Method and device for signaling information about beam set for beam management in wireless communication system

Abstract

The present disclosure is for signaling information about a beam set for beam management in a wireless communication system. An operation method of a terminal may involve: determining a first and second set of beams for beam management on the basis of artificial intelligence (AI) / machine learning (ML); receiving at least one reference signal corresponding to at least one beam belonging to the second set; predicting information about at least one beam included in the first set on the basis of the at least one reference signal; and transmitting the information about the at least one beam to a base station. The information about the at least one beam included in the first set may be predicted on the basis of a measurement result for the at least one reference signal corresponding to the at least one beam included in the second set.

Description

Method and device for signaling information about a beam set for beam management in a wireless communication system

[0001] The present disclosure relates to beam management in a wireless communication system, and to a method and apparatus for signaling information about a beam set for beam management.

[0002] Communication networks (e.g., 5G communication networks, 6G communication networks, etc.) are being developed to provide improved communication services compared to existing communication networks (e.g., long term evolution (LTE), advanced LTE-A (LTE-A), etc.). 5G communication networks (e.g., new radio (NR) communication networks) can support frequency bands above 6 GHz as well as frequency bands below 6 GHz. That is, 5G communication networks can support FR1 bands and / or FR2 bands. 5G communication networks can support various communication services and scenarios compared to LTE communication networks. For example, usage scenarios of 5G communication networks can include enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communication (URLLC), massive Machine Type Communication (mMTC), etc.

[0003] Compared to 5G, 6G communication networks can support a wider range of communication services and scenarios. 6G communication networks can meet requirements for ultra-high performance, ultra-high bandwidth, ultra-high space, ultra-high precision, ultra-intelligence, and / or ultra-reliability. 6G communication networks can support diverse and wide frequency bands and be applied to various usage scenarios (e.g., terrestrial communications, non-terrestrial communications, sidelink communications, etc.).

[0004] The use of artificial intelligence (AI) is expanding in the communications sector. AI can be utilized in a variety of areas, including network operation monitoring, predictive maintenance, network security and fraud prevention, customer service, and intelligent customer relationship management (CRM) systems.

[0005] AI can also be used to reduce the complexity of signaling between terminals and base stations or to improve its accuracy. To this end, research is being conducted on methods for utilizing AI models to replace some or all of the signaling for channel measurement, beam management, positioning, and other tasks, or to improve performance.

[0006] Meanwhile, the technology that serves as the background for the invention is written to promote understanding of the background for the invention, and may include content that is not a prior art already known to a person with ordinary skill in the field to which the technology belongs.

[0007] The purpose of the present disclosure to solve the above problems is to provide a method and device for effectively signaling information about a beam set for beam management.

[0008] The purpose of the present disclosure is to provide a method and device for effectively performing AI (artificial intelligence) / ML (machine learning) based beam management.

[0009] An object of the present disclosure is to provide a method and apparatus for configuring a first set of beams to be the subject of inference and a second set of beams used for inference.

[0010] According to one embodiment of the present disclosure for achieving the above object, a method of operating a terminal in a wireless communication system may include determining a first set and a second set of beams for artificial intelligence (AI) / machine learning (ML)-based beam management, receiving at least one reference signal corresponding to at least one beam belonging to the second set, predicting information about at least one beam included in the first set based on the at least one reference signal, and transmitting the information about the at least one beam to a base station. The information about at least one beam included in the first set may be predicted based on a measurement result for at least one reference signal corresponding to at least one beam included in the second set.

[0011] According to one embodiment of the present disclosure for achieving the above object, a method of operating a base station in a wireless communication system may include determining a first set and a second set of beams for artificial intelligence (AI) / machine learning (ML)-based beam management, transmitting at least one reference signal corresponding to at least one beam belonging to the second set, and receiving information about at least one beam included in the first set, which is predicted based on the at least one reference signal. The information about at least one beam included in the first set may be predicted based on a measurement result for at least one reference signal corresponding to at least one beam included in the second set.

[0012] According to one embodiment of the present disclosure for achieving the above object, in a wireless communication system, a terminal includes at least one transceiver, at least one processor, and at least one memory operably connected to the at least one processor and storing instructions that, when executed by the processor, control the terminal to perform operations, wherein the operations may include determining a first set and a second set of beams for artificial intelligence (AI) / machine learning (ML)-based beam management, receiving at least one reference signal corresponding to at least one beam belonging to the second set, predicting information about at least one beam included in the first set based on the at least one reference signal, and transmitting information about the at least one beam to a base station. The information about the at least one beam included in the first set may be predicted based on a measurement result for at least one reference signal corresponding to at least one beam included in the second set.

[0013] According to one embodiment of the present disclosure for achieving the above object, in a wireless communication system, a base station includes at least one transceiver, at least one processor, and at least one memory operably connected to the at least one processor and storing instructions that, when executed by the processor, control the terminal to perform operations, wherein the operations may include determining a first set and a second set of beams for artificial intelligence (AI) / machine learning (ML)-based beam management, transmitting at least one reference signal corresponding to at least one beam belonging to the second set, and receiving information about at least one beam included in the first set, which is predicted based on the at least one reference signal. The information about at least one beam included in the first set may be predicted based on a measurement result for at least one reference signal corresponding to at least one beam included in the second set.

[0014] The proposed technique proposes a method for effective beam management.

[0015] Figure 1 illustrates a wireless communication system according to an embodiment of the present disclosure.

[0016] FIG. 2 illustrates a block diagram of a communication node according to an embodiment of the present disclosure.

[0017] FIG. 3a illustrates a block diagram of a transmission path of a communication node according to an embodiment of the present disclosure.

[0018] FIG. 3b illustrates a block diagram of a receiving path of a communication node according to an embodiment of the present disclosure.

[0019]

[0020] FIG. 4 illustrates a device for inferring or learning an AI / ML (artificial intelligence / machine learning) model according to an embodiment of the present disclosure.

[0021] Figure 5 illustrates the structure of a neural network according to an embodiment of the present disclosure.

[0022] FIG. 6 illustrates an AI / ML framework according to an embodiment of the present disclosure.

[0023] FIG. 7 illustrates a procedure for AI / ML-based beam management according to an embodiment of the present disclosure.

[0024] FIGS. 8A to 8C illustrate configuration examples of beam sets according to embodiments of the present disclosure.

[0025] FIG. 9 illustrates an example of a two-dimensional representation of beams of a base station according to an embodiment of the present disclosure.

[0026] FIGS. 10A and 10B illustrate examples of patterns of beam set B according to embodiments of the present disclosure.

[0027] This disclosure may be subject to various modifications and various embodiments. Specific embodiments are illustrated and described in detail in the drawings. However, this is not intended to limit the disclosure to specific embodiments, but rather to encompass all modifications, equivalents, and alternatives falling within the spirit and technical scope of the disclosure.

[0028] While terms such as "first" and "second" may be used to describe various components, these components should not be limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present disclosure, a first component could be referred to as a "second component," and similarly, a second component could also be referred to as a "first component." The term "and / or" may refer to a combination of multiple related items described herein or to any of multiple related items described herein.

[0029] In the present disclosure, “at least one of A and B” may mean “at least one of A or B” or “at least one of combinations of one or more of A and B.” Additionally, in the present disclosure, “at least one of A and B” may mean “at least one of A or B” or “at least one of combinations of one or more of A and B.”

[0030] In the present disclosure, (re)transmission may mean “transmission,” “retransmission,” or “transmission and retransmission,” (re)setting may mean “setting,” “resetting,” or “setting and resetting,” (re)connection may mean “connection,” “reconnection,” or “connection and reconnection,” and (re)connection may mean “connection,” “reconnection,” or “connection and reconnection.”

[0031] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0032] The terminology used in this disclosure is only used to describe specific embodiments and is not intended to limit the present disclosure. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this disclosure, it should be understood that the terms "comprises" or "has" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0033] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which this disclosure pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0034] Hereinafter, preferred embodiments of the present disclosure will be described in more detail with reference to the attached drawings. In order to facilitate an overall understanding in describing the present disclosure, the same reference numerals will be used for identical components in the drawings, and redundant descriptions of identical components will be omitted. In addition to the embodiments explicitly described in the present disclosure, operations may be performed according to combinations of embodiments, extensions of embodiments, and / or modifications of embodiments. The performance of some operations may be omitted, and the order of operation may be changed.

[0035] In an embodiment, even if a method (e.g., transmitting or receiving a signal) performed by a first communication node among communication nodes is described, a corresponding second communication node can perform a method (e.g., receiving or transmitting a signal) corresponding to the method performed by the first communication node. That is, if an operation of a UE (user equipment) is described, a corresponding base station can perform an operation corresponding to the operation of the UE. Conversely, if an operation of a base station is described, a corresponding UE can perform an operation corresponding to the operation of the base station.

[0036] A base station may be referred to as a NodeB, an evolved NodeB, a gNodeB (next generation node B), a gNB, a device, an apparatus, a node, a communication node, a BTS (base transceiver station), a RRH (radio remote head), a TRP (transmission reception point), a RU (radio unit), an RSU (road side unit), a radio transceiver, an access point, an access node, etc. A UE may be referred to as a terminal, a device, an apparatus, a node, a communication node, an end node, an access terminal, a mobile terminal, a station, a subscriber station, a mobile station, a portable subscriber station, an OBU (on-broad unit), etc.

[0037] In the present disclosure, signaling may be at least one of upper layer signaling, MAC signaling, or PHY (physical) signaling. A message used for upper layer signaling may be referred to as an "upper layer message" or an "upper layer signaling message." A message used for MAC signaling may be referred to as a "MAC message" or a "MAC signaling message." A message used for PHY signaling may be referred to as a "PHY message" or a "PHY signaling message." Upper layer signaling may refer to a transmission and reception operation of system information (e.g., a master information block (MIB), a system information block (SIB)) and / or an RRC message. MAC signaling may refer to a transmission and reception operation of a MAC control element (CE). PHY signaling may refer to a transmission and reception operation of control information (e.g., downlink control information (DCI), uplink control information (UCI), sidelink control information (SCI)).

[0038] In the present disclosure, “an operation (e.g., a transmission operation) is set” may mean that “setting information for the operation (e.g., an information element, a parameter)” and / or “information instructing the performance of the operation” is signaled. “An information element (e.g., a parameter) is set” may mean that the information element is signaled. In the present disclosure, “a signal and / or a channel” may mean a signal, a channel, or “a signal and a channel,” and a signal may be used to mean “a signal and / or a channel.”

[0039] The communication network to which the embodiment is applied is not limited to what is described below, and the embodiment may be applied to various communication networks (e.g., 4G communication networks, 5G communication networks, and / or 6G communication networks). Here, the communication network may be used in the same sense as the communication system.

[0040] Figure 1 is a conceptual diagram illustrating an embodiment of a communication system.

[0041] Referring to FIG. 1, the communication system (100) may include a plurality of communication nodes (110-1, 110-2, 110-3, 120-1, 120-2, 130-1, 130-2, 130-3, 130-4, 130-5, 130-6). In addition, the communication system (100) may further include a core network (e.g., a serving-gateway (S-GW), a packet data network (PDN)-gateway (P-GW), a mobility management entity (MME)). If the communication system (100) is a 5G communication system (e.g., a new radio (NR) system), the core network may include an access and mobility management function (AMF), a user plane function (UPF), a session management function (SMF), etc.

[0042] A plurality of communication nodes (110 to 130) can support a communication protocol specified in the 3rd generation partnership project (3GPP) standard (e.g., LTE communication protocol, LTE-A communication protocol, NR communication protocol, etc.). The plurality of communication nodes (110 to 130) may support CDMA (code division multiple access) technology, WCDMA (wideband CDMA) technology, TDMA (time division multiple access) technology, FDMA (frequency division multiple access) technology, OFDM (orthogonal frequency division multiplexing) technology, Filtered OFDM technology, CP (cyclic prefix)-OFDM technology, DFT-s-OFDM (discrete Fourier transform-spread-OFDM) technology, OFDMA (orthogonal frequency division multiple access) technology, SC (single carrier)-FDMA technology, NOMA (non-orthogonal multiple access) technology, GFDM (generalized frequency division multiplexing) technology, FBMC (filter bank multi-carrier) technology, UFMC (universal filtered multi-carrier) technology, SDMA (space division multiple access) technology, etc. Each of the plurality of communication nodes may have the following structure.

[0043] Figure 2 is a block diagram illustrating an embodiment of a communication node constituting a communication system.

[0044] FIG. 2 illustrates an example of a wireless device (200) in a wireless communication system according to one embodiment of the present disclosure. The wireless device (200) according to the embodiment of the present disclosure may be a mobile terminal such as a smartphone, tablet PC, or wearable device, but is not limited thereto.

[0045] Referring to FIG. 2, the wireless device (200) may include at least one control unit (210), at least one memory (220), at least one power supply unit (230), at least one transceiver unit (240), at least one input unit (250), at least one output unit (260), and / or at least one antenna (270).

[0046] The control unit (210) can control the memory (220) and / or the transceiver unit (240), and can be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in the present disclosure. The memory (220) can be connected to the control unit (210) and can store various information related to the operation of the control unit (210). For example, the memory (220) can perform some or all of the controls controlled by the control unit (210), or store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in the present disclosure. The configuration of the memory is not limited in a specific manner. For example, it can be configured as at least one of a read-only memory (ROM) and a random access memory (RAM).

[0047] At least one control unit (210) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in the present disclosure may be implemented using firmware or software in the form of codes, instructions, and / or a set of instructions. Here, the firmware or software may execute another program stored in a memory (220), such as an OS. The control unit (210) may be implemented to support beamforming or directional routing operations in which signals from at least one antenna (270) are differently weighted to effectively steer signals outgoing in a desired direction.

[0048] Additionally, at least one control unit (210) may be coupled to a backhaul or network interface. The wireless device (200) may communicate with other wireless devices through the backhaul or network interface. The control unit (210) may include at least one processor. The processor may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present disclosure are performed.

[0049] At least one transceiver (240) may be connected to the control unit (210) and may transmit and / or receive a wireless signal via at least one antenna (270). The transceiver (240) may include a transmitter and / or a receiver. The at least one transceiver (240) may transmit user data, control information, wireless signals / channels, etc. mentioned in the methods and / or operation flowcharts of the present disclosure to at least one other device. For example, the at least one transceiver (240) may be connected to at least one control unit (210) and may transmit and receive wireless signals. In addition, the at least one control unit (210) may control the at least one transceiver (240) to transmit user data, control information, or a wireless signal to at least one other device. The at least one transmitter (240) may receive a signal transmitted by another wireless device from at least one antenna (270). Additionally, at least one transceiver (240) may downconvert or upconvert the received signal to generate a baseband signal. At least one antenna (270) may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports).

[0050] The input unit (250) can obtain information such as user input, video, and audio, and can include various input means such as various mechanical / electronic input means, cameras, and microphones. The output unit (260) is for providing information to users by generating output related to sight, hearing, or touch, and can include a display, a speaker, a vibration module, and the like. The wireless device (200) supplies power through the power supply unit (230), and the power supply unit (230) can include a wired / wireless charging circuit, a battery, and the like.

[0051] Referring again to FIG. 1, the communication system (100) may include a plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) and a plurality of terminals (130-1, 130-2, 130-3, 130-4, 130-5, 130-6). Each of the first base station (110-1), the second base station (110-2), and the third base station (110-3) may form a macro cell. Each of the fourth base station (120-1) and the fifth base station (120-2) may form a small cell. The fourth base station (120-1), the third terminal (130-3), and the fourth terminal (130-4) may be within the cell coverage of the first base station (110-1). The second terminal (130-2), the fourth terminal (130-4), and the fifth terminal (130-5) may be within the cell coverage of the second base station (110-2). The fifth base station (120-2), the fourth terminal (130-4), the fifth terminal (130-5), and the sixth terminal (130-6) may be within the cell coverage of the third base station (110-3). The first terminal (130-1) may be within the cell coverage of the fourth base station (120-1). The sixth terminal (130-6) may be within the cell coverage of the fifth base station (120-2).

[0052] Here, each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) may be referred to as a NodeB (NB), an evolved NodeB (eNB), a gNB, an advanced base station (ABS), a high reliability-base station (HR-BS), a base transceiver station (BTS), a radio base station, a radio transceiver, an access point, an access node, a radio access station (RAS), a mobile multihop relay-base station (MMR-BS), a relay station (RS), an advanced relay station (ARS), a high reliability-relay station (HR-RS), a home NodeB (HNB), a home eNodeB (HeNB), a road side unit (RSU), a radio remote head (RRH), a transmission point (TP), a transmission and reception point (TRP), etc.

[0053] Each of the plurality of terminals (130-1, 130-2, 130-3, 130-4, 130-5, 130-6) may be referred to as a user equipment (UE), terminal equipment (TE), advanced mobile station (AMS), high reliability-mobile station (HR-MS), terminal, access terminal, mobile terminal, station, subscriber station, mobile station, portable subscriber station, node, device, on board unit (OBU), etc.

[0054] Meanwhile, each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) may operate in a different frequency band or may operate in the same frequency band. Each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) may be connected to each other via an ideal backhaul link or a non-ideal backhaul link, and may exchange information with each other via the ideal backhaul link or the non-ideal backhaul link. Each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) may be connected to the core network via the ideal backhaul link or the non-ideal backhaul link. Each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) can transmit a signal received from the core network to the corresponding terminal (130-1, 130-2, 130-3, 130-4, 130-5, 130-6), and can transmit a signal received from the corresponding terminal (130-1, 130-2, 130-3, 130-4, 130-5, 130-6) to the core network.

[0055] Additionally, each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) may support MIMO transmission (e.g., single user (SU)-MIMO, multi user (MU)-MIMO, massive MIMO, etc.), coordinated multipoint (CoMP) transmission, carrier aggregation (CA) transmission, transmission in an unlicensed band, sidelink communication (e.g., device to device communication (D2D), proximity services (ProSe)), Internet of Things (IoT) communication, dual connectivity (DC), etc. Here, each of the plurality of terminals (130-1, 130-2, 130-3, 130-4, 130-5, 130-6) can perform an operation corresponding to the base station (110-1, 110-2, 110-3, 120-1, 120-2) and an operation supported by the base station (110-1, 110-2, 110-3, 120-1, 120-2). For example, the second base station (110-2) can transmit a signal to the fourth terminal (130-4) based on the SU-MIMO scheme, and the fourth terminal (130-4) can receive a signal from the second base station (110-2) by the SU-MIMO scheme. Alternatively, the second base station (110-2) can transmit signals to the fourth terminal (130-4) and the fifth terminal (130-5) based on the MU-MIMO method, and each of the fourth terminal (130-4) and the fifth terminal (130-5) can receive signals from the second base station (110-2) based on the MU-MIMO method.

[0056] Each of the first base station (110-1), the second base station (110-2), and the third base station (110-3) can transmit a signal to the fourth terminal (130-4) based on the CoMP scheme, and the fourth terminal (130-4) can receive a signal from the first base station (110-1), the second base station (110-2), and the third base station (110-3) based on the CoMP scheme. Each of the plurality of base stations (110-1, 110-2, 110-3, 120-1, 120-2) can transmit and receive a signal with terminals (130-1, 130-2, 130-3, 130-4, 130-5, 130-6) within its cell coverage based on the CA scheme. Each of the first base station (110-1), the second base station (110-2), and the third base station (110-3) can control sidelink communication between the fourth terminal (130-4) and the fifth terminal (130-5), and each of the fourth terminal (130-4) and the fifth terminal (130-5) can perform sidelink communication under the control of the second base station (110-2) and the third base station (110-3), respectively.

[0057]

[0058] FIG. 3 illustrates a device for inferring or learning an AI / ML (artificial intelligence / machine learning) model according to one embodiment of the present disclosure.

[0059] The device for inferring or learning the AI / ML model of FIG. 3 may be an embodiment of the wireless device (200) of the device of FIG. 2. Accordingly, the communication unit, control unit, and storage unit of FIG. 3 may each correspond to the transceiver unit (240), control unit (210), and storage unit (220) of FIG. 2. For convenience of explanation, the device for inferring or learning the AI / ML model will be described below assuming that it is a server. In addition, if the server is connected to the outside world via wireless communication, the communication unit may include the antenna (270) of FIG. 2. The control unit may perform AL / ML data learning or inference. The artificial intelligence model may be implemented in various forms. For example, the artificial intelligence model may be a model based on a neural network. The neural network model may include a deep neural network (DNN), a recurrent neural network (RNN), a bidirectional recurrent deep neural network (BRDNN), and the like.

[0060] The communication unit can communicate with external devices, and the server can receive various information through the communication unit. Therefore, the communication unit can receive monitoring information, user input information, etc. for training AI / ML models, and transmit them to the control unit. To this end, the communication unit can communicate with external devices via wired or wireless communication. The server can transmit and receive signals to external devices such as artificial satellites, mobile devices, and autonomous vehicles through the communication unit. Wireless communication may include cellular communication, short-range wireless communication, or global navigation satellite system (GNSS) communication.

[0061] The storage unit can store learning models, input data, output data, etc. Accordingly, the storage unit can store artificial intelligence models learned by the control unit or updated artificial intelligence, and can provide data when necessary according to commands from the control unit.

[0062] The control unit may include an AL / ML management unit, a model learning unit, and a model inference unit. The AL / ML management unit can manage the communication unit, the model inference unit, and the model learning unit so that the inference or learning task can be performed efficiently. For example, the management unit can receive monitoring information through the communication unit, receive output from the model inference unit, and then check the performance of the inference. The management unit can transmit the checked performance to the model learning unit and instruct the model learning unit to learn the AI / ML model based on the performance. In other words, the management unit can transmit performance feedback information to the model inference unit, and the model inference unit can use the performance feedback information to instruct the learning goal or as a reward for reinforcement learning. In addition, the control unit can instruct the AI / ML model to be used by the server or an external device, or instruct the activation / deactivation of the use of the AI / ML model. For convenience of explanation, Fig. 3 illustrates the server as including both the model learning unit and the model inference unit. However, depending on the purpose of the server, either the model learning unit or the model inference unit may be included. In addition, the structure of the aforementioned FIG. 3 can be applied not only to servers but also to other devices such as terminals, wireless devices, autonomous vehicles, and mobile devices where inference or learning occurs.

[0063] FIG. 4a is a block diagram illustrating an embodiment of a transmission path, and FIG. 4b is a block diagram illustrating an embodiment of a reception path.

[0064] Referring to FIGS. 4A and 4B, a transmission path (410) may be implemented in a communication node that transmits a signal, and a reception path (420) may be implemented in a communication node that receives a signal. The transmission path (410) may include a channel coding and modulation block (411), an S-to-P (serial-to-parallel) block (512), an N IFFT (Inverse Fast Fourier Transform) block (413), a P-to-S (parallel-to-serial) block (414), a CP (cyclic prefix) addition block (415), and an UC (up-converter) (UC) (416). The receiving path (420) may include a DC (down-converter) (421), a CP removal block (422), an S-to-P block (423), an N FFT block (424), a P-to-S block (425), and a channel decoding and demodulation block (426). Here, N may be a natural number.

[0065] In the transmission path (410), information bits may be input to a channel coding and modulation block (411). The channel coding and modulation block (411) may perform a coding operation (e.g., a low-density parity check (LDPC) coding operation, a polar coding operation, etc.) and a modulation operation (e.g., a quadrature phase shift keying (QPSK), a quadrature amplitude modulation (QAM), etc.) on the information bits. The output of the channel coding and modulation block (411) may be a sequence of modulation symbols.

[0066] The S-to-P block (412) can convert modulation symbols in the frequency domain into parallel symbol streams to generate N parallel symbol streams. N can be an IFFT size or an FFT size. The N IFFT block (413) can perform an IFFT operation on the N parallel symbol streams to generate signals in the time domain. The P-to-S block (414) can convert the output (e.g., parallel signals) of the N IFFT block (413) into a serial signal to generate a serial signal.

[0067] The CP addition block (415) can insert a CP into a signal. The UC (416) can up-convert the frequency of the output of the CP addition block (415) to an RF (radio frequency) frequency. Additionally, the output of the CP addition block (415) can be filtered at the baseband before up-conversion.

[0068] A signal transmitted from a transmission path (410) may be input to a reception path (420). An operation in the reception path (420) may be the reverse operation of the operation in the transmission path (410). A DC (421) may down-convert the frequency of the received signal to a baseband frequency. A CP removal block (422) may remove a CP from a signal. The output of the CP removal block (422) may be a serial signal. An S-to-P block (423) may convert the serial signal into parallel signals. An N FFT block (424) may perform an FFT algorithm to generate N parallel signals. A P-to-S block (425) may convert the parallel signals into a sequence of modulation symbols. A channel decoding and demodulation block (426) may perform a demodulation operation on the modulation symbols and perform a decoding operation on the result of the demodulation operation to restore data.

[0069] In FIGS. 4A and 4B , Discrete Fourier Transform (DFT) and Inverse DFT (IDFT) may be used instead of FFT and IFFT. Each of the blocks (e.g., components) in FIGS. 4A and 4B may be implemented by at least one of hardware, software, or firmware. For example, some of the blocks in FIGS. 4A and 4B may be implemented by software, and the remaining blocks may be implemented by hardware or a “combination of hardware and software.” In FIGS. 4A and 4B , a block may be subdivided into multiple blocks, multiple blocks may be integrated into a single block, some blocks may be omitted, and blocks supporting other functions may be added.

[0070] Figure 5 illustrates an example of a neural network structure applicable to the present disclosure. In a neural network, the input layer is the first layer that receives external data. The input layer can receive raw data in various forms and pass it on after feature processing. Feature processing here refers to extracting characteristic parts of the raw data. The number of neurons in the input layer can be determined based on the characteristics and requirements of the given data.

[0071] In a neural network, hidden layers can consist of one or more layers located between the input and output layers. Therefore, unlike Figure 5, hidden layers can consist of two or more layers. Neurons in the hidden layers can represent transformed forms of input data, and the depth and width of the hidden layers can determine the complexity of the AI / ML model. Hidden layers can be used to implement nonlinear relationships between the input and output layers.

[0072] The output layer is the final layer of the neural network and provides the final output value. Based on the final output value of the output layer, the error between the neural network's prediction and the actual target value can be calculated.

[0073] Therefore, data input through the input layer can pass through one or more hidden layers before being passed to the output layer. During this process, features of the input data can be extracted. Neural networks can be trained using algorithms such as backpropagation and gradient descent. Learning can be fed back by rewarding the errors calculated in the output layer. Based on this feedback, the weights of each layer can be updated. This training process can be repeated to optimize the inference of AI / ML models.

[0074] Terms related to AI to be used in this disclosure may be defined as follows.

[0075] AI / ML-enabled features refer to specific functions that utilize AI / ML. Examples of AI / ML-enabled features include CSI measurements, beam management, and positioning.

[0076] An AI / ML model (AI / ML Model) refers to an algorithm that applies AI / ML technology to generate a set of outputs based on a set of inputs. In this disclosure, the AI / ML model may not refer to the algorithm itself, but rather to a set of parameter values ​​that specify the AI / ML model. For example, an AI / ML model may be expressed as a set of weights in a neural network.

[0077] AI / ML model delivery refers to the transfer of an AI / ML model from one entity to another. The entities can include network nodes / functions (e.g., gNBs, LMFs, etc.), UEs, standalone servers, etc. Therefore, an AI / ML model trained by a first entity can be transferred to a second entity.

[0078] AI / ML model inference refers to the process of generating a set of outputs based on a set of inputs using a trained AI / ML model within a single entity.

[0079] AI / ML model testing is the process of evaluating the performance of the final AI / ML model using a dataset different from the one used for training and validation. It can be included as a sub-step of training. Unlike AI / ML model validation, testing involves further tuning the model.

[0080] AI / ML model training refers to the process of training an AI / ML model in a data-driven manner to learn input / output relationships and obtain a trained AI / ML model that can be used for inference.

[0081] AI / ML model transfer refers to the transfer of an AI / ML model from a sender to a receiver. Transfer can be wireless or wired, and can include parameters of a known model structure or parameters related to a new model. The transferred parameters can include parameters for the entire model or a portion of the model.

[0082] AI / ML model validation is a sub-process of training that evaluates the quality of an AI / ML model using a dataset different from the one used for training. It can be used to determine model parameters for not only the training dataset but also generalized datasets.

[0083] Data collection refers to the collection of data by network nodes, management entities, or UEs for AI / ML model training, data analysis, and inference.

[0084] Federated learning (or federated training) refers to a machine learning technique that trains AI / ML models by performing local model training on multiple distributed edge nodes (e.g., UEs, gNBs) using local data. Federated learning may require multiple interactions between nodes involved in edge AI / ML models. Local data exchange is not essential.

[0085] Functionality identification refers to the process of identifying AI / ML functionality so that it can be shared between the network and the UE. Information about AI / ML functionality can be shared during the functionality identification process.

[0086] Management instructions refer to the information necessary to ensure proper inference operations. Management instructions may include selecting / deactivating / switching AI / ML models or AI / ML functions, performing AI / ML tasks, and performing other alternative operations.

[0087] Model activation means activating an AI / ML model for a specific AI / ML supported function, and model deactivation means deactivating an AI / ML model for a specific AI / ML supported function.

[0088] Model download refers to the transmission of an AI / ML model from the network to the UE, and the UE receives the AI / ML model. Model identification refers to the identification of an AI / ML model based on a shared functional identifier between the network and the UE. Information about the AI / ML model may be shared during model identification. Model upload refers to the transmission of a model from the UE to the network.

[0089] Model monitoring refers to the process of monitoring the inference performance of an AI / ML model, and model parameter update refers to the process of updating the model's parameters.

[0090] Model selection refers to selecting one of several models to activate for a single AI / ML-enabled function. Model selection can occur simultaneously with model activation. Model switching refers to deactivating the currently activated AI / ML model for a specific AI / ML-enabled function and activating a different AI / ML model. Model updating refers to the process of updating model parameters and / or model structure.

[0091] AI / ML models can be classified based on the location of inference. A network-side AI / ML model refers to an AI / ML model in which inference is performed entirely within the network. A two-sided AI / ML model refers to a pair of AI / ML models in which inference is performed jointly by the UE and the network. Joint inference means that some inference is performed first in the UE, and the remaining portion is performed in the gNB. A UE-side AI / ML model refers to an AI / ML model in which inference is performed entirely within the UE.

[0092] The following learning methods can be used in AI / ML learning. Reinforcement learning (RL) refers to the process of training an AI / ML model based on inputs (i.e., states) and feedback signals (e.g., rewards) generated by the model's output (e.g., actions) in an environment in which the model interacts. Semi-supervised learning refers to the process of training a model using a mixture of labeled and unlabeled data. Supervised learning refers to the process of training a model based on inputs and their corresponding labels. Unsupervised learning refers to the process of training a model using unlabeled data.

[0093]

[0094] Hereinafter, the initial connection procedure between a terminal and a base station will be described. If the initial connection procedure is performed with the base station due to reasons such as the terminal's power on / off operation or loss of coverage, an identification procedure between the base station and the terminal may be required. First, the terminal may perform an initial cell search operation with the base station. The terminal may perform monitoring to receive a synchronization signal. The synchronization signal may be at least one of a primary synchronization signal (PSS) and a secondary synchronization signal (SSS). The terminal may receive a physical broadcast channel (PBCH) signal from the base station to obtain broadcast information within the cell. Based on the physical broadcast channel, the terminal may obtain information about the cell using at least one of the MIB or SIB. A block including all of the PSS, SSS, and PBCH may be referred to as a synchronization signal block (SSB).

[0095] A terminal can perform a random access procedure. The terminal can transmit a preamble to the base station and receive a random access response (RAR) from the base station. The RAR message can include a temporary identifier. The terminal can transmit MSG3 (or an RRC connection request message) using the scheduling information in the RAR, and the base station can perform a contention resolution procedure by transmitting MSG4 (or a contention resolution message) to the terminal in response to MSG3.

[0096] The base station can perform beam management based on the RACH occasion used for transmitting the preamble used in the random access procedure. For example, the base station can determine the beam on which the terminal received the synchronization signal based on the RACH occasion in which the preamble was transmitted. A synchronization signal can also be included as a reference signal for indicating the QCL relationship, and the QCL relationship can be established based on the SSB received during the initial access procedure.

[0097] Additionally, a channel measurement procedure may be performed for beam management. The terminal may receive a reference signal from the base station. Based on this, the terminal may report channel state information (CSI) to the base station. The channel state information may include at least one of reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-noise ratio (SNR). The base station may use the received channel state information to adjust beamforming for the terminal or optimize radio resource allocation. For channel measurement, the base station may transmit configuration information for channel measurement to the terminal. The configuration information for channel measurement may include information related to a measurement target, a measurement cycle, and the like.

[0098]

[0099] Enhancing CSI Feedback with Artificial Intelligence

[0100] Terminals and base stations can utilize artificial intelligence to perform channel state estimation and channel state reporting procedures. Using AI can enhance the CSI feedback process. For example, AI models can be used for spatial-frequency domain CSI compression. A bilateral model can be used for spatial-frequency domain CSI compression. Preprocessing, postprocessing, quantization, and dequantization can be included in the CSI compression process. AI / ML-based CSI compression can be performed based on existing frameworks, such as the aforementioned CSI feedback procedure. AI models can also be used for time-domain CSI prediction. Since CSI measurements are performed at the terminal, a UE-side model can be used.

[0101] For CSI compression using a two-sided model, three types of AI / ML model training collaboration can be considered. The first type of training collaboration refers to joint training of two-sided models on a single side or entity, and can be performed on either the UE side or the network side. The decision on which side to train can be based on at least one of the following factors: whether the model can be maintained exclusively, privacy protection, whether specific models are supported, whether device-specific optimization is required, flexibility in model updates, the possibility of developing / updating each model, whether to build a unified CSI reconfiguration model for different UEs, whether to build a unified CSI generation model for different networks, scalability, and model performance.

[0102] The second type of collaborative training involves jointly training two-sided models on both the network and UE sides. The third type of collaborative training involves separate training on the network and UE sides, with the UE training the CSI generation portion and the network training the CSI reconfiguration portion. Joint training requires that the generation and reconstruction models be trained in the same loop through forward and backpropagation, and can be performed on at least one node. Separate training refers to sequential training initiated on either the UE or network side.

[0103] In the second training collaboration type, joint training can include both simultaneous and sequential training. Sequential training in the second training collaboration type can begin with network-side training.

[0104] In the second training collaboration type, whether to choose concurrent or sequential training can be determined based on at least one of the following: whether the model can be maintained exclusively, whether the model can be maintained exclusively, privacy protection, flexibility in whether to support a specific model, whether to optimize for each device, flexibility in model update, possibility of development / update of each model, whether to build a unified CSI reset model for different UEs, whether to build a unified CSI generation model for different networks, scalability, whether the training data distribution can match the inference device, software / hardware compatibility, and model performance. In the third training collaboration type, whether the training is performed first on the network or on the UE can be determined based on the same conditions as those considered in the second training collaboration type, such as whether to maintain exclusivity and whether to protect privacy.

[0105] Data generation for AI models can be implemented in various ways. For example, in the CSI compression use case, training data for model training can be generated by the UE / gNB. For the network portion of two-sided model inference, input data can be generated by the UE and terminated at the base station. For the UE portion of two-sided model inference, input data can be used internally within the UE.

[0106] Additionally, for network-side performance monitoring, calculated performance metrics or data for performance metric calculations, if necessary, can be generated by the UE and terminated at the base station. In CSI compression use cases using a double-sided model, pairing information based on model identification can be established to select a CSI generation model compatible with the CSI reconstruction model used at the base station.

[0107] The following can be applied to CSI prediction using AI. Training data for model training can be generated by the UE. For UE-side model inference, input data can be used internally within the UE. Data for performance metrics or performance metric calculations required for network-side performance monitoring can be generated by the UE and terminated at the gNB.

[0108]

[0109] Beam management using artificial intelligence

[0110] Base station and terminal beam settings can be managed through artificial intelligence (AI). For convenience, beam set B refers to the beam set where measurements are performed using AI / ML model input, and beam set A refers to the beam set determined based on AI / ML model inference. Beam sets A and B may contain beam information for the same frequency range.

[0111] Artificial intelligence can be used to infer spatial domain downlink beams for beam set A based on measurements of beam set B. As another example, artificial intelligence can be used to infer temporal downlink beams for beam set A based on past measurements of beam set B, where beam set A and beam set B may be different sets or beam set B may be a subset of beam set A.

[0112] Additionally, the input of the artificial intelligence model can be formed from various combinations. For example, the input of the artificial intelligence can include at least one of an L1-RSRP measurement based on beam set B, other auxiliary information, a channel impulse response (CIR) based on beam set B, and a downlink Tx / Rx beam identifier (ID) associated with the L1-RSRP measurement of beam set B.

[0113] The aforementioned artificial intelligence model can be designed to infer a beam including at least one of a downlink reception beam and a downlink transmission beam. In addition, the output of the artificial intelligence model can include at least one of a transmission beam, a reception beam, an L1-RSRP of the transmission beam, an L1-RSRP of the reception beam, an angle of the transmission beam, an angle of the reception beam, and other information.

[0114] The beam management method using an AI model is not limited to the aforementioned method. The AI ​​model can be configured in various ways by configuring inputs and outputs with various combinations of settings for beam sets A and B, performance monitoring, data collection, and auxiliary information.

[0115] Learning and inference methods can also be implemented in various ways. For example, artificial intelligence can be learned or trained using an AI / ML (artificial intelligence / machine learning) model. Learning and training can be performed by the network or the UE. Furthermore, learning and inference can be performed on different devices. For example, learning can be performed on the network and inference on the UE. Split learning can be performed in such a way that some of the learning is performed on a first device and some on a second device. Similarly to learning, split inference can be performed using multiple devices. Input data for inference can also be generated in various ways. For example, input data can be generated on the UE, and inference can be performed using the input data on the network. Furthermore, input data generated on the UE can be used for inference within the UE.

[0116]

[0117] Enhanced positioning method using artificial intelligence

[0118] Terminals and base stations can perform positioning procedures that utilize artificial intelligence to determine the terminal's location. To improve positioning accuracy, methods that directly determine location through AI / ML models and methods that determine auxiliary location can be considered.

[0119] The method of directly determining location through an AI / ML model outputs the UE's location, and channel fingerprinting based on channel observations can be used as input for the AI / ML model. Fingerprinting is a positioning technique based on probabilistic modeling that utilizes noise and surrounding environmental information for location tracking. Therefore, fingerprinting utilizes existing devices, such as wireless access points (APs), to construct a fingerprint map based on signal strength values, and the terminal's location can be determined based on the channel fingerprint generated by the terminal's channel observation.

[0120] The way AI / ML models determine auxiliary positions can include outputting new and / or enhanced values ​​of existing measurements, inputting LOS / NLOS identification, timing and / or angle of measurements, probability of measurement, etc.

[0121] Specifically, the following methods may be considered:

[0122] - UE-based positioning, direct AI / ML or AI / ML-assisted positioning using UE-side models

[0123] - UE-assisted / LMF-based positioning, AI / ML-assisted positioning using UE-side models

[0124] - UE-assisted / LMF-based positioning, direct AI / ML positioning using LMF-side models

[0125] - NG-RAN node auxiliary positioning, AI / ML auxiliary positioning using gNB-side model

[0126] - NG-RAN node auxiliary positioning, direct AI / ML positioning using LMF side model

[0127] Data generation for AI models can be implemented in various ways. For example, for training AI / ML models for positioning, training data can be generated by the UE / PRU / gNB / LMF. For LMF-side model inference, input data can be generated by the UE / gNB and terminated in the LMF. For gNB-side model inference, input data can be used internally within the gNB. For UE-side model inference, input data can be used internally within the UE. For LMF-side performance monitoring, if necessary, calculated performance metrics or data for performance metric calculation can be generated by the UE / gNB and terminated in the LMF. For gNB-side performance monitoring, if necessary, calculated performance metrics or data for performance metric calculation can be generated at least by the gNB.

[0128]

[0129]

[0130] Among the agenda items for discussion on AI / ML for NR air interface to be discussed in Rel-19 RAN (radio access network) WG1 (working group 1), the contents of the WID (work item description) document on AI / ML based beam management are as shown in [Table 1] below.

[0131] Provide specification support for the following aspects:……- Beam management - DL Tx beam prediction for both UE-sided model and NW-sided model, encompassing [RAN1 / RAN2]:o Spatial-domain DL Tx beam prediction for Set A of beams based on measurement results of Set B of beams (“”o Temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams (“”o Specify necessary signalling / mechanism(s) to facilitate LCM operations specific to the Beam Management use cases, if anyo Enabling method(s) to ensure consistency between training and inference regarding NW-side additional conditions (if identified) for inference at UENOTE: Strive for common framework design to support both BM-Case1 and BM-Case2

[0132] In 3GPP Rel-18 RAN WG1, discussions were held at the SI (study item) stage to develop standards utilizing AI / ML. Specifically, studies were conducted on three major technology categories: CSI feedback, beam management, and positioning accuracy enhancement. Among these, beam management and positioning accuracy enhancement will be discussed at the WI (working item) stage in Rel-19. [Table 1] shows the development scope of AI / ML-based beam management to be discussed in Rel-19. As presented above, Rel-19 considers DL transmit (Tx) beam prediction, and the AI / ML models used here are considered as UE-sided models and NW-sided models. Here, the UE-side model and the NW-side model may have a structure in which the UE and NW each perform at least one operation for an AI / ML procedure, rather than utilizing an AI / ML model coupled between the UE and NW. The detailed development scope includes the following four topics.

[0133] - Spatial domain beam prediction for set A beam based on set B beam measurement results: BM-Case1

[0134] - Time domain beam prediction for set A beam based on set B beam measurement results: BM-Case2

[0135] - Signaling and mechanisms required for beam management-related LCM (life cycle management) operations.

[0136] - A method to ensure continuity between training and inference related to additional conditions of NW for inference at the UE level.

[0137] Additionally, standardization aims to develop a common framework that can support both BM-Case1 and BM-Case2, if possible.

[0138] Research on AI / ML in 3GPP Rel-18 led to the TR 38.843 document, which covers the NR air interface based on AI / ML. Specifically, the standard defines an AI / ML framework that will be commonly used in AI / ML for the NR air interface, and includes descriptions of three representative use cases (e.g., CSI feedback, positioning accuracy enhancement, beam management, etc.), performance evaluation results, and expected specification changes. Subsequently, Rel-19 RAN WG1 will develop standards for positioning accuracy enhancement and beam management, and conduct further research on CSI-RS feedback. The aforementioned AI / ML framework is as shown in Fig. 6.

[0139] FIG. 6 illustrates an AI / ML framework according to an embodiment of the present disclosure. Depending on the physical entity where operations for the AI / ML framework as illustrated in FIG. 6 are performed, AI / ML models are classified into NW-side AI / ML models, UE-side AI / ML models, and two-side AI / ML models. Among these, the NW-side AI / ML model and the UE-side AI / ML model process all AI / ML operations at either the base station or the terminal, while the two-side AI / ML model jointly performs AI / ML operations at the base station and the terminal. Therefore, the two-side AI / ML model requires significantly more data transmission for AI / ML than the NW-side AI / ML model and the UE-side AI / ML model, and may have significantly greater complexity. Therefore, standardization of the NW-side AI / ML model and the UE-side AI / ML model is expected in Rel-19.

[0140] In AI / ML-based beam management, beams are categorized into multiple sets. Set A of beams, Set B of beams, and Set C of beams can be defined, and the technical meaning of each can be defined as shown in [Table 2] below.

[0141] Clause 5.2.1 in TR38.843...The following are selected as representative sub-use cases:- BM-Case1: Spatial-domain Downlink beam prediction for Set A of beams based on measurement results of Set B of beams...- BM-Case2: Temporal Downlink beam prediction for Set A of beams based on the historic measurement results of Set B of beams...Set B is a set of beams whose measurements are taken as inputs of the AI / ML model.Clause 6.3.2.3 in TR38.843...-(Opt 2D)For the case that Set B of beams (pairs) is a subset of measured beams (pairs) Set C (where Set C is fixed across training and inference), compared to the case with all measurements of measured beam Set C as AI inputs-with Top K=1 / 2of the measurements of Set C,…

[0142] To summarize, a set A of beams (hereinafter referred to as 'beam set A') includes a set of beams to be predicted via AI / ML, a set B of beams (hereinafter referred to as 'beam set B') includes a set of beams to be used as input data to AI / ML to predict beams within beam set A, and a set C of beams (hereinafter referred to as 'beam set C') includes a set of beams on which beam measurement is performed. Here, the beams included in beam set C may or may not be used as input data for AI / ML.

[0143] The DL transmit beams in any area served by the base station, e.g., the entire coverage area or an area within a specific wide-beam, are N in total. DL_TxBeam If a set of beams is called a set U, then the beams of beam set A and the beams of beam set B become elements of set U, and beam set A and beam set B are each subsets of set U.

[0144]

[0145] FIG. 7 illustrates a procedure for AI / ML-based beam management according to an embodiment of the present disclosure. As shown in FIG. 7, a terminal and a base station interact for the procedure for AI / ML-based beam management.

[0146] Referring to Figure 7, in step S701, the base station transmits a capability information inquiry message to the terminal. After connecting to the base station through the initial connection procedure, the terminal establishes a connection and can receive a request for capability information from the base station.

[0147] In step S703, the terminal transmits a capability information message to the base station. The terminal may transmit information about hardware or functional capabilities related to communication to the base station. At this time, according to an embodiment of the present disclosure, the terminal may transmit information related to AI / ML-based beam management. Specifically, the capability information message may include information indicating support for AI / ML-based beam management features, information indicating support for UE-side AI / ML beam management features, information about a model for beam management, and the like. Through this, the base station can determine that beam management for the terminal can be performed based on AI / ML.

[0148] In step S705, the base station transmits a message including information about a beam set configuration. Here, the beam set configuration includes information about a first beam set including beams to be inferred and a second beam set including beams used to obtain input data for inference. According to an embodiment of the present disclosure, the information about the beam set configuration may explicitly or implicitly indicate beam(s) belonging to the first beam set and beam(s) belonging to the second beam set. To indicate the beams, a beam index, a reference signal index, a resource index, an indicator based on a representation method of the beams, physical information of the beam (e.g., direction, etc.), etc. may be used.

[0149] In step S707, the base station transmits at least one reference signal. At this time, the base station transmits at least one reference signal corresponding to at least one beam belonging to the second beam set. Although not illustrated in FIG. 7, configuration information or control information regarding the transmission of the reference signal may be signaled prior to transmitting the reference signal. The terminal may receive at least one reference signal belonging to the second beam set based on the configuration information or control information.

[0150] In step S709, the terminal performs channel measurements. In other words, the terminal acquires channel quality information by performing measurements on at least one reference signal belonging to the second beam set. For example, the channel quality information may include reference signal received power (RSRP). At this time, channel quality information for one or more beams is acquired.

[0151] In step S711, the terminal infers information about at least one beam belonging to the first beam set. That is, by using channel quality information about at least one reference signal belonging to the second beam set as input data, the terminal generates information about at least one beam belonging to the first beam set through an AI / ML model. At this time, the information obtained through inference may be the same type of information as the information obtained through the previous channel measurement, or may be a different type of information. For example, the information obtained through inference may include at least one of RSRP, PMI (precoding matrix indicator), RI (rank indicator), and CQI (channel quality information). As another example, the information obtained through inference may include probability information. Here, the object of the probability information may be defined in various ways, such as the probability that each beam satisfies a specific criterion, the probability that each beam has a specific performance requirement, etc. As a specific example, the probability information may include a probability value that each beam belongs to the top K beams (e.g., the best K beams selected based on a specific metric (e.g., L1-RSRP, etc.)). As another example, the information obtained through inference may include a value representing the characteristics of the beam, specifically, a vector value of a latent space.

[0152] At step S713, the terminal transmits information about the inferred channel quality. For this purpose, although not illustrated in FIG. 7, configuration information or control information for feedback on channel quality information may be signaled. The terminal may transmit information about at least one beam belonging to the first beam set inferred using the AI / ML model to the base station through resources identified based on the configuration information or control information.

[0153] AI / ML-based beam management can be performed according to the aforementioned procedure. In the aforementioned procedure, the base station signals the configuration of beam sets to the terminal. Here, the beam sets may include beam set A and beam set B, as described above with reference to Table 2. The present disclosure below describes various embodiments for the configuration and signaling of beam sets.

[0154] FIGS. 8A to 8C illustrate configuration examples of beam sets according to embodiments of the present disclosure.

[0155] Figure 8a shows 64 (e.g. N DL_TxBeam =64) is an example showing the configuration of beam set A and beam set B in the entire beams. Referring to Fig. 8a, beam set A includes beam #1, beam #2, beam #62, beam #63, beam #64, etc., and beam set B includes beam #2, beam #4, beam #5, beam #62, beam #63, beam #64, etc. In the example of Fig. 8a, beam set A and beam set B are configured without any particular rule. As in the example of Fig. 8a, two beam sets can be configured randomly, but a more efficient configuration can be achieved considering performance and complexity. For example, a case like Fig. 8b is possible.

[0156] Figure 8b shows 64 (e.g. N DL_TxBeam=64) is an example in which beam set A is configured to include beams with consecutive indices among all beams. In Fig. 8b, beam set A is configured with beams of beam IDs 33 to 48, and beam set B is configured with some of the beams of beam set A. In other words, beam set B is a subset of beam set A. In this way, when beam sets A and beam sets B are configured, this can be understood as beam management based on a method of using measurements and / or information for a small number of beams as inputs to AI / ML to predict characteristics for surrounding beams that are correlated with the beams (e.g., beams used as inputs). Extending the example of Fig. 8b, there may be a case in which beam set A beams are predicted by measuring one beam, and there may be a case in which beam sets A and beam sets B are the same.

[0157] In the example of Fig. 8b, beam set A is composed of continuous beam IDs. However, as in Fig. 8c, it is also possible for beam set A to be composed of discontinuous beam IDs, and beam set B to be composed of a subset of beam set A.

[0158]

[0159] The present disclosure proposes a DL transmission beam management scheme, which is mainly discussed in Rel-19 AI / ML based beam management.

[0160] FIG. 9 illustrates an example of a two-dimensional representation of beams of a base station according to an embodiment of the present disclosure. Referring to FIG. 9, the area covered by DL transmission beams can be two-dimensionally represented based on azimuth and elevation angles, as in the first representation (910) of FIG. 9. In the first representation (910), eight areas are defined by azimuth and four areas are defined by elevation, thereby representing 32 beams belonging to beam set A. The beams represented in FIG. 9 correspond to all or part of the beams that can be generated by the base station. The second representation (920) of FIG. 9 is a result of transforming the first representation (910) into rectangular coordinates. In the second representation (920), one rectangular block represents one of the beams belonging to beam set A to be estimated by AI / ML.

[0161] First, the terminal can notify the base station of its capability for AI / ML or its capability for AI / ML-based beam management. Prior to this step, the base station can request a capability report from the terminal. If the base station decides to perform UE-side AI / ML-based beam management, the base station can provide the terminal with information on beams of beam set A. The information provided to the terminal can be used in performing operations for UE-side AI / ML-based beam management, and can include at least one of the number of beams of beam set A, the azimuth size or beam width of each beam, and the elevation size of each beam. In particular, as in the second representation (920) of FIG. 9, when beam set A can be expressed by the number of rows (e.g., n_row) and the number of columns (e.g., n_col), at least one of information on n_row and n_col, the azimuth for each column, and the elevation for each column may be further included in the information on the beams of beam set A, or may replace the aforementioned items. Alternatively, only all or part of the items included in the information for the aforementioned beam set A may be used.

[0162] Additionally, the base station must inform the terminal of information about beam set B. In the case where beam set B is a subset of beam set A, the data format for informing the terminal of the configuration of beam set B may take various forms. For example, one of the following formats, format 1, format 2, and format 3, may be applied.

[0163] - Format 1: The base station can map each beam to one bit and indicate information about beam set B to the terminal in bitmap format. For example, in the case of FIG. 10a, beams included in beam set B can be indicated using a bitmap, such as [10001000 00100010 01000100 00010001], and in the case of FIG. 10b, beams included in beam set B can be indicated using a bitmap, such as [01001001 10010010 00100100 10010010].

[0164] - Format 2: It can be indicated by the lowest column index of the beams included in the beam set B in each row and the distance information between the elements of the beam set B in each row. In the case of Fig. 10a, the lowest column index among the beams belonging to the beam set B in each row, that is, the start column index, is [0,2,1,3], respectively, and the distance between the elements of the beam set B in each row is constant as 4. In addition, in the example of Fig. 10b, the lowest column index per row, that is, the start column index, is [1,0,2,0], respectively, and the distance between the elements of the beam set B in each row is 3. Therefore, the base station can inform the terminal of the configuration of the beam set B by indicating the start column index of the beam in each row and the distance between the elements in each row.

[0165] - Format 3: It can indicate a bitmap that is commonly applied to all rows and the cyclic shift value of that bitmap for each row. This method indicates a bitmap of [10001000] and a cyclic shift value of [0,2,1,3] to express beam set B of Fig. 10a. In the case of Fig. 10b, the configuration of beam set B can be indicated with a bitmap of [10010010] and a cyclic shift value of [1,0,2,0].

[0166] Format 2 and Format 3 of the above description can be applied when beam set B has a specific pattern, while Format 1 can also indicate an irregular pattern of beam set B. Therefore, considering the beam set pattern, etc., a method of selecting and indicating Format 1, Format 2, and Format 3 can also be applied as needed. For example, when a regular pattern is to be indicated, Format 3 can be used, and when an irregular pattern is to be indicated, Format 1 can be used.

[0167] The above-described formats 1, 2, and 3 can be changed to formats with the rows and columns reversed. For example, in the case of format 1, a bitmap is created as [01001001 10010010 00100100 10010010] to indicate Fig. 10b, but a bitmap with the rows and columns reversed, such as [0101 1000 0010 0101 1000 0010 0101 1000], can be created. Format 2 and format 3 can also use similar modified formats.

[0168]

[0169] To indicate information about the beam set B described above, various embodiments may be utilized.

[0170] In the first method, beam set B information in a format identical or similar to formats 1 / 2 / 3 described above can be transmitted to the terminal via MAC_CE or RRC configuration. This method may require frequent configuration when frequently updating information about beam set B. If, as described above, multiple formats are utilized to indicate beam set B information, an additional indicator is required to indicate which format the data is for.

[0171] In a second method, a method of pre-defining candidates for a large amount of pattern information of beam set B that can occur in a specification document, for example, in a format identical or similar to format 1, format 2, and format 3, together with a pattern ID, can be applied. Through this, the base station can select one of the pre-defined patterns and indicate the pattern of beam set B to the terminal through the pattern ID. At this time, in addition to the pattern ID, the base station can indicate additional information (e.g., the validity time for the pattern, other pattern IDs to be used sequentially, etc.). The following [Table 3] is an example of expressing patterns in the format 3, and candidate patterns can be defined in a similar manner for format 1 or format 2.

[0172] Pattern IDn_row, n_colPattern BitmapCyclingShift............100(4,8)[10001000][1 0 2 3]101(4,8)[10001000][0 2 1 3]102(4,8)[10010010][1 0 2 3]103(4,8)[10010010][0 2 1 3]104(4,8)..................

[0173] In a third method, one or more candidates for the pattern of the aforementioned beam set B are configured in advance by the base station to the terminal via RRC signaling, the beam set B for AI / ML operation is selected as one of the candidates, and the identifier of the selected candidate is indicated via DCI, MAC_CE, and RRC configuration. In this case, the candidates that the base station configures to the terminal can be defined similarly to the second method. In UE-side AI / ML-based beam management, since the terminal can use the result of beam management as an input of AI / ML, beam set B and beam set C can be said to be the same. Therefore, the base station may not provide information about beam set C to the terminal.

[0174]

[0175] The operations of the method according to the present disclosure can be implemented as a computer-readable program or code on a computer-readable recording medium. A computer-readable recording medium includes any type of recording device that stores information readable by a computer system. Furthermore, a computer-readable recording medium can be distributed across network-connected computer systems, allowing the computer-readable program or code to be stored and executed in a distributed manner.

[0176] Additionally, the computer-readable recording medium may include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. The program instructions may include not only machine language codes produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc.

[0177] While some aspects of the present disclosure have been described in the context of a device, they may also represent a description of a corresponding method, wherein a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method may also be described as a corresponding block or item or a feature of a corresponding device. Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, at least one or more of the most significant method steps may be performed by such a device.

[0178] A programmable logic device (e.g., a field-programmable gate array) may be used to perform some or all of the functions of the methods described in the present disclosure. The field-programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described in the present disclosure. In general, the methods are preferably performed by some hardware device.

[0179] Although the present disclosure has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present disclosure without departing from the spirit and scope of the present disclosure as set forth in the claims below.

Claims

1. The operation method of a terminal in a wireless communication system is as follows: Determining the first set and the second set of beams for AI (artificial intelligence) / ML (machine learning) based beam management; Receiving at least one reference signal corresponding to at least one beam belonging to the second set; Predicting information about at least one beam included in the first set based on at least one reference signal; comprising transmitting information about at least one beam to a base station, A method wherein information about at least one beam included in the first set is predicted based on a measurement result for at least one reference signal corresponding to at least one beam included in the second set.

2. In claim 1, A method wherein the first set comprises beams having consecutive indices.

3. In claim 1, The first set comprises groups containing beams having consecutive indices, A method wherein the second set is configured to include at least one beam included in each of the groups.

4. In claim 1, Transmitting capability information for the AI / ML-based beam management to the base station; A method further comprising receiving at least one message comprising a configuration for the first set and a configuration for the second set.

5. In claim 1, Further comprising receiving at least one message comprising a configuration for the first set and a configuration for the second set, A method wherein the configuration for the first set includes at least one of the number of beams included in the first set, the azimuth size or beam width of each of the beams, the elevation size of each of the beams, the number of rows and the number of columns for two-dimensional representation of the beams, the azimuth for each column, and the elevation for each column.

6. In claim 1, Further comprising receiving at least one message comprising a configuration for the first set and a configuration for the second set, A method wherein the configuration for the second set includes information about beams included in the second set.

7. In claim 6, A method wherein information about beams included in the second set includes a bitmap in which each bit is mapped to a candidate beam.

8. In claim 6, A method wherein information about beams included in the second set includes a starting column index and a row index interval per row of a two-dimensional representation of the beams.

9. In claim 6, A method wherein information about beams included in the second set comprises a bitmap of a representative row of a two-dimensional representation of the beams and a cyclic shift value for at least one remaining row.

10. In claim 6, A method wherein the configuration for the second set includes an identifier indicating at least one pattern among candidate patterns configured through predefined or signaling for beams included in the second set.

11. In claim 6, A method wherein the configuration for the second set includes information about the validity time of at least one pattern among candidate patterns configured through predefined or signaling for beams included in the second set.

12. In claim 1, A method wherein the information about at least one beam includes at least one of RSRP, PMI (precoding matrix indicator), RI (rank indicator), CQI (channel quality information), and a vector value of a latent space indicating the characteristics of the beam.

13. The method of operation of a base station in a wireless communication system is as follows: Determining the first set and the second set of beams for AI (artificial intelligence) / ML (machine learning) based beam management; Transmitting at least one reference signal corresponding to at least one beam belonging to the second set; Receiving information about at least one beam included in the first set, the beam being predicted based on at least one reference signal; A method wherein information about at least one beam included in the first set is predicted based on a measurement result for at least one reference signal corresponding to at least one beam included in the second set.

14. In claim 13, Receiving capability information for the AI / ML-based beam management from the terminal; A method further comprising transmitting to the terminal at least one message comprising a configuration for the first set and a configuration for the second set.

15. In a wireless communication system, at a terminal, At least one transmitter / receiver; at least one processor; and At least one memory operably connected to said at least one processor and storing instructions that, when executed by said processor, control said terminal to perform operations; The above actions are, Determining the first set and the second set of beams for AI (artificial intelligence) / ML (machine learning) based beam management; Receiving at least one reference signal corresponding to at least one beam belonging to the second set; Predicting information about at least one beam included in the first set based on at least one reference signal; comprising transmitting information about at least one beam to a base station, A terminal, wherein information about at least one beam included in the first set is predicted based on a measurement result for at least one reference signal corresponding to at least one beam included in the second set.

16. In a wireless communication system, at a base station, At least one transmitter / receiver; at least one processor; and At least one memory operably connected to said at least one processor and storing instructions that, when executed by said processor, control said terminal to perform operations; The above actions are, Determining the first set and the second set of beams for AI (artificial intelligence) / ML (machine learning) based beam management; Transmitting at least one reference signal corresponding to at least one beam belonging to the second set; Receiving information about at least one beam included in the first set, the beam being predicted based on at least one reference signal; A base station, wherein information about at least one beam included in the first set is predicted based on a measurement result for at least one reference signal corresponding to at least one beam included in the second set.

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