station, wherein the base station transmits initialization information to the Quantum Thomson sampling device After transmitting, receiving first best combination information from the Quantum Thomson sampling device, transmitting group configuration information to each of a plurality of terminals based on the first best combination information, and based on feedback information for each of a plurality of terminals Group feedback information may be calculated, transmitted to the Quantum Thomson sampling device, and second best combination information may be received from the Quantum Thomson sampling device.

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 initialized as a Quantum Thomson sampling device. information, receives first best combination information from a quantum Thomson sampling device, transmits group configuration information to each of a plurality of terminals based on the first best combination information, and through a reflector selected from each of the plurality of terminals Transmits a signal to each of a plurality of terminals, receives feedback information from each of a plurality of terminals, calculates group feedback information based on the feedback information for each of a plurality of terminals, and transmits it to a quantum Thomson sampling device, and second best combination information may be received from the Quantum Thomson sampling device.

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 wherein the at least one command causes the device to send initialization information to the Quantum Thomson sampling device, receive first best combination information from the Quantum Thomson sampling device, and group configuration information based on the first best combination information. to each of the plurality of terminals, transmits a signal to each of the plurality of terminals through a reflector selected from each of the plurality of terminals, receives feedback information from each of the plurality of terminals, and Group feedback information may be calculated based on the feedback information, transmitted to the Quantum Thomson sampling device, and second best combination information may be received from the Quantum Thomson sampling device.

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 initialization information may be information generated based on mapping a plurality of reflectors and a plurality of terminals.

In addition, as an example of the present disclosure, first best combination information may be derived through a Quantum Thomson sampling device based on mapping information that can be combined between a plurality of reflectors and a plurality of terminals based on initialization information.

In addition, as an example of the present disclosure, group feedback information calculated by the base station based on feedback information of a plurality of terminals may be overall performance information.

Also, as an example of the present disclosure, reward information generated by comparing overall performance information with predetermined expected performance information may be provided as an input of a Quantum Thomson sampling device.

In addition, as an example of the present disclosure, based on the reward information, the quantum Thomson sampling device may derive second best combination information by performing learning based on quantum Thomson sampling.

In addition, as an example of the present disclosure, when learning is performed based on quantum Thomson sampling, a plurality of quantization bits are set based on mapping information that can be combined with a plurality of reflectors and a plurality of terminals, and each of the plurality of quantization bits is After the ry-gate operation is performed through the Hadamard gate, quantized bits can be output.

Also, as an example of the present disclosure, the ry-gate operation may include a phase weight value corresponding to each of a plurality of quantization bits, and the phase weight value may be updated based on reward information.

Also, as an example of the present disclosure, second best combination information may be derived based on quantized bits output after the ry-gate operation is performed.

Also, as an example of the present disclosure, the quantized bits output after the ry-gate operation is performed may be provided as a unitary operation updater based on the second best combination information.

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

In embodiments based on the present disclosure, a terminal and a base station may transmit and receive signals using a reflector.

In embodiments based on the present disclosure, a method for mapping a reflector and a terminal may be provided in consideration of an environment in which a plurality of reflectors and a plurality of terminals coexist.

In embodiments based on the present disclosure, a method for mapping a reflector and a terminal based on quantum Thomson sampling may be provided.

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 exemplary communication system according to an embodiment of the present disclosure.

2 is a diagram showing an example of a wireless device according to an embodiment of the present disclosure.

3 is a diagram illustrating another example of a wireless device according to an embodiment of the present disclosure.

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

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

6 is a diagram illustrating an example of AI (Artificial Intelligence) according to an embodiment of the present disclosure.

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

8 is a diagram showing an electromagnetic spectrum according to an embodiment of the present disclosure.

9 is a diagram illustrating a method of performing communication based on a possible reflector according to an embodiment of the present disclosure.

10 is a diagram illustrating a smart radio environment (SRE) using a reflector according to an embodiment of the present disclosure.

11 is a diagram illustrating a method of selecting a reflector through a Thompson sampling method according to an embodiment of the present disclosure.

12 is a diagram illustrating a specific method of performing Thomson sampling according to an embodiment of the present disclosure.

13 is a diagram illustrating a method of matching a reflector and a terminal in an SRE environment based on quantum Thomson sampling.

14 is a diagram illustrating a method of matching a reflector and a terminal in an SRE environment based on quantum Thomson sampling.

15 is a diagram illustrating a specific method of performing quantum Thomson sampling according to an embodiment of the present disclosure.

16 is a diagram illustrating a 1-qubit mode according to an embodiment of the present disclosure.

17 is a diagram illustrating a 1-qubit unitary operation updater according to an embodiment of the present disclosure.

18 is a diagram showing simulation results of quantum Thomson sampling according to an embodiment of the present disclosure.

19 is a flowchart illustrating a method of operating a base station in a wireless communication 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. there is.

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.

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

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. there is. 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. there is.

The learning processor unit 640c may learn a model composed of an artificial neural network using learning data. The running processor unit 640c may perform AI processing together with the running processor unit of the AI server (FIG. 1, 140). The learning processor unit 640c may process information received from an external device through the communication unit 610 and/or information stored in the memory unit 630 . 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 for adjusting a radio channel environment using a reflector and performing communication between a base station and a terminal will be described. For example, the reflector may be an intelligent reflect surface (IRS). Also, as an example, the reflector may be large intelligent surfaces (LIS). That is, the shape of the reflector may be implemented in various ways, and may not be limited to a specific shape. For example, in the following, it is referred to as a reflector for convenience of description. In addition, when a plurality of reflectors and a plurality of terminals coexist in a wireless channel environment, selection of the reflector may be performed through an artificial intelligence system operating based on a quantum computer-based algorithm. After that, the base station and the terminal can control the reflector to adjust the radio channel environment to perform communication, and a method for this will be described below.

For example, when a signal is transmitted in the THz band, it may be vulnerable to path loss. Considering the above points, in order to overcome the limitations of a multi-access communication environment, a terminal and a base station may perform communication through a reflector. The terminal and the base station change the multi-access communication channel through the reflector, and based on this, multi-paths can be secured and transmission efficiency can be increased by improving throughput.

For example, the reflector may create a new radio path by reflecting the radio signal sent from the base station toward the terminal. Since the reflector is not involved in communication other than adjusting the angle of reflection of the signal, there may be advantages in terms of complexity and power consumption. However, when a reflector is applied, a plurality of reflectors may be used in consideration of communication between a base station and a plurality of terminals.

In this case, when multiple access wireless communication is performed in an environment in which a plurality of reflectors and a plurality of terminals exist, overall transmission performance may vary according to mapping between the plurality of reflectors and the plurality of terminals. That is, mapping between the reflector and the terminal needs to be performed in consideration of overall transmission performance. Therefore, in order to optimize the overall performance, it is necessary to determine which reflecting plate is to be assigned to which terminal.

For example, one reflector may be assigned to only one terminal, which may be a single reflector mode. However, even in the case of a single reflector mode, the number of channels to be determined according to the number of terminals may increase to a factorial level when the total number of cases in which the number of reflectors and the number of terminals are considered is considered. That is, the complexity of selecting the reflector may increase. In addition, in the case of a multi-reflector mode in which a plurality of reflectors are allocated to each terminal, the complexity of selecting the reflector may further increase.

As another example, in the case of a mobile terminal (e.g. vehicle) or an environment in which complex terrain features exist in a cell, the optimal reflector selection needs to be determined in real time, and there may be limitations in the method of receiving channel information as feedback. In this case, when the selection of the optimal reflector is simplified based on classical computing, performance may deteriorate. On the other hand, direct optimization with a given cost metric may conversely limit real-time processing. Considering the above points, a method based on a new computing method that minimizes complexity while improving accuracy may be required for selecting a reflector, and a method for this will be described below.

9 is a diagram illustrating a method of performing communication based on a reflector according to an embodiment of the present disclosure. Referring to FIG. 9(a), in conventional wireless communication, a base station 910 may directly communicate with a terminal 920, and a channel environment between the base station 910 and the terminal 920 may be h(t). . Here, when the transmission signal is x(t) and the added noise is n(t), the signal received at the receiving end may be modeled as in Equation 1 below.

In this case, as an example, referring to FIG. 9(b), when the base station 910 and the terminal 920 communicate using the reflector 930, the signal received at the receiving end is modeled as in Equation 2 below. can

here

Is the channel environment between the base station 910 and the reflector 930,

may be a channel environment of the terminal 920 and the reflector 930. Also, θ(t) may be a characteristic of each reflector. For example, the reflector may adjust at least one or more reflective elements in the reflector to exhibit performance higher than that of an existing wireless communication received signal, thereby increasing transmission efficiency.

10 is a diagram illustrating a smart radio environment (SRE) using a reflector according to an embodiment of the present disclosure. Referring to FIG. 10 , an environment in which a plurality of reflectors and a plurality of terminals coexist may be considered. Here, the K reflectors 1030-1, 1030-2, and 1030-3 may be mapped one-to-one with the terminal based on the single reflector mode. That is, K terminals 1020-1, 1020-2, and 1020-3 among the N terminals 1020-1, 1020-2, 1020-3, and 1020-4 are K reflectors 1030-1, 1030-2, 1030-3) may be mapped. At this time, for example, each of the

reflectors

1, 2, ... , K (1030-1, 1030-2, 1030-3) can select each terminal in order, but this is not an optimal choice and performance gain may be limited.

For example, when a reflector is selected in a wireless communication system, the base station 1010 includes reflectors 1030-1, 1030-2, and 1030-3 and terminals 1020-1, 1020-2, and 1020 respectively. -3, 1020-4) must be aware of the channels to be optimally selected. In addition, a limitation may exist because an optimal selection may be possible only when the base station maps the reflector and the terminal in consideration of the number of all combinations of the reflector and the terminal.

For example, the base station is trying to check the channel between each of the reflectors (1030-1, 1030-2, 1030-3) and each of the terminals (1020-1, 1020-2, 1020-3, 1020-4) In this case, each of the terminals 1020-1, 1020-2, 1020-3, and 1020-4 is a channel {h[k, n]} may be measured and reported to the base station 1010. In the case of performing the above-described process, complexity may be very high, and significant loss of wireless communication uplink capacity may occur. Therefore, the base station 1010 needs to reduce complexity by selecting an optimal combination through trial and error without feedback on channel information from the respective terminals 1020-1, 1020-2, 1020-3, and 1020-4. there is.

Also, as an example, the number of cases in which the reflector is selected may be N!/(N-K)!. More specifically, in the process of selecting a terminal corresponding to each reflector in an environment where K reflectors and N terminals exist, it is necessary to consider the order of the reflectors, and accordingly, unlike the combination operation, the number of cases is N! /(N-K)!

As a specific example, when the number K and N of the reflectors and terminals gathered around one side of the cell are N=K=10, respectively, the number of cases for selecting the reflector may be 3,628,800, and one combination of these is selected. It can be. Therefore, in the case of performing the reflector selection, existing conventional computing may have a limit due to the amount of computation in selecting an optimal combination for the reflector selection. That is, when the reflector selection is performed through calculation based on computing performance, real-time optimization may be difficult due to complicated calculation. Also, for example, if the reflector is selected in a simplified method, performance loss in comparison to optimal capacity may occur, and thus there may be a limit to the practical benefits of installing the reflector.

In view of the foregoing, the following describes a method for performing reflector selection.

As an example, FIG. 11 is a diagram illustrating a method of selecting a reflector through a Thompson sampling method according to an embodiment of the present disclosure. Referring to FIG. 11, the Thomson sampling method may be applied during reinforcement learning to select an optimized reflector in an SRE environment. As an example, Thomson sampling may be a method of receiving reward information for each parameter and updating parameters in consideration of the reward. Thereafter, a beta distribution may be applied to the updated parameter to determine the next operation based on the largest value among sampled values.

As a specific example, in FIG. 11, for convenience of description, two reflectors 1130-1 and 1130-2 (N=2) and three terminals 1120-1, 1120-2 and 1120-3 (K= 3) represents the environment in which it exists. In this case, the number of terminals K is an arbitrary number of terminals that the base station processes at one time for selecting the reflector, and may be smaller than or equal to the total number of terminals in the cell. However, this is only one example for convenience of description, and is not limited to the above-described embodiment.

At this time, terminal 1 (1120-1), terminal 2 (1120-2), and terminal 3 (1120-3) may be optimized by being mapped to reflector 1 (1130-1) and reflector 2 (1130-2). For example, among the mapping combinations of terminals and reflectors, reflector 1 (1130-1) and terminal 1 (1120-1) are mapped, and reflector 2 (1130-2) and terminal 2 (1120-2) are mapped. An optimal combination can be considered. Here, the base station has six combinations (3 The number of *2 = 6) is provided as Thomson sampling, and a combination index that is probabilistically highest at the current point in time among the number of 6 cases can be obtained. That is, the base station 1110 may obtain a result derived by applying Thomson sampling based on information on selectable combinations, and may not be limited to a specific form. After that, the base station may transmit a signal to each terminal through each reflector based on the determined combination. For example, in FIG. 11, the optimal combination is when reflector 1 (1130-1) and terminal 1 (1120-1) are mapped, and reflector 2 (1130-2) and terminal 2 (1120-2) are mapped. , The base station 1110 may transmit a signal to terminal 1 1120-1 through reflector 1 1130-1 and transmit a signal to terminal 2 1120-2 through reflector 2 1130-2. . After that, each of terminal 1 (1120-1) and terminal 2 (1120-2) may measure the reception performance of the signal received from the base station 1110 through the reflector and feed back the measured information to the base station. Here, the base station 1110 may calculate the sum of reception performances of the terminals 1120-1 and 1120-2. After that, the base station 1110 may compare the calculated sum of reception performance with a predetermined expected performance value, and provide result information to Thomson sampling. Thomson sampling may be updated based on the above, and the updated result may be reflected in the selection of the next reflector.

Here, as an example, the Thomson sampling device 1140 may be a device that exists separately from the base station 1110 or may be implemented through a cloud. That is, the base station 1110 provides reflector and terminal information to the Thomson sampling device 1140 implemented through a cloud or a device, and the Thomson sampling device 1140 may be initialized based on the transmitted information. After that, the Thomson sampling device 1140 may generate optimal reflector-to-vehicle mapping combination information based on the set initial value, and transmit it to the base station 1110 . Thereafter, the base station 1110 communicates with the terminal using the reflector and provides the obtained feedback information to the Thomson sampling device 1140, and the Thomson sampling device 1140 performs an update through the information to obtain an optimum Combinations can be updated.

As another example, it may be possible for the base station 1110 to perform Thomson sampling without a separate Thomson sampling device 1140 being provided. That is, as an artificial intelligence system that performs Thomson sampling, the above-described functions for the Thomson sampling device 1140 may be implemented in the base station 1140. Through the above, the base station 1140 may directly perform Thomson sampling without exchanging information with a separate device or cloud, and is not limited to a specific embodiment.

As another example, as an artificial intelligence system that performs Thomson sampling, the above-described function for the Thomson sampling device 1140 may be implemented in respective terminals, which will be described later.

12 is a diagram illustrating a specific method of performing Thomson sampling according to an embodiment of the present disclosure. 12 may be an operation of the Thomson sampling device of FIG. 11 . Thomson sampling may be used for selecting a reflector in the above-described SRE environment, but may not be limited thereto. For example, Thomson sampling may be applied even when stochastic optimization or stochastic selection is required in a wireless communication environment. As a more specific example, Thomson sampling can also be used for optimal frequency selection for each terminal.

Referring to FIG. 12 , when Thomson sampling is performed, whether or not the standard performance is achieved is input as True/False, and selected state information may be output. For example, the base station may calculate overall performance based on feedback information received from each terminal based on a previous mapping combination of the reflector and the terminal, and provide the overall performance information to Thomson sampling. At this time, whether or not the overall performance has achieved the standard performance can be provided as a True/False input of “Probability function update”. For example, the index of the previously obtained maximum value is the existing beta distribution factors a[l* ], b[l*] may be updated, and may be as shown in Table 2 below. Here, l* may be an index corresponding to the previously obtained maximum value.

As a specific example, when Thomson sampling is performed, a probability value for a selection value may be generated for each of the M candidates corresponding to the total number of cases. At this time, Thomson sampling can be configured as a, b beta distribution by setting the probability value of each case to a Bernoulli distribution and accumulating the probability function for the selection value, as shown in Equations 3 and 4 below. can In this case, for each case of the beta distribution, the factors may be “a[l]” and “b[l]”, and l may be a number for a given case.

Next, the selection value probability value applied according to Table 2 described above is sent to the maximum selection unit through the result bus, and through this, the index of the maximum value is output so that an updated reflector and terminal mapping combination can be indicated.

That is, the base station provides the overall performance information according to the previous combination as reward information to the Thomson sampling device based on Thomson sampling, and the Thomson sampling device updates each factor of the beta distribution through the reward information to optimize the distance between the reflector and the terminal. The combination can be effectively checked, and operation can be performed even if the channel between the reflector and the terminal is not recognized.

However, when Thomson sampling is used, the amount of calculation for each case may increase because a probability value must be obtained through a selection value probability function for all cases. That is, when the number of candidates M increases in FIG. 12 , the probability value may also increase and thus the complexity may increase in proportion. For example, in consideration of the above points, the number of logarithmically increasing cases may be considered to reduce the amount of computation for each case. In addition, as an example, there is a need to simplify the calculation of probability values in consideration of the fact that the amount of calculation increases, and considering the above point, a quantum computing-based quantum Thompson sampling method may be considered.

At this time, as an example, Quantum Thomson sampling may be implemented as a single device or provided as a result performed in a cloud system. That is, the base station provides reflector and terminal information to a quantum Thomson sampling device implemented through a cloud or a device, and the quantum Thomson sampling device may be initialized based on the transmitted information. Thereafter, the quantum Thomson sampling device may generate optimal reflector and terminal mapping combination information based on the set initial values, and transmit the information to the base station. Thereafter, the base station communicates with the terminal using the reflector and provides the obtained feedback information to the quantum Thomson sampling device, and the quantum Thomson sampling device performs an update based on the information to confirm an optimal combination.

As another example, it may be possible for the base station to perform Thomson sampling without a separate quantum Thomson sampling device. That is, as an artificial intelligence system that performs Thomson sampling, functions for the Quantum Thomson sampling device can be implemented in the base station. Through the above, the base station can directly perform quantum Thomson sampling without exchanging information with a separate device or cloud, and is not limited to a specific embodiment.

As another example, as an artificial intelligence system that performs quantum Thomson sampling, a function for a quantum Thomson sampling device may be implemented in respective terminals, which will be described later.

As an example, FIGS. 13 and 14 are diagrams illustrating a method of matching a reflector and a terminal in an SRE environment based on quantum Thomson sampling. At this time, as an example, quantum Thomson sampling is not limited to the reflector selection, and may be applied when stochastic optimization or stochastic selection is required in a wireless communication environment. For example, quantum Thomson sampling may be applied even when the terminal selects a base station for connection, which will be described later. Also, as an example, quantum Thomson sampling may be applied even when a terminal selects a resource to be used, which will be described later.

As a specific example, referring to FIG. 13 , terminal 1 (1320-1), terminal 2 (1320-2) and terminal 3 (1320-3) and reflector 1 (1330-1) and reflector 2 (1330-2) are environment can be taken into account. However, this is only one example for convenience of description, and is not limited to the above-described embodiment. At this time, as an example, reflector 1 (1330-1) and terminal 1 (1320-1) are mapped, and reflector 2 (1330-2) and terminal 2 (1320-2) are mapped among the mapping combinations of the terminals and the reflector. The case where the case is an optimal combination can be considered. Here, the base station has six combinations (3 The number of *2 = 6) is provided as quantum Thomson sampling, and an optimal combination index can be probabilistically obtained at the current time among the number of 6 cases.

After that, the base station 1310 may transmit a signal to each terminal through each reflector based on the determined combination. For example, in FIG. 13, the case where reflector 1 (1330-1) and terminal 1 (1320-1) are mapped and reflector 2 (1330-2) and terminal 2 (1320-2) are mapped is an optimal combination. , The base station 1310 may transmit a signal to terminal 1 (1320-1) through reflector 1 (1330-1) and transmit a signal to terminal 2 (1320-2) through reflector 2 (1330-2). . After that, each of terminal 1 (1320-1) and terminal 2 (1320-2) may measure the reception performance of the signal received from the base station 1310 through the reflector and feed back the measured information to the base station. Here, the base station 1310 may calculate the sum of reception performances of the terminals 1320-1 and 1320-2. After that, the base station 1310 may compare the calculated sum of reception performance with a predetermined expected performance value, and provide result information to quantum Thomson sampling. Quantum Thomson sampling compares overall performance with expected performance, updates beta distribution factors a and b, and derives optimal combination information based on the largest value among sampled values.

As a specific example, referring to FIG. 14 , based on FIG. 13 , the base station 1410 may provide combination information on matching between the reflector and the terminal to the quantum Thomson sampling device 1430 . That is, the base station 1410 may provide information for initialization to the quantum Thomson sampling device 1430 . In this case, the quantum Thomson sampling device 1430 may obtain first best combination information based on the information received from the base station. For example, the Quantum Thomson sampling device 1430 may include previously learned information about an optimal combination, and may derive first optimal combination information based on this, but is not limited to a specific embodiment. Also, as an example, the quantum Thomson sampling device 1430 may be implemented in the base station 1410, as described above. Then, the base station 1410 may set mapping information of the reflector obtained based on quantum Thomson sampling and each terminal as group configuration information, and transmit the group configuration information to each terminal. That is, the base station 1410 may transmit mapping information between the reflector and the terminal to terminal 1 1420 - 1 and terminal 2 1420 - 2 based on quantum Thomson sampling. After that, the base station 1410 may control the mapped reflector to transmit a signal to each terminal through the reflector corresponding to each terminal. After that, terminal 1 (1420-1) and terminal 2 (1420-2) may transmit feedback information on the signal received from the base station 1410 to the base station 1410 through the reflector. The base station 1410 calculates the sum of the reception performance of the terminal 1 1420-1 and the terminal 2 1420-2 based on the feedback information obtained from the terminal 1 1420-1 and the terminal 2 1420-2. can be calculated After that, the base station 1310 may compare the calculated sum of reception performance with a preset expected performance value, and provide result information to quantum Thomson sampling as group feedback information or reward information. The quantum Thomson sampling device 1430 may be learned based on the above, and then an optimal combination may be selected by updating the beta distribution factors a and b. After that, the Thomson sampling device 1430 may transmit updated second best combination information to the base station 1410 . The base station 1410 may check the mapped reflector for each terminal based on the second optimal combination information. After that, the base station 1410 may transmit information on the mapped reflector to respective terminals and perform signal transmission to the terminals through the mapped reflector. Through the foregoing, the base station 1410 may perform communication by updating mapping between the reflector and the terminal in real time.

Here, a specific operation of quantum Thomson sampling may be performed through a unitary operation updater based on qubits without using a probability function update. At this time, as an example, whether or not the standard performance is achieved may be input as True/False to the unitary operation updater, and state information selected from the unitary operation updater may be output. For example, the base station may calculate overall performance based on feedback information received from each terminal based on a previous mapping combination of the reflector and the terminal, and provide the overall performance information to Thomson sampling. At this time, whether the overall performance achieves the reference performance may be provided to the unitary operation updater.

At this time, as an example, referring to FIG. 15 , a quantum computing part may be provided at the top of the quantum Thomson sampling. At this time, based on the quantum computing part, first, L number of qubits enter the Hadamard gate, respectively, and quantized information is generated, which may be transitioned to a quantum superposition state. Then, the transformed qubits can be quantized according to the given weight through unitary operation (W). For example, it may be possible to assign a higher weight to a specific reflector based on the unitary operation (W), and may not be limited to the above-described embodiment. In this case, as an example, the unitary operation for each qubit may be as shown in Equation 5 below.

In addition, in quantum Thomson sampling, L may be greater than or equal to M, which is the total number of log cases. At this time, even when L increases in size through log, a very small value can be derived. Next, the results of the qubits after quantum calculations can be measured and output to the outside through a classical computing bus. In addition, the unitary operation updater may obtain overall performance information for an optimal combination of qubits based on the result as an input. At this time, the unitary operation updater may update the weight of the existing unitary operation with reference to two input values (Best Probabilistic Selection Result, Performance > Expectation?).

As another example, FIG. 16 is a diagram illustrating a 1 qubit mode for selecting one of two selection combinations as an example of FIG. 15 . As an example, referring to FIG. 16 , in the quantum computing part at the top, a qubit first enters the Hadamard gate, quantized information is generated, and the quantum superposition state can be transitioned. Then, the transformed qubits can be quantized according to a given weight through unitary operation (W), as described above. Here, a weight may be reflected based on Ry-gate, which is one of the unitary operations. In Ry-gate, the ratio of 0 to 1 in the transformed qubit can be different based on the θ value. That is, the weight for each transformed qubit can be determined through θ of the Ry-gate. In this case, for example, when a qubit is quantized through a bloch sphere after passing through a Hadamard gate, the probability of obtaining 0 and 1 may be the same. Therefore, when a unitary operation operates based on a block sphere, there may be a limit to assigning a weight. However, as an example, when a reflector is selected, a weight may be assigned to increase the probability of selecting a specific reflector, and through this, flexible selection of the reflector may be possible. For example, the weight may be determined based on performance obtained by the base station. For example, the base station may set a high weight for a reflector corresponding to an ACK-received signal and set a low weight for a reflector corresponding to a NACK-received signal. However, this is only one example and is not limited to the above-described embodiment. That is, each transformed qubit can be provided to the unitary operation updater after passing through the Hadamard gate and then being weighted through Ry-gate (θ) as a unitary operation. That is, the qubit for which Ry-gate has been performed can be provided as a unitary operation updater while outputting the result to the outside through a classical computing bus with a result measured as 0 or 1. In this case, the unitary operation updater may update the existing phase weight (θ) of the ry-gate operation by referring to two input values (best probabilistic selection result, performance > expectation?), and control may be performed through this.

Also, as an example, FIG. 17 is a diagram illustrating a 1-qubit unitary operation updater in FIG. 16 . For example, referring to FIG. 17(a) , the phase value θ is initially 0 so that a combination in which a first reflector is allocated to a first terminal and a second reflector is provided to a second terminal among two reflector combinations is selected. A case in which −π/2 is updated may be considered. Here, an algorithm applied to the 1-qubit unitary operation updater may be shown in Table 3 below.

Here, the operation principle of the block diagram can be maintained as it is when the value of y[k] at point k is True according to the value of r[k]. On the other hand, if the value of y[k] at point k is False according to the value of r[k], it becomes 1-y[k] and can enter the Generate Target θ step. At this time, the Generate Target θ step may output θo[k] as a reference phase value according to the input value. In this case, when the input is 1, the output may be ?π/2. Also, when the input is 0, the output may be π/2.

θ[k] may be updated based on Equation 6 below in a gradient method using the above-described value. In this case, for a maximum of two combinations, quantum Thomson sampling may be as shown in Equation 7 and FIG. 18 according to changes in success probabilities of each case.

As another example, quantum Thomson sampling may be applied not only to mapping between a reflector and a terminal, but also to a case where stochastic optimization or stochastic selection is required. As a specific example, quantum Thomson sampling may be applied in a process in which a terminal selects a base station to which an access is to be performed. As an example, the terminal may receive optimal base station information through quantum Thomson sampling after initialization by exchanging base station related information with a quantum Thomson sampling device. Here, as an example, the function of the quantum tom sampling device may be implemented in the terminal. That is, the terminal may directly perform quantum Thomson sampling by reflecting information on accessable base stations, and the specific quantum Thomson sampling operation may operate based on the ry-gate (θ) operation and unitary operation updater as described above. And, the specific operation method may be as described above.

As another example, quantum Thomson sampling may be applied in a process of selecting a frequency used by the base station and the terminal for communication. For example, quantum Thomson sampling may be performed based on a separate quantum Thomson sampling device or may be implemented and operated in a terminal or base station, and is not limited to a specific embodiment. At this time, information related to frequency selection may be provided and initialized for quantum Thomson sampling, and an optimal frequency may be selected through quantum Thomson sampling. For example, UEs within a cell may use different frequencies to avoid collision. Therefore, quantum Thomson sampling can perform optimal frequency selection based on available frequencies and mapping information on terminals using the frequencies. Then, the quantum Thomson sampling may be updated based on the reward information, and the specific quantum Thomson sampling operation may operate based on the ry-gate (θ) operation and the unitary operation updater as described above, which is as described above can be the same

Referring to FIG. 19, the base station may transmit initialization information to the Quantum Thomson sampling device (S1910). At this time, the initialization information is based on mapping that can be combined in an environment where a plurality of reflectors and a plurality of terminals exist as described above. It may be generated information. That is, the initialization information may be initial information for deriving best combination information in consideration of mapping between the reflector and the terminal. Then, the quantum Thomson sampling device may derive first best combination information based on the received initialization information. The base station may receive the first best combination information derived by the quantum Thomson sampling device (S1920). Here, the first best combination information is a reflector derived based on mapping information that can be combined in a plurality of reflectors and a plurality of terminals. and mapping information of the UE.

After that, the base station may generate group configuration information based on the first best combination information and transmit the group configuration information to each of a plurality of terminals (S1930). It can be transmitted as group configuration information, and the terminal can recognize the reflector to which it is mapped through this. After that, the base station may transmit a signal to each of the plurality of terminals through a reflector selected from each of the plurality of terminals (S1940). Then, the base station may receive feedback information from each of the plurality of terminals. ( S1950) At this time, the base station may calculate group feedback information from feedback information for each of a plurality of terminals. Here, the group feedback information may be total reception performance information considering the reception performance of each terminal. Thereafter, the base station may transmit the derived group feedback information to the Quantum Thomson sampling device (S1960). Then, the base station may receive second best combination information from the Quantum Thomson sampling device (S1970). For example, overall performance information as the above-described group feedback information may be compared with preset expected performance information, and reward information may be generated through this. In this case, the reward information may be updated information for factors a and b based on the above-described beta distribution, as described above. Here, the above-described reward information may be provided as an input of the Quantum Thomson sampling device. More specifically, the reward information may be provided as a unitary operation updater. After that, the phase value applied to the ry-gate may be updated based on the reward information in the unitary operation updater. That is, the quantum Thomson sampling device may derive second best combination information by performing learning through quantum Thomson sampling based on the reward information.

Here, when learning is performed based on quantum Thomson sampling, a plurality of quantization bits may be set based on mapping information that can be combined between a plurality of reflectors and a plurality of terminals. In this case, each of the plurality of quantization bits may output quantized bits by reflecting a phase value as weight information in a ry-gate operation through a Hadamard gate.

That is, the ry-gate operation may include a phase weight value corresponding to each of a plurality of quantization bits, and the phase weight value may be updated based on reward information. Then, second best combination information may be derived based on the quantized bits output after the ry-gate operation is performed, and based on this, mapping between the reflector and the terminal may be performed.

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). there is.

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 2nd 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 THzWave 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 initialization information to the Quantum Thomson sampling device;

receiving first best combination information from the quantum Thomson sampling device;

transmitting group configuration information to each of a plurality of terminals based on the first best combination information;

transmitting a signal to each of the plurality of terminals through a reflector selected from each of the plurality of terminals;

Receiving feedback information from each of the plurality of terminals;

Calculating group feedback information based on feedback information for each of the plurality of terminals and transmitting the group feedback information to the Quantum Thomson sampling device; and

Receiving second best combination information from the Quantum Thomson sampling device; including, a base station operating method.
According to claim 1,

The initialization information is information generated based on mapping a plurality of reflectors and the plurality of terminals, the base station operating method.
According to claim 2,

Based on the initialization information, the first best combination information is derived through the quantum Thomson sampling device based on mapping information that can be combined of the plurality of reflectors and the plurality of terminals.
According to claim 3,

The group feedback information calculated by the base station based on the feedback information of the plurality of terminals is overall performance information.
According to claim 4,

Reward information generated by comparing the overall performance information with predetermined expected performance information is provided as an input to the Quantum Thomson sampling device.
According to claim 5,

Based on the reward information, the quantum Thomson sampling device derives the second best combination information by performing learning based on quantum Thomson sampling.
According to claim 6,

When learning is performed based on quantum Thomson sampling, a plurality of quantization bits are set based on mapping information that can be combined between the plurality of reflectors and the plurality of terminals, and each of the plurality of quantization bits passes through a Hadamard gate to ry A method of operating a base station that outputs quantized bits after a -gate operation is performed.
According to claim 7,

The ry-gate operation includes a phase weight value corresponding to each of a plurality of quantization bits, and the phase weight value is updated based on the reward information.
According to claim 7,

The second best combination information is derived based on the quantized bits output after the ry-gate operation is performed.
According to claim 9,

The quantized bit output after the ry-gate operation is performed is provided as a unitary operation updater based on the second best combination information.
In a terminal operating method in a wireless communication system,

Receiving group configuration information from a base station;

checking a corresponding reflector based on the received group configuration information;

receiving a signal from the base station through the reflector; and

Generating feedback information based on the received signal and transmitting it to the base station; Including,

The base station transmits initialization information to the Quantum Thomson sampling device, receives first best combination information from the Quantum Thomson sampling device, and transmits the group configuration information to each of a plurality of terminals based on the first best combination information and calculating group feedback information based on the feedback information for each of the plurality of terminals, transmitting the group feedback information to the Quantum Thomson sampling device, and receiving second best combination information from the Quantum Thomson sampling device.
In a base station of a wireless communication system,

transceiver; and

A processor connected to the transceiver;

the processor,

Sending initialization information to a Quantum Thomson sampling device through the transceiver;

Receiving first best combination information from the Quantum Thomson sampling device through the transceiver;

Transmitting group configuration information to each of a plurality of terminals based on the first best combination information through the transceiver;

Transmitting a signal to each of the plurality of terminals through a reflector selected from each of the plurality of terminals through the transceiver;

Receiving feedback information from each of the plurality of terminals through the transceiver,

Calculate group feedback information based on feedback information for each of the plurality of terminals through the transceiver, transmit it to the quantum Thomson sampling device, and

and receiving second best combination information from the Quantum Thomson sampling device through the transceiver.
In the terminal of the wireless communication system,

transceiver; and

A processor connected to the transceiver;

the processor,

Receiving group configuration information from a base station through the transceiver;

Checking a corresponding reflector based on the received group configuration information;

Receiving a signal from the base station through the reflector through the transceiver, and

Generate feedback information based on the received signal through the transceiver and transmit it to the base station,

The base station transmits initialization information to the Quantum Thomson sampling device, receives first best combination information from the Quantum Thomson sampling device, and transmits the group configuration information to each of a plurality of terminals based on the first best combination information and calculating group feedback information based on the feedback information for each of the plurality of terminals, transmitting the group feedback information to the Quantum Thomson sampling device, and receiving second best combination information from the Quantum Thomson sampling device.
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,

Send initialization information to the Quantum Thomson sampling device,

Receiving first best combination information from the Quantum Thomson sampling device;

Transmitting group configuration information to each of a plurality of terminals based on the first best combination information;

Transmitting a signal to each of the plurality of terminals through a reflector selected from each of the plurality of terminals,

Receiving feedback information from each of the plurality of terminals,

Group feedback information is calculated based on the feedback information for each of the plurality of terminals, transmitted to the Quantum Thomson sampling device, and

and receiving second best combination information from the Quantum Thomson sampling device.
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 apparatus transmits initialization information to a Quantum Thomson sampling device, receives first best combination information from the Quantum Thomson sampling device, and transmits group configuration information to each of a plurality of terminals based on the first best combination information, , Transmits a signal to each of the plurality of terminals through a reflector selected from each of the plurality of terminals, receives feedback information from each of the plurality of terminals, and based on the feedback information for each of the plurality of terminals A computer readable medium for calculating group feedback information, transmitting to the Quantum Thomson sampling device, and receiving second best combination information from the Quantum Thomson sampling device.