Device and method for performing communication using a reconfigurable intelligent surface in a wireless communication system

Abstract

The present disclosure is for performing communication using a reconfigurable intelligent surface (RIS) in a wireless communication system, and the method of operation of a first base station may include the steps of transmitting a reference signal to a first terminal, receiving first channel information measured based on the reference signal from the first terminal, receiving second channel information from a second base station, determining a reflection pattern of the RIS (reconfigurable intelligent surface) based on the first channel information and the second channel information, and transmitting a data signal to the first terminal.

Description

Technology Field

[0001] The following description relates to a wireless communication system, and to an apparatus and method for performing communication using a reconfigurable intelligent surface (RIS) in a wireless communication system. Background Technology

[0002] Wireless access systems are being widely deployed to provide various types of communication services, such as voice and data. Generally, 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 multiple access systems include CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access) systems.

[0003] In particular, as many communication devices require large communication capacities, enhanced mobile broadband (eMBB) communication technology is being proposed as an improvement over existing radio access technology (RAT). Furthermore, communication systems are being proposed that consider not only massive machine type communications (mmTC), which connects multiple devices and objects to provide various services anytime and anywhere, but also services and user equipment (UE) that are sensitive to reliability and latency. Various technical configurations are being proposed to support this. The problem to be solved

[0004] The present disclosure may provide an apparatus and method for efficiently performing communication using a reconfigurable intelligent surface (RIS) in a wireless communication system.

[0005] The present disclosure may provide an apparatus and method for transmitting a signal to a user in a shadow area using an RIS in a wireless communication system.

[0006] The present disclosure may provide an apparatus and method for optimizing the reflection pattern of an RIS in a wireless communication system.

[0007] The present disclosure may provide an apparatus and method for determining a reflection pattern of an RIS based on the total transmission rate in a wireless communication system.

[0008] The present disclosure may provide an apparatus and method for acquiring channel information of a surrounding base station to determine the reflection pattern of an RIS in a wireless communication system.

[0009] The present disclosure may provide an apparatus and method for reducing gain for a channel between a user and an RIS that does not use the RIS in a wireless communication system.

[0010] The present disclosure may provide an apparatus and method for operating an RIS without reducing the communication performance of other service providers in a wireless communication system.

[0011] The present disclosure may provide an apparatus and method for determining weights between a user using RIS and a user not using RIS in a wireless communication system based on a channel environment.

[0012] The present disclosure may provide an apparatus and method for optimizing the reflection pattern of an RIS using a Riemannian conjugate gradient (RCG) algorithm in a wireless communication system.

[0013] The technical objectives to be achieved in this disclosure are not limited to those mentioned above, and other unmentioned technical problems may be considered by those skilled in the art to which the technical configuration of this disclosure applies, based on the embodiments of this disclosure described below. means of solving the problem

[0014] As an example of the present disclosure, a method performed by a first base station in a wireless communication system may include the steps of transmitting a reference signal to a first terminal, receiving first channel information measured based on the reference signal from the first terminal, receiving second channel information from a second base station, determining a reflection pattern of a reconfigurable intelligent surface (RIS) based on the first channel information and the second channel information, and transmitting a data signal to the first terminal. The first channel information may include information related to a first RIS channel including a channel between the first base station and the RIS and a channel between the RIS and the first terminal, and the second channel information may include information related to a second RIS channel including a channel between the second base station and the RIS and a channel between the RIS and the second terminal.

[0015] As an example of the present disclosure, a method performed by a first terminal in a wireless communication system may include the steps of receiving a reference signal from a first base station, transmitting first channel information measured based on the reference signal to the first base station, and receiving a data signal from the first base station through a reconfigurable intelligent surface (RIS). The reflection pattern of the RIS is determined based on a first RIS channel and a second RIS channel, wherein the first RIS channel includes a channel between the first base station and the RIS and a channel between the RIS and the first terminal, and the second RIS channel may include a channel between the second base station and the RIS and a channel between the RIS and the second terminal.

[0016] As an example of the present disclosure, a first base station in a wireless communication system comprises a transceiver and a processor connected to the transceiver, wherein the processor transmits a reference signal to a first terminal, receives first channel information measured based on the reference signal from the first terminal, receives second channel information from a second base station, determines a reflection pattern based on the first channel information and the second channel information, and controls the transmission of a data signal to the first terminal, wherein the first channel information includes information related to a first RIS channel including a channel between the first base station and the RIS and a channel between the RIS and the first terminal, and the second channel information may include information related to a second RIS channel including a channel between the second base station and the RIS and a channel between the RIS and the second terminal. In a wireless communication system, a first terminal comprises a transceiver and a processor connected to the transceiver, wherein the processor receives a reference signal from a first base station, receives first channel information measured based on the reference signal from the first base station, and controls the first base station to receive a data signal through a reconfigurable intelligent surface (RIS), wherein the reflection pattern of the RIS is determined based on a first RIS channel and a second RIS channel, the first RIS channel includes a channel between the first base station and the RIS and a channel between the RIS and the first terminal, and the second RIS channel may include a channel between the second base station and the RIS and a channel between the RIS and the second terminal.

[0017] As an example of the present disclosure, a communication device comprises at least one processor and at least one computer memory connected to the at least one processor and storing instructions that direct operations as executed by the at least one processor, wherein the operations may include: transmitting a reference signal to a first terminal; receiving first channel information measured based on the reference signal from the first terminal; receiving second channel information from a second base station; determining a reflection pattern of a reconfigurable intelligent surface (RIS) based on an objective function generated based on the first channel information and the second channel information; and transmitting a data signal to the first terminal. The first channel information may include information related to a first RIS channel including a channel between the communication device and the RIS and a channel between the RIS and the first terminal, and the second channel information may include information related to a second RIS channel including a channel between the second base station and the RIS and a channel between the RIS and the second terminal.

[0018] As an example of the present disclosure, a non-transitory computer-readable medium storing at least one instruction comprises said at least one instruction executable by a processor, said at least one instruction, said at least one instruction, said at least one instruction, said device transmits a reference signal to a first terminal, receives first channel information measured based on the reference signal from the first terminal, receives second channel information from a second base station, determines a reflection pattern of a reconfigurable intelligent surface (RIS) based on an objective function generated based on the first channel information and the second channel information, and controls the transmission of a data signal to the first terminal, said first channel information comprises information related to a first RIS channel including a channel between the device and the RIS and a channel between the RIS and the first terminal, and said second channel information may comprise information related to a second RIS channel including a channel between the second base station and the RIS and a channel between the RIS and the second terminal.

[0019] The embodiments of the present disclosure described above are merely some of the preferred embodiments of the present disclosure, and various embodiments reflecting the technical features of the present disclosure can be derived and understood by those skilled in the art based on the detailed description of the present disclosure to be described below. Effects of the invention

[0020] The following effects may be achieved by embodiments based on the present disclosure.

[0021] According to the present disclosure, communication can be performed efficiently using a reconfigurable intelligent surface (RIS).

[0022] The effects obtainable from the embodiments of the present disclosure are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by a person skilled in the art to which the technical configuration of the present disclosure applies from the description of the embodiments of the present disclosure below. That is, unintended effects resulting from implementing the configuration described in the present disclosure can also be derived by a person skilled in the art from the embodiments of the present disclosure. Brief explanation of the drawing

[0023] The drawings attached below are intended to aid in understanding the present disclosure and may provide embodiments of the present disclosure together with the detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with one another to form new embodiments. Reference numerals in each drawing may denote structural elements. FIG. 1 illustrates an example of a communication system applicable to the present disclosure. FIG. 2 illustrates an example of a wireless device applicable to the present disclosure. FIG. 3 illustrates another example of a wireless device applicable to the present disclosure. FIG. 4 illustrates an example of a portable device applicable to the present disclosure. FIG. 5 illustrates an example of a vehicle or autonomous vehicle applicable to the present disclosure. FIG. 6 illustrates an example of Artificial Intelligence (AI) applicable to the present disclosure. FIG. 7 illustrates a method for processing a transmission signal applicable to the present disclosure. FIG. 8 illustrates an example of a communication structure that can be provided in a 6G system applicable to the present disclosure. FIG. 9 illustrates an electromagnetic spectrum applicable to the present disclosure. FIG. 10 illustrates a THz communication method applicable to the present disclosure. FIG. 11 illustrates a wireless channel environment according to one embodiment of the present disclosure. FIG. 12 illustrates an intelligent wireless environment according to one embodiment of the present disclosure. FIG. 13 illustrates a conventional wireless channel environment and an intelligent wireless channel environment according to one embodiment of the present disclosure. FIG. 14 illustrates an example of a communication environment considering multiple service providers according to one embodiment of the present disclosure. FIG. 15 illustrates an example of a procedure in which a first base station, according to one embodiment of the present disclosure, determines a beamformer and a reconfigurable intelligent surface (RIS) reflection pattern by considering channel information of a second base station. FIG. 16 illustrates an example of a procedure using a Riemannian conjugate gradient (RCG) algorithm to optimize the reflection pattern of a RIS according to one embodiment of the present disclosure. FIG. 17 illustrates an example of a procedure in which a first terminal, according to one embodiment of the present disclosure, performs communication using a reflection pattern of an RIS that takes into account channel information of a second base station. FIG. 18 illustrates an example of signaling for performing communication using a reflection pattern of an RIS determined based on channel information of a first base station and channel information of a second base station according to one embodiment of the present disclosure. FIG. 19 illustrates an example of a simulation environment for a comparative experiment according to one embodiment of the present disclosure. FIG. 20 illustrates an example of a comparative experiment in which the total transmission rate changes based on the transmission power according to one embodiment of the present disclosure. FIG. 21 is a balance parameter according to one embodiment of the present disclosure. An example of the performance of the total transmission rate according to is illustrated. Specific details for implementing the invention

[0024] The following embodiments are combinations of the components and features of the present disclosure in a predetermined form. Each component or feature may be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, some components and / or features may be combined to constitute embodiments of the present disclosure. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of any embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.

[0025] In the description of the drawings, procedures or steps that could obscure the gist of the present disclosure have not been described, nor have procedures or steps that are understandable to those skilled in the art been described.

[0026] Throughout the specification, when a part is described as "comprising" or "including" a component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "...part," "...unit," and "module" as used in the specification refer to a unit that performs at least one function or operation, and this may be implemented in hardware, software, or a combination of hardware and software. Additionally, "one (a or an)," "one," "the," and similar related terms may be used in the context describing the present disclosure (particularly in the context of the following claims) in both singular and plural forms, unless otherwise indicated in the specification or clearly contradicted by the context.

[0027] In this specification, the embodiments of the present disclosure are described with a focus on the data transmission and reception relationship between a base station and a mobile station. Here, the base station refers to a terminal node of a network that communicates directly with a mobile station. Specific operations described in this document as being performed by a base station may, in some cases, be performed by an upper node of the base station.

[0028] That is, in a network consisting of multiple network nodes including a base station, various operations performed for communication with a mobile station may be performed by the base station or other network nodes other than the base station. In this case, 'base station' may be replaced by terms such as fixed station, Node B, eNB (eNode B), gNB (gNode B), ng-eNB, advanced base station (ABS), or access point.

[0029] Additionally, in the embodiments of the present disclosure, the term terminal may be replaced with terms such as user equipment (UE), mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, or advanced mobile station (AMS).

[0030] Furthermore, the transmitting end refers to a fixed and / or mobile node that provides data or voice services, and the receiving end refers to a fixed and / or mobile node that receives data or voice services. Therefore, in the case of the uplink, a mobile station can be the transmitting end and a base station can be the receiving end. Similarly, in the case of the downlink, a mobile station can be the receiving end and a base station can be the transmitting end.

[0031] Embodiments of the present disclosure may be supported by standard documents disclosed in at least one of wireless access systems, such as IEEE 802.xx systems, 3GPP (3rd Generation Partnership Project) systems, 3GPP LTE (Long Term Evolution) systems, 3GPP 5G (5th generation) NR (New Radio) systems and 3GPP2 systems, and in particular, embodiments of the present disclosure may be supported by 3GPP TS (technical specification) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents.

[0032] In addition, the embodiments of the present disclosure may be applied to other wireless access systems and are not limited to the systems described above. For example, they may be applicable to systems applied after the 3GPP 5G NR system and are not limited to specific systems.

[0033] That is, obvious steps or parts not described in the embodiments of the present disclosure may be described by referring to the aforementioned documents. Additionally, all terms disclosed in this document may be explained by the aforementioned standard documents.

[0034] Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary embodiments of the present disclosure and is not intended to represent the only embodiment in which the technical configuration of the present disclosure can be implemented.

[0035] Additionally, specific terms used in the embodiments of the present disclosure are provided to aid in understanding the present disclosure, and the use of such specific terms may be modified in other forms without departing from the technical spirit of the present disclosure.

[0036] The following technology can be applied to various wireless access systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access).

[0037] For the sake of clarity in the following description, the explanation is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical concept of the present invention is not limited thereto. LTE may refer to technology from 3GPP TS 36.xxx Release 8 onwards. Specifically, LTE technology from 3GPP TS 36.xxx Release 10 onwards is referred to as LTE-A, and LTE technology from 3GPP TS 36.xxx Release 13 onwards may be referred to as LTE-A pro. 3GPP NR may refer to technology from TS 38.xxx Release 15 onwards. 3GPP 6G may refer to technology from TS Release 17 and / or Release 18 onwards. "xxx" indicates a standard document detail number. LTE / NR / 6G may be collectively referred to as 3GPP systems.

[0038] Regarding the background technology, terms, abbreviations, etc. used in this disclosure, reference may be made to matters described in standard documents published prior to the present invention. For example, reference may be made to standard documents 36.xxx and 38.xxx.

[0039] Communication systems applicable to the present disclosure

[0040] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods, and / or flowcharts of the disclosure disclosed in this document may be applied to various fields requiring wireless communication / connection (e.g., 5G) between devices.

[0041] Examples are provided in more detail below with reference to the drawings. In the following drawings and descriptions, the same reference numerals may represent the same or corresponding hardware blocks, software blocks, or function blocks unless otherwise described.

[0042] FIG. 1 is a drawing illustrating an example of a communication system to which the present disclosure applies.

[0043] Referring to FIG. 1, the communication system (100) to which the present disclosure applies includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using wireless access technology (e.g., 5G NR, LTE) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (extended reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Thing) device (100f), and an AI (artificial intelligence) device / server (100g). For example, the vehicle may include a vehicle equipped with wireless communication capabilities, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, etc. Here, the vehicle (100b-1, 100b-2) may include an unmanned aerial vehicle (UAV) (e.g., a drone). XR devices (100c) include AR (augmented reality) / VR (virtual reality) / MR (mixed reality) devices and can be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc. Portable devices (100d) may include a smartphone, a smart pad, a wearable device (e.g., a smartwatch, smart glasses), a computer (e.g., a laptop, etc.). Home appliances (100e) may include a TV, a refrigerator, a washing machine, etc. IoT devices (100f) may include sensors, smart meters, etc. For example, base stations (120) and networks (130) may also be implemented as wireless devices, and a specific wireless device (120a) may operate as a base station / network node for other wireless devices.

[0044] Wireless devices (100a to 100f) can be connected to a network (130) through a base station (120). AI technology may be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (100g) through the network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g., NR) network. The wireless devices (100a to 100f) may communicate with each other through the base station (120) / network (130), but they may also communicate directly (e.g., sidelink communication) without going through the base station (120) / network (130). For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). Also, IoT devices (100f) (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0045] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base station (120) and between base station (120) / base station (120). Here, wireless communication / connection can be established through various wireless access technologies (e.g., 5G NR), such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between base stations (150c) (e.g., relay, IAB (integrated access backhaul)). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on the various proposals of the present disclosure, at least some of the following may be performed: a process for setting various configuration information for transmitting / receiving wireless signals, a process for various signal processing (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), a resource allocation process, etc.

[0046] Communication systems applicable to the present disclosure

[0047] FIG. 2 is a drawing illustrating an example of a wireless device that can be applied to the present disclosure.

[0048] Referring to FIG. 2, the first wireless device (200a) and the second wireless device (200b) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (200a), the second wireless device (200b)} may correspond to {the wireless device (100x), the base station (120)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 1.

[0049] The first wireless device (200a) includes one or more processors (202a) and one or more memories (204a), and may additionally include one or more transceivers (206a) and / or one or more antennas (208a). The processor (202a) controls the memory (204a) and / or transceivers (206a) and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or sequences of operation disclosed in this document. For example, the processor (202a) may process information within the memory (204a) to generate a first information / signal and then transmit a wireless signal containing the first information / signal through the transceiver (206a). Additionally, the processor (202a) may receive a wireless signal containing a second information / signal through the transceiver (206a) and then store information obtained from the signal processing of the second information / signal in the memory (204a). 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 store software code containing instructions for performing some or all of the processes controlled by the processor (202a) or for performing the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams disclosed in this document. Here, the processor (202a) and memory (204a) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). A transceiver (206a) may be connected 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 receiver. The transceiver (206a) may be combined with an RF (radio frequency) unit. In the present disclosure, a wireless device may refer to a communication modem / circuit / chip.

[0050] The second wireless device (200b) includes one or more processors (202b) and one or more memories (204b), and may additionally include one or more transceivers (206b) and / or one or more antennas (208b). The processor (202b) controls the memory (204b) and / or transceivers (206b) and may be configured to implement the descriptions, functions, procedures, proposals, methods and / or flowcharts of operation disclosed herein. For example, the processor (202b) may process information within the memory (204b) to generate a third information / signal and then transmit a wireless signal containing the third information / signal through the transceiver (206b). Additionally, the processor (202b) may receive a wireless signal containing a fourth information / signal through the transceiver (206b) and then store information obtained from the signal processing of the fourth information / signal in the memory (204b). Memory (204b) may be connected to the processor (202b) and may store various information related to the operation of the processor (202b). For example, memory (204b) may store software code containing instructions for performing some or all of the processes controlled by the processor (202b) or for performing the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. Here, the processor (202b) and memory (204b) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). A transceiver (206b) may be connected 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 receiver. The transceiver (206b) may be used interchangeably with an RF unit. In the present disclosure, a wireless device may refer to a communication modem / circuit / chip.

[0051] Hereinafter, hardware elements of the wireless device (200a, 200b) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (202a, 202b). For example, one or more processors (202a, 202b) may implement one or more layers (e.g., functional layers such as PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource control), and SDAP (service data adaptation protocol). One or more processors (202a, 202b) may generate one or more PDUs (Protocol Data Units) and / or one or more SDUs (service data units) according to the descriptions, functions, procedures, proposals, methods and / or flowcharts of operation disclosed in this document. One or more processors (202a, 202b) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. One or more processors (202a, 202b) may generate a signal (e.g., baseband signal) containing a PDU, SDU, message, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this document and provide it to one or more transceivers (206a, 206b). One or more processors (202a, 202b) may receive a signal (e.g., baseband signal) from one or more transceivers (206a, 206b) and may obtain a PDU, SDU, message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document.

[0052] 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, 202b). The descriptions, functions, procedures, proposals, methods, and / or 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, etc. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be contained in one or more processors (202a, 202b) or stored in one or more memories (204a, 204b) and driven by one or more processors (202a, 202b). The descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be implemented using firmware or software in the form of code, instructions, and / or sets of instructions.

[0053] One or more memories (204a, 204b) may be connected to one or more processors (202a, 202b) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. One or more memories (204a, 204b) may be composed of ROM (read-only memory), RAM (random access memory), EPROM (erasable programmable read-only memory), flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. One or more memories (204a, 204b) may be located inside and / or outside of one or more processors (202a, 202b). Additionally, 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.

[0054] One or more transceivers (206a, 206b) may transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or operation flowcharts, etc., of this document to one or more other devices. One or more transceivers (206a, 206b) may receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts, etc., disclosed in this document from one or more other devices. For example, one or more transceivers (206a, 206b) may be connected to one or more processors (202a, 202b) and may transmit and receive wireless signals. For example, one or more processors (202a, 202b) may control one or more transceivers (206a, 206b) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (202a, 202b) may control one or more transceivers (206a, 206b) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (206a, 206b) may be connected to one or more antennas (208a, 208b), and one or more transceivers (206a, 206b) may be configured to transmit and receive user data, control information, wireless signals / channels, etc., as described in the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document through one or more antennas (208a, 208b). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (206a, 206b) can convert the received wireless signal / channel, etc. from an RF band signal to a baseband signal in order to process the received user data, control information, wireless signal / channel, etc. using one or more processors (202a, 202b).One or more transceivers (206a, 206b) can convert user data, control information, wireless signals / channels, etc. processed using one or more processors (202a, 202b) from baseband signals to RF band signals. To this end, one or more transceivers (206a, 206b) may include (analog) oscillators and / or filters.

[0055] Wireless device structure applicable to the present disclosure

[0056] FIG. 3 is a drawing illustrating another example of a wireless device to which the present disclosure applies.

[0057] Referring to FIG. 3, the wireless device (300) corresponds to the wireless device (200a, 200b) of FIG. 2 and may be composed of various elements, components, units, and / or modules. For example, the wireless device (300) may include a communication unit (310), a control unit (320), a memory unit (330), and additional elements (340). The communication unit may include a communication circuit (312) and transceiver(s) (314). For example, the communication circuit (312) may include one or more processors (202a, 202b) and / or one or more memories (204a, 204b) of FIG. 2. For example, the transceiver(s) (314) may include one or more transceivers (206a, 206b) and / or one or more antennas (208a, 208b) of FIG. 2. The control unit (320) is electrically connected to the communication unit (310), the memory unit (330), and additional elements (340) and controls the general operation of the wireless device. For example, the control unit (320) may control the electrical / mechanical operation of the wireless device based on a program / code / command / information stored in the memory unit (330). Additionally, the control unit (320) may transmit information stored in the memory unit (330) to an external (e.g., another communication device) via a wireless / wired interface through the communication unit (310), or store information received from an external (e.g., another communication device) via a wireless / wired interface through the communication unit (310) in the memory unit (330).

[0058] The additional element (340) may be configured in various ways depending on 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 implemented in the form of a robot (Fig. 1, 100a), a vehicle (Fig. 1, 100b-1, 100b-2), an XR device (Fig. 1, 100c), a portable device (Fig. 1, 100d), a home appliance (Fig. 1, 100e), an IoT device (Fig. 1, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or financial device), a security device, a climate / environment device, an AI server / device (Fig. 1, 140), a base station (Fig. 1, 120), a network node, etc. Wireless devices can be used in a movable or fixed location depending on the use—e.g., service.

[0059] In FIG. 3, various elements, components, units / parts, and / or modules within the wireless device (300) may be entirely interconnected via a wired interface, or at least a portion may be wirelessly connected via a communication unit (310). For example, within the wireless device (300), the control unit (320) and the communication unit (310) may be wired, and the control unit (320) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (310). Additionally, each element, component, unit / part, and / or module within the wireless device (300) may include one or more additional elements. For example, the control unit (320) may be composed of one or more sets of processors. For example, the control unit (320) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (330) may be composed of RAM, DRAM (dynamic RAM), ROM, flash memory, volatile memory, non-volatile memory and / or a combination thereof.

[0060] Portable device to which the present disclosure is applicable

[0061] FIG. 4 is a drawing illustrating an example of a portable device to which the present disclosure applies.

[0062] FIG. 4 illustrates a portable device to which the present disclosure applies. The portable device may include a smartphone, a smartpad, a wearable device (e.g., a smart watch, smart glasses), a portable computer (e.g., a laptop, etc.). The portable 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).

[0063] Referring to FIG. 4, the portable device (400) may include 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). The antenna unit (408) may be configured as part of the communication unit (410). Blocks 410 to 430 / 440a to 440c correspond to blocks 310 to 330 / 340 of FIG. 3, respectively.

[0064] The communication unit (410) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (420) can control the components of the portable device (400) to perform various operations. The control unit (420) may include an application processor (AP). The memory unit (430) can store data / parameters / programs / code / commands required for the operation of the portable device (400). Additionally, the memory unit (430) can store input / output data / information, etc. The power supply unit (440a) supplies power to the portable device (400) and may include wired / wireless charging circuits, batteries, etc. The interface unit (440b) can support the connection between the portable device (400) and other external devices. The interface unit (440b) may include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (440c) can receive or output video information / signals, audio information / signals, 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, etc.

[0065] For example, in the case of data communication, the input / output unit (440c) acquires information / signals (e.g., touch, text, voice, image, video) input by the user, and the acquired information / signals can be stored in the memory unit (430). The communication unit (410) converts the information / signals stored in the memory into wireless signals and can directly transmit the converted wireless signals to another wireless device or to a base station. Additionally, the communication unit (410) can receive wireless signals from another wireless device or base station and then restore the received wireless signals to their original information / signals. The restored information / signals are stored in the memory unit (430) and then can be output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (440c).

[0066] Types of wireless devices to which the present disclosure is applicable

[0067] FIG. 5 is a drawing illustrating an example of a vehicle or autonomous vehicle to which the present disclosure applies.

[0068] FIG. 5 illustrates a vehicle or autonomous vehicle to which the present disclosure applies. The vehicle or autonomous vehicle may be implemented as a mobile robot, vehicle, train, manned or unmanned aerial vehicle (AV), ship, etc., and is not limited to the form of a vehicle.

[0069] Referring to FIG. 5, a vehicle or autonomous vehicle (500) may include 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 (540d). The antenna unit (550) may be configured as part of the communication unit (510). Blocks 510 / 530 / 540a to 540d correspond to blocks 410 / 430 / 440 of FIG. 4, respectively.

[0070] The communication unit (510) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations (roadside units), etc.), and servers. The control unit (520) can control elements of the vehicle or autonomous vehicle (500) to perform various operations. The control unit (520) may include an electronic control unit (ECU).

[0071] FIG. 6 is a drawing illustrating an example of an AI device to which the present disclosure applies. For example, the AI ​​device may be implemented as a stationary device or a mobile device, such as a TV, projector, smartphone, PC, laptop, digital broadcasting terminal, tablet PC, wearable device, set-top box (STB), radio, washing machine, refrigerator, digital signage, robot, vehicle, etc.

[0072] Referring to FIG. 6, the AI ​​device (600) may include a communication unit (610), a control unit (620), a memory unit (630), an input / output unit (640a / 640b), a learning processor unit (640c), and a sensor unit (640d). Blocks 610 to 630 / 640a to 640d may correspond to blocks 310 to 330 / 340 of FIG. 3, respectively.

[0073] The communication unit (610) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning model, control signal, etc.) with external devices such as other AI devices (e.g., FIG. 1, 100x, 120, 140) or AI servers (Fig. 1, 140) using wired and wireless communication technology. To this end, the communication unit (610) can transmit information within the memory unit (630) to an external device or transmit signals received from an external device to the memory unit (630).

[0074] The control unit (620) can 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. The control unit (620) can perform the determined operation by controlling the components of the AI ​​device (600). For example, the control unit (620) can request, search, receive, or utilize data from the learning processor unit (640c) or the memory unit (630), and can control the components of the AI ​​device (600) to execute a predicted operation or an operation determined to be desirable among at least one executable operation. Additionally, the control unit (620) can collect historical information, including the operation content of the AI ​​device (600) or user feedback regarding the operation, and store it in the memory unit (630) or the learning processor unit (640c), or transmit it to an external device such as an AI server (Fig. 1, 140). The collected historical information can be used to update the learning model.

[0075] The memory unit (630) can store data that supports various functions of the AI ​​device (600). For example, the memory unit (630) can store data obtained from the input unit (640a), data obtained from the communication unit (610), output data from the learning processor unit (640c), and data obtained from the sensing unit (640). Additionally, the memory unit (630) can store control information and / or software code required for the operation / execution of the control unit (620).

[0076] The input unit (640a) can acquire various types of data from outside the AI ​​device (600). For example, the input unit (620) can acquire training data for model training and input data to which the training model is applied. The input unit (640a) may include a camera, a microphone and / or a user input unit, etc. The output unit (640b) can generate output related to visual, auditory, or tactile senses, etc. The output unit (640b) may include a display unit, a speaker and / or a haptic module, etc. The sensing unit (640) can obtain at least one of internal information of the AI ​​device (600), surrounding environment information of the AI ​​device (600), and user information using various sensors. The sensing unit (640) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone and / or radar, etc.

[0077] The learning processor unit (640c) can train a model composed of an artificial neural network using training data. The learning processor unit (640c) can perform AI processing together with the learning processor unit of the AI ​​server (Fig. 1, 140). The learning processor unit (640c) can process information received from an external device through the communication unit (610) and / or information stored in the memory unit (630). Additionally, the output value of the learning processor unit (640c) can be transmitted to an external device through the communication unit (610) and / or stored in the memory unit (630).

[0078] FIG. 7 is a diagram illustrating a method for processing a transmission signal applicable to the present disclosure. For example, the transmission signal may be processed by a signal processing circuit. In this case, the signal processing circuit (700) may include a scrambler (710), a modulator (720), a layer mapper (730), a precoder (740), a resource mapper (750), and a signal generator (760). In this case, for example, the operation / function of FIG. 7 may be performed in the processor (202a, 202b) and / or transceiver (206a, 206b) of FIG. 2. Also, for example, the hardware elements of FIG. 7 may be implemented in the processor (202a, 202b) and / or transceiver (206a, 206b) of FIG. 2. For example, blocks 710 to 760 may be implemented in the processor (202a, 202b) of FIG. 2. Additionally, blocks 710 to 750 may be implemented in the processor (202a, 202b) of FIG. 2, and block 760 may be implemented in the transceiver (206a, 206b) of FIG. 2, but are not limited to the above-described embodiment.

[0079] A codeword can be converted into a wireless signal through the signal processing circuit (700) of FIG. 7. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transmission block (e.g., UL-SCH transmission block, DL-SCH transmission block). The wireless signal may be transmitted through various physical channels (e.g., PUSCH, PDSCH). Specifically, the codeword can be converted into a scrambled bit sequence by a scrambler (710). The scrambled sequence used for scrambling is generated based on an initialization value, which may include ID information of a wireless device, etc. The scrambled bit sequence may be modulated into a modulation symbol sequence by a modulator (720). The modulation method may include pi / 2-BPSK (pi / 2-binary phase shift keying), m-PSK (m-phase shift keying), m-QAM (m-quadrature amplitude modulation), etc.

[0080] A complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (730). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (740) (precoding). The output z of the precoder (740) can be obtained by multiplying the output y of the layer mapper (730) by an N*M precoding matrix W, where N is the number of antenna ports and M is the number of transmission layers. Here, the precoder (740) can perform precoding after performing transform precoding (e.g., a discrete Fourier transform (DFT)) on the complex modulation symbols. Alternatively, the precoder (740) can perform precoding without performing transform precoding.

[0081] A resource mapper (750) can map the modulation symbols of each antenna port to a time-frequency resource. The time-frequency resource may include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. A signal generator (760) generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to another device through each antenna. To this end, the signal generator (760) may include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, etc.

[0082] The signal processing process for a received signal in a wireless device can be configured as the inverse of the signal processing process (710–760) of FIG. 7. For example, a wireless device (e.g., 200a, 200b of FIG. 2) can receive a wireless signal from the outside through an antenna port / transceiver. The received wireless signal can be converted into a baseband signal through a signal restorer. To this end, the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Subsequently, the baseband signal can be restored into a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a de-scrambling process. The codeword can be restored into the original information block through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0083] 6G communication system

[0084] The 6G (wireless communication) system aims for (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) reduced 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 seen in 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. In other words, Table 1 represents the requirements of the 6G system.

[0085] Per device peak data rate 1 Tbps E2E latency 1 ms Maximum spectral efficiency 100 bps / Hz Mobility support up to 1000 km / hr Satellite integration Fully AI Fully Autonomous vehicle Fully XR Fully Haptic Communication Fully

[0086] At this time, 6G systems can have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLC), mMTC (massive machine type communications), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0087] FIG. 8 is a drawing illustrating an example of a communication structure that can be provided in a 6G system applicable to the present disclosure.

[0088] Referring to Fig. 8, the 6G system is expected to have 50 times higher simultaneous wireless communication connectivity than the 5G wireless communication system. URLLC, a key feature of 5G, is expected to become an even more dominant technology in 6G communication by providing end-to-end latency of less than 1ms. In this case, the 6G system will have much better volumetric spectrum efficiency than the frequently used area spectrum efficiency. The 6G system can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices in the 6G system may not need to be charged separately.

[0089] Core implementation technology of 6G systems

[0090] - Artificial Intelligence (AI)

[0091] The most critical and newly introduced technology for 6G systems is AI. AI was not involved in 4G systems. 5G systems will support AI partially or to a very limited extent. However, 6G systems will be supported by AI for complete automation. Advancements in machine learning will create more intelligent networks for real-time communication in 6G. Introducing AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. In other words, AI can increase efficiency and reduce processing latency.

[0092] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play a significant role in M2M, machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in brain-computer interfaces (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.

[0093] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have primarily focused on the application layer and network layer, particularly in the field of wireless resource management and allocation. However, such research is increasingly advancing toward the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of signal processing and communication mechanisms based on AI drivers rather than traditional communication frameworks in terms of fundamental signal processing and communication mechanisms. Examples include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO (multiple input multiple output) mechanisms, and AI-based resource scheduling and allocation.

[0094] Machine learning can be used for channel estimation and channel tracking, and for power allocation and interference cancellation in the physical layer of the downlink (DL). In addition, machine learning can be used for antenna selection, power control, and symbol detection in MIMO systems.

[0095] However, the application of DNNs for transmission at the physical layer may have the following problems.

[0096] Deep learning-based AI algorithms require a vast amount of training data to optimize training parameters. However, due to limitations in acquiring training data from specific channel environments, a large amount of offline training data is used. Consequently, static training on training data in specific channel environments can lead to contradictions between the dynamic characteristics and diversity of wireless channels.

[0097] Furthermore, current deep learning primarily targets real signals. However, signals at the physical layer of wireless communication are complex signals. Further research is needed on neural networks that detect complex domain signals to match the characteristics of wireless communication signals.

[0098] Below, we will take a closer look at machine learning.

[0099] Machine learning refers to a series of operations for training machines to create machines capable of performing tasks that humans can or find difficult to do. Machine learning requires data and learning models. Data learning methods in machine learning can be broadly classified into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0100] The purpose of neural network training is to minimize output errors. It is a process that repeatedly inputs training data into a neural network, calculates the error between the network's output and the target for the training data, and updates the weights of each node by backpropagating the error from the output layer to the input layer in a direction that reduces the error.

[0101] Supervised learning uses training data with correct answers labeled, whereas unsupervised learning may not have correct answers labeled. That is, for example, in the case of supervised learning regarding data classification, the training data may consist of data where each training data point is labeled with a category. Labeled training data is input into a neural network, and an error can be calculated by comparing the network's output (category) with the labels of the training data. The calculated error is backpropagated within the neural network (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to this backpropagation. The amount of change in the connection weights of each node being updated can be determined by the learning rate. The neural network's calculations on the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, efficiency can be increased by using a high learning rate in the early stages of neural network training to enable the network to quickly achieve a certain level of performance, and accuracy can be increased by using a low learning rate in the later stages of training.

[0102] The learning method may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted from the transmitting end at the receiving end in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

[0103] Learning models correspond to the human brain, and while the most basic linear models can be considered, a machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0104] The neural network cores used for learning methods are broadly classified into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann machines (RNN), and these learning models can be applied.

[0105] THz (Terahertz) communication

[0106] THz communication can be applied in 6G systems. For example, data transfer rates can be increased by increasing bandwidth. This can be achieved by using sub-THz communication with wide bandwidth and applying advanced large-scale MIMO technology.

[0107] FIG. 9 illustrates the electromagnetic spectrum applicable to the present disclosure. For example, referring to FIG. 9, 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 to 3 mm. The 100 GHz to 300 GHz band range (Sub-THZ band) is considered the main part of the THz band for cellular communication. Adding the Sub-THZ band to the mmWave band increases 6G cellular communication capacity. Among the defined THz bands, 300 GHz to 3 THz is located in the far-infrared (IR) frequency band. The 300 GHz to 3 THz band is part of the broadband but lies at the boundary of the broadband and immediately following the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarity to RF.

[0108] Key characteristics of THz communication include (i) widely available bandwidth to support very high data transmission rates, and (ii) high path loss occurring at high frequencies (highly directional antennas are indispensable). The narrow beam width generated by highly directional antennas reduces interference. The small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array technologies that can overcome range limitations.

[0109] Terahertz (THz) wireless communication

[0110] FIG. 10 is a drawing illustrating a THz communication method applicable to the present disclosure.

[0111] Referring to Fig. 10, THz wireless communication utilizes THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 10¹² Hz) for wireless communication, and can refer to terahertz (THz) band wireless communication using very high carrier frequencies of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) they penetrate non-metallic / non-polar materials well compared to visible light / infrared light, and have high directivity and beam focusing capabilities due to their shorter wavelength compared to RF / millimeter waves.

[0112] Specific embodiments of the present disclosure

[0113] The present disclosure relates to the control of a reconfigurable intelligent surface (RIS) in a system utilizing the RIS, and proposes a technique for determining a reflection pattern applied to the RIS. Furthermore, the present disclosure proposes a technique for operating the RIS in a communication environment considering multiple service providers.

[0114] The RIS may also be referred to as an intelligent reflect surface (IRS) and is not limited to a specific name. For convenience of explanation in this disclosure, the description focuses on the RIS, but is not limited to that name. Specifically, in the various embodiments described below, structures and operations related to an RIS equipped with a reflect surface are described, but the RIS may be replaced by a relay station or an IAB (integrated access and backhaul) node having limited functions. Here, "limited functions" means that it is implemented with low hardware capabilities or operates with some functions blocked depending on the operating mode.

[0115] Current wireless communication technology can be controlled through endpoint optimization that adapts to the channel environment (H). When optimization is performed at the transmitter and receiver, the transmitter and receiver can increase transmission efficiency by adjusting at least one of beamforming, power control, and adaptive modulation to match the channel environment (H) between the transmitter and receiver. In this case, the channel environment may be random, uncontrolled, and naturally fixed. That is, in existing communication systems, the method of controlling each endpoint to optimize for the channel environment while the channel environment is fixed could be performed. Therefore, the transmitter and receiver have no choice but to perform optimization to adapt to the channel and transmit and receive data through this. However, in environments such as non-line of sight (NLOS) in dead zones or environments where signal loss is high and multipath is unlikely to exist, like 6G THz, it may be difficult to overcome Shannon's Capacity Limit through endpoint optimization alone, and communication operators may find it difficult to expect throughput that meets their desired requirements.

[0116] Considering the above points, communication can be performed in the new communication system based on a smart radio environment. In this case, by using an RIS in the smart radio environment, the wireless channel can be used as a controllable factor, such as a transceiver.

[0117] In other words, parameters for the wireless channel can be added as factors used to optimize wireless communication transmission. Through this, it may be possible to reconfigure the channel or overcome Shannon's channel capacity limitations, which are problems that are impossible to solve in existing communication systems. However, in an intelligent wireless environment, there is a need to optimize wireless communication transmission by measuring the channels added by the RIS and considering the RIS together with the transceiver, and consequently, the optimization process may become complex.

[0118] FIG. 11 illustrates a wireless channel environment according to an embodiment of the present disclosure. Referring to FIG. 11, in a conventional communication system, the wireless channel environment (H) may be in a naturally fixed and uncontrollable random state. Accordingly, the transmitter (1110) and the receiver (1120) can find an optimized transmission and reception method by adapting to the channel. The transmitter (1110) and the receiver (1120) can measure the channel state through a signal (e.g., a reference signal) and be controlled to perform optimization based on the measured channel state. However, as described above, there may be limitations on data transmission in NLOS environments, such as terahertz environments where signal loss is large and multipath application is difficult, and in dead zones. For example, [Equation 1] below may represent Shannon's capacity limit. In this case, even if precoding and processing are applied to the transmission signal P in [Equation 1] to increase it, there may be limitations on increasing the channel capacity if the size of the channel |H| is small.

[0119] When the wireless channel environment is fixed, there may be a limit to increasing the channel capacity based on [Equation 1]. In this case, communication using RIS can secure multiple paths between the transmitter (1110) and the receiver (1120) and can increase the aforementioned channel |H|. That is, in an intelligent wireless environment, the wireless channel environment based on RIS can be an adjustable factor, and through this, the channel capacity can be increased.

[0120]

[0121] In [Equation 1], C is the channel capacity, |HP| 2 ε₀ represents the transmitted signal power that passed through the channel, and σ represents the standard deviation of the noise.

[0122] For example, FIG. 12 illustrates an intelligent wireless environment according to an embodiment of the present disclosure. Referring to FIG. 12, in an intelligent wireless channel environment, the wireless channel |H| may be a factor for optimization. More specifically, in FIG. 11 described above, optimization may be performed at the transmitter (1110) and receiver (1120) based on "max{f(Tx, Rx)}" as an endpoint optimization, as described above. However, in FIG. 12, optimization may be performed at the transmitter (1210) and receiver (1220) based on "max{f(Tx, Rx, H)}" as an endpoint optimization. That is, in an intelligent wireless environment, the channel |H| may be used as a factor for optimization based on an intelligent reflector.

[0123] FIGS. 13a and FIG. 13b illustrate a conventional wireless channel environment and an intelligent wireless channel environment according to an embodiment of the present disclosure. For example, referring to FIG. 13a, the conventional wireless channel environment may be P1. Also, referring to FIG. 13b, the intelligent wireless channel environment may be P2. In this case, when a signal x is transmitted from a transmitting end through a wireless channel in FIG. 13a and FIG. 13b, respectively, the receiving end may receive a signal y. In this case, the probability of P1 in the conventional wireless channel environment is fixed, and the receiving end (decoder) may transmit feedback to the transmitting end through a measurement of the transmitted signal. The transmitting end may perform optimization to adapt to the wireless channel environment through the feedback from the receiving end. As a more specific example, the receiving end may measure the CQI (channel quality indicator) for the transmitted signal based on a reference signal transmitted by the transmitting end and provide feedback. Based on the feedback information, the transmitting end may adjust the MCS (modulation coding scheme) and provide information regarding this to the receiving end to perform communication.

[0124] On the other hand, referring to FIG. 13b, in an intelligent wireless channel environment, a wireless channel environment P2 is recognized, and the wireless channel environment can be changed through RIS control. At the same time, the receiver can perform a measurement of the received transmission signal and transmit feedback thereon to the transmitter. That is, the transmitter can perform optimization by receiving feedback information based on RIS control and feedback information from the receiver. At this time, the transmitter can change the wireless channel environment by adjusting the RIS, and optimization considering the wireless channel environment and the transmitter can be performed.

[0125] Due to the aforementioned characteristics, RIS is attracting attention as one of the important technologies for improving communication systems for various purposes in post-5G wireless communication systems, and research related to RIS is actively underway. Conventional communication systems could improve communication performance by utilizing multiple antennas for antenna gain and beamforming gain. However, communication systems using active components, such as radio frequency chains, can consume a large amount of power. In contrast, RIS is composed of passive components capable of changing phase. Therefore, communication systems using RIS can achieve the communication performance improvements that could be obtained through the use of multiple antennas with relatively low cost and power.

[0126] However, communication systems using RIS may present problems that alter the communication environments of different service providers. In a communication environment considering multiple service providers, each service provider has its own cell, and base stations for each cell operate in different frequency bands. Since one service provider operates a different frequency band than other service providers when operating communication services, interference between the cells of different service providers is not considered. However, since RIS, which is composed of passive components, does not possess signal processing capabilities in the baseband, RIS can cause phase shifts across the entire band rather than affecting signals in a specific band. Therefore, when a specific service provider uses RIS to increase channel gain, users communicating in other frequency bands also receive reflected signals through the RIS; consequently, users may experience unintended performance degradation due to the interaction between reflected signals and direct signals. Accordingly, the present disclosure proposes an RIS utilization technique that can achieve the purpose of RIS operation while reducing such performance degradation.

[0127] In the present disclosure, vertical vectors and matrices are denoted in boldface letters, and their transposes and Hermitian transposes are and It is denoted as . The set of complex numbers is It is written as, Is Represents a set of complex matrices of size square matrix A Regarding, Tr( A ) Is A Represents the sum of the diagonal elements of a vector a For, the diagonal matrix having that element as a diagonal component is diag( a It is denoted as ). Vector a The ℓ2-norm value of It is denoted as such, and the matrix A The Frobenius-norm value of It is notated as follows. Mean μ and variance The normal distribution having is It is written as. Is It represents an identity matrix of size. and and represent the magnitude and real part of the complex number a, respectively. represents the Hadamard product. represents a big-O representation.

[0128] In addition, the symbols used to describe the proposed technology in this disclosure may be interpreted as shown in [Table 2] below.

[0129] Number of antennas at the i-th base station Number of users in the i-th cell Number of RIS components of the i-th cell signal from the first user of the i-th cell Beamformer corresponding to the nth user Maximum transmission power of the i-th base station Downlink channel between the i-th base station and the RIS of the i-th cell Downlink channel between the nth user and the RIS of the second base station and the second cell Downlink channel between the first users Reflection coefficient matrix in RIS

[0130] The present disclosure proposes a technology for operating an RIS in a communication environment considering multiple service providers. It is common for different service providers to use different operating frequencies. Therefore, interference between cells operated by different providers can be ignored. However, if a first service provider installs an RIS composed of passive components, the channel environment of the frequency band used by a second service provider may also change. That is, users serviced by the second service provider receive signals through the direct channel and signals reflected from the RIS installed by the first service providers simultaneously, and if the first service provider controls the RIS, the service users may experience performance degradation.

[0131] FIG. 14 illustrates an example of a communication environment considering multiple service providers according to one embodiment of the present disclosure. In FIG. 14, a first base station (1420-1) and a second base station (1420-2) refer to base stations belonging to different service providers. The first cell users (1410-1) include at least one terminal serviced by the first base station (1420-1), and the second cell users (1410-2) include at least one terminal serviced by the second base station (1420-2).

[0132] Considering beamforming at base station i, the transmitted signal vector for users in the i-th cell Is It can be assumed that is satisfied. Also, the beamforming matrix corresponding to the transmitted signal vector Is It satisfies that. That is, the transmission power is the maximum transmission power of base station i. It is preferable that it does not exceed. In the present disclosure, the set of users in the i-th cell is It is defined as. At this time, the first cell users are Users (1410-1) belonging to the base station have a direct channel blocked, and to overcome this, as shown in FIG. 14, the first service provider installs and operates the RIS (1430) in the first cell.

[0133] set belonging to The downlink reception signal of the nth user can be expressed as shown in [Equation 2] below.

[0134]

[0135] In [Mathematical Formula 2], is a set belonging to downlink reception signal of the th user, is of the i-th cell Downlink channel between the nth user and RIS (1430), is the reflection coefficient matrix of RIS(1430), is the downlink channel between base station i and RIS (1430), is the set of users in the i-th cell, is of the i-th cell Beamformer corresponding to the nth user, is of the i-th cell The signal from the th user, represents noise. Here, each element of the RIS (1430) is a passive element, and the diagonal vector of the reflection coefficient matrix The element value of Satisfies.

[0136] Since the RIS (1430) does not have signal processing capabilities in the baseband, it can affect all frequency bands equally. Therefore, users (1410-2) in the second cell are also affected by the RIS (1430) installed in the first cell. At this time, the set belonging to The downlink reception signal at the nth user can be expressed as shown in [Equation 3] below.

[0137]

[0138] In [Mathematical Formula 3], is a set belonging to Downlink reception signal at the nth user, is the second base station (1420-2) and the second cell Downlink channel between the nth users, is of the i-th cell Downlink channel between the nth user and RIS (1430), is the reflection coefficient matrix of RIS(1430), is the element value vector of the passive component of RIS(1430), is the downlink channel between base station i and RIS (1430), is of the i-th cell Beamformer corresponding to the nth user, is of the i-th cell The signal from the th user, means noise.

[0139] In a channel environment such as FIG. 14, in order to optimize the reflection pattern of the RIS (1430), a procedure may be required for the first base station (1420-1) to acquire information related to the channel. Accordingly, the present disclosure proposes various embodiments in which the first base station (1420-1) acquires information related to the channel of the first cell user (141-1) as well as the second cell user (1410-2), and controls the RIS (1430) based on the acquired information.

[0140] As shown in FIG. 14, various paths are formed between a base station and a terminal depending on the use of the RIS. For example, a path between a base station and a terminal, a path between a base station and the RIS, and a path between the RIS and the terminal are formed. For convenience of explanation below, in this disclosure, a channel on the path between a base station and a terminal is referred to as a 'direct channel'. In addition, a channel on a path including a path between a base station and the RIS and a path between the RIS and the terminal, which is the channel through which a signal transmitted from a base station and reflected by the RIS reaches a first terminal, may be referred to as a 'reflected channel', 'RIS channel', 'RIS reflected channel', or other terms having an equivalent technical meaning.

[0141] FIG. 15 illustrates an example of a procedure in which a first base station determines the reflection patterns of a beamformer and an RIS by considering channel information of a second base station according to one embodiment of the present disclosure. FIG. 15 illustrates a method performed by the first base station, and the first base station may receive channel information for optimizing the reflection patterns of a beamformer and an RIS by using the procedure of FIG. 15.

[0142] Referring to FIG. 15, in step S1501, the first base station transmits a reference signal to the first terminal. At this time, the reference signal may reach the terminal after being reflected by the RIS. Here, the reference signal may include at least one of CSI-RS (channel state information reference signal), DMRS (demodulate reference signal), and PT-RS (phase-tracking reference signal).

[0143] In step S1503, the first base station receives first channel information measured based on a reference signal from the first terminal. The first channel information may include information related to a reflected channel through the RIS. Here, the reflected channel includes a channel between the first base station and the RIS, and a channel between the RIS and the first terminal. That is, the first channel information may include information related to a first RIS channel, which includes a channel between the first base station and the RIS, and a channel between the RIS and the first terminal. In this case, the information related to the first RIS channel is information about an integrated channel passing through the first base station, the RIS, and the first terminal, and information about the channel between the first base station and the RIS, and information about the channel between the RIS and the first terminal, may not be expressed individually.

[0144] If information on the channel between the first base station and the RIS and information on the channel between the RIS and the first terminal are individually required to optimize the reflection pattern of the RIS, additional procedures may be required.

[0145] For example, when the first base station and the RIS are used in a fixed manner, a pre-measured fixed value may be used for the channel value between the first base station and the RIS. Accordingly, the first base station can estimate information related to the channel between the RIS and the first terminal based on the first channel information and the channel value between the first base station and the RIS.

[0146] As another example, the first base station and the RIS may perform separate procedures for estimating the channel between the first base station and the RIS. That is, the first base station transmits a reference signal to the RIS and can receive channel information between the first base station and the RIS from the RIS. The first base station can estimate channel information between the RIS and the terminal using the first channel information received from the first terminal and the channel information between the first base station and the RIS received from the RIS.

[0147] In step S1505, the first base station receives second channel information from the second base station. The second channel information may include channel information related to the second terminal. The second base station may perform a channel estimation procedure with the second terminal to generate the second channel information. That is, the second base station may transmit a reference signal to the second terminal and receive second channel information from the second terminal. Subsequently, the second base station may transmit the second channel information to the first base station. The second channel information may include channel information related to the RIS. For example, the second channel information may include information related to a second RIS channel, which includes a channel between the second base station and the RIS and a channel between the RIS and the second terminal.

[0148] In step S1507, the first base station determines the reflection pattern of the RIS based on the first channel information and the second channel information. The first base station determines an objective function such that the gain of the first RIS channel of a user belonging to the first base station is increased and the gain of the second RIS channel of a user belonging to the second base station is decreased, and can optimize the reflection pattern of the RIS based on the objective function.

[0149] In step S1509, the first base station transmits a data signal to the first terminal using the RIS. The first base station can control the RIS using a determined reflection pattern of the RIS. That is, the first base station transmits a control signal to the RIS to reflect the signal according to the determined reflection pattern. Accordingly, the first base station can transmit a data signal to the first terminal using the RIS.

[0150] In the embodiment described with reference to FIG. 15, the base station determines the reflection pattern of the RIS based on an objective function. According to one embodiment, the objective function may be defined based on the gain of the first RIS channel and the gain of the second RIS channel. For example, the objective function may be determined based on a function obtained by subtracting the value obtained by multiplying the gain of the second RIS channel by a weight from the value obtained by the gain of the first RIS channel. Accordingly, the first base station can increase the gain of the first RIS channel and reduce the impact of the RIS on the channel of the second terminal by performing optimization in the direction of increasing the objective function. Additionally, the first base station can adjust the weight according to the channel environment and, by adjusting the weight, can determine whether to focus on increasing the gain of the first RIS channel or on decreasing the gain of the second RIS channel. The first base station can perform optimization of the reflection pattern of the RIS using the determined objective function, and the optimization can be performed through various algorithms. For example, the RCG (Riemannian conjugate gradient) algorithm described below can be used as an optimization algorithm.

[0151] According to various embodiments, the first base station has a first beamformer F1 and a reflection pattern of the RIS. Determines. To this end, the procedure disclosed in FIG. 15 may be used, and the first base station may receive necessary channel information from the second base station. Provided, the second base station Direct channel information between users belonging to The second beamformer is determined using only the RIS. That is, the second base station can perform communication without considering the RIS.

[0152] Each beamformer matrix can be determined using SLNR (signal-to-leakage-and-noise-ratio) based beamforming with given channel information, and can be determined so that equal power is distributed to each user. Since the beamformer can be determined before optimizing the reflection pattern of the RIS, it is determined based on the initial reflection pattern of the RIS. The initial reflection pattern of the RIS can be determined in various ways, such as random or pre-set reflection patterns of the RIS.

[0153] Referring to [Mathematical Equation 3], RIS is It can also affect users belonging to it. In addition, since the second base station determines F2 solely through direct channel information, RIS If you design it only for users belonging to Users belonging to [the base station] may experience severe performance degradation. Therefore, the first base station must determine the reflection pattern of the RIS by taking into account all users belonging to each base station.

[0154] In [Mathematical Formula 2] The RIS reflection channel of the nth user can be expressed as shown in [Equation 4] below.

[0155]

[0156] In [Mathematical Formula 4], is of the i-th cell Downlink channel between the nth user and RIS, is the reflection coefficient matrix of RIS, is the element value vector of the passive component of the RIS, is the downlink channel between base station i and RIS, silver It means.

[0157] Using [Mathematical Formula 4] The total channel gain through the reflection channels of all users belonging to can be expressed as follows [Equation 5].

[0158]

[0159] In [Mathematical Formula 5] represents the element value vector of the passive component of the RIS, and silver It means, silver It means.

[0160] Here, cast It can be understood as the total reflection channel of all users belonging to it. Similarly, The unintended channels affected by RIS for users belonging to [Equation 4] can be expressed as [Equation 6] below.

[0161]

[0162] In [Mathematical Equation 6], is of the i-th cell Downlink channel between the nth user and RIS, is the reflection coefficient matrix of RIS, is the element value vector of the passive component of the RIS, is the downlink channel between base station i and RIS, Is It means.

[0163] Here, Is It can be understood as an unintended channel for all users belonging to it.

[0164] The first base station is Because RIS is used to overcome failures caused by obstacles or other reasons in the direct paths of users belonging to, [Equation 5] The reflection pattern of the RIS can be optimized with the goal of designing it so that the value can be maximized. In addition, the first base station It must prevent users belonging to from experiencing unintended performance degradation caused by the RIS. Therefore, the first base station It can be optimized in a direction that reduces the value. Therefore, the base station Value and It is necessary to perform optimization by considering all values. Therefore, if the two aforementioned objectives are designed as a single optimization problem, problem (P1) can be derived as shown in [Equation 7] below.

[0165]

[0166] In [Equation 7], is the element value vector of the passive component of the RIS, is the weight, Is The Frobenius norm of the entire reflection channel of all users belonging to, Is The Frobenius norm of the unintended channel of all users belonging to , M represents the number of elements of RIS.

[0167] In problem (P1), the relative weights for the two objectives can be adjusted by λ. For example, the first base station is controlled by the RIS controlled by the first base station. A high λ value can be used to give strong weight to minimizing changes in the channel environment of users belonging to it. Conversely, the first base station is A low λ value can be used to give weight to enhancing the channel gain of users belonging to it. Therefore, the first base station can adjust the λ value based on the channel environment. For example, a channel gain sufficient to satisfy the quality of service Users belonging to are being secured, In the case where users belonging to are not secured, the first base station uses a high λ value Optimization can be achieved by assigning more weight to performance changes for users belonging to it. In the opposite case, the first base station uses a low λ value. Optimization can be achieved by allocating more weight to the performance improvement of users belonging to it.

[0168] In [Equation 7], the Hermite matrix having a size of M×M Using [Equation 7], [Equation 8] can be simplified as follows.

[0169]

[0170] In [Mathematical Equation 8], Is , is the element value vector of the passive components of RIS, and M represents the number of components of RIS.

[0171] The objective function of the problem (P1′) is It can be expressed in the form of a simple quadratic function, but There is a constraint that each element of must have a magnitude of 1. Therefore, problem (P1′) is not a convex problem. Consequently, various methods can be used to solve the non-convex problem (P1′). For example, the RCG algorithm can be used. The objective function of problem (P1′) is continuous and differentiable, and a complex circle manifold It forms a stationary point. Therefore, the first base station can obtain a stationary point using the RCG algorithm.

[0172] FIG. 16 illustrates an example of a procedure using an RCG algorithm to optimize the reflection pattern of an RIS according to one embodiment of the present disclosure. FIG. 16 illustrates a method performed by a first base station.

[0173] Referring to FIG. 16, in step S1601, the first base station determines the Riemann gradient. In this disclosure, the objective function of [Equation 8] It is expressed as. The Riemann gradient is at a certain point It is defined as the direction in which the objective function increases most significantly in the restricted tangent space. The Riemann gradient can be determined through projection into the tangent space based on the Euclidean gradient. The Euclidean gradient of the objective function is It can be expressed as. Therefore, the first base station is the Liemann gradient can be determined as shown in [Equation 9] below.

[0174]

[0175] In [Equation 9], is the Riemann gradient at the t-th point, is the t-th point of the tangent space, is the Euclidean gradient of the objective function, means the Hadamard product.

[0176] In step S1603, the base station determines a transport operator capable of moving the search direction vector from the previous tangent space to another tangent space. The first base station uses the Riemann gradient obtained in step S1601. search direction using can be obtained similarly to the conjugate gradient method in Euclidean space. However, when the first base station performs optimization on a manifold, the two search directions are and They may not lie in the same tangent space. If the two search directions are not in the same tangent space, the first base station cannot use a method of simply adding the two search directions. Therefore, a mapping process between the two search directions can be performed through a movement operator. Manifold The move operator used in can be expressed as shown in [Equation 10] below.

[0177]

[0178] In [Mathematical Formula 10], is the search direction vector in the previous tangent space A move operator that can move to the next tangent space, is the t-th point of the tangent space, is the t-th search direction, is the Hadamard product, and Re(a) represents the real part of the complex number a.

[0179] Therefore, the first base station uses a movement operator to determine the next search direction as shown in [Equation 11] below. can decide.

[0180]

[0181] In [Mathematical Equation 11], is the Riemann gradient at the t+1th point, is the t+1th search direction, is the search direction vector in the previous tangent space A move operator that can move to the next tangent space, represents the parameter of the move operator.

[0182] In [Equation 11] It can be determined by parameters of various methods. For example, the first base station may use parameters determined using either the Polak-Riebiere method or the Fletcher-Reeves method.

[0183] The Polak-Riebiere method utilizes a ratio calculated by dividing the difference between the current gradient and the previous gradient by the current gradient, and is known to be advantageous when the gradient changes significantly. The Fletcher-Reeves method utilizes a ratio calculated by dividing the length of the current gradient by the length of the previous gradient, and is generally known to have a fast convergence speed. Therefore, a method for determining parameters can be established based on the computing power channel conditions of the first base station, etc. In this disclosure, it is assumed that the parameters were determined using the Polak-Riebiere method.

[0184] In step S1605, the first base station performs vector contraction. The first base station must perform the vector contraction process to move to the next point using the search direction obtained in step S1603. A point on the manifold has a step size About When moved by a certain amount, the point after the movement may not exist on the manifold. Therefore, the first base station must perform a vector contraction process, which can be expressed by [Equation 12] below, so that the point can exist on the corresponding manifold in tangent space.

[0185]

[0186] In [Mathematical Equation 12], is the t-th point of the tangent space, is the step size to move on the manifold, is the t-th search direction, is a reflection pattern It refers to the transpose matrix with the element size changed to 1.

[0187] Afterwards, the first base station Steps S1601 through S1605 are repeated until convergence occurs. The initial setup for using the RCG algorithm can be implemented in various ways and is not limited to a specific method. Therefore, the first base station uses the RCG algorithm to obtain an optimized reflection pattern vector as shown in [Table 3] below. can decide.

[0188] Algorithm 1 Proposed balancing RIS reflection coefficients design Initialization 1: Initialize point 2: Initialize search direction 3: Iteration counter t = 0 Iterative update 4: repeat 5: Choose step size 6: Find next point by retraction in [equation 12] 7: Compute Riemannian gradient according to [equation 9] 8: Choose 9: Conduct vector transport to update as in [equation 11]10: t ← t+111: until Convergence Output:

[0189] The first base station has an optimized reflection pattern coefficient vector φ based on the above-described method. * It can determine. For optimization calculation, the first base station uses the initial beamformer vector F1 as the initial reflection pattern coefficient It can be determined based on [this]. Additionally, for further optimization, the first base station may update the first beamformer vector F1 based on channel information to which the determined reflection pattern is applied. FIG. 17 illustrates an example of a procedure in which a first terminal according to an embodiment of the present disclosure performs communication using a reflection pattern of an RIS that considers the channel information of a second base station. FIG. 17 illustrates a method performed by the first terminal.

[0190] Referring to FIG. 17, in step S1701, the first terminal receives a reference signal from the first base station. The first base station requires channel information between the first base station and the first terminal to determine the reflection pattern of the RIS. Therefore, the first base station and the first terminal can perform a measurement procedure.

[0191] In step S1703, the first terminal transmits first channel information to the first base station. The first terminal may determine the first channel information based on a reference signal received from the base station and transmit the first channel information to the base station. For example, the first channel information may include channel information regarding the RIS.

[0192] In step S1705, the first terminal receives a signal from the first base station through the RIS. At this time, the RIS can be controlled by the first base station. The reflection pattern of the RIS can be determined based on first channel information and second channel information. At this time, the second channel information may include channel information between the first base station service provider and the second base station and the second terminal of another service provider. The reflection pattern of the RIS can be determined in a direction such that the channel gain from the first base station to the first terminal increases, and the channel gain that the second terminal is unintentionally affected by due to RIS control decreases. For example, the reflection pattern of the RIS can be determined through an RCG algorithm such as [Table 4].

[0193] FIG. 18 illustrates an example of signaling for performing communication using a RIS (1830) reflection pattern determined based on channel information of a first base station (1820-1) and channel information of a second base station (1820-2) according to one embodiment of the present disclosure.

[0194] Referring to FIG. 18, in step S1801, base stations (1820-1, 1820-2), terminals (1810-1, 1810-1) and RIS (1830) perform an RRC connection procedure. The first base station (1820-1) and the second base station (1820-2) are base stations operated by different service providers, and a procedure to establish a connection (e.g., Xn connection) between the first base station (1820-1) and the second base station (1820-2) may be required to transmit second channel information. Additionally, if the first base station (1820-1) wirelessly controls the RIS (1830), a procedure to establish a connection between the first base station (1820-1) and the RIS (1830) may be required. Additionally, to measure channel information, a procedure to establish a connection (e.g., RRC connection) between the first base station (1820-1) and the first terminal (1830-1) may be performed, and a procedure to establish a connection between the second base station (1820-2) and the second terminal (1810-2) may also be performed. Furthermore, an initial connection procedure may be performed between the first base station (1820-1) and the first terminal (1830-1), and between the second base station (1820-2) and the second terminal (1810-2).

[0195] In step S1803, the first base station (1820-1) transmits a first reflection pattern to the RIS (1830). The first reflection pattern is an initial reflection pattern and can be set to measure first channel information. In step S1805, the first base station (1820-1) transmits a first reference signal to the first terminal (1830-1). For example, the first reference signal may include one of CSI-RS, DMRS, or PT-RS. In step S1807, the first terminal (1830-1) transmits first channel information to the first base station (1820-1). The first channel information is information determined based on the channel between the first base station (1820-1) and the RIS (1830) and the channel between the RIS (1830) and the first terminal (1830-1), and refers to information related to the channel used by the first base station (1820-1) and the first terminal (1830-1).

[0196] In step S1809, the second base station (1820-2) transmits a second reference signal to the second terminal (1810-2). In step S1811, the second terminal (1810-2) transmits second channel information to the second base station (1820-2). The second channel information may include information related to a second RIS channel, which includes a channel between the second base station (1820-2) and the RIS (1830), and a channel between the RIS and the second terminal (1810-2). In step S1813, the second base station (1820-2) transmits the second channel information to the first base station (1820-1). To optimize the reflection pattern of the RIS, the first base station (1820-1) may need information related to the terminals being serviced by the second base station. For example, the second channel information may include the number of terminals being serviced by the second base station (1820-2).

[0197] In step S1815, the first base station (1820-1) optimizes the RIS (1830) beamformer and reflection pattern. The first base station (1820-1) can obtain all channel information necessary for optimization based on the first channel information and the second channel information. The first base station (1820-1) can determine the second reflection pattern based on the increase or decrease in channel gain caused by the RIS (1830). For example, the second reflection pattern can be optimized with the goal of increasing the gain of the first RIS (1830) channel, which includes the channel between the first base station (1820-1) and the RIS (1830) and the channel between the RIS (1830) and the first terminal (1830-1), and decreasing the gain of the second RIS (1830) channel, which includes the channel between the second base station (1820-2) and the RIS (1830) and the channel between the RIS (1830) and the second terminal (1810-2). According to one embodiment, for optimization, the first base station (1820-1) may use the objective function of [Equation 7] and determine weight values ​​based on the channel environment.

[0198] The method for determining the beamforming vector can be implemented in various ways based on the channel environment and the computing power of the base station. For example, the first base station (1820-1) can determine the beamforming vector based on the signal-to-leakage-and-noise-ratio (SLNR) for a given channel environment. For example, the first base station (1820-1) can determine the first beamformer vector F1 based on channel information to which a determined reflection pattern is applied for further optimization.

[0199] At this time, the first base station (1820-1) can determine the beamforming vector F1 so as to provide equal power distribution to each user. However, the second base station (1820-2) determines the beamforming vector F2 using only the second channel information, i.e., direct channel information, without regard to the RIS (1830). Therefore, additional optimization of the beamforming vector F2 may not be performed.

[0200] In step S1817, the first base station (1820-1) transmits a second reflection pattern to the RIS (1830). The second reflection pattern includes an optimized reflection pattern. Thus, the RIS (1830) can adjust passive elements based on the received second reflection pattern. In step S1819, the first base station (1820-1) transmits a data signal to the first terminal (1830-1). The first terminal (1830-1) can receive the data signal through a reflection channel formed by the first RIS (1830) based on the optimized reflection pattern and beamforming.

[0201] In step S1821, the second base station (1820-2) transmits a data signal to the second terminal (1810-2). Since the second reflection pattern determined by the first base station (1820-1) reduces the influence on the second RIS (1830) channel, the second terminal (1810-2) can receive the data signal from the second base station (1820-2).

[0202] The theoretical complexity of the algorithm proposed in this disclosure can be expressed as follows, assuming N1 = N2 = N and K1 = K2 = K. First, for a given RIS (1830) reflection coefficient vector The complexity required to calculate the value is It can be expressed as such. The complexity of the RCG algorithm mainly occurs in the part where the Euclidean gradient is calculated, and It can be expressed as. Therefore, the overall complexity of the final algorithm is It is expressed as, and at this timeI represents the total number of iterations of the algorithm proposed in this disclosure.

[0203] In this disclosure, the results of a comparative experiment between the optimization method disclosed herein and an existing method will be described. Through the following comparative experiment, it can be seen that transmission and reception can be performed efficiently using the optimization method of this disclosure.

[0204] FIG. 19 illustrates an example of a simulation environment for a comparative experiment according to one embodiment of the present disclosure. FIG. 19 is a drawing showing the comparative experiment environment set in the present disclosure based on a 3D coordinate system.

[0205] FIG. 19 shows the locations of the first base station (BS 1), the second base station (BS 2), and terminals belonging to each base station in the xy plane. Users can be positioned arbitrarily within each indicated area. Additionally, the height of each base station is set to 15m, the height of the RIS is set to 10m, and the height of the users is set to 1m. Furthermore, it is assumed that the first base station, the second base station, and the RIS have a uniform planar array (UPA) structure.

[0206] In addition, it was assumed that all communication channels have a Rician channel model, and the angle of the line of sight (LOS) channel was calculated separately based on the positions of the transmitter and receiver. The angle of the non-line of sight (NLoS) channel was arbitrarily determined to have a specific variance relative to the LOS channel. The total system bandwidth was assumed to be 10 MHz, and the path loss exponent in large-scale fading was assumed to be 4.5 for the channel between each base station and each user, 2.4 for the channel between the RIS and each user, and 2.5 for the channel between each base station and the RIS.

[0207] Regarding the number of NLoS channels, it was assumed that there are 8 NLoS channels between each base station and each user, 4 NLoS channels between the RIS and each user, and 8 NLoS channels between each base station and the RIS. Additionally, regarding the Ra'asian factor, it was assumed that the channel between each base station and each user is 3 dB, the channel between the RIS and each user is 5 dB, and the channel between each base station and the RIS is 5 dB. Furthermore, to consider homogeneity among cells, the number of antennas for each base station was set identically as N1 = N2 = N, and the number of users belonging to each base station was set identically as K1 = K2 = K. Also, the maximum transmit power of each branch station is P T,1 = P T,2 = P T It was set to be the same.

[0208] The performance metric uses the sum of the transmission rates of users in each cell. belonging to The maximum transmission rate achievable for the nth user can be expressed as follows [Equation 13].

[0209]

[0210] In [Mathematical Equation 13], Is belonging to Maximum transmission rate achievable for the nth user, is the element value vector of the passive component of the RIS, Is , is the i-th cell's Beamformer corresponding to the nth user, silver It refers to the dispersion of noise experienced by the nth user.

[0211] likewise belonging to The maximum transmission rate achievable for the nth user can be expressed as [Equation 14].

[0212]

[0213] In [Mathematical Equation 14], Is belonging to Maximum transmission rate achievable for the nth user, is the element value vector of the passive component of the RIS, is the i-th cell's Beamformer corresponding to the nth user, Is , silver It refers to the dispersion of noise experienced by the nth user.

[0214] Using [Equation 13] and [Equation 14], The sum of the transmission rates of users belonging to is It can be calculated as, The sum of the transmission rates of users belonging to is It can be calculated as.

[0215] Each beamforming matrix can be determined for a given channel using SLNR (signal-to-leakage-and-noise-ratio) based beamforming, and is determined so that equal power can be distributed to each user. As mentioned above, since the second base station does not consider RIS, the beamformer vector F2 of the second base station is solely direct channel information It is determined solely through.

[0216] The proposed technique and the technique compared in FIGS. 20 and 21 below are shown as follows. First, the optimization in which the λ value defined in each [Equation 7] is set to 0 can be viewed as not considering the channel gain for the second base station at all, because it calculates the value regarding the channel gain for the second base station as 0. That is, the first base station Without considering the channels of users belonging to at all, This can be viewed as a case where the reflection coefficient of the RIS is determined to maximize the gain through the RIS channel of users belonging to [the group]. When λ is set to 0, the total transmission rate associated with the first base station is denoted as 'λ=0 - Cell 1', and the total transmission rate associated with the second base station is denoted as 'λ=0 - Cell 2'. Furthermore, in an environment considering the channel of the second base station using the technique employing the algorithm proposed in this disclosure, the total transmission rate associated with the first base station is denoted as 'Proposed - Cell 1', and the total transmission rate associated with the second base station is denoted as 'Proposed - Cell 2'. Finally, in the case where the RIS does not exist, the first base station In cases where there was no impact on users belonging to , the total transmission rate related to the second base station was indicated as 'No RIS - Cell 2'.

[0217] FIG. 20 illustrates an example of a comparative experiment in which the total transmission rate changes based on the transmission power according to one embodiment of the present disclosure. FIG. 20 N =8, M =96, K =6, λ=20dbm is set, and represents the total transmission rate of each cell according to the transmission power.

[0218] First, Figure 20 indicates that as the transmission power increases, the overall total transmission rate increases. Additionally, it can be seen that the transmission rate for the second base station is higher in the case of No RIS - Cell 2 than in the case of λ=0 - Cell 2. These results indicate that the first base station If the RIS is controlled without considering the users belonging to, It indicates that users belonging to [group] experience unintended performance degradation due to channels added by the RIS. Furthermore, performance degradation occurs increasingly significantly as the transmission power increases. Therefore, FIG. 20 indicates that the method proposed in the present disclosure can reduce unintended performance degradation. That is, in the case of Proposed - Cell 1, performance degradation does not occur significantly compared to the case of λ=0 - Cell 1, and in the case of Proposed - Cell 1, a significant performance increase occurs compared to the case of λ=0 - Cell 2, thereby reducing performance degradation caused by the RIS. Therefore, FIG. 20 indicates that the invention proposed in the present disclosure can efficiently prevent the deterioration of communication performance of other service providers when one service provider uses the RIS.

[0219] FIG. 21 illustrates an example of the performance of the total transmission rate according to a balance parameter λ according to one embodiment of the present disclosure. The experimental results of FIG. 21 are shown in FIG. 21 N =8, M =96, K =6, P T This result was derived assuming =30dbm.

[0220] Figure 21 shows that the total transmission rate for the second base station increases as the parameter value λ increases. This result indicates that as the value of λ increases, due to the RIS This means that the degradation of communication performance experienced by users belonging to is reduced. Additionally, Fig. 21 indicates that the total transmission rate for the first base station decreases as the value of λ increases. These results indicate that as the value of λ increases This means that the performance of the first cell is reduced because the RIS is designed with greater consideration for the performance degradation occurring to users belonging to it.

[0221] Through the method proposed in the present disclosure, a base station can efficiently operate an RIS in a communication environment that considers multiple service providers. The present disclosure relates to a method for considering the communication environment of other providers when one service provider uses an RIS. Therefore, it is not limited to the algorithms and procedures described above. That is, the base station can optimize the reflection pattern of the RIS through the RCG algorithm or other algorithms.

[0222] It is evident that the examples of the proposed methods described above may also be included as one of the implementation methods of the present disclosure and thus can be regarded as a type of proposed method. Furthermore, while the proposed methods described above may be implemented independently, they may also be implemented in the form of a combination (or merger) of some proposed methods. Rules may be defined so that information regarding the application of the proposed methods (or information regarding the rules of the proposed methods) is communicated by a base station to a terminal via a predefined signal (e.g., a physical layer signal or an upper layer signal).

[0223] The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential features described herein. Accordingly, the above detailed description should not be interpreted restrictively in all respects and should be considered exemplary. The scope of the present disclosure shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are included within the scope of the present disclosure. Furthermore, embodiments may be constructed by combining claims that are not explicitly related in the claims, or new claims may be included by amendments made after filing. Industrial applicability

[0224] Embodiments of the present disclosure can be applied to various wireless access systems. Examples of various wireless access systems include the 3GPP (3rd Generation Partnership Project) or 3GPP2 systems.

[0225] The embodiments of the present disclosure can be applied not only to the various wireless access systems mentioned above but also to all technical fields utilizing the various wireless access systems. Furthermore, the proposed method can be applied to mmWave and THz communication systems utilizing the ultra-high frequency band.

[0226] Additionally, embodiments of the present disclosure may be applied to various applications such as autonomous vehicles and drones.

Claims

Claim 1 A method performed by a first base station in a wireless communication system, comprising: transmitting a reference signal to a first terminal; receiving first channel information measured based on the reference signal from the first terminal; receiving second channel information from a second base station; determining a reflection pattern of a reconfigurable intelligent surface (RIS) based on the first channel information and the second channel information; and transmitting a data signal to the first terminal, wherein the first channel information includes information related to a first RIS channel including a channel between the first base station and the RIS and a channel between the RIS and the first terminal, and the second channel information includes information related to a second RIS channel including a channel between the second base station and the RIS and a channel between the RIS and the second terminal. Claim 2 A method according to claim 1, wherein the reflection pattern is determined based on an objective function determined based on the first channel information and the second channel information. Claim 3 A method according to claim 2, wherein the objective function is determined based on a function obtained by subtracting a value obtained by multiplying a value related to the gain of the second RIS channel by a weight from a value related to the gain of the first RIS channel, and the reflection pattern is determined to maximize the objective function. Claim 4 In claim 3, the reflection pattern is determined by the RCG (Riemannian conjugate gradient) algorithm based on the objective function. Claim 5 A method according to claim 3, wherein the step of determining the reflection pattern comprises: determining the Riemann gradient of the objective function with respect to a first point associated with the RIS reflection pattern; determining a first search direction based on the Riemann gradient; determining a translation operator that moves from a previous tangent space to a next tangent space; determining a second search direction based on the translation operator; determining a second point based on the first point and the second search direction; and determining a third point that indicates the reflection pattern through vector contraction of the second point. Claim 6 A method according to claim 5, wherein the second search direction is determined based on a value obtained by multiplying the movement operator by a parameter, and the parameter is determined based on a ratio calculated by dividing the length of the current slope by the length of the previous slope. Claim 7 In claim 3, the weight is determined based on the channel environment of the first base station and the channel environment of the second base station. Claim 8 The method of claim 1 further comprises the step of determining an initial reflection pattern of the RIS; and the step of determining a first beamforming vector, wherein the first beamforming vector is determined based on the signal-to-leakage-and-noise-ratio (SLNR) using the initial reflection pattern. Claim 9 A method according to claim 8, wherein the step of transmitting a data signal to the first terminal comprises the step of updating the first beamforming vector to the second beamforming vector based on the reflection pattern determined based on the first channel information and the second channel information. Claim 10 In claim 1, the first base station and the second base station use different frequency bands. Claim 11 In claim 1, the first base station and the second base station are operated by another service provider. Claim 12 A method performed by a first terminal in a wireless communication system, comprising: receiving a reference signal from a first base station; transmitting first channel information measured based on the reference signal to the first base station; and receiving a data signal from the first base station through a reconfigurable intelligent surface (RIS), wherein the reflection pattern of the RIS is determined based on a first RIS channel and a second RIS channel, the first RIS channel includes a channel between the first base station and the RIS and a channel between the RIS and the first terminal, and the second RIS channel includes a channel between the second base station and the RIS and a channel between the RIS and the second terminal. Claim 13 A method according to claim 12, wherein the reflection pattern is determined to maximize an objective function determined based on a function of subtracting a value obtained by multiplying a value related to the gain of the second RIS channel by a weight from a value related to the gain of the first RIS channel. Claim 14 In claim 12, the first base station and the second base station are operated by different service providers. Claim 15 A first base station in a wireless communication system comprises: a transceiver; and a processor connected to the transceiver, wherein the processor transmits a reference signal to a first terminal, receives first channel information measured based on the reference signal from the first terminal, receives second channel information from a second base station, determines a reflection pattern based on the first channel information and the second channel information, and controls the transmission of a data signal to the first terminal, wherein the first channel information includes information related to a first RIS channel including a channel between the first base station and the RIS and a channel between the RIS and the first terminal, and the second channel information includes information related to a second RIS channel including a channel between the second base station and the RIS and a channel between the RIS and the second terminal. Claim 16 A first terminal in a wireless communication system comprises: a transceiver; and a processor connected to the transceiver, wherein the processor receives a reference signal from a first base station, receives first channel information measured based on the reference signal from the first base station, and controls the first base station to receive a data signal through a reconfigurable intelligent surface (RIS), wherein the reflection pattern of the RIS is determined based on a first RIS channel and a second RIS channel, wherein the first RIS channel includes a channel between the first base station and the RIS and a channel between the RIS and the first terminal, and the second RIS channel includes a channel between the second base station and the RIS and a channel between the RIS and the second terminal. Claim 17 A communication device comprising: at least one processor; and at least one computer memory connected to the at least one processor and storing instructions that direct operations as executed by the at least one processor, wherein the operations include: transmitting a reference signal to a first terminal; receiving first channel information measured based on the reference signal from the first terminal; receiving second channel information from a second base station; determining a reflection pattern of a reconfigurable intelligent surface (RIS) based on an objective function generated based on the first channel information and the second channel information; and transmitting a data signal to the first terminal, wherein the first channel information includes information related to a first RIS channel including a channel between the communication device and the RIS and a channel between the RIS and the first terminal, and the second channel information includes information related to a second RIS channel including a channel between the second base station and the RIS and a channel between the RIS and the second terminal. Claim 18 A non-transitory computer-readable medium storing at least one instruction, comprising said at least one instruction executable by a processor, wherein said at least one instruction controls a device to transmit a reference signal to a first terminal, receive first channel information measured based on said reference signal from the first terminal, receive second channel information from a second base station, determine a reflection pattern of a reconfigurable intelligent surface (RIS) based on an objective function generated based on said first channel information and said second channel information, and transmit a data signal to the first terminal, wherein the first channel information comprises information related to a first RIS channel including a channel between the device and the RIS and a channel between the RIS and the first terminal, and said second channel information comprises information related to a second RIS channel including a channel between the second base station and the RIS and a channel between the RIS and the second terminal.

Images