WO2022231084A1 - Method and device for transmitting signal in wireless communication system - Google Patents

Abstract

According to an example disclosed herein, an operation method of a terminal comprises the steps of: receiving a radio resource control (RRC) message, including sounding configuration information, from a base station; transmitting a first sounding reference signal (SRS) to the base station on the basis of the received sounding configuration information; and transmitting a second SRS to the base station via an intelligent reflecting surface (IRS) on the basis of the received sounding configuration information. The base station estimates channels on the basis of the first SRS and the second SRS. The second SRS is transmitted periodically, and the channel estimation is based on the period of the second SRS.

Description

Method and apparatus for transmitting a signal in a wireless communication system

The following description relates to a wireless communication system, and to an apparatus and method for channel estimation in a wireless communication system.

Wireless access systems have been widely deployed to provide various types of communication services such as voice and data. In general, a wireless access system is a multiple access system that can support communication with multiple users by sharing available system resources (bandwidth, transmission power, etc.). Examples of the multiple access system include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, and a single carrier frequency (SC-FDMA) system. division multiple access) systems.

In particular, as many communication devices require a large communication capacity, an enhanced mobile broadband (eMBB) communication technology has been proposed compared to the existing radio access technology (RAT). In addition, a communication system that considers reliability and latency sensitive services/user equipment (UE) as well as massive machine type communications (mMTC) that provides various services anytime, anywhere by connecting multiple devices and things has been proposed. . For this purpose, various technical configurations have been proposed.

The present disclosure may provide an apparatus and method for channel estimation in a wireless communication system.

The present disclosure may provide an apparatus and method for channel estimation in a wireless communication system including an intelligent reflecting surface (IRS).

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

As an example of the present disclosure, a method of operating a terminal in a wireless communication system includes receiving a radio resource control (RRC) message including sounding configuration information from a base station, the received sound Transmitting a first sounding reference signal (SRS) to the base station based on the sounding configuration information and transmitting a second SRS based on the received sounding configuration information through an intelligent reflecting surface (IRS) and transmitting to the base station. The base station estimates a channel based on the first SRS and the second SRS. The second SRS is transmitted periodically, and the channel estimation is based on a period of the second SRS. The channel estimation may be based on the number of elements L of the IRS and the number of repetition periods Q of the second SRS. When the Q is less than the L, an IRS learning matrix based on pseudo-orthogonality may be used for the channel estimation. When the Q is greater than or equal to the L, an IRS learning matrix based on orthogonality may be used for the channel estimation. When the Q is greater than or equal to the L, an IRS learning matrix based on linear independence may be used for the channel estimation. The terminal may receive feedback information of the second SRS from the base station through the IRS.

As an example of the present disclosure, in a wireless communication system, a terminal includes a transceiver and a processor connected to the transceiver. The processor controls the transceiver to receive a radio resource control (RRC) message including sounding configuration information from the base station. The processor controls the transceiver to transmit a first sounding reference signal (SRS) to the base station based on the received sounding configuration information. The processor controls the transceiver to transmit a second SRS to the base station through an intelligent reflecting surface (IRS) based on the received sounding configuration information. The base station estimates a channel based on the first SRS and the second SRS. The second SRS is transmitted periodically, and the channel estimation is based on a period of the second SRS. The channel estimation may be based on the number of elements L of the IRS and the number of repetition periods Q of the second SRS. When the Q is less than the L, an IRS learning matrix based on pseudo-orthogonality may be used for the channel estimation. When the Q is greater than or equal to the L, an IRS learning matrix based on orthogonality may be used for the channel estimation. When the Q is greater than or equal to the L, an IRS learning matrix based on linear independence may be used for the channel estimation. The processor may control the transceiver to receive feedback information of the second SRS from the base station through the IRS.

As an example of the present disclosure, a communication device includes at least one processor and at least one computer memory coupled to the at least one processor and storing instructions for instructing operations as executed by the at least one processor. . The processor controls the communication device to receive a radio resource control (RRC) message including sounding configuration information from the base station. The processor controls the communication device to transmit a first sounding reference signal (SRS) to the base station based on the received sounding configuration information. The processor controls the communication device to transmit a second SRS to the base station through an intelligent reflecting surface (IRS) based on the received sounding configuration information. The base station estimates a channel based on the first SRS and the second SRS. The second SRS is transmitted periodically, and the channel estimation is based on a period of the second SRS.

As an example of the present disclosure, a non-transitory computer-readable medium storing at least one instruction may be executable by a processor to store the at least one instruction. include The at least one instruction instructs the computer-readable medium to receive a radio resource control (RRC) message including sounding configuration information from a base station. The at least one instruction instructs the computer-readable medium to transmit a first sounding reference signal (SRS) to the base station based on the received sounding configuration information. The at least one instruction instructs the computer-readable medium to transmit a second SRS to the base station through an intelligent reflecting surface (IRS) based on the received sounding configuration information. The base station estimates a channel based on the first SRS and the second SRS. The second SRS is transmitted periodically, and the channel estimation is based on a period of the second SRS.

As an example of the present disclosure, a method of operating a base station in a wireless communication system includes transmitting a radio resource control (RRC) message including sounding configuration information to a terminal, the sounding configuration Receiving a first sounding reference signal (SRS) based on information from the terminal and receiving a second SRS based on the sounding configuration information from the terminal through an intelligent reflecting surface (IRS) includes The base station estimates a channel based on the first SRS and the second SRS. The second SRS is transmitted periodically, and the channel estimation is based on a period of the second SRS.

As an example of the present disclosure, a base station in a wireless communication system includes a transceiver and a processor connected to the transceiver. The processor controls the transceiver to transmit a radio resource control (RRC) message including sounding configuration information to the terminal. The processor controls the transceiver to receive a first sounding reference signal (SRS) based on the sounding configuration information from the terminal. The processor controls the transceiver to receive a second SRS from the terminal through an intelligent reflecting surface (IRS) based on the sounding configuration information. The base station estimates a channel based on the first SRS and the second SRS. The second SRS is transmitted periodically, and the channel estimation is based on a period of the second SRS.

Aspects of the present disclosure described above are only some of the preferred embodiments of the present disclosure, and various embodiments in which the technical features of the present disclosure are reflected are detailed descriptions of the present disclosure that will be described below by those of ordinary skill in the art can be derived and understood based on

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

According to the present disclosure, channel estimation may be performed in a wireless communication system including an intelligent reflecting surface (IRS).

According to the present disclosure, channel estimation may be performed in a wireless communication system including an intelligent reflecting surface (IRS).

According to the present disclosure, the overhead of channel estimation can be reduced.

According to the present disclosure, accurate channel estimation is possible based on an adjustable learning matrix in a communication system including an IRS.

Effects obtainable in the embodiments of the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned are the technical fields to which the technical configuration of the present disclosure is applied from the description of the embodiments of the present disclosure below. It can be clearly derived and understood by those of ordinary skill in the art. That is, unintended effects of implementing the configuration described in the present disclosure may also be derived by those of ordinary skill in the art from the embodiments of the present disclosure.

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

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

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

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

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

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

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

7 is a diagram illustrating a method of processing a transmission signal applicable to the present disclosure.

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

9 is a diagram illustrating an electromagnetic spectrum applicable to the present disclosure.

10 is a diagram illustrating a THz communication method applicable to the present disclosure.

11 shows an example of an intelligent reflective surface (IRS) communication system applicable to the present disclosure.

12 illustrates an example of a channel estimation technique applicable to the present disclosure.

13A and 13B show simulation results according to examples of the present disclosure.

14 illustrates an example of a channel estimation procedure applicable to the present disclosure.

15 shows an example of a terminal operation procedure applicable to the present disclosure.

16 illustrates a base station operation procedure applicable to the present disclosure.

The following embodiments combine elements and features of the present disclosure in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. In addition, some components and/or features may be combined to configure an embodiment of the present disclosure. The order of operations described in embodiments of the present disclosure may be changed. Some configurations or features of one embodiment may be included in other embodiments, or may be replaced with corresponding configurations or features of other embodiments.

In the description of the drawings, procedures or steps that may obscure the subject matter of the present disclosure are not described, and procedures or steps that can be understood at the level of those skilled in the art are also not described.

Throughout the specification, when a part is said to "comprising or including" a certain component, it does not exclude other components unless otherwise stated, meaning that other components may be further included. do. In addition, terms such as "... unit", "... group", and "module" described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, "a or an", "one", "the" and like related terms are used differently herein in the context of describing the present disclosure (especially in the context of the following claims). Unless indicated or clearly contradicted by context, it may be used in a sense including both the singular and the plural.

In the present specification, embodiments of the present disclosure have been described focusing on a data transmission/reception relationship between a base station and a mobile station. Here, the base station has a meaning as a terminal node of a network that directly communicates with the mobile station. A specific operation described as being performed by the base station in this document may be performed by an upper node of the base station in some cases.

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

In addition, in embodiments of the present disclosure, a terminal includes a user equipment (UE), a mobile station (MS), a subscriber station (SS), a mobile subscriber station (MSS), It may be replaced by terms such as a mobile terminal or an advanced mobile station (AMS).

In addition, a transmitting end refers to a fixed and/or mobile node that provides a data service or a voice service, and a receiving end refers to a fixed and/or mobile node that receives a data service or a voice service. Accordingly, in the case of uplink, the mobile station may be the transmitting end and the base station may be the receiving end. Similarly, in the case of downlink, the mobile station may be the receiving end, and the base station may be the transmitting end.

Embodiments of the present disclosure are wireless access systems IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP LTE (Long Term Evolution) system, 3GPP 5G (5th generation) NR (New Radio) system, and 3GPP2 system among It may be supported by standard documents disclosed in at least one, and in particular, embodiments of the present disclosure are 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. can be

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

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

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

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

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

In the following, in order to clarify the following description, it is described based on a 3GPP communication system (e.g. (eg, LTE, NR, etc.), but the technical spirit of the present invention is not limited thereto. LTE is 3GPP TS 36.xxx Release 8 or later Specifically, LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may mean technology after TS 38.xxx Release 15. 3GPP 6G may mean technology after TS Release 17 and/or Release 18. "xxx" means standard document detail number LTE/NR/6G may be collectively referred to as a 3GPP system.

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

Communication system applicable to the present disclosure

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

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

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

Referring to FIG. 1 , a communication system 100 applied to the present disclosure includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (eg, 5G NR, LTE), and may be referred to as a communication/wireless/5G device. Although not limited thereto, the wireless device includes a robot 100a, a vehicle 100b-1, 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, and a home appliance. appliance) 100e, an Internet of Thing (IoT) device 100f, and an artificial intelligence (AI) device/server 100g. For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous driving vehicle, a vehicle capable of performing inter-vehicle communication, and the like. Here, the vehicles 100b-1 and 100b-2 may include an unmanned aerial vehicle (UAV) (eg, a drone). The XR device 100c includes augmented reality (AR)/virtual reality (VR)/mixed reality (MR) devices, and includes a head-mounted device (HMD), a head-up display (HUD) provided in a vehicle, a television, It may be implemented in the form of a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, and the like. The portable device 100d may include a smart phone, a smart pad, a wearable device (eg, smart watch, smart glasses), and a computer (eg, a laptop computer). The home appliance 100e may include a TV, a refrigerator, a washing machine, and the like. The IoT device 100f may include a sensor, a smart meter, and the like. For example, the base station 120 and the network 130 may be implemented as a wireless device, and a specific wireless device 120a may operate as a base station/network node to other wireless devices.

The wireless devices 100a to 100f may be connected to the network 130 through the base station 120 . AI technology may be applied to the wireless devices 100a to 100f , and the wireless devices 100a to 100f may be connected to the AI server 100g through the network 130 . The network 130 may be configured using a 3G network, a 4G (eg, LTE) network, or a 5G (eg, NR) network. The wireless devices 100a to 100f may communicate with each other through the base station 120/network 130, but communicate directly without going through the base station 120/network 130 (eg, sidelink communication) You may. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (eg, vehicle to vehicle (V2V)/vehicle to everything (V2X) communication). Also, the IoT device 100f (eg, a sensor) may communicate directly with another IoT device (eg, a sensor) or other wireless devices 100a to 100f.

Wireless communication/ connection 150a, 150b, and 150c may be performed between the wireless devices 100a to 100f/base station 120 and the base station 120/base station 120 . Here, the wireless communication/connection includes various uplink/downlink communication 150a and sidelink communication 150b (or D2D communication), and communication between base stations 150c (eg, relay, integrated access backhaul (IAB)). This may be achieved through radio access technology (eg, 5G NR). Through the wireless communication/ connection 150a, 150b, and 150c, the wireless device and the base station/wireless device, and the base station and the base station may transmit/receive radio signals to each other. For example, the wireless communication/ connection 150a, 150b, 150c may transmit/receive signals through various physical channels. To this end, based on various proposals of the present disclosure, various configuration information setting processes for transmission/reception of radio signals, various signal processing processes (eg, channel encoding/decoding, modulation/demodulation, resource mapping/demapping, etc.) , at least a part of a resource allocation process, etc. may be performed.

Communication system applicable to the present disclosure

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

Referring to FIG. 2 , a first wireless device 200a and a second wireless device 200b may transmit and receive wireless signals through various wireless access technologies (eg, LTE, NR). Here, {first wireless device 200a, second wireless device 200b} is {wireless device 100x, base station 120} of FIG. 1 and/or {wireless device 100x, wireless device 100x) } can be matched.

The first wireless device 200a includes one or more processors 202a and one or more memories 204a, and may further include one or more transceivers 206a and/or one or more antennas 208a. The processor 202a controls the memory 204a and/or the transceiver 206a and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. For example, the processor 202a may process information in the memory 204a to generate first information/signal, and then transmit a wireless signal including the first information/signal through the transceiver 206a. In addition, the processor 202a may receive the radio signal including the second information/signal through the transceiver 206a, and then store the information obtained from the signal processing of the second information/signal in the memory 204a. The memory 204a may be connected to the processor 202a and may store various information related to the operation of the processor 202a. For example, the memory 204a may provide instructions for performing some or all of the processes controlled by the processor 202a, or for performing the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including Here, the processor 202a and the memory 204a may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 206a may be coupled to the processor 202a and may transmit and/or receive wireless signals via one or more antennas 208a. The transceiver 206a may include a transmitter and/or a receiver. The transceiver 206a may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, a wireless device may refer to a communication modem/circuit/chip.

The second wireless device 200b includes one or more processors 202b, one or more memories 204b, and may further include one or more transceivers 206b and/or one or more antennas 208b. The processor 202b controls the memory 204b and/or the transceiver 206b, and may be configured to implement the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. For example, the processor 202b may process information in the memory 204b to generate third information/signal, and then transmit a wireless signal including the third information/signal through the transceiver 206b. In addition, the processor 202b may receive the radio signal including the fourth information/signal through the transceiver 206b, and then store information obtained from signal processing of the fourth information/signal in the memory 204b. The memory 204b may be connected to the processor 202b and may store various information related to the operation of the processor 202b. For example, the memory 204b may provide instructions for performing some or all of the processes controlled by the processor 202b, or for performing the descriptions, functions, procedures, suggestions, methods, and/or operational flowcharts disclosed herein. may store software code including Here, the processor 202b and the memory 204b may be part of a communication modem/circuit/chip designed to implement a wireless communication technology (eg, LTE, NR). The transceiver 206b may be coupled to the processor 202b and may transmit and/or receive wireless signals via one or more antennas 208b. Transceiver 206b may include a transmitter and/or receiver. 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.

Hereinafter, hardware elements of the wireless devices 200a and 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 include one or more layers (eg, physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), and radio resource (RRC). control) and a functional layer such as service data adaptation protocol (SDAP)). The one or more processors 202a, 202b may be configured to process one or more Protocol Data Units (PDUs) and/or one or more Service Data Units (SDUs) according to the description, function, procedure, proposal, method, and/or operational flowcharts disclosed herein. can create One or more processors 202a, 202b may generate messages, control information, data, or information according to the description, function, procedure, proposal, method, and/or flow charts disclosed herein. The one or more processors 202a, 202b generate a signal (eg, a baseband signal) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and/or methods disclosed herein. , to one or more transceivers 206a, 206b. One or more processors 202a, 202b may receive signals (eg, baseband signals) from one or more transceivers 206a, 206b, and the descriptions, functions, procedures, proposals, methods, and/or flowcharts of operation disclosed herein. PDUs, SDUs, messages, control information, data, or information may be acquired according to the fields.

One or more processors 202a, 202b may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. 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 of operations disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, and the like. The descriptions, functions, procedures, proposals, methods, and/or flowcharts of operation disclosed in this document may contain firmware or software configured to perform one or more processors 202a, 202b, or stored in one or more memories 204a, 204b. It may be driven by the above processors 202a and 202b. The descriptions, functions, procedures, suggestions, methods, and/or flowcharts of operations disclosed herein may be implemented using firmware or software in the form of code, instructions, and/or sets of instructions.

One or more memories 204a, 204b may be coupled to one or more processors 202a, 202b and may store various types of data, signals, messages, information, programs, codes, instructions, and/or instructions. One or more memories 204a, 204b may include read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), flash memory, hard drives, registers, cache memory, computer readable storage media and/or It may consist of a combination of these. One or more memories 204a, 204b may be located inside and/or external to one or more processors 202a, 202b. Also, one or more memories 204a, 204b may be coupled to one or more processors 202a, 202b through various technologies, such as wired or wireless connections.

The one or more transceivers 206a, 206b may transmit user data, control information, radio signals/channels, etc. referred to in the methods and/or operational flowcharts of this document, to one or more other devices. One or more transceivers 206a, 206b may receive user data, control information, radio signals/channels, etc. referred to in the descriptions, functions, procedures, suggestions, methods, and/or flow charts, etc. disclosed herein, from one or more other devices. have. For example, one or more transceivers 206a, 206b may be coupled 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. In addition, 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. Further, one or more transceivers 206a, 206b may be coupled to one or more antennas 208a, 208b, and the one or more transceivers 206a, 206b may be connected via one or more antennas 208a, 208b. , procedures, proposals, methods and/or operation flowcharts, etc. may be set to transmit and receive user data, control information, radio signals/channels, and the like. In this document, one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (eg, antenna ports). The one or more transceivers 206a, 206b converts the received radio signal/channel, etc. from the RF band signal to process the received user data, control information, radio signal/channel, etc. using the one or more processors 202a, 202b. It can be converted into a baseband signal. One or more transceivers 206a, 206b may convert user data, control information, radio 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.

Wireless device structure applicable to the present disclosure

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

Referring to FIG. 3 , a wireless device 300 corresponds to the wireless devices 200a and 200b of FIG. 2 , and includes various elements, components, units/units, and/or modules. ) may consist of For example, the wireless device 300 may include a communication unit 310 , a control unit 320 , a memory unit 330 , and an additional element 340 . The communication unit may include communication circuitry 312 and transceiver(s) 314 . For example, communication circuitry 312 may include one or more processors 202a, 202b 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 the additional element 340 and controls general operations of the wireless device. For example, the controller 320 may control the electrical/mechanical operation of the wireless device based on the program/code/command/information stored in the memory unit 330 . In addition, the control unit 320 transmits the information stored in the memory unit 330 to the outside (eg, another communication device) through the communication unit 310 through a wireless/wired interface, or externally (eg, through the communication unit 310) Information received through a wireless/wired interface from another communication device) may be stored in the memory unit 330 .

The additional element 340 may be variously configured according to the type of the 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 include a robot ( FIGS. 1 and 100a ), a vehicle ( FIGS. 1 , 100b-1 , 100b-2 ), an XR device ( FIGS. 1 and 100c ), and a mobile device ( FIGS. 1 and 100d ). ), home appliances (FIG. 1, 100e), IoT device (FIG. 1, 100f), digital broadcasting terminal, hologram device, public safety device, MTC device, medical device, fintech device (or financial device), security device, climate/ It may be implemented in the form of an environment device, an AI server/device ( FIGS. 1 and 140 ), a base station ( FIGS. 1 and 120 ), and a network node. The wireless device may be mobile or used in a fixed location depending on the use-example/service.

In FIG. 3 , various elements, components, units/units, and/or modules in the wireless device 300 may be entirely interconnected through a wired interface, or at least some of them may be wirelessly connected through the communication unit 310 . For example, in the wireless device 300 , the control unit 320 and the communication unit 310 are connected by wire, and the control unit 320 and the first unit (eg, 130 , 140 ) are connected wirelessly through the communication unit 310 . can be connected In addition, each element, component, unit/unit, and/or module within the wireless device 300 may further include one or more elements. For example, the controller 320 may include one or more processor sets. For example, the controller 320 may be configured as a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphic processing processor, a memory control processor, and the like. As another example, the memory unit 330 may include a RAM, a dynamic RAM (DRAM), a ROM, a flash memory, a volatile memory, a non-volatile memory, and/or a combination thereof. can be configured.

Mobile device to which the present disclosure is applicable

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

4 illustrates a portable device applied to the present disclosure. The portable device may include a smart phone, a smart pad, a wearable device (eg, a smart watch, smart glasses), and a portable computer (eg, a laptop computer). The mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).

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

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

For example, in the case of data communication, the input/output unit 440c obtains information/signals (eg, touch, text, voice, image, video) input from the user, and the obtained information/signals are stored in the memory unit 430 . can be saved. The communication unit 410 may convert the information/signal stored in the memory into a wireless signal, and transmit the converted wireless signal directly to another wireless device or to a base station. Also, after receiving a radio signal from another radio device or a base station, the communication unit 410 may restore the received radio signal to original information/signal. The restored information/signal may be stored in the memory unit 430 and output in various forms (eg, text, voice, image, video, haptic) through the input/output unit 440c.

Types of wireless devices to which the present disclosure is applicable

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

5 illustrates a vehicle or an autonomous driving vehicle applied to the present disclosure. The vehicle or autonomous driving vehicle may be implemented as a mobile robot, vehicle, train, manned/unmanned aerial vehicle (AV), ship, etc., but is not limited to the shape of the vehicle.

Referring to FIG. 5 , the vehicle or autonomous driving vehicle 500 includes an antenna unit 508 , a communication unit 510 , a control unit 520 , a driving unit 540a , a power supply unit 540b , a sensor unit 540c and autonomous driving. A unit 540d may be included. The antenna unit 550 may be configured as a part of the communication unit 510 . Blocks 510/530/540a-540d correspond to blocks 410/430/440 of FIG. 4, respectively.

The communication unit 510 may transmit/receive signals (eg, data, control signals, etc.) to and from external devices such as other vehicles, base stations (eg, base stations, roadside units, etc.), and servers. The controller 520 may control elements of the vehicle or autonomous vehicle 500 to perform various operations. The controller 520 may include an electronic control unit (ECU).

6 is a diagram illustrating an example of an AI device applied to the present disclosure. For example, AI devices include TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc. It may be implemented as a device or a mobile device.

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

The communication unit 610 uses wired and wireless communication technology to communicate with external devices such as other AI devices (eg, FIGS. 1, 100x, 120, 140) or an AI server ( FIGS. 1 and 140 ) and wired and wireless signals (eg, sensor information, user input, learning model, control signal, etc.). To this end, the communication unit 610 may transmit information in the memory unit 630 to an external device or transmit a signal received from the external device to the memory unit 630 .

The controller 620 may determine at least one executable operation of the AI device 600 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. Then, the controller 620 may control the components of the AI device 600 to perform the determined operation. For example, the control unit 620 may request, search, receive, or utilize the data of the learning processor unit 640c or the memory unit 630, and is determined to be a predicted operation or desirable among at least one executable operation. Components of the AI device 600 may be controlled to execute the operation. In addition, the control unit 620 collects history information including user feedback on the operation contents or operation of the AI device 600 and stores it in the memory unit 630 or the learning processor unit 640c, or the AI server ( 1 and 140), and the like may be transmitted to an external device. The collected historical information may be used to update the learning model.

The memory unit 630 may store data supporting various functions of the AI device 600 . For example, the memory unit 630 may store data obtained from the input unit 640a , data obtained from the communication unit 610 , output data of the learning processor unit 640c , and data obtained from the sensing unit 640 . Also, the memory unit 630 may store control information and/or software codes necessary for the operation/execution of the control unit 620 .

The input unit 640a may acquire various types of data from the outside of the AI device 600 . For example, the input unit 620 may obtain training data for model learning, input data to which the learning model is applied, and the like. The input unit 640a may include a camera, a microphone, and/or a user input unit. The output unit 640b may generate an output related to sight, hearing, or touch. The output unit 640b may include a display unit, a speaker, and/or a haptic module. The sensing unit 640 may obtain at least one of internal information of the AI device 600 , surrounding environment information of the AI device 600 , and user information by using various sensors. The sensing unit 640 may include a proximity sensor, an illumination sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and/or a radar. have.

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

7 is a diagram illustrating a method of processing a transmission signal applied to the present disclosure. As an 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, as an example, the operation/function of FIG. 7 may be performed by the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 . Also, as an example, the hardware elements of FIG. 7 may be implemented in the processors 202a and 202b and/or the transceivers 206a and 206b of FIG. 2 . As an example, blocks 710 to 760 may be implemented in the processors 202a and 202b of FIG. 2 . In addition, blocks 710 to 750 may be implemented in the processors 202a and 202b of FIG. 2 , and block 760 may be implemented in the transceivers 206a and 206b of FIG. 2 , and the embodiment is not limited thereto.

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

The complex modulation symbol sequence may be mapped to one or more transport layers by a layer mapper 730 . Modulation symbols of each transport layer may be mapped to corresponding antenna port(s) by a precoder 740 (precoding). The output z of the precoder 740 may be obtained by multiplying the output y of the layer mapper 730 by the precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transport layers. Here, the precoder 740 may perform precoding after performing transform precoding (eg, discrete fourier transform (DFT) transform) on the complex modulation symbols. Also, the precoder 740 may perform precoding without performing transform precoding.

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

The signal processing process for the received signal in the wireless device may be configured in reverse of the signal processing processes 710 to 760 of FIG. 7 . For example, the wireless device (eg, 200a or 200b of FIG. 2 ) may receive a wireless signal from the outside through an antenna port/transceiver. The received radio signal may 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. Thereafter, the baseband signal may be restored to a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a descrambling process. The codeword may be restored to the original information block through decoding. Accordingly, the signal processing circuit (not shown) for the received signal may include a signal reconstructor, a resource de-mapper, a post coder, a demodulator, a de-scrambler, and a decoder.

6G communication system

6G (wireless) systems have (i) very high data rates per device, (ii) very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) battery- It aims to reduce energy consumption of battery-free IoT devices, (vi) ultra-reliable connections, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system may have four aspects such as "intelligent connectivity", "deep connectivity", "holographic connectivity", and "ubiquitous connectivity", and the 6G system can satisfy the requirements shown in Table 1 below. That is, Table 1 is a table showing the requirements of the 6G system.

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

In this case, the 6G system includes enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), AI integrated communication, and tactile Internet (tactile internet), high throughput (high throughput), high network capacity (high network capacity), high energy efficiency (high energy efficiency), low backhaul and access network congestion (low backhaul and access network congestion) and improved data security ( It may have key factors such as enhanced data security.

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

Referring to FIG. 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 important technology by providing an end-to-end delay of less than 1 ms in 6G communication. At this time, the 6G system will have much better volumetric spectral efficiency, unlike the frequently used area spectral efficiency. 6G systems can provide very long battery life and advanced battery technology for energy harvesting, so mobile devices in 6G systems may not need to be charged separately.

Core implementation technology of 6G system

- Artificial Intelligence (AI)

The most important and new technology to be introduced in 6G systems is AI. AI was not involved in the 4G system. 5G systems will support partial or very limited AI. However, the 6G system will be AI-enabled for full automation. Advances in machine learning will create more intelligent networks for real-time communication in 6G. Incorporating AI into communications can simplify and enhance real-time data transmission. AI can use numerous analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.

Time-consuming tasks such as handovers, network selection, and resource scheduling can be performed instantly by using AI. AI can also play an important role in M2M, machine-to-human and human-to-machine communication. In addition, AI can be a rapid communication in the BCI (brain computer interface). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

Recently, attempts have been made to integrate AI with wireless communication systems, but these are focused on the application layer, network layer, and especially deep learning, wireless resource management and allocation. come. However, these studies are gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission in the physical layer are appearing. AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in a fundamental signal processing and communication mechanism. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanism, It may include AI-based resource scheduling and allocation.

Machine learning may be used for channel estimation and channel tracking, and may be used for power allocation, interference cancellation, etc. in a physical layer of a downlink (DL). In addition, machine learning may be used for antenna selection, power control, symbol detection, and the like in a MIMO system.

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

Deep learning-based AI algorithms require large amounts of training data to optimize training parameters. However, due to a limitation in acquiring data in a specific channel environment as training data, a lot of training data is used offline. This is because static training on training data in a specific channel environment may cause a contradiction between dynamic characteristics and diversity of a wireless channel.

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

Hereinafter, machine learning will be described in more detail.

Machine learning refers to a set of actions that trains a machine to create a machine that can perform tasks that humans can or cannot do. Machine learning requires data and a learning model. In machine learning, data learning methods can be roughly divided into three types: supervised learning, unsupervised learning, and reinforcement learning.

Neural network learning is to minimize errors in output. Neural network learning repeatedly inputs learning data to the neural network, calculates the output and target errors of the neural network for the training data, and backpropagates the neural network error from the output layer of the neural network to the input layer in the direction to reduce the error. ) to update the weights of each node in the neural network.

Supervised learning uses training data in which the correct answer is labeled in the training data, and in unsupervised learning, the correct answer may not be labeled in the training data. That is, for example, the training data in the case of supervised learning related to data classification may be data in which categories are labeled for each of the training data. The labeled training data is input to the neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data. The calculated error is back propagated in the reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back propagation. A change amount of the connection weight of each node to be updated may be determined according to a learning rate. The computation of the neural network on the input data and the backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of learning of a neural network, a high learning rate can be used to increase the efficiency by allowing the neural network to quickly obtain a certain level of performance, and in the late learning period, a low learning rate can be used to increase the accuracy.

The learning method may vary depending on the characteristics of the data. For example, in a case where the purpose of accurately predicting data transmitted from a transmitter in a communication system is accurately predicted by a receiver, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

The learning model corresponds to the human brain, and the most basic linear model can be considered. ) is called

The neural network cord used as a learning method is largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent boltzmann machine (RNN) methods. and such a learning model can be applied.

THz (Terahertz) communication

THz communication may be applied in the 6G system. For example, the data rate may be increased by increasing the bandwidth. This can be accomplished by using sub-THz communication with a wide bandwidth and applying advanced large-scale MIMO technology.

9 is a diagram illustrating an electromagnetic spectrum applicable to the present disclosure. As an example, referring to FIG. 9 , a THz wave, also known as sub-millimeter radiation, generally represents a frequency band between 0.1 THz and 10 THz with a corresponding wavelength in the range of 0.03 mm-3 mm. The 100GHz-300GHz band range (Sub THz band) is considered a major part of the THz band for cellular communication. Sub-THz band Addition to mmWave band increases 6G cellular communication capacity. Among the defined THz bands, 300GHz-3THz is in the far-infrared (IR) frequency band. The 300GHz-3THz band is part of the broadband, but at the edge of the wideband, just behind the RF band. Thus, this 300 GHz-3 THz band exhibits similarities to RF.

The main characteristics of THz communication include (i) widely available bandwidth to support very high data rates, and (ii) high path loss occurring at high frequencies (high directional antennas are indispensable). The narrow beamwidth produced by the highly directional antenna reduces interference. The small wavelength of the THz signal allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This allows the use of advanced adaptive nesting techniques that can overcome range limitations.

terahertz (THz) wireless communication

10 is a diagram illustrating a THz communication method applicable to the present disclosure.

Referring to FIG. 10, THz wireless communication uses wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1THz=1012Hz). It can mean communication. THz wave is located between the RF (Radio Frequency)/millimeter (mm) and infrared bands, (i) It penetrates non-metal/non-polar materials better than visible light/infrared light, and has a shorter wavelength than RF/millimeter wave, so it has high straightness. Beam focusing may be possible.

specific embodiments of the present invention

Intelligent reflecting surface (IRS) is attracting attention as one of methods for increasing communication speed in communication after 5G. The existing method of using multiple antennas may improve communication speed through antenna gain and beamforming gain. However, the multi-antenna requires an active element such as a radio frequency (RF) chain. Accordingly, there may be problems in terms of cost and power consumption for the device to deploy a large-scale antenna. The IRS may be configured with only a passive element. Accordingly, the IRS can obtain a gain that can be obtained with multiple antennas using relatively low cost and power.

When the communication system including the IRS estimates the channel, the channel estimation method of the existing communication system not including the IRS cannot be used. In addition, the IRS including only passive elements cannot transmit and receive independent signals. Therefore, it is difficult to independently estimate the channel between the base station and the IRS and the channel between the terminal and the IRS. The base station and the terminal can observe only the cascaded channel through the IRS. Accordingly, a communication system including the IRS needs a new channel estimation method, and the present disclosure proposes a channel estimation method of a communication system including the IRS.

Existing channel estimation techniques for communication systems including IRS focus on estimation accuracy. Therefore, in the conventional technique, the length of the training sequence required for estimation is very long. In addition, the length of the training column increases in proportion to the number of antennas and the number of IRS elements. In addition, when a large-scale IRS is used to support a high communication speed, the length of the training sequence may be longer than a coherence time. Therefore, the existing channel estimation method may be substantially impossible to communicate in this case. Accordingly, the present disclosure proposes a channel estimation method based on an adjustable learning sequence in a communication system including an IRS.

In the present disclosure, vertical vectors and matrices may be expressed in boldface letters. A transpose and a Hermitian transpose may be denoted by ()T and ()H, respectively. The set of complex numbers can be written as C. C ^a×b represents the set of complex matrices of size a×b. For a matrix A, its a-th row and b-th column can be written as A(a,:) and A(:,b) have. The Kronecker product of a matrix is

can be denoted as For some matrix A, A ^(-R) and A ^(-L) represent its right inverse and left inverse, respectively. For a vector a, a diagonal matrix having its elements as diagonal components can be written as diag(a). For any vector a, that

value is

can be expressed as A multivariate normal distribution with a mean vector μ and a covariance matrix Σ is

can be denoted as Ia represents an identity matrix having a size of a×a.

Table 2 below shows terms used in the present disclosure.

11 shows an example of an intelligent reflective surface (IRS) communication system applicable to the present disclosure. 12 illustrates an example of a channel estimation technique applicable to the present disclosure.

In the present disclosure, in a single-user multiple-input multiple-output (SU-MIMO) system based on time division duplexing (TDD), a base station (BS) transmits a channel using an uplink signal. We propose a method for estimating In the present disclosure, it is assumed that the IRS is controlled by the base station. An object of the present disclosure is the estimation of a cascaded MIMO channel and the application of an adjustable training sequence.

The base station may determine the element setting value of the IRS. The received signal at time t may be expressed as Equation 1a below.

[Equation 1a]

It is assumed that channel estimation is made within a coherence time. Channels can be marked without a time index. The transmit beamformer has a unit norm to satisfy the transmit power limitation (

) the transmitted signal does not lose its generality. It is assumed that the transmit signal is a constant that satisfies the transmit power limit (

), for example, s[t]=1.

On the other hand, when the IRS element is turned off (turning off), the received signal at the base station may be expressed as in Equation 1b below.

[Equation 1b]

The base station may estimate the channel H _UB between the terminal and the base station based on the received signal. The base station may cancel the influence of H _UB on the received signal based on the estimated value. Accordingly, the base station can estimate information on the concatenated channel composed of H _IB and H _UI . Equation 2 below represents a concatenated channel.

[Equation 2]

In Equation 2(a), the chain channel

is a one-dimensional matrix

is expressed as a weighted sum of The present disclosure is a one-dimensional matrix that can express the same concatenated channel instead of H _IB and H _UI

We propose a method for estimating

Hereinafter, a method for estimating a channel between the terminal and the base station will be described.

In the present disclosure, a user equipment (UE) is used interchangeably with a user. In order to estimate the channel H _UB between the UE and the base station that does not go through the IRS, the device may set the size of all IRS elements to 0 (

).

Also, the time range of the device

A matrix can be formed by concatenating the received signals within. Equation 3 below represents a matrix to which a received signal is connected.

[Equation 3]

In Equation 3,

is a matrix related to the received signal.

Is

A matrix associated with a transmit beamformer in For example,

is a matrix in which transmit beamformers are connected within a time range.

Is

It is a matrix to which the receive noise in . For example,

is a matrix to which reception noises are connected within a time range. Beamformer

If is designed as a matrix in which a right inverse matrix exists, the device may estimate the channel H _UB using the received signal. Equation 4 below represents the estimated channel H _UB .

[Equation 4]

Beamformer F _UB

It can be designed by normalizing M rows in a discrete Fourier transform (DFT) matrix of size. That is, the device can design a beamformer by normalizing only M rows in the DFT matrix. In this case, the right inverse of F _UB is the Hermetic transpose matrix of F _UB

can be obtained by normalizing The apparatus may remove the influence of H _UB on subsequent received signals by using the estimated channel between the terminal and the base station. The influence of H _UB may be removed from the received signals using the estimated channel, and this is expressed as Equation 5 below.

[Equation 5]

Hereinafter, a method of estimating one-dimensional matrices will be described.

The present disclosure is a time range

one-dimensional matrix during

We propose a method for estimating In addition, the present disclosure provides a state in which all IRS elements are turned on (

) is assumed. time span is length

It can be divided into Q periods. At time t, the element setting value may be expressed as in Equation 6a below.

[Equation 6a]

qth cycle

The IRS element setting value during the period may be fixed as shown in Equation 6b below.

[Equation 6b]

Also, the beamformer in each period may be designed with the same matrix. The beamformer can be expressed as Equation 7 below.

[Equation 7]

The beamformer F _UIB may be designed as a matrix having a right inverse matrix, like F _UB . In addition, the beamformer F _UIB is

It can be made by normalizing only M rows in a DFT matrix of size.

The device may obtain a concatenated channel by multiplying the matrix formed by concatenating the reception signals of the q-th period by the right inverse matrix of the beamformer F _UIB . The concatenated channel can be expressed as in Equation 8 below.

[Equation 8]

In Equation 8, the above equation represents a concatenated channel obtained by multiplying a matrix connecting received signals by a right inverse matrix of the beamformer F _UIB . the formula below

denotes a matrix in which the reception noise of the q-th period is connected. When there is no noise, the concatenated channel can be expressed as Equation 9a below.

[Equation 9a]

The concatenated channel may be expressed as in Equation 9b below.

[Equation 9b]

12 shows the sequence of each rank I channel matrix. Specifically, FIG. 12 shows a sequence of a 1 ^st IRS element, a sequence of a 2 ^nd IRS element, and a sequence of an L-th element.

The following Equation 9c represents a sequence of each rank I channel matrix and a low correlation between the two sequences.

[Equation 9c]

The following equations (10a) and (10b) represent equations connecting the equations for all Q periods.

[Equation 10a]

[Equation 10b]

In Equation 10a,

denotes an IRS training matrix. The IRS learning matrix may have linear independence between columns. Since the IRS learning matrix has linear independence between columns, when a left inverse of the IRS learning matrix exists, one-dimensional matrices can be estimated. Equation 11 below is a one-dimensional matrix

represent their estimates.

[Equation 11]

In order to have a left inverse matrix, the number Q of repetition periods must be greater than or equal to the number of IRS elements L. When many IRS elements are installed to maximize the performance gain of the IRS,

The condition causes the length of the training column to be longer than the correlation time. Therefore, so that the length of the training column can be adjusted regardless of L, as shown in Equation 12 below

can be designed.

[Equation 12]

In the formula below in Equation 12,

If ,

may be designed as a matrix having column orthogonality. Accordingly,

can be achieved. On the other hand,

If ,

cannot have a left inverse matrix. In addition,

cannot satisfy hot orthogonality. but,

can be designed as a matrix having pseudo-orthogonality.

If , matrix

Is

It can be designed by normalizing Q arbitrary rows in a discrete Fourier transform matrix of size. designed like this

Estimation of one-dimensional matrices using

[Equation 13]

In the formula below in Equation 13, the matrix

By the unit modulus property of the elements of

Therefore, part (a) can be established. In Equation 13, A is

value to offset the amplification effect of Equations in Equation 13 are all values satisfying Equation 12, regardless of the magnitude of Q and L.

can be commonly applied to If Q is greater than L to ensure column-to-column orthogonality of the IRS training matrix, all

and for k

The value can be 0. therefore,

can be achieved Accordingly, the following Equation 14 may be established.

[Equation 14]

Equation 14 shows that in the absence of noise, complete estimation of one-dimensional matrices is possible as shown in Equation 11. If Q is less than L

pseudo orthogonality of

to ensure Accordingly, one-dimensional matrices can be estimated.

Equation 15 below shows an example of a channel estimation technique.

[Equation 15]

13A and 13B show simulation results according to examples of the present disclosure.

13A is a diagram in which the antenna of the base station, the antenna of the terminal, and the IRS elements are arranged in a uniform planar array (UPA) structure of 4Х2, 2Х2, and 4Х2, respectively, using Q=16 repetition periods greater than L=8. Shows the performance of channel estimation. The noise variance is N ₀ =-89 dBm. Quantization is assumed to be B=2 bits. The IRS element setting value was set to the maximum frequency efficiency (SE) among the (2 ² ) ⁸ IRS element setting values through exhaustive search. 13A includes a result (perfect) using the complete one-dimensional matrix information for comparison and a result (random) in which a phase value of an IRS element is arbitrarily set. When the information of a complete one-dimensional matrix is used (perfect), the maximum SE appears. When an IRS training matrix with guaranteed orthogonality between columns is used (orthogonal) and when linear independence between columns is guaranteed (independent), performance is close to perfect. The random results presented for comparison show that the set values of IRS elements that are not designed efficiently do not achieve high SE.

13B shows the performance of channel estimation according to the number of repetition periods Q when Q<L in the same environment as FIG. 13A. As Q increases, pseudo-orthogonality between columns becomes stronger. Accordingly, the channel estimation is accurate, and a high SE appears. If it is significantly smaller than L=8, higher SE than a random design appears even if Q=2. 13B shows an SE close to the case of having complete channel information (ie, perfect case) even in the case of a repetition period of about Q=L/2=4. Therefore, when L is very large, SE can be guaranteed even if the device sets Q small enough to estimate the channel within the correlation time.

According to the present disclosure, the device may have an adjustable train of learning. Accordingly, the device can estimate the intact channel using a sufficiently long training column when the scale of the intelligent reflective surface is small. When the communication system uses a large number of antennas and surface elements (IRS elements) to support high communication rates, the device can adjust the training heat to a short time and present a channel estimation result that can be obtained with a realistic burden within the correlation time. .

14 illustrates an example of a channel estimation procedure applicable to the present disclosure. 14 is a diagram illustrating an example of the channel estimation procedure described above with reference to FIGS. 11 and 12 . Steps S1401 to S1405 indicate the procedure for the q-th pilot. As described above, the pilot may be transmitted periodically, and the procedure may be repeated for each period. In step S1401, the base station may determine the element setting value of the IRS. The base station may transmit IRS configuration information including an IRS element configuration value to the IRS. In step S1403, the UE may transmit a pilot signal. Specifically, the terminal may transmit a pilot signal to the base station through the IRS. For example, the terminal may transmit a sounding reference signal (SRS) to the base station through the IRS. The IRS receiving the pilot signal may transmit it to the base station. The base station may estimate the channel based on the received pilot signal. For example, the base station may estimate the channel based on the received SRS. The above procedure may be repeated in other cycles. The base station may complete channel estimation in every period and feed back channel information including the channel estimation result. That is, the base station may transmit feedback information for channel estimation to the terminal through the IRS.

15 shows an example of a terminal operation procedure applicable to the present disclosure. 15 shows an example of an operation procedure of the terminal described above with reference to FIGS. 11 to 14 . In step S1501, the terminal may receive a radio resource control (radio resource control, RRC) message. Specifically, the UE may receive an RRC message including SRS configuration information. For example, the terminal may receive a system information block (SIB) message including SRS configuration information. In step S1503, the terminal may transmit a reference signal to the base station through the IRS. Specifically, the terminal may transmit a sounding reference signal to the base station based on the received SRS configuration information. In addition, the terminal may transmit the SRS to the base station through the IRS based on the received SRS configuration information. Here, the terminal may periodically transmit a reference signal as described above with reference to FIGS. 11 to 14 . The base station may estimate the channel based on the received SRS. For example, the base station may estimate the channel based on the SRS directly received from the terminal and the SRS received from the IRS. The channel estimation may be based on the number of elements L of the IRS and the number of repetition periods Q of the SRS received from the IRS. For example, when Q is less than L, an IRS learning matrix based on pseudo-orthogonality may be used for channel estimation. As another example, when Q is greater than or equal to L, an IRS learning matrix based on orthogonality may be used for the channel estimation. As another example, when the Q is greater than or equal to the L, an IRS learning matrix based on linear independence may be used for the channel estimation.

In step S1505, the terminal may receive feedback on the reference signal from the base station. For example, the terminal may receive feedback on the SRS from the base station through the IRS. Specifically, the terminal may receive feedback information including a channel estimation result based on pilot signals of a plurality of periods.

16 illustrates a base station operation procedure applicable to the present disclosure. 16 shows an example of an operation procedure of the base station described above with reference to FIGS. 11 to 14 . In step S1601, the base station may transmit a radio resource control (radio resource control, RRC) message to the terminal. Specifically, the base station may transmit an RRC message including SRS configuration information to the terminal. For example, the base station may transmit a system information block (SIB) message including SRS configuration information. In step S1603, the base station may receive a reference signal from the terminal through the IRS. Specifically, the base station may receive the SRS based on the received SRS configuration information. In addition, the base station may receive the SRS based on the received SRS configuration information from the terminal through the IRS. Here, as described above with reference to FIGS. 11 to 14 , the terminal periodically transmits a reference signal, and the base station may receive it. As described above, the base station can estimate the channel based on the received SRS. For example, the base station may estimate the channel based on the SRS directly received from the terminal and the SRS received from the IRS. The channel estimation may be based on the number of elements L of the IRS and the number of repetition periods Q of the SRS received from the IRS. For example, when Q is less than L, an IRS learning matrix based on pseudo-orthogonality may be used for channel estimation. As another example, when Q is greater than or equal to L, an IRS learning matrix based on orthogonality may be used for the channel estimation. As another example, when the Q is greater than or equal to the L, an IRS learning matrix based on linear independence may be used for the channel estimation.

In step S1605, the base station may transmit feedback on the reference signal to the terminal. For example, the base station may transmit feedback on the SRS to the terminal through the IRS. Specifically, as described above, the base station may transmit feedback information including a channel estimation result based on pilot signals of a plurality of periods.

Since examples of the above-described proposed method may also be included as one of the implementation methods of the present disclosure, it is obvious that they may be regarded as a kind of proposed method. In addition, the above-described proposed methods may be implemented independently, or may be implemented in the form of a combination (or merge) of some of the proposed methods. The rule can be defined so that the information on whether the proposed methods are applied (or information on the rules of the proposed methods) is notified by the base station to the terminal through a predefined signal (eg, a physical layer signal or a higher layer signal). .

The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential characteristics described in the present disclosure. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the present disclosure should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are included in the scope of the present disclosure. In addition, claims that are not explicitly cited in the claims may be combined to form an embodiment or may be included as a new claim by amendment after filing.

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

Embodiments of the present disclosure may be applied not only to the various radio access systems, but also to all technical fields to which the various radio access systems are applied. Furthermore, the proposed method can be applied to mmWave and THz communication systems using very high frequency bands.

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

Claims (16)

1. A method of operating a terminal in a wireless communication system, the method comprising:

   Receiving a radio resource control (RRC) message including sounding configuration information from the base station;

   transmitting a first sounding reference signal (SRS) to the base station based on the received sounding configuration information; and

   Including; transmitting a second SRS to the base station through an intelligent reflecting surface (IRS) based on the received sounding configuration information;

   The base station estimates a channel based on the first SRS and the second SRS,

   The second SRS is transmitted periodically, wherein the channel estimation is based on a period of the second SRS.
2. According to claim 1,

   The channel estimation is based on the number of elements L of the IRS and the number of repetition periods Q of the second SRS.
3. 3. The method of claim 2,

   When the Q is less than the L, the channel estimation uses an IRS learning matrix based on pseudo-orthogonality.
4. 3. The method of claim 2,

   When the Q is greater than or equal to the L, the channel estimation is an IRS learning matrix based on orthogonality is used.
5. 3. The method of claim 2,

   When the Q is greater than or equal to the L, the channel estimation is an IRS learning matrix based on linear independence is used.
6. According to claim 1,

   Receiving feedback information of the second SRS from the base station through the IRS.
7. In a terminal in a wireless communication system,

   transceiver; and

   a processor coupled to the transceiver;

   The processor is

   Control the transceiver to receive a radio resource control (RRC) message including sounding configuration information from the base station,

   controlling the transceiver to transmit a first sounding reference signal (SRS) to the base station based on the received sounding configuration information;

   Controlling the transceiver to transmit a second SRS to the base station through an intelligent reflecting surface (IRS) based on the received sounding configuration information,

   The base station estimates a channel based on the first SRS and the second SRS,

   The second SRS is transmitted periodically, and the channel estimation is based on a period of the second SRS.
8. 8. The method of claim 7,

   The channel estimation is based on the number of elements L of the IRS and the number of repetition periods Q of the second SRS.
9. 9. The method of claim 8,

   When the Q is less than the L, the channel estimation uses an IRS learning matrix based on pseudo-orthogonality.
10. 9. The method of claim 8,

    If the Q is greater than or equal to the L, the channel estimation is an IRS learning matrix based on orthogonality is used.
11. 9. The method of claim 8,

    When the Q is greater than or equal to the L, the channel estimation is an IRS learning matrix based on linear independence is used.
12. 8. The method of claim 7,

    The processor is

    Controlling the transceiver to receive the feedback information of the second SRS from the base station through the IRS.
13. A communication device comprising:

    at least one processor;

    at least one computer memory coupled to the at least one processor and storing instructions for instructing operations as executed by the at least one processor;

    The processor is the communication device,

    Control to receive a radio resource control (RRC) message including sounding configuration information from the base station,

    control to transmit a first sounding reference signal (SRS) to the base station based on the received sounding configuration information;

    Controlling to transmit a second SRS to the base station through an intelligent reflecting surface (IRS) based on the received sounding configuration information,

    The base station estimates a channel based on the first SRS and the second SRS,

    The second SRS is transmitted periodically, wherein the channel estimation is based on a period of the second SRS.
14. A non-transitory computer-readable medium storing at least one instruction, comprising:

    at least one instruction executable by a processor;

    The at least one instruction includes the computer readable medium,

    Instructs to receive a radio resource control (RRC) message including sounding configuration information from the base station,

    Instructing the base station to transmit a first sounding reference signal (SRS) based on the received sounding configuration information,

    Instructing to transmit a second SRS to the base station through an intelligent reflecting surface (IRS) based on the received sounding configuration information,

    The base station estimates a channel based on the first SRS and the second SRS,

    The second SRS is transmitted periodically, wherein the channel estimate is based on a period of the second SRS.
15. A method of operating a base station in a wireless communication system, the method comprising:

    transmitting a radio resource control (RRC) message including sounding configuration information to the terminal;

    receiving a first sounding reference signal (SRS) based on the sounding configuration information from the terminal; and

    Receiving a second SRS from the terminal through an intelligent reflecting surface (IRS) based on the sounding configuration information; Including,

    The base station estimates a channel based on the first SRS and the second SRS,

    The second SRS is transmitted periodically, wherein the channel estimation is based on a period of the second SRS.
16. In a base station in a wireless communication system,

    transceiver; and

    a processor coupled to the transceiver;

    The processor is

    Control the transceiver to transmit a radio resource control (RRC) message including sounding configuration information to the terminal,

    Control the transceiver to receive a first sounding reference signal (SRS) based on the sounding configuration information from the terminal,

    Controlling the transceiver to receive a second SRS from the terminal through an intelligent reflecting surface (IRS) based on the sounding configuration information,

    The base station estimates a channel based on the first SRS and the second SRS,

    The second SRS is transmitted periodically, and the channel estimation is based on a period of the second SRS.

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