Device and method for performing online learning in wireless communication system

Abstract

The present disclosure is for performing online learning in a wireless communication system. This method performed by a terminal may comprise the steps of: transmitting a first message requesting information about neighboring regions; receiving a second message including the information about the neighboring regions; evaluating one or more neighboring regions on the basis of the information about the neighboring regions; and causing a channel task model to perform online learning on the basis of the evaluation result. The online learning may be performed on at least some regions evaluated to have an out-of-distribution probability of a threshold value or greater for the channel task model, among the one or more neighboring regions.

Description

Device and method for performing online learning in a wireless communication system

[0001] The following description relates to a wireless communication system, and to a device and method for performing online learning in a wireless communication system.

[0002] Wireless access systems are widely deployed to provide various types of communication services, such as voice and data. Typically, wireless access systems are multiple access systems that support communications with multiple users by sharing available system resources (e.g., bandwidth, transmission power). Examples of multiple access systems include code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), orthogonal frequency division multiple access (OFDMA), and single-carrier frequency division multiple access (SC-FDMA).

[0003] In particular, as numerous communication devices demand greater communication capacity, enhanced mobile broadband (eMBB) communication technologies are being proposed, improving upon existing radio access technology (RAT). Furthermore, massive machine type communications (mMTC), which connects multiple devices and objects to provide diverse services anytime and anywhere, as well as communication systems that consider reliability and latency-sensitive services / user equipment (UE), are being proposed. Various technological configurations are being proposed for these solutions.

[0004] The present disclosure can provide a device and method for effectively performing online learning in a wireless communication system.

[0005] The present disclosure may provide a device and method for assisting online learning of a channel task model in a wireless communication system.

[0006] The present disclosure may provide a device and method for reducing the occurrence of out-of-distribution (OOD) of a channel task model in a wireless communication system.

[0007] The present disclosure may provide a device and method for evaluating the OOD probability of a channel task model in a wireless communication system.

[0008] The present disclosure may provide a device and method for signaling information related to OOD evaluation for at least one neighboring area in a wireless communication system.

[0009] The present disclosure may provide a device and method for providing information related to a probability distribution of a channel for neighboring areas of a terminal in a wireless communication system.

[0010] The present disclosure may provide a device and method for limiting neighboring areas subject to OOD evaluation in a wireless communication system.

[0011] The present disclosure may provide a device and method for acquiring information for learning about a channel of at least one neighboring area in a wireless communication system.

[0012] The present disclosure may provide a device and method for generating sample data for learning about a channel of at least one neighboring area in a wireless communication system.

[0013] The present disclosure may provide a device and method for reducing the cost of online learning based on the mobility of a terminal in a wireless communication system.

[0014] The technical objectives to be achieved in the present disclosure are not limited to those mentioned above, and other technical tasks not mentioned can be considered by a person having ordinary skill in the technical field to which the technical configuration of the present disclosure is applied from the embodiments of the present disclosure described below.

[0015] As an example of the present disclosure, a method performed by a terminal in a wireless communication system may include the steps of transmitting a first message requesting information about a neighboring area, receiving a second message including information about the neighboring area, performing an evaluation on at least one neighboring area based on the information about the neighboring area, and performing online learning for a channel task model based on a result of the evaluation. The online learning may be performed on at least some areas, among the at least one neighboring area, that are evaluated to have an out-of-distribution probability greater than a threshold value for the channel task model.

[0016] As an example of the present disclosure, in a wireless communication system, a terminal includes a transceiver and a processor connected to the transceiver, wherein the processor controls the terminal to transmit a first message requesting information about a neighboring area, receive a second message including information about the neighboring area, perform an evaluation on at least one neighboring area based on the information about the neighboring area, and perform online learning for a channel task model based on a result of the evaluation, wherein the online learning can be performed on at least some areas among the at least one neighboring area that are evaluated to have an out-of-distribution probability greater than a threshold value for the channel task model.

[0017] As an example of the present disclosure, a communication device includes at least one processor, and at least one computer memory connected to the at least one processor and storing instructions that, when executed by the at least one processor, direct operations, wherein the operations may include: transmitting a first message requesting information about a neighboring area; receiving a second message including information about the neighboring area; performing an evaluation of at least one neighboring area based on the information about the neighboring area; and performing online learning for a channel task model based on a result of the evaluation. The online learning may be performed for at least some areas, among the at least one neighboring area, that are evaluated to have an out-of-distribution probability greater than a threshold value for the channel task model.

[0018] As an example of the present disclosure, a non-transitory computer-readable medium storing at least one instruction includes at least one instruction executable by a processor, wherein the at least one instruction controls a device to transmit a first message requesting information about a neighboring area, receive a second message including information about the neighboring area, perform an evaluation on at least one neighboring area based on the information about the neighboring area, and perform online learning for a channel task model based on a result of the evaluation, wherein the online learning can be performed on at least some areas among the at least one neighboring area that are evaluated to have an out-of-distribution probability greater than a threshold value for the channel task model.

[0019] The above-described aspects of the present disclosure are only some of the preferred embodiments of the present disclosure, and various embodiments reflecting the technical features of the present disclosure can be derived and understood by a person having ordinary skill in the art based on the detailed description of the present disclosure to be described below.

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

[0021] According to the present disclosure, online learning can be performed effectively.

[0022] The effects that can be obtained from the embodiments of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art to which the technical configuration of the present disclosure is applied, from the description of the embodiments of the present disclosure below. In other words, unintended effects resulting from implementing the configuration described in the present disclosure can also be derived from the embodiments of the present disclosure by those skilled in the art.

[0023] The accompanying drawings are intended to aid understanding of the present disclosure and, together with detailed descriptions, may provide embodiments of the present disclosure. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to form new embodiments. Reference numerals in each drawing may indicate structural elements.

[0024] Figure 1 illustrates an example of a communication system applicable to the present disclosure.

[0025] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0026] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure.

[0027] Figure 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure.

[0028] FIG. 5 illustrates an example of a communication structure that can be provided in a 6G (6th generation) system applicable to the present disclosure.

[0029] Figure 6 illustrates an electromagnetic spectrum applicable to the present disclosure.

[0030] FIG. 7 illustrates a THz wireless communication transceiver applicable to the present disclosure.

[0031] Figure 8 illustrates a THz signal generation method applicable to the present disclosure.

[0032] FIG. 9 illustrates a wireless communication transceiver applicable to the present disclosure.

[0033] Figure 10 illustrates a transmitter structure applicable to the present disclosure.

[0034] Figure 11 illustrates a modulator structure applicable to the present disclosure.

[0035] Figure 12 illustrates the structure of a perceptron included in an artificial neural network applicable to the present disclosure.

[0036] Figure 13 illustrates an artificial neural network structure applicable to the present disclosure.

[0037] Figure 14 illustrates an example of a functional framework for application of artificial intelligence technology applicable to the present disclosure.

[0038] Figure 15 illustrates an example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0039] Figure 16 illustrates another example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0040] Figure 17 illustrates another example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0041] Figure 18 illustrates an AI technology-based communication procedure applicable to the present disclosure.

[0042] Figure 19 illustrates an example of movement of a terminal between different channel environments in a wireless communication system.

[0043] Figure 20 illustrates an example of an adversarial generative neural network model that processes channel information.

[0044] FIG. 21 illustrates functional structures of devices in a wireless communication system according to one embodiment of the present disclosure.

[0045] FIG. 22 illustrates an example of a procedure for performing online learning in a wireless communication system according to one embodiment of the present disclosure.

[0046] FIG. 23 illustrates an example of a procedure for providing information for online learning in a wireless communication system according to one embodiment of the present disclosure.

[0047] FIG. 24 illustrates an example of a procedure for performing online learning in response to out-of-distribution detection in a wireless communication system according to one embodiment of the present disclosure.

[0048] FIG. 25 illustrates an example of a channel point map in a wireless communication system according to one embodiment of the present disclosure.

[0049] FIG. 26 illustrates examples of candidate channel points in a wireless communication system according to one embodiment of the present disclosure.

[0050] The following embodiments combine the components 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 without being combined with other components or features. Furthermore, some components and / or features may be combined to form embodiments of the present disclosure. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment.

[0051] In the description of the drawings, procedures or steps that may obscure the gist of the present disclosure are not described, and procedures or steps that can be understood by a person skilled in the art are also not described.

[0052] Throughout the specification, when a part is said to "comprising" or "including" a component, this does not mean that other components may be included, but rather that other components may be excluded, unless otherwise specifically stated. In addition, terms such as "...part," "...unit," and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software. In addition, the words "a" or "an," "one," "the," and similar related words may be used in the context of describing the present disclosure (especially in the context of the claims below) to include both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0053] Embodiments of the present disclosure described herein focus on the data transmission and reception relationship between a base station and a mobile station. Here, the base station is understood as a terminal node of a network that directly communicates with the mobile station. Certain operations described herein as being performed by the base station may, in some cases, be performed by an upper node of the base station.

[0054] That is, in a network consisting of multiple network nodes including a base station, various operations performed for communication with a mobile station may be performed by the base station or other network nodes other than the base station. In this case, the term 'base station' may be replaced by terms such as fixed station, Node B, eNB (eNode B), gNB (gNode B), ng-eNB, advanced base station (ABS), or access point.

[0055] Additionally, in the embodiments of the present disclosure, the term terminal may be replaced with terms such as user equipment (UE), mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, or advanced mobile station (AMS).

[0056] Additionally, a transmitter refers to a fixed and / or mobile node that provides data or voice services, and a receiver refers to a fixed and / or mobile node that receives data or voice services. Therefore, for uplink, a mobile station can be the transmitter, and a base station can be the receiver. Similarly, for downlink, a mobile station can be the receiver, and a base station can be the transmitter.

[0057] Embodiments of the present disclosure may be supported by standard documents disclosed in at least one of wireless access systems, such as IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP Long Term Evolution (LTE) system, 3GPP 5th generation (5G) NR (New Radio) system and 3GPP2 system, and in particular, embodiments of the present disclosure may be supported by 3GPP TS (technical specification) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents.

[0058] Furthermore, the embodiments of the present disclosure can be applied to other wireless access systems and are not limited to the systems described above. For example, they can be applied to systems implemented after the 3GPP 5G NR system and are not limited to a specific system.

[0059] That is, obvious steps or parts not described in the embodiments of the present disclosure can be explained by referring to the above documents. In addition, all terms disclosed in this document can be explained by the above standard documents.

[0060] Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the present disclosure and is not intended to represent the only embodiments in which the technical configurations of the present disclosure may be implemented.

[0061] Additionally, specific terms used in the embodiments of the present disclosure are provided to aid in understanding of the present disclosure, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the present disclosure.

[0062] The following technology can be applied to various wireless access systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access).

[0063] For clarity, the following description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical spirit of the present disclosure is not limited thereto. LTE may refer to technology after 3GPP TS 36.xxx Release 8. 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 refer to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and / or Release 18. "xxx" refers to a standard document detail number. LTE / NR / 6G may be collectively referred to as a 3GPP system.

[0064] For background information, terms, abbreviations, etc. used in this disclosure, reference may be made to standard documents published prior to this disclosure. For example, reference may be made to standard documents 36.xxx and 38.xxx.

[0065] Communication system applicable to the present disclosure

[0066] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts of the present disclosure disclosed in this document may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0067] Hereinafter, more specific examples will be provided with reference to the drawings. In the drawings / descriptions below, the same drawing reference numerals may represent identical or corresponding hardware blocks, software blocks, or functional blocks, unless otherwise described.

[0068] Figure 1 illustrates an example of a communication system applied to the present disclosure.

[0069] 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 (e.g., LTE, LTE-A, LTE-A pro, NR, 5G, 5G-A, 6G) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (extended reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI (artificial intelligence) device / server (100g). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicles (100b-1, 100b-2) may include unmanned aerial vehicles (UAVs) (e.g., drones). The XR devices (100c) include augmented reality (AR) / virtual reality (VR) / mixed reality (MR) devices, and may be implemented in the form of head-mounted devices (HMDs), head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. The portable devices (100d) may include smartphones, smart pads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.), etc. The home appliances (100e) may include TVs, refrigerators, washing machines, etc. The IoT devices (100f) may include sensors, smart meters, etc.For example, the base station (120) and the network (130) may also be implemented as wireless devices, and a specific wireless device (120a) may act as a base station / network node to other wireless devices.

[0070] Wireless devices (100a to 100f) can be connected to a network (130) via a base station (120). AI technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (100g) via a network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR), or a 6G network. The wireless devices (100a to 100f) can communicate with each other via the base station (120) / network (130), but can also communicate directly (e.g., sidelink communication) without going through the base station (120) / network (130). For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). Additionally, an IoT device (100f) (e.g., a sensor) can communicate directly with another IoT device (e.g., a sensor) or another wireless device (100a to 100f).

[0071] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a to 100f) / base stations (120), and base stations (120) / base stations (120). Here, the wireless communication / connection can be established through various wireless access technologies such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and base station-to-base station communication (150c) (e.g., relay, IAB (integrated access backhaul)). Through the wireless communication / connection (150a, 150b, 150c), the wireless device and base station / wireless device, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, the wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of the present disclosure, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), resource allocation processes, etc. may be performed.

[0072] Devices applicable to the present disclosure

[0073] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0074] Referring to FIG. 2, the wireless device (200) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, LTE-A, LTE-A pro, NR, 5G, 5G-A, 6G). The wireless device (200) includes at least one processor (202) and at least one memory (204), and may additionally include at least one transceiver (206) and / or at least one antenna (208).

[0075] The processor (202) controls the memory (204) and / or the transceiver (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (206). In addition, the processor (202) may receive a wireless signal including second information / signal via the transceiver (206), and then store information obtained from signal processing of the second information / signal in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may store software code including instructions for performing some or all of the processes controlled by the processor (202), or for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology. The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via at least one antenna (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF (radio frequency) unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0076] Hereinafter, the hardware elements of the wireless device (200) will be described in more detail. Although not limited thereto, at least one protocol layer may be implemented by at least one processor (202). For example, at least one processor (202) may implement at least one layer (e.g., a functional layer such as physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), radio resource control (RRC), and service data adaptation protocol (SDAP)). At least one processor (202) may generate at least one Protocol Data Unit (PDU) and / or at least one Service Data Unit (SDU) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. At least one processor (202) may generate a message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. At least one processor (202) can generate a signal (e.g., a baseband signal) including a PDU, an SDU, a message, control information, data or information according to the functions, procedures, proposals and / or methods disclosed in this document, and provide the signal to at least one transceiver (206). At least one processor (202) can receive a signal (e.g., a baseband signal) from at least one transceiver (206) and obtain the PDU, SDU, message, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed in this document.

[0077] At least one processor (202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. The at least one processor (202) may be implemented by hardware, firmware, software, or a combination thereof. For example, at least one application specific integrated circuit (ASIC), at least one digital signal processor (DSP), at least one digital signal processing device (DSPD), at least one programmable logic device (PLD), or at least one field programmable gate array (FPGA) may be included in the at least one processor (202). The descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document may be included in the at least one processor (202), or may be stored in at least one memory (204) and executed by the at least one processor (202). The descriptions, functions, procedures, suggestions, methods and / or flowcharts disclosed in this document may be implemented using firmware or software in the form of code, instructions and / or sets of instructions.

[0078] At least one memory (204) can be connected to at least one processor (202) and can store various forms of data, signals, messages, information, programs, codes, instructions and / or commands. The at least one memory (204) can be configured as a read only memory (ROM), a random access memory (RAM), an erasable programmable read only memory (EPROM), a flash memory, a hard drive, a register, a cache memory, a computer readable storage medium and / or a combination thereof. The at least one memory (204) can be located internally and / or externally to the at least one processor (202). In addition, the at least one memory (204) can be connected to the at least one processor (202) via various technologies such as a wired or wireless connection.

[0079] At least one transceiver (206) can transmit user data, control information, wireless signals / channels, etc., mentioned in the methods and / or flowcharts of this document to at least one other device. At least one transceiver (206) can receive user data, control information, wireless signals / channels, etc. mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts disclosed in this document from at least one other device. For example, at least one transceiver (206) can be connected to at least one processor (202) and can transmit and receive wireless signals. For example, at least one processor (202) can control at least one transceiver (206) to transmit user data, control information, or wireless signals to at least one other device. Furthermore, at least one processor (202) can control at least one transceiver (206) to receive user data, control information, or wireless signals from at least one other device. In addition, at least one transceiver (206) may be connected to at least one antenna (208), and at least one transceiver (206) may be configured to transmit and receive user data, control information, wireless signals / channels, etc. mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts disclosed in this document through at least one antenna (208). In this document, at least one antenna may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports). At least one transceiver (206) may convert the received wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using at least one processor (202). At least one transceiver (206) may convert the processed user data, control information, wireless signals / channels, etc. from baseband signals to RF band signals using at least one processor (202).For this purpose, at least one transceiver (206) may include an (analog) oscillator and / or filter.

[0080] The components of the wireless device described with reference to FIG. 2 may be referred to by different terms in terms of functionality. For example, the processor (202) may be referred to as a control unit, the transceiver (206) as a communication unit, and the memory (204) as a storage unit. In some cases, the communication unit may be used to mean at least a portion of the processor (202) and the transceiver (206).

[0081] The structure of the wireless device described with reference to FIG. 2 can be understood as the structure of at least a portion of various devices. For example, the structure of the wireless device illustrated in FIG. 2 can be at least a portion of various devices described with reference to FIG. 1 (e.g., a robot (100a), a vehicle (100b-1, 100b-2), an XR device (100c), a portable device (100d), a home appliance (100e), an IoT device (100f), an AI device / server (100g)). Furthermore, according to various embodiments, in addition to the components illustrated in FIG. 2, the device may further include other components.

[0082] For example, the device may be a portable device such as a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), or a portable computer (e.g., a laptop, etc.). In this case, the device may further include at least one of a power supply unit that supplies power and includes a wired / wireless charging circuit, a battery, etc., an interface unit that includes at least one port for connection with another device (e.g., an audio input / output port, a video input / output port), and an input / output unit for inputting and outputting image information / signals, audio information / signals, data, and / or information input from a user.

[0083] For example, the device may be a mobile device such as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, etc. In this case, the device may further include at least one of a driving unit including at least one of an engine, a motor, a power train, wheels, brakes, and a steering unit of the device, a power supply unit including a wired / wireless charging circuit, a battery, etc. that supplies power, a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, an autonomous driving unit that performs functions such as path maintenance, speed control, and destination setting, and a position measurement unit that obtains location information of the mobile device through a global positioning system (GPS) and various sensors.

[0084] For example, the device may be an XR device such as an HMD, a head-up display (HUD) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, etc. In this case, the device may further include at least one of a power supply unit that supplies power and includes a wired / wireless charging circuit, a battery, etc., an input / output unit that obtains control information, data, etc. from the outside and outputs the generated XR object, and a sensor unit that senses status information, environmental information, and user information of the device or the surroundings of the device.

[0085] For example, the device may be a robot that can be classified into industrial, medical, household, military, etc. types depending on the purpose or field of use. In this case, the device may further include at least one of a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, and a driving unit that performs various physical actions, such as moving the robot joints.

[0086] For example, the device may be an AI device such as a TV, a projector, a smartphone, a PC, a laptop, a digital broadcasting terminal, a tablet PC, a wearable device, a set-top box (STB), a radio, a washing machine, a refrigerator, digital signage, a robot, a vehicle, etc. In this case, the device may further include at least one of an input unit that acquires various types of data from the outside, an output unit that generates output related to sight, hearing, or touch, a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, and a training unit that trains a model composed of an artificial neural network using learning data.

[0087] The structure of the wireless device illustrated in FIG. 2 may be understood as a part of a RAN node (e.g., a base station, DU, RU, RRH, etc.). That is, the device illustrated in FIG. 2 may be a RAN node. In this case, the device may further include a wired transceiver for front haul and / or back haul communications. However, if the front haul and / or back haul communications are based on wireless communications, at least one transceiver (206) illustrated in FIG. 2 may be used for front haul and / or back haul communications, and a wired transceiver may not be included.

[0088] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure. For example, the transmission signal may be processed by a signal processing circuit. At this time, the signal processing circuit (300) may include a scrambler (310), a modulator (320), a layer mapper (330), a precoder (340), a resource mapper (350), and a signal generator (360). At this time, as an example, the operations / functions of FIG. 3 may be performed in the processor (202) and / or the transceiver (206) of FIG. 2. Furthermore, as an example, the hardware elements of FIG. 3 may be implemented in the processor (202) and / or the transceiver (206) of FIG. 2. As an example, blocks 310 to 360 may be implemented in the processor (202) of FIG. 2. Additionally, blocks 310 to 350 may be implemented in the processor (202) of FIG. 2, and block 360 may be implemented in the transceiver (206) of FIG. 2, and are not limited to the above-described embodiment.

[0089] The codeword can be converted into a wireless signal through the signal processing circuit (300) of FIG. 3. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., an UL-SCH transport block, a DL-SCH transport block). The wireless signal may be transmitted through various physical channels (e.g., a PUSCH, a PDSCH). Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (310). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (320). The modulation scheme may include pi / 2-binary phase shift keying (pi / 2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), etc.

[0090] A complex modulation symbol sequence can be mapped to at least one transmission layer by a layer mapper (330). The modulation symbols of each transmission layer can be mapped to corresponding antenna port(s) by a precoder (340). The output z of the precoder (340) can be obtained by multiplying the output y of the layer mapper (330) by an N×M precoding matrix W. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (340) can perform precoding after performing transform precoding (e.g., discrete Fourier transform (DFT) transform) on the complex modulation symbols. Additionally, the precoder (340) can perform precoding without performing transform precoding.

[0091] The resource mapper (350) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (360) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (360) can include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like.

[0092] The signal processing process for a received signal in a wireless device may be configured in reverse order of the signal processing process (310 to 360) of FIG. 3. For example, a wireless device (e.g., 200 of FIG. 2) may receive a wireless signal from the outside through an antenna port / transceiver. The received wireless signal may be converted into a baseband signal through a signal restorer. For this purpose, 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 demapper process, a postcoding process, a demodulation process, and a descrambling process. The codeword may be restored to the original information block through decoding. Therefore, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.

[0093] Figure 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure. Figure 4 illustrates operations of a terminal (410) and a base station (420) transmitting and / or receiving data and operations performed prior thereto.

[0094] Referring to FIG. 4, in step 401, the terminal (410) and the base station (420) perform synchronization. For example, the terminal (410) performs an initial cell search operation. Specifically, the terminal (410) can detect at least one synchronization signal transmitted from the base station (420) according to a predefined rule. Here, the synchronization signal can include multiple synchronization signals classified according to structure or purpose (e.g., primary synchronization signal, secondary synchronization signal). Through this, the terminal (410) can check the boundary of the frame, subframe, slot, and / or symbol of the base station (420) and obtain information about the base station (420) (e.g., cell identifier).

[0095] In step 403, the terminal (410) obtains system information transmitted from the base station (420). The system information is information related to the properties, characteristics, and / or capabilities of the base station (420) required to access the base station (420) and use the service, and may be classified according to the content (e.g., whether it is essential for access), transmission structure (e.g., channel used, whether provided on-demand), etc., and may be classified into, for example, a master information block (MIB) and a system information block (SIB). If necessary, the terminal (410) may transmit a signal requesting system information before receiving the system information. However, the request and provision of the system information may be performed after the random access procedure described below.

[0096] In step 405, the terminal (410) and the base station (420) perform a random access procedure. The terminal (410) may transmit and / or receive at least one message (e.g., a random access preamble, a random access response (RAR) message, etc.) for the random access procedure based on information related to the random access channel of the base station (420) obtained through system information (e.g., channel position, channel structure, supported preamble structure, etc.). For example, the terminal (410) may transmit a preamble (e.g., MSG1) through the random access channel, receive an RAR message (e.g., MSG2), transmit a message (e.g., MSG3) including information related to the terminal (410) (e.g., identification information) to the base station (420) using scheduling information included in the RAR message, and receive a message (e.g., MSG4) for contention resolution and / or connection establishment. As another example, MSG1 and MSG3 may be sent and received as one message, or MSG2 and MSG4 may be sent and received as one message.

[0097] In step 407, the terminal (410) and the base station (420) perform signaling of control information. Here, the control information may be defined in various layers, such as a layer that controls a connection (e.g., a radio resource control (RRC) layer), a layer that handles mapping between logical channels and transport channels (e.g., a media access control (MAC) layer), and a layer that handles physical channels (e.g., a physical (PHY) layer). For example, the terminal (410) and the base station (420) may perform at least one of signaling for establishing a connection, signaling for determining settings related to communication, and signaling for indicating allocated resources.

[0098] In step 409, the terminal (410) and the base station (420) transmit and / or receive data. In other words, the terminal (410) and the base station (420) can process, transmit, and / or receive data based on the signaling of the control information. For example, when transmitting data, the terminal (410) or the base station (420) can perform at least one of channel encoding, rate matching, scrambling, constellation mapping, layer mapping, waveform modulation, antenna mapping, and resource mapping on the information bits. Conversely, when receiving data, the terminal (410) or the base station (420) can perform at least one of signal extraction from resources, waveform demodulation for each antenna, signal arrangement considering layer mapping, constellation demapping, descrambling, and channel decoding.

[0099] 6G communication systems and core implementation technologies of 6G systems

[0100] The 5G system defines various operating bands within FR1 (frequency range 1), which covers 410 MHz to 7125 MHz, and FR2 (frequency range 2), which covers 24,250 MHz to 71,000 MHz. Various frequencies are being discussed as operating bands for the subsequent 6G system, and the use of higher frequencies than 5G systems is also being considered for wider bandwidth and higher transmission speeds. One such band is the THz (terahertz) frequency band, which covers approximately 100 GHz to 10 THz. The THz frequency band is a band that has both the transparency of radio waves and the straightness of light waves, and communications using the THz frequency band are expected to play a transitional role from existing radio-centered communications to lightwave-based communications.

[0101] 6G systems utilizing the THz frequency band have the following goals: i) very high data rates per device, ii) a very large number of connected devices, iii) global connectivity, iv) very low latency, v) reduced energy consumption of battery-free IoT devices, vi) ultra-reliable connectivity, and vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: “intelligent connectivity,” “deep connectivity,” “holographic connectivity,” and “ubiquitous connectivity,” and the 6G system can be designed to satisfy the requirements as shown in [Table 1] below.

[0102] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100 bps / HzMobility supportup to 1000 km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0103] At this time, the 6G system may have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security. FIG. 5 illustrates an example of a communication structure that can be provided in a 6G system applicable to the present disclosure. Referring to FIG. 5, the 6G system is expected to have simultaneous wireless communication connectivity that is 50 times higher than that of a 5G wireless communication system. URLLC, a key feature of 5G, is expected to become even more crucial in 6G communications by providing end-to-end latency of less than 1 ms. Furthermore, 6G systems will boast significantly higher volumetric spectral efficiency than the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, potentially eliminating the need for separate charging for mobile devices in 6G systems.

[0104] As core implementation technologies of the 6G system, technologies such as artificial intelligence (AI), THz (terahertz) communication, optical wireless technology, FSO backhaul network, massive MIMO technology, blockchain, 3D networking, quantum communication, unmanned aerial vehicles, cell-free communication, wireless information and energy transfer (WIET), integration of sensing and communication, integration of access backhaul networks, holographic beamforming, big data analysis, and large intelligent surface (LIS) can be adopted.

[0105] For example, THz communication is a communication that utilizes a spectrum in a frequency band between 0.1 THz and 10 THz with a corresponding wavelength in the range of 0.03 mm to 3 mm as shown in Fig. 6, and can be implemented using circuit elements having a structure as shown in Fig. 7. In addition, optical wireless technology is a technology that generates and modulates THz signals using optical elements, and can be implemented based on devices having structures as shown in Figs. 8, 9, 10, and 11.

[0106] In addition, AI can be implemented based on various models such as neural networks and machine learning (machine models). For example, an AI model of a neural network structure can be based on the structure of a perceptron as in Fig. 12. Referring to Fig. 12, an artificial neural network can be composed of multiple perceptrons. Depending on the structure of the perceptron, the input vector x = {x1, x2, , xd} is input, each component is given a weight {W1, W2, , Wd} are multiplied, and after all the results are added up, the activation function σ(·) is applied. If the structure of a large artificial neural network is expanded from the simplified perceptron structure shown in Fig. 12, the input vector can be applied to perceptrons of different dimensions. When perceptrons are stacked, a neural network having an input layer, a hidden layer, and an output layer as in Fig. 13 can be constructed.

[0107] AI technology utilizing the structure of the neural network as described above can be operated based on a functional framework as shown in FIG. 14 below. FIG. 14 illustrates an example of a functional framework for application of AI technology applicable to the present disclosure. First, the data collection block (1410) performs data preparation on input data collected from objects (e.g., UE, RAN node, network node, etc.) to generate training data (1411) and / or inference data (1411) including processed input data. The model training block (1420) performs training on an AI model using the training data (1411) and provides information on the trained model to the model inference block (1430). The model inference block (1430) generates an output (1416) by performing inference and / or prediction using the inference data (1411). Additionally, the model inference block (1430) can provide model performance feedback (1414) to the model training block (1420). Here, the output (1416) refers to the inference output of the AI ​​model generated by the model inference block (1430), and the details of the inference output may vary depending on the use case. The actor block (1440) triggers or performs a specified task / action based on the output (1416). The actor block (1440) can trigger a task / action for another object (e.g., at least one UE, at least one RAN node, at least one network node, etc.) or for itself. Any one of the functions illustrated in FIG. 14 described above may be performed by two or more entities including the RAN, the network node, the network operator's OAM, or the UE in collaboration. This may be referred to as a split AI operation.

[0108] FIG. 15 illustrates an example of a procedure for utilizing an AI model applicable to the present disclosure. FIG. 15 illustrates a case where a model training function (e.g., a function of a model training block (1420)) is included in a network node, and a model inference function (e.g., a function of a model inference block (1430)) is included in a RAN node. Referring to FIG. 15 , in step 1, RAN node 1 and RAN node 2 transmit input data (e.g., training data) for training an AI model to the network node. Here, RAN node 1 and RAN node 2 may also transmit data collected from the UE (e.g., measurements of the UE related to RSRP, RSRQ, SINR of the serving cell and neighboring cells, the UE's position, velocity, etc.) to the network node. In step 2, the network node trains the AI ​​model using the received training data. In step 3, the network node distributes / updates the AI ​​model to RAN node 1 and / or RAN node 2. RAN node 1 and / or RAN node 2 may continue model training based on the received AI model. In this procedure, it is assumed that the AI ​​model is deployed / updated only to RAN node 1. In step 4, RAN node 1 receives input data (e.g., inference data) for AI model inference from the UE and RAN node 2. In step 5, RAN node 1 performs inference based on the AI ​​model using the received inference data to generate output data (e.g., a prediction or a decision). In step 6, if applicable, RAN node 1 may send model performance feedback to network nodes. In step 7, RAN node 1, RAN node 2, and UE (or 'RAN node 1 and UE', or 'RAN node 1 and RAN node 2') perform actions based on the output data. For example, in case of load balancing operation, the UE may move from RAN node 1 to RAN node 2.In step 8, RAN node 1 and RAN node 2 transmit feedback information to the network nodes.

[0109] FIG. 16 illustrates another example of a procedure for utilizing an AI model applicable to the present disclosure. FIG. 16 illustrates a case where a model training function (e.g., a function of a model training block (1420)) and a model inference function (e.g., a function of a model inference block (1430)) are included in a RAN node. Referring to FIG. 16, in step 1, a UE and a RAN node 2 transmit input data (e.g., training data) for training an AI model to a RAN node 1. In step 2, RAN node 1 trains an AI model using the received training data. In step 3, RAN node 1 receives input data (e.g., inference data) for AI model-based inference from the UE and RAN node 2. In step 4, RAN node 1 performs AI model-based inference using the received inference data to generate output data (e.g., a prediction or decision). In step 5, RAN node 1, RAN node 2, and UE (or 'RAN node 1 and UE', or 'RAN node 1 and RAN node 2') perform actions based on the output data. For example, in case of load balancing operation, the UE may move from RAN node 1 to RAN node 2. In step 6, RAN node 2 transmits feedback information to RAN node 1.

[0110] FIG. 17 illustrates another example of a procedure for utilizing an AI model applicable to the present disclosure. FIG. 17 illustrates a case where a model training function (e.g., a function of a model training block (1420)) is included in a RAN node, and a model inference function (e.g., a function of a model inference block (1430)) is included in a UE. Referring to FIG. 17, in step 1, a UE transmits input data (e.g., training data) for training an AI model to a RAN node. Here, the RAN node may collect data from various UEs and / or other RAN nodes. In step 2, the RAN node trains an AI model using the received training data. In step 3, the RAN node distributes / updates the AI ​​model to the UE. The UE may continue model training based on the received AI model. In step 4, input data (e.g., inference data) for inference based on the AI ​​model is received from the UE, the RAN node, and / or other UEs. In step 5, the UE generates output data (e.g., predictions or decisions) by performing AI model-based inference using the received inference data. In step 6, if applicable, the UE may transmit model performance feedback to the RAN node. In step 7, the UE and the RAN node perform actions based on the output data. In step 8, the UE transmits feedback information to the RAN node.

[0111] According to the aforementioned framework and procedures, an AI model can be trained and utilized in a wireless communication system. In the aforementioned framework and procedures, various data, such as input data, training data, and inference data, are introduced. The specific content of the aforementioned data may vary depending on the task for which the AI ​​model is utilized. For example, information used in various embodiments of the present disclosure described below may be included in the aforementioned data.

[0112] Figure 18 illustrates an AI technology-based communication procedure applicable to the present disclosure. The detailed procedures illustrated in Figure 18 can be combined with various embodiments of the present disclosure described below. For example, data generated according to various embodiments of the present disclosure can be used for operations (e.g., configuration, training, inference, and / or data transmission / reception) in at least one of the detailed procedures illustrated in Figure 18. As another example, the results of the inference illustrated in Figure 18 can be used to transmit and / or receive data according to various embodiments of the present disclosure.

[0113] Referring to FIG. 18, in step 1801, at least one of a UE (1810), a RAN node (1820), and a network node (1830) performs an initial access procedure. For example, in this step, at least one of an initial cell search operation, a system information acquisition operation, a random access operation, and a registration operation may be performed. In step 1803, at least one of the UE (1810), the RAN node (1820), and the network node (1830) performs a configuration procedure. Through the configuration procedure, parameters, resources, connections, and / or entities necessary for performing subsequent procedures in layers between the UE (1810) and the RAN node (1820) and / or in at least one layer between the UE (1810) and the network node (1830) may be determined and / or created. In this case, the configuration procedure may be performed based on information, status, and / or characteristics of an AI model used for subsequent training and inference.

[0114] At step 1805, at least one of the UE (1810), the RAN node (1820), and the network node (1830) performs a model training procedure. At least one of the UE (1810), the RAN node (1820), and the network node (1830) may collect training data and perform learning using the training data. For example, the model training procedure may be performed as described with reference to FIG. 15, FIG. 16, or FIG. 17. If an offline-trained model is used, this step may be omitted.

[0115] At step 1807, at least one of the UE (1810), the RAN node (1820), and the network node (1830) performs a task using the trained model. That is, the task may be performed based on the results of inference and / or prediction using the trained model. For example, the task may be a procedure belonging to a communication protocol, and may be a preparatory operation for subsequent data transmission and / or reception, or may be related to data transmission and / or reception, or may be related to data processing (e.g., encoding, decoding, etc.).

[0116] At step 1809, at least one of the UE (1810), the RAN node (1820), and the network node (1830) transmits and / or receives data. At this time, the result of the task performed at step 1807 may be used. In some cases, the task performed at step 1807 may include transmitting and / or receiving data, in which case this step may be omitted as it is part of step 1807.

[0117] Specific embodiments of the present disclosure

[0118] The present disclosure relates to a technique for performing online learning in a wireless communication system, and more particularly, to a technique for acquiring data for online learning and performing learning on a channel task model while considering the probability of experiencing a new environment that has not been learned.

[0119] task is input Output from Mapping function to , and outputs. If this mapping is implemented as a machine learning model, the joint probability of input data and output data Data set with is an AI / ML model Set values ​​based on It is used for learning AI / ML models can provide output for input. The task is a loss function with can be expressed as . Also, two tasks have the same model Even if you use If the distribution of , they can be distinguished into different tasks. A task can only be defined by a single stationary distribution. The data distribution can change as time or a certain step continues to change. In this case, The task that changes by can be expressed as

[0120] In physical layer mobile communications, a set of channel-dependent transmission and reception operations can be defined as a channel task, an AI / ML term, from a data-driven machine learning perspective. As mentioned above, channel tasks can be divided into regression and classification. Tasks in which a machine learning model infers continuous values ​​belong to regression, and tasks in which discrete values ​​are inferred belong to classification. For example, channel tasks related to regression may include channel estimation, synchronization acquisition and tracking, beam tracking, and channel state information (CSI) prediction. In addition, channel tasks related to classification may include tasks that distinguish classes, such as rank indicator (RI), channel quality indicator (CQI), and precoding matrix indicator (PMI) estimation, modulation and coding scheme (MCS) detection, and beam index detection. These aspects may vary slightly depending on the requirements and design of the communication system. Channel Task , at, It can be at least one of physical layer signals such as a reference signal, a synchronization signal, and a data signal.

[0121] Unlike existing mathematical model-based approaches, this approach relies on data. That is, AI / ML models are trained and utilized based on large-scale training data. Physical layer tasks primarily perform operations to overcome the radio channel between the base station and the terminal. For example, when the base station transmits a CSI-RS, the terminal receives it and uses the received CSI-RS data to train and utilize a deep neural network to characterize the characteristics of the radio channel.

[0122] However, when a terminal encounters a communication channel environment for which it has not been trained as it moves, its performance will degrade. In particular, terminals with limited memory and computational capacity have deep neural networks of limited complexity, and thus are more likely to encounter new channel distributions that they have never learned before due to movement. When the deep neural network encounters these new channels, communication quality degradation, such as data truncation, can occur. These problems can be detected as a decrease in SNR (signal-to-noise ratio), BER (bit error rate), SER (symbol error rate), and BER (block error rate) while performing physical layer tasks. The aforementioned problems can occur in situations such as those illustrated in Figure 19.

[0123] Figure 19 illustrates an example of a terminal moving between different channel environments in a wireless communication system. Referring to Figure 19, a terminal (1910) moves within the coverage areas of base stations (1910-1 to 1910-5). Accordingly, the terminal (1910) sequentially moves through regions corresponding to channel task areas T0 to T4. In the example of Figure 19, each region may have a different channel probability distribution. For example, a channel task is a physical layer task directly or indirectly related to a channel, and may handle a beam index prediction or classification problem. In the case of a beam index prediction problem, when a new beam index is added as the terminal moves from area T0 to area T1, the channel environment changes due to the appearance of the new beam index, which can be understood as a different task.

[0124] The problem of predicting the external distribution of channel tasks

[0125] Data distribution of the channel environment experienced by the terminal As the distribution continues to change, the AI / ML model may forget what it has learned or perform worse than a certain level in a new distribution. This phenomenon is called out-of-distribution (OOD). Conversely, if the AI / ML model learns the distribution well or does not forget it, it is called in-distribution (IND). The determination of out-of-distribution and in-distribution depends not only on the change in the channel environment distribution but also on how well the AI / ML model responds. In other words, the determination of out-of-distribution and in-distribution depends not only on the channel environment distribution but also on the AI / ML model. From this perspective, the out-of-distribution and in-distribution of channel tasks should be considered to optimize the performance of physical layer communication. If the capacity of the terminal AI / ML model is finite and the channel has a heavy-tail distribution characteristic, the probability of out-of-distribution may increase. When a terminal (1910) moves as shown in Fig. 19, if an external distribution occurs at the boundary point of the task, problems such as communication disconnection will occur and performance will deteriorate.

[0126] Wireless channel modeling using a generative model

[0127] Unlike discriminative models, which estimate some judgment or value for data, generative models can model data distributions. Given data x, a discriminative model obtains y from the conditional probability p(Y|X) that best fits the data distribution. A generative model is interested in the data distribution p(Y,X) itself, and when x is provided, it probabilistically samples a value within Y. With the development of neural network technology, it has become possible to train a neural network to infiltrate complex probability distributions related to images, voices, videos, text, etc., and then generate realistic data as the output of the neural network using a probability distribution that is easy to generate. Types of such generative models include generative adversarial networks (GANs), variational autoencoders (VAEs), normalizing flow (NF), and diffusion models.

[0128] Figure 20 illustrates an example of a generative adversarial network that processes channel information. In Figure 20, (i, j) is the index of a MIMO antenna, and z is a Gaussian distribution. When two input values ​​are input, they are converted into embedding values ​​by the embedding module (2012), and virtual channel samples are generated by the generation network G (2014). At this time, the virtual channel samples and the actual (ground truth, GT) channel samples are input to the discrimination network D (2024), and through training, the output of the generation network G (2014) and the distribution of the actual channel samples can become similar. Here, (i, j) applies some additional information from a generation perspective and becomes a conditional embedding for neural network learning. The conditional information can be extended beyond the MIMO index to include channel context, such as the actual terminal location environment. In this case, as actual data, both channel samples and channel context information must be provided.

[0129] The extension of conditional information can be applied not only to GANs but also to other generative models. One example of a channel task, channel estimation, can be utilized from a generative network perspective. In a communication system, when an instantaneous channel is provided, channel estimation is performed using techniques such as MMSE, taking into account the probabilistic characteristics of the instantaneous channel. In this case, the statistical characteristics of the probability distribution (e.g., covariance, noise power) are utilized rather than the probability distribution itself. In contrast, channel estimation using a generative network can provide channel estimation results by applying a channel context, performing multiple channel samplings from the generative network probability distribution, and then going through an optimization process to select the best value among the selected samples.

[0130] Heavy tail distribution characteristics of communication channel environments

[0131] In future communication systems like 6G, communication channel characteristics may exhibit thick-tailed distributions because diverse channel distributions can coexist. This is because as frequencies increase, such as millimeter-wave or terahertz waves, in addition to propagation straightness, radio waves combine with diverse local environments and diffraction, scattering, and reflection characteristics, allowing channel environments to exhibit unique distributions depending on the region.

[0132] It is possible to distinguish the propagation environments in which reference signals in the millimeter-wave or terahertz bands influence channel tasks. Unlike propagation characteristics in lower-frequency bands, signals in the millimeter-wave or terahertz bands exhibit greater linearity. Furthermore, these high-frequency propagation environments can exhibit numerous variables, including static buildings and terrain, as well as moving objects, antenna placement, and terminal movement. For example, the technical report (TR) 38.901 standard presents the following various propagation channels:

[0133] a) Urban microcells (UMi) with outdoor-to-outdoor (O2O) and outdoor-to-indoor (O2I) (e.g. streets and building forests, open areas)

[0134] b) UMa (urban macrocell) with O2O and I2I

[0135] c) Indoor: Environment, shopping malls. Typical office environments include open cubicle areas and walled spaces.

[0136] d) Backhaul, including outdoor rooftop backhaul and street canyon scenarios with small cells in urban areas.

[0137] e) D2D / V2V. Device-to-device access in open spaces, canyons, and indoor scenarios. V2V is vehicle-to-vehicle communication.

[0138] f) Other scenarios such as stadiums (e.g., open roof) and gymnasiums (e.g., closed roof).

[0139] g) Indoor industry scenario

[0140] Locality refers to the extent to which the communication channel probability distribution is more dependent on the location of the terminal in the radio environment. The geographical distribution of a specific terminal and its dynamic state have the greatest impact on locality. This suggests that regional characteristics can be associated with the channel probability distribution. Because this locality further diversifies the channel probability distribution, the data distribution of AI / ML models can vary significantly.

[0141] A system according to various embodiments of the present disclosure may utilize a channel point map for online continuous learning. A channel point map may be defined as a set of channel distribution distances or similarity distances between channel points at locations x (x∈X) in an area X and at specific times. A set of discontinuous channel measurement points in a continuous three-dimensional space may be constructed through a cooperative process of multiple base stations and terminals. The longer the channel measurement distance from one point to another, the higher the likelihood that the channel distribution is unfamiliar to the terminal, which may increase the difficulty of performing online learning. In this case, the failure probability of the channel task may increase, but the terminal may reduce this probability by recognizing the channel point distances in advance using the channel point map and performing online learning. In the present disclosure, the channel point map may be referred to as a 'channel distance map', a 'channel distribution map', a 'channel distribution distance map', or other terms having equivalent technical meanings thereto.

[0142] According to various embodiments of the present disclosure, a channel distribution distance may be considered for online learning. The channel distribution distance refers to the difference between channel measurement points. If the probability distributions of channel information y1 and y2 of two measurement points x1 and x2 in a channel point map are p1 and p2, the channel distribution distance is the probability distance. can be expressed as . Here, can be explained by one of the definitions shown in [Table 2] below.

[0143] IE (information element) description (Manhattan) -distance Euclidean -distance Cosine similarity Dot product KL divergence Total variation distance p-Wasserstein distance JS (Jensen-Shannon) divergence

[0144] According to various embodiments of the present disclosure, for communication between a base station and a terminal or between terminals, the terminal can utilize a channel point map to evaluate the probability of an out-of-distribution of an expected channel point based on its velocity and acceleration vector, and perform online learning before encountering an out-of-distribution point. Accordingly, it will be possible to minimize or reduce the probability of out-of-sync of a channel task in the physical layer.

[0145] The structure of an AI / ML model according to various embodiments of the present disclosure is as shown in FIG. 21. FIG. 21 illustrates the functional structures of devices in a wireless communication system according to one embodiment of the present disclosure. FIG. 21 illustrates the channel task model structure of a base station or terminal.

[0146] Referring to FIG. 21, a first device (2110) operating as a transmitter includes a transmit entity (2112), a transmit model (2114), and a transmit signal processing unit (2116). A second device (2120) operating as a receiver includes a receive signal processing unit (2122), a receive model (2124a), an OOD detector (2124b), a channel model (2124c), and a receive entity (2126).

[0147] The transmitting entity (2112) performs overall control and processing for data transmission. In particular, according to various embodiments, the transmitting entity (2112) provides information necessary for the operation of the transmitting model (2114) and generates and provides input data for the task model. The transmitting model (2114), as part of the task model, generates output values ​​to be transmitted through a channel based on the input data. The transmitting signal processing unit (2116) performs processing for transmitting the output values ​​of the transmitting model (2114) through a wireless channel, for example, physical layer processing including at least one of channel encoding, modulation, resource mapping, and frequency conversion. The function of the transmitting signal processing unit (2116) may vary depending on the function of the task model.

[0148] The receiving signal processing unit (2122) performs physical layer processing including at least one of frequency conversion, resource de-mapping, demodulation, and channel decoding to interpret a signal received through a wireless channel. The function of the receiving signal processing unit (2122) may vary depending on the function of the task model. The receiving model (2124a) is part of the task model and generates output data based on the output values ​​of the transmitting model (2114) received through the wireless channel, i.e., performs a task. The OOD detector (2124b) detects an external distribution. The channel model (2124c) generates channel information for online learning. The receiving entity (2126) performs overall control and processing for data reception. In particular, according to various embodiments, the receiving entity (2126) obtains the output data of the receiving model (2124a) and processes the obtained output data according to the task. In addition, the receiving entity (2126) may provide information necessary for the operation of other blocks.

[0149] Transmission model (2114) is set to Machine learning model or neural network model with , and the receiver model (2124a) is set to Machine learning model or neural network model with The transmission model (2112) acts as a kind of precoder and may have a finite number of quantized settings, taking into account the complexity of the transmitter and receiver. For example, the transmission model (2112) may be implemented as a multiplier that multiplies a beamforming vector based on a DFT codebook or as a complex neural network model. The OOD detector (2124b) is a model that evaluates the external distribution probability. According to various embodiments, the model included in the OOD detector (2124b) Silver model Implemented as a neural network added to or model can be designed to take a portion of the same model output.

[0150] For example, model If a neural network classifies n CQIs, the output contains one value out of n. If it is designed to take n softmax values ​​immediately before the output, the model is an OOD model (2124b). can be implemented. It is possible to determine OOD by determining the variance for n values ​​and comparing it with a threshold value. If n is 15, if the CQI value is classified as 7 by normal operation, the softmax output will have a large value at 7. However, in the OOD situation, all n values ​​have low values, and the variance is likely to be small. As another example, in the case of channel estimation operation, an independent neural network that determines whether it is OOD or not is binary trained using non-OOD data and OOD data, thereby generating a model that is an OOD model (2124b). can be implemented.

[0151] The receiving model (2124a) and the OOD detector (2124b) provide side information Use additional information The terminal context information may include sensor information including the terminal's location, acceleration, etc., or information signaled from the first device (2110).

[0152] Channel model (2124c) is a channel latent space Any element in We perform sampling for the model By using Channels with high similarity to can be generated. That is, the channel model (2124c) is a channel with a large data dimension. to a low-dimensional latent space The channel model (2124c) may be implemented as a dimension reduction inverse function model of the transformation that maps to or as a decoder of a generative model. The implementation of the channel model (2124c) may be determined according to the capability of the terminal. If the channel model (2124c) is a dimension reduction inverse function model, the channel model (2124c) may be implemented as a plurality of channel candidates that the second device (2120) may experience at a point corresponding to the terminal context of the point where the second device (2120) is expected to move. , and gives the minimum loss to the current channel task. It is possible to perform optimization of .

[0153] According to one embodiment, the model If the dimensionality reduction inverse function model is a first device (2110), the first device (2110) generates multiple latent spaces from the channel statistics database of the corresponding channel point. When the value of is transmitted to the second device (2120), the second device (2120) passes the received information through a dimension reduction inverse function model to select a channel candidate. You can get it here, is a low-dimensional vector and can be transmitted with small signaling overhead.

[0154] According to one embodiment, the model If the first device (2110) is a generative model, the second device (2120) transmits the latent space probability distribution information of the point corresponding to the context of the point where the second device (2120) is expected to move to the second device (2120). The second device (2120) selects multiple channel candidates that the second device (2120) may experience. It generates through probability distribution sampling and gives minimum loss to the current channel task. can perform optimization of the channel latent space. Any element in When sampling is performed and input into the generative model, the second device (2120) Channels with high similarity to can be generated. The channel information generated in this way can be used for external distribution and online learning.

[0155] Additionally, the second device (2120) may include a channel point encoder for evaluating a channel point map given a channel, and corresponding elements of the latent space given the channel and context at the current channel and the target channel point. can be decided.

[0156] Evaluation of extrinsic predictions and online learning methods for k-candidate sets

[0157] According to various embodiments, an external distribution prediction evaluation and online learning can be performed for a k-candidate set. A candidate channel point is a channel point at a location where a terminal is expected to move. The greater the channel distribution distance between the current location and the candidate channel point or the smaller the similarity in the channel latent space, the higher the probability that the candidate channel point will have an external distribution. Considering the terminal complexity and signaling burden, k candidates are selected from the current location. The terminal determines a k-candidate set in a certain space in the direction of the moving speed and acceleration vector. In this case, according to one embodiment, an adaptive control can be performed in which the size k of the k-candidate set increases as the speed or acceleration increases, and the size k of the k-candidate set decreases as the speed or acceleration decreases.

[0158] Next, extrinsic distribution evaluation and online learning for channel points are performed. In this disclosure, for convenience of explanation, the channel distribution distance or distance in the channel latent space is referred to as "channel distance." Extrinsic distribution evaluation and online learning can be performed as follows.

[0159] Step-1) Channel distribution distance threshold in k-candidate set according to mobility Select a larger channel point.

[0160] Step 2) Perform external distribution evaluation for all channel points in the previous step. To evaluate external distribution, the terminal may obtain representative sample data X for the corresponding channel point from the base station or the master terminal for V2X communication, and utilize an external distribution detector (OOD detector).

[0161] Step-3) When an external distribution occurs, the terminal performs online learning using representative sample data for the corresponding channel point or representative sample data generated by a generative model in the channel point space.

[0162] The following describes the operation of devices for online learning according to various embodiments of the present disclosure. In the following description, the base station operates as a transmitter and the terminal operates as a receiver. However, the embodiments described below can be applied not only to the base station and the terminal, but also to the terminal and terminal performing sidelink communication. In this case, at least some of the operations performed by the base station in the embodiments described below can be understood as operations of the master terminal of V2X.

[0163] FIG. 22 illustrates an example of a procedure for performing online learning in a wireless communication system according to one embodiment of the present disclosure. FIG. 22 illustrates an example of a method performed by a terminal.

[0164] Referring to Figure 22, in step S2201, the terminal transmits a message requesting information about a neighboring area. For example, the terminal may request the base station to transmit a broadcast message (e.g., system information) or a unicast message containing information about the neighboring area. Here, the neighboring area is understood as an area adjacent to the geographical area where the terminal is currently located, either physically adjacent or adjacent in terms of channel distribution.

[0165] In step S2203, the terminal receives a message containing information about a neighboring area. The terminal receives the message transmitted in response to the request in step S2201. The message may include a broadcast message (e.g., system information) or a unicast message. The information about the neighboring area includes information for evaluating or measuring at least one neighboring area. In one embodiment, the information about the neighboring area may include at least one of information related to external distribution evaluation and information related to channel distribution.

[0166] In step S2205, the terminal performs an evaluation of at least one neighboring region. Through this, the terminal can obtain information about channels related to at least one neighboring region. According to various embodiments, the terminal determines whether the region (e.g., candidate channel point) is an area that generates an outlier distribution, i.e., an area with a channel distribution that differs by a certain degree or more from the training data used to train a model for a task. To this end, the terminal determines the channel distribution distance between the current location and the candidate channel points, obtains sample data for the channels of candidate channel points having a channel distribution distance greater than a threshold, and determines whether an outlier distribution exists using the sample data.

[0167] In step S2207, the terminal performs online learning based on the evaluation. In other words, the terminal performs online learning on the channel task model using the evaluation results. Specifically, if at least one neighboring region (e.g., channel point) with an external distribution occurrence probability greater than a threshold is identified, the terminal acquires representative sample data for the identified at least one neighboring region and performs learning on the task model using the acquired representative sample data. In other words, online learning can be performed on at least some of the at least one neighboring region that is evaluated to have an external distribution probability greater than a threshold for the channel task model.

[0168] FIG. 23 illustrates an example of a procedure for providing information for online learning in a wireless communication system according to an embodiment of the present disclosure. FIG. 23 illustrates an example of a method performed by a base station.

[0169] Referring to Figure 23, in step S2301, the base station receives a message requesting information about a neighboring area. For example, the terminal may request the base station to transmit a broadcast message (e.g., system information) or a unicast message containing information about the neighboring area. Here, the neighboring area may be understood as an area adjacent to the geographical area where the terminal is currently located, either physically adjacent or adjacent in terms of channel distribution.

[0170] In step S2303, a message containing information about a neighboring area of ​​the base station is transmitted. The base station transmits the message in response to the request in step S2301. The message may include a broadcast message (e.g., system information) or a unicast message. The information about the neighboring area includes information for evaluating or measuring at least one neighboring area. In one embodiment, the information about the neighboring area may include at least one of information related to external distribution evaluation and information related to channel distribution.

[0171] Figure 24 illustrates an example of a procedure for performing online learning in response to external distribution detection in a wireless communication system according to one embodiment of the present disclosure. Figure 24 illustrates an example of a method performed by a terminal.

[0172] Referring to FIG. 24, at step S2401, the terminal performs a procedure for generating a channel point map. The channel point map is information related to channel distribution for areas within the coverage area, is stored and managed by the base station, and can be provided to the terminals. The channel point map is generated based on measurements made by the terminals, and the terminals can perform measurements under the control of the base station and report the measurement results.

[0173] In step S2403, the terminal performs an external distribution evaluation on candidate channel points. For example, the terminal may obtain a channel point map at the current location and perform an external distribution evaluation based on the channel point map. To this end, the terminal may request and receive information related to the channel point map from a base station or another terminal. Furthermore, the terminal determines multiple candidate channel points that are movable from the current location and estimates an external distribution probability based on the channel distribution for the determined multiple candidate channel points. The external distribution probability may be estimated using a model for external distribution evaluation.

[0174] In step S2405, the terminal acquires sample data for channel points where an external distribution has occurred. The occurrence of an external distribution refers to a situation where the external distribution probability is greater than or equal to a threshold. In other words, the terminal acquires sample data for at least one channel point among candidate channel points that has an external distribution probability greater than or equal to the threshold. Here, the sample data is channel-related information generated based on the channel distribution. The sample data may be received from a base station or generated by the terminal's generation model.

[0175] In step S2407, the terminal performs training using sample data. In other words, the terminal trains a channel task model using the sample data. Accordingly, the channel task model can be trained to operate in a channel distribution at at least one channel point where an external distribution is expected to occur, and as a result, the occurrence of an external distribution at that at least one channel point can be prevented.

[0176] According to the aforementioned embodiments, online learning can be performed on a channel task model to learn the channel distribution of channel points where terminal movement is expected. Here, the specific external distribution evaluation and online learning operations may vary depending on the terminal's operating mode. The external distribution evaluation and online learning operations according to the terminal's operating mode (e.g., idle mode, connected mode, etc.) are as follows.

[0177] In idle mode, the terminal wakes up from low power mode every n-DRX (discoutinuos reception) cycle and performs an evaluation of channel distance and external distribution. This is to prevent external distribution in advance during radio link setup based on paging messages through the paging channel and to minimize in-sync time. Since it wakes up every n-DRX cycle, power consumption can be minimized. The terminal can receive information related to channel points and distributed distances in the current cell through the SIB (system information block).

[0178] The terminal analyzes the current mobility vector to obtain a channel distribution map corresponding to the current cell location, selects k candidate channel points based on the mobility vector, and requests the base station for the channel distribution map or channel potential space information of the current point based on the current location. Here, the request to the base station can be performed according to the SIB acquisition procedure, i.e., according to the on-demand system information request procedure. Thereafter, the base station transmits information related to the channel point map corresponding to the current location to the terminal via the SIB. Then, the terminal performs external distribution evaluation and online learning for the target k candidate channel points for each n-DRX cycle.

[0179] Here, the information transmitted from the base station to the terminal via SIB may include at least one of the items listed in [Table 3] below.

[0180] IE(information element)DescriptionMeasurement periodn-DRX(evaluate k candidate channel points every n DRX cycles).Reference signalReference signal for channel measurement. At least one of all available reference signals such as CSI-RS, DM-RS, PT-RS, etc.Channel distance metric type Distance metric type. Channel model ID Channel model The identifier of the IDOOD model. Generates a channel distribution. OOD evaluation model IDOOD model Identifier of . Evaluates the external distribution. OOD evaluation criteria. OOD judgment criteria. The threshold of the OOD model. If it is lower than the threshold, it is judged as OOD. Number of k-candidates. The number of channel point candidates. Channel map type. The type of channel point map (e.g., generative or low-dimensional inverse transform).

[0181] As information transmitted from a base station to a terminal, when the channel model is a low-dimensional inverse feedback model, information related to a channel point may include at least one of the items listed in [Table 4] below.

[0182] IE(information element)Description(description)Reference channel point IDReference channel point identifier.Reference channel contextContext information containing reference channel point location information.Set of channel point z vectorMultiple vector values ​​for generating channel candidates for reference channel points.

[0183] Information related to the channel point distance may be transmitted for each of the k candidates surrounding the reference channel point. If the channel model is a low-dimensional inverse feedback model, the information related to each channel point distance may include at least one of the items listed in [Table 5] below.

[0184] IE(information element)Description Candidate IDk An identifier for distinguishing between candidates. Candidate point distance vector A distance vector from a reference channel point to the corresponding code point. Set of channel point z vectork - Multiple vector values ​​for generating channel candidates for candidate channel points.

[0185] As information transmitted from a base station to a terminal, if the channel model is a generative model, information related to a channel point may include at least one of the items listed in [Table 6] below.

[0186] IE(information element)Description(description)Reference channel point IDReference channel point identifier.Reference channel contextContext information containing reference channel point location information.Set of channel point z vectorProbability distribution information for generating channel candidates for reference channel points.Contains at least one of probability distribution information, mean, and variance.

[0187] Information related to the channel point distance may be transmitted for each of the k candidates surrounding the reference channel point. If the channel model is a generative model, the information related to each channel point distance may include at least one of the items listed in [Table 7] below.

[0188] IE(information element)DescriptionCandidate IDAn identifier for distinguishing between k candidates.Candidate point distance vectorThe distance vector from the reference channel point to the corresponding code point.Set of channel point z vectorProbability distribution information for generating channel candidates for the reference channel point.Contains at least one of the probability distribution information, mean, and variance.

[0189] In connected mode, the terminal can perform external distribution evaluation on k channel point candidates and perform online learning. For example, the terminal can obtain channel point information for each beam associated with a cell list or CSI-RS during handover.

[0190] A terminal requests information related to a channel point map from a base station by transmitting a message requesting channel point information (e.g., a CP_INFO_REQ message). Specifically, the terminal analyzes the current mobility vector to obtain a channel distribution map corresponding to the current cell location, selects k candidate channel points based on the mobility vector, and requests the base station for the channel distribution map or channel potential space information of the current location based on the current location. This operation can be performed when moving from the current reference channel point to another channel point area. Thereafter, the base station transmits a message (e.g., a CP_INFO_RES message) containing information related to the channel point map corresponding to the current location to the terminal. Then, the terminal performs external distribution evaluation and online learning for the target k candidate channel points.

[0191] Here, the information transmitted from the base station to the terminal via a message may include at least one of the items listed in [Table 8] below.

[0192] IE (information element) Description Measurement period n-slots (evaluate k candidate channel points every n slots) Reference signal Reference signal for channel measurement. At least one of all available reference signals, such as CSI-RS, DM-RS, PT-RS, etc. Channel distance metric type Distance metric type. Channel model ID Channel model The identifier of the IDOOD model. Generates a channel distribution. OOD evaluation model IDOOD model Identifier of . Evaluates the external distribution. OOD evaluation criteria. OOD judgment criteria. The threshold of the OOD model. If it is lower than the threshold, it is judged as OOD. Number of k-candidates. The number of channel point candidates. Channel map type. The type of channel point map (e.g., generative or low-dimensional inverse transform).

[0193] As information transmitted from a base station to a terminal, when the channel model is a low-dimensional inverse feedback model, information related to a channel point may include at least one of the items listed in [Table 9] below.

[0194] IE(information element)Description(description)Reference channel point IDReference channel point identifier.Reference channel contextContext information containing reference channel point location information.Set of channel point z vectorMultiple vector values ​​for generating channel candidates for reference channel points.

[0195] Information related to the channel point distance may be transmitted for each of the k candidates surrounding the reference channel point. If the channel model is a low-dimensional inverse feedback model, the information related to each channel point distance may include at least one of the items listed in [Table 10] below.

[0196] IE(information element)Description Candidate IDk An identifier for distinguishing between candidates. Candidate point distance vector A distance vector from a reference channel point to the corresponding code point. Set of channel point z vectork - Multiple vector values ​​for generating channel candidates for candidate channel points.

[0197] As information transmitted from a base station to a terminal, if the channel model is a generative model, information related to a channel point may include at least one of the items listed in [Table 11] below.

[0198] IE(information element)Description(description)Reference channel point IDReference channel point identifier.Reference channel contextContext information containing reference channel point location information.Set of channel point z vectorProbability distribution information for generating channel candidates for reference channel points.Contains at least one of probability distribution information, mean, and variance.

[0199] Information related to the channel point distance may be transmitted for each of the k candidates surrounding the reference channel point. If the channel model is a generative model, the information related to each channel point distance may include at least one of the items listed in [Table 12] below.

[0200] IE(information element)DescriptionCandidate IDAn identifier for distinguishing between k candidates.Candidate point distance vectorThe distance vector from the reference channel point to the corresponding code point.Set of channel point z vectorProbability distribution information for generating channel candidates for the reference channel point.Contains at least one of the probability distribution information, mean, and variance.

[0201] Figure 25 illustrates an example of a channel point map in a wireless communication system according to one embodiment of the present disclosure. Figure 25 illustrates a situation where a database (2502) of reference channel points in a latent space based on a channel distribution map or channel similarity is stored at a base station. Accordingly, the base station can provide training data related to designated reference channel points to the terminal.

[0202] Figure 26 illustrates examples of candidate channel points in a wireless communication system according to one embodiment of the present disclosure. Figure 26 illustrates candidate channel points according to the direction of the mobility vector of each terminal. Based on the current location, external distribution evaluation and online learning can only be performed when the channel distribution distance to the candidate channel point is large. For external distribution evaluation and online learning, the base station can provide representative online training data.

[0203] When a terminal performs online continuous learning and encounters a new, unfamiliar channel environment, communication interruptions may occur due to learning delays. However, as in the various embodiments described above, by pre-evaluating the external distribution of k-candidate channel points based on the channel distribution map and terminal mobility information and performing online learning, the probability of channel task out-of-sync can be reduced.

[0204] In the k-candidate distribution, the value of k can be adaptively determined in proportion to the mobility. This minimizes the cost of unnecessary external distribution evaluation. In what dimension is the channel point map dense? It satisfies the geometric probability with a Poisson distribution, and the terminal speeds in one direction. When moving to , the terminal visits a new channel point at time intervals of an exponential distribution, where the time interval is is proportional to the exponential distribution average time to evaluate the external distribution and complete online learning. Assuming that it takes that long, the approximate probability that k-candidates cannot handle online learning is as follows [Mathematical Formula 1].

[0205]

[0206] In [Equation 1], is the approximate probability that k-candidates will not be able to handle online learning, is the density of the Poisson distribution of geometric probability that the channel point map satisfies, represents the terminal's movement speed, and K represents the number of candidate channel points.

[0207] Referring to [Mathematical Formula 1], if the probability is the same, the smaller the speed, A trade-off relationship of decreasing value is confirmed. Furthermore, since extrinsic evaluation requires representative training data for candidate channels, unnecessary transfer of representative training data can be prevented by pre-selecting the target of extrinsic evaluation using a channel distribution map before extrinsic evaluation.

[0208] The proposed methods described above can be implemented independently, but they can also be implemented as a combination (or merge) of some of the proposed methods. Rules can be defined so that the base station notifies the terminal of the applicability of the proposed methods (or information about the rules of the proposed methods) through a predefined signal (e.g., a physical layer signal or a higher layer signal).

[0209] The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential features described herein. Therefore, the above detailed description should not be construed as limiting in all respects but rather as illustrative. 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 intended to be included within the scope of the present disclosure. Furthermore, claims that are not explicitly cited in the claims may be combined to form an embodiment or incorporated into a new claim through a post-filing amendment.

[0210] Embodiments of the present disclosure can be applied to various wireless access systems. Examples of various wireless access systems include the 3rd Generation Partnership Project (3GPP) or 3GPP2 systems.

[0211] The embodiments of the present disclosure can be applied not only to the various wireless access systems described above, but also to all technical fields that utilize these various wireless access systems. Furthermore, the proposed method can also be applied to mmWave and THz communication systems utilizing ultra-high frequency bands.

[0212] Additionally, embodiments of the present disclosure can be applied to various applications such as autonomous vehicles and drones.

Claims

1. A method performed by a terminal in a wireless communication system, A step of transmitting a first message requesting information about a neighboring area; A step of receiving a second message including information about the neighboring area; A step of performing an evaluation of at least one neighboring area based on information about the neighboring area; A step of performing online learning on a channel task model based on the results of the above evaluation is included. A method in which the above online learning is performed for at least some regions among the at least one neighboring region, which are evaluated to have an out-of-distribution probability greater than a threshold value for the channel task model.

2. In claim 1, A method wherein the information about the above neighboring area includes a channel point map representing information related to the probability distribution of channels of a plurality of points.

3. In claim 2, A method wherein the at least one neighboring region includes at least one point among the plurality of points that has a probability distribution distance greater than a threshold value from the current point of the terminal.

4. In claim 1, The steps for performing the above online learning are: A step of receiving representative sample data for channels of at least a portion of the area from a base station; A method comprising the step of performing learning on the channel task model using the representative sample data.

5. In claim 1, The steps for performing the above online learning are: A step of generating representative sample data for channels in at least some of the above regions; A method comprising the step of performing learning on the channel task model using the representative sample data.

6. In claim 1, Information about the above neighboring areas may be included in system information or unicast messages.

7. In claim 1, A method wherein information about said neighboring area includes at least one of a performance period of said evaluation, a number of said at least one neighboring area, a type of channel point map, a distance metric used in said channel point map, an identifier of a model for said evaluation, an identifier of a channel point, and a distance to a channel point.

8. In a wireless communication system, at the terminal, Transmitter and receiver; and comprising a processor connected to the above transceiver, The above processor, Send a first message requesting information about the neighboring area, Receive a second message containing information about the neighboring area; Based on the information about the above neighboring areas, an evaluation is performed on at least one neighboring area, Control to perform online learning for the channel task model based on the results of the above evaluation, The above online learning is performed on at least some regions among the at least one neighboring region, which are evaluated to have an out-of-distribution probability greater than a threshold value for the channel task model.

9. In communication devices, At least one processor; At least one computer memory coupled to said at least one processor and storing instructions that direct operations when executed by said at least one processor, The above actions are, A step of transmitting a first message requesting information about a neighboring area; A step of receiving a second message including information about the neighboring area; A step of performing an evaluation of at least one neighboring area based on information about the neighboring area; A step of performing online learning on a channel task model based on the results of the above evaluation is included. A communication device in which the above online learning is performed for at least some regions among the at least one neighboring region, which are evaluated to have an out-of-distribution probability greater than a threshold value for the channel task model.

10. In a non-transitory computer-readable medium storing at least one instruction, comprising at least one instruction executable by the processor; At least one of the above commands causes the device to: Send a first message requesting information about the neighboring area, Receive a second message containing information about the neighboring area; Based on the information about the above neighboring areas, an evaluation is performed on at least one neighboring area, Control to perform online learning for the channel task model based on the results of the above evaluation, A non-transitory computer-readable medium in which the online learning is performed for at least some regions among the at least one neighboring region, which are evaluated to have an out-of-distribution probability greater than a threshold value for the channel task model.

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