Apparatus and method for performing online learning in wireless communication system

Abstract

The objective of the present disclosure is to perform online learning in a wireless communication system. A method performed by a first device in a wireless communication system may comprise the steps of: receiving configuration information related to measurement; receiving at least one signal for measurement; performing measurement on the basis of the at least one signal; and transmitting a report related to the result of the measurement. The configuration information may include information related to a feature model and a metric model used to generate a channel point map, which is information required for online learning of a model for performing a channel task.

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 generating information related to OOD evaluation for at least one neighboring area in a wireless communication system.

[0008] The present disclosure may provide an apparatus and method for performing measurements to generate information related to an OOD evaluation for at least one neighboring area in a wireless communication system.

[0009] The present disclosure may provide an apparatus and method for reducing the overhead of measurements for generating information related to OOD evaluation for at least one neighboring area in a wireless communication system.

[0010] The present disclosure may provide a device and method for providing information related to OOD evaluation for at least one neighboring area in a wireless communication system.

[0011] 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.

[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 first device in a wireless communication system may include the steps of receiving configuration information related to measurement, receiving at least one signal for measurement, performing measurement based on the at least one signal, and transmitting a report related to a result of the measurement. The configuration information may include information related to a feature model and a metric model used to generate a channel point map, which is information required for online learning of a model for performing a channel task.

[0016] As an example of the present disclosure, a terminal in a wireless communication system includes a transceiver and a processor connected to the transceiver, wherein the processor receives configuration information related to measurement, receives at least one signal for measurement, performs measurement based on the at least one signal, and controls transmission of a report related to a result of the measurement, wherein the configuration information may include information related to a feature model and a metric model used to generate a channel point map, which is information necessary for online learning of a model for performing a channel task.

[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 direct operations when executed by the at least one processor, wherein the operations may include: receiving configuration information related to a measurement; receiving at least one signal for measurement; performing a measurement based on the at least one signal; and transmitting a report related to a result of the measurement. The configuration information may include information related to a feature model and a metric model used to generate a channel point map, which is information necessary for online learning of a model for performing a channel task.

[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 receive configuration information related to a measurement, receive at least one signal for measurement, perform a measurement based on the at least one signal, and transmit a report related to a result of the measurement, wherein the configuration information may include information related to a feature model and a metric model used to generate a channel point map, which is information necessary for online learning of a model for performing a channel task.

[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 shows an example of the result of low-dimensional conversion of channel information in a wireless communication system.

[0043] FIG. 20 illustrates an example of a model for generating a channel point map in a wireless communication system according to one embodiment of the present disclosure.

[0044] Figure 21 shows an example of a generative network model that classifies channel classes.

[0045] FIG. 22 illustrates an example of a channel distribution distance according to one embodiment of the present disclosure.

[0046] FIG. 23 illustrates an example of a procedure for assisting in generating a channel point map in a wireless communication system according to one embodiment of the present disclosure.

[0047] FIG. 24 illustrates an example of a procedure for generating a channel point map in a wireless communication system according to one embodiment of the present disclosure.

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

[0049] FIG. 26 illustrates an example of a procedure for performing measurements to generate a channel point map in a wireless communication system according to one embodiment of the present disclosure.

[0050] FIG. 27 illustrates an example of a channel point map in a wireless communication system according to one embodiment of the present disclosure.

[0051] FIG. 28 illustrates another example of a channel point map in a wireless communication system according to one embodiment of the present disclosure.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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).

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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).

[0065] 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.

[0066] 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.

[0067] Communication system applicable to the present disclosure

[0068] 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.

[0069] 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.

[0070] Figure 1 illustrates an example of a communication system applied to the present disclosure.

[0071] 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.

[0072] 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).

[0073] 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.

[0074] Devices applicable to the present disclosure

[0075] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0076] 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).

[0077] 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.

[0078] 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.

[0079] 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 driven 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 6G communication systems and core implementation technologies of 6G systems

[0102] 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.

[0103] 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.

[0104] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100 bps / HzMobility supportup to 1000 km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] Specific embodiments of the present disclosure

[0120] The present disclosure relates to a technology for performing online learning in a wireless communication system, and more specifically, to a technology for obtaining, storing, and managing information on various channel distributions in advance so as to provide data for online learning by considering the probability of experiencing a new environment that has not been learned.

[0121] 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. Tasks can be broadly classified into regression and classification depending on the output value form. In addition, two tasks can be expressed as 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 probability 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

[0122] In physical layer mobile communications, a set of channel-dependent transmission and reception operations can be defined as a channel task, a term used in AI / ML from a data-driven machine learning perspective. In other words, various operations performed at the physical layer of a wireless communication system can be implemented as AI / ML-based tasks. In this case, a 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.

[0123] Channel tasks can be divided into regression and classification. Tasks in which a machine learning model infers continuous values ​​fall into regression, while tasks in which discrete values ​​are inferred fall into classification. For example, channel tasks related to regression may include channel estimation, acquisition and tracking, beam tracking, and channel state information (CSI) prediction. Furthermore, 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.

[0124] 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.

[0125] However, as a terminal moves and encounters a communication channel environment for which it has not been trained, its performance will degrade. In particular, terminals with limited memory and computational capacity have deep neural networks of limited complexity, making it more likely to encounter new channel distributions that have not been trained before. When a deep neural network encounters these new channels, communication quality degradation, such as data truncation, can occur. These problems can be detected as degradations in signal-to-noise ratio (SNR), bit error rate (BER), symbol error rate (SER), and block error rate (BER) during physical layer tasks.

[0126] The aforementioned problem stems from the causality of the terminal's AI / ML model performing related channel tasks based solely on current and past data during online learning. To address this, it is necessary to provide relevant information about the channel task in advance so that the task can be performed with future data in mind. Therefore, to provide the terminal with information about future channel tasks, the terminal and base station need a procedure to jointly build a database containing information related to mutually agreed-upon channel tasks. Furthermore, a method is needed to efficiently deliver training data samples for future channel tasks to the terminal. Therefore, the present disclosure proposes a technique for resolving the channel task problem between the base station and the terminal by cooperatively generating a channel point map, and for the base station to provide and utilize this information to the terminal for online learning.

[0127] Locality of communication channel environment

[0128] The regional nature of a communication channel environment refers to the distribution of communication propagation characteristics that are more distinctive in each region. As frequencies increase, such as millimeter waves and terahertz waves, propagation becomes more linear. As radio waves interact with diverse regional environments and diffraction, scattering, and reflection characteristics, channel regionality emerges, with unique distributions specific to each region. This characteristic can result in long-tailed distributions in the channel propagation probability distribution. For example, the following regional characteristics are identified in the TR (technical report) 38.901 standard.

[0129] a) Urban microcells (UMi) with outdoor-to-outdoor (O2O) and outdoor-to-indoor (O2I) (e.g. streets and building forests, open areas)

[0130] b) UMa (urban macrocell) with O2O and I2I

[0131] c) Indoor: Environment, shopping malls. Typical office environments include open cubicle areas and walled spaces.

[0132] d) Backhaul, including outdoor rooftop backhaul and street canyon scenarios with small cells in urban areas.

[0133] e) D2D / V2V. Device-to-device access in open spaces, canyons, and indoor scenarios. V2V is vehicle-to-vehicle communication.

[0134] f) Other scenarios such as stadiums (e.g., open roof) and gymnasiums (e.g., closed roof).

[0135] g) Indoor industry scenario

[0136] Locality can be defined as areas where the communication channel probability distribution is similar within the radio environment a terminal faces. The radio environment that impacts the reference signal task in the millimeter-wave or terahertz bands can be distinguished. In the millimeter-wave or terahertz bands, signals propagate more linearly than in lower-frequency bands. Furthermore, these high-frequency radio environments can be highly variable, encompassing not only static buildings and terrain, but also moving objects, antenna placement, and terminal movement. The geographical distribution of a specific terminal and its dynamic state can have the greatest impact on locality. This suggests that local characteristics can be associated with channel probability distributions. Because these localities further diversify channel probability distributions, the data distributions for AI / ML models can vary significantly.

[0137] Channel charting

[0138] Channel charting refers to mapping high-dimensional CSI information into manageable, low-dimensional spatial geometry. The output of channel charting is a channel chart. The proximity between points on the channel chart represents their proximity in real space. Specifically, using AI / ML technology, it is possible to train channel charts to derive spatial or location information from CSI. In other words, the trained channel chart captures the spatial geometry surrounding the UE and effectively encodes the relative or logical UE location.

[0139] The advantage of channel charting is that it can complement existing methods for determining the location of a terminal. For example, using channel charts may eliminate the need for positioning information from the Global Navigation Satellite System (GNSS). Because channel charts utilize self-supervised learning, they can reduce the need for line-of-sight (LoS) propagation conditions or costly measurement campaigns.

[0140] A base station or access point passively collects high-dimensional CSI (e.g., complex-valued frequency and time coefficients, possibly from multiple antennas) from multiple transmitting UEs and / or UE locations. The base station or access point then extracts CSI features that describe the large-scale fading properties contained in the collected CSI. Finally, a channel chart utilizes dimensionality reduction techniques to perform low-dimensional learning to preserve as much information as possible from the high-dimensional CSI. Nearby points on the channel chart correspond to close locations in real space.

[0141] Figure 19 illustrates an example of the result of low-dimensional transformation of channel information in a wireless communication system. Referring to Figure 19, a base station (1920) generates a low-dimensional chart (1904) as a result of learning on a specific plane by low-dimensionally transforming channel data (1902) measured in a two-dimensional space. It is confirmed that a path expressed as a VIP in an actual physical space is mapped to the low-dimensional chart (1904). The physical path expressed as a VIP in the channel data (1902) is also displayed in the channel chart (1904). Although the path in the channel chart (1904) is not explicitly expressed as a VIP, it includes distance information locally.

[0142] contrastive learning

[0143] Contrastive learning involves extracting data features and performing representation learning by comparing or contrasting the extracted features. For example, the task of classifying dogs, cats, and lions from an image is typically performed using a simple CNN-based classification neural network. However, classification problems can also be solved through contrastive learning. If representation learning is performed by contrasting the features of dogs, cats, and lions, and mapping objects with similar features closer to the representation space and objects with different features farther away, the results can be provided by referencing the representation space when a classification task requests image classification. Tasks generated by utilizing the results of representation learning can be called downstream tasks.

[0144] As mentioned above, the reason we perform complex representation learning and then use downstream tasks without using a CNN neural network is because contrastive learning is self-supervised learning. This means that feature learning is possible without the task of generating training data labels. In other words, labeling cat images as "cat" is not required.

[0145] This disclosure proposes the concept of a channel distribution map that has undergone contrastive learning as part of representation learning. This disclosure aims to leverage the advantageous characteristic of significantly reducing system complexity, which requires measuring and managing a large amount of data, by eliminating or minimizing the need for labeling channels in contrastive learning. The structure of a contrastive learner may include an encoder that extracts data features, a metric transformer that compares the data features or representations extracted from the encoder, and an embedding space that maps features to a representation space based on the comparison results. In downstream tasks, the representation space can be utilized as input.

[0146] The present disclosure proposes various embodiments for the concept and utilization of a channel point map. Channel charts are learned to preserve geometric distances using CSI. In contrast, channel point maps according to various embodiments are learned to project terminal context, such as terminal location, velocity, acceleration, and position, along with empirical channel probability distributions and probability distribution distance information between two points, onto the map. Channel point maps according to various embodiments can serve as a basis for assisting terminals or base stations in predicting and providing future channel information in online learning. To this end, the terminal and / or base station can determine the empirical probability distribution of the channel by measuring high-dimensional CSI and then projecting the CSI into low-dimensional data.

[0147] FIG. 20 illustrates an example of a model for generating a channel point map in a wireless communication system according to an embodiment of the present disclosure. Referring to FIG. 20, a feature encoder (2002) converts high-dimensional CSI into low-dimensional CSI and extracts channel features. In addition, the feature encoder (2002) determines an empirical probability distribution v through accumulation and normalization of feature information. A metric model (2004) is trained through contrastive learning based on similarity and maps the input empirical probability distribution v to an embedding space Z.

[0148] channel point

[0149] A channel point is associated with terminal context information, such as two-dimensional or three-dimensional spatial vector position, velocity, acceleration, etc. at a given time, and can be defined as a single point having channel information z through a feature encoder and metric model. For example, a channel point can be expressed as in [Mathematical Equation 1] below.

[0150]

[0151] In [Mathematical Formula 1], q is the channel point, t is the time, z is the embedding spatial channel information, is context information, f( ) is a metric model, e( ) is a feature encoder, and H represents a channel.

[0152] A channel point map represents the collective space of channel points in a terminal's 3D map. Through contrastive learning, a channel point map can place points with similar channel distribution characteristics close together and dissimilar points farther apart in the embedding space.

[0153] channel point distribution

[0154] The channel point distribution is defined as the empirical probability distribution p(z) of channel measurement information z at measurement point q. The empirical distribution refers to the sample statistical distribution of the distribution of the actual population. Statistics define data as the output of a measurement function. For example, here, the output distribution of the measurement function (e.g., e(H)) can be an empirical distribution. For example, if the measurement function is the average of all matrix values, the output can be the complex average of the channel I value and the channel Q value. As another example, if the measurement function is a neural network that classifies the class of the channel, it is as shown in FIG. 21. Referring to FIG. 21, the neural network (xx02) e(H) that classifies the channel class generates a softmax output. The channel class can be classified as Uma (urban microcell), RMa (rual microcell), Umi (urban microcell), etc., and probability values ​​for L classes are output. Normalizing the softmax output of a neural network yields a probability vector with L classes.

[0155] Channel point distribution distance

[0156] Two measurement points and The probability distribution of and Then, the channel point distribution distance is the probability distance can be defined as. The probability distance can be used as a similarity metric in contrastive learning. Here, can be defined as one of the definitions shown in [Table 2] below.

[0157] IE (information element) description (Manhattan) -distance Euclidean -distance Cosine similarity Dot product KL divergence Total variation distance p-Wasserstein distance JS (Jensen-Shannon) divergence

[0158] Additionally, the similarity metric can be included in the loss function of contrastive learning along with the physical distance between specific locations, as in [Equation 3] below.

[0159]

[0160] In [Equation 3], is a similarity metric, and is the context, and is the probability distribution of channel information, is the distance function, means weight.

[0161] Fig. 22 illustrates an example of a channel distribution distance according to an embodiment of the present disclosure. Referring to Fig. 22, currently, a terminal (2210) is configured to determine a channel distribution At this time, movement of the terminal (2210) is expected, and the channel distribution at the location after movement and current channel distribution The greater the difference between the two, the greater the probability of occurrence of an external distribution.

[0162] Channel Point Map and Reference Channel Points and Areas

[0163] Channel Point Maps are maps that show specific areas and their associated context. It is defined as a set of information about channel points belonging to a network. Channel points can be generated through downlink or uplink CSI measurements of numerous terminals. If the base station were to accept and manage all of the numerous channel points and provide them to terminals, the signaling burden for transmitting channel point information to support online learning would be very large.

[0164] To address the signaling overhead, the system can cluster multiple sets of channel points, grouping points with similar channel distributions into a single region. One region is defined as the reference channel region, and the center point of that region is defined as the reference channel point. Accordingly, a density of reference channel points can be defined. In channel regions with high similarity, terminals can omit measurements, thereby minimizing measurement reports to the base station. Generating reference channel points in this manner improves continuous learning performance as the density increases, but this trades off between higher terminal energy consumption for measurement and increased signaling overhead at that point.

[0165] In mathematical terms, the reference channel point q in region X i Location of the nearest terminal x UE is the reference channel point region of the terminal. The channel point region satisfies the Voronoi diagram, which divides the plane into a set of points with the closest distance to a specific point. Region and location The distance between If defined as , the Voronoi region can be defined as follows [Mathematical Formula 2].

[0166]

[0167] In [Mathematical Formula 2], R is the Voronoi region, x is the location, X is the region, x refis the reference channel area, d(x,x (j) ) are at positions x and x (j) It means the distance between the liver and the liver.

[0168] Sharing Channel Point Map Information

[0169] Channel point map information can be shared as follows. The base station can provide at least a portion of the channel point map to the terminal. For example, when the terminal requests channel point information, the base station can transmit the reference channel point information to which the terminal's context belongs, as well as channel distribution differential information between the reference point and the current point. This method reduces the signaling burden because, even as the number of channel points increases, only differential information with a small number of bits is transmitted.

[0170] According to the proposed technology, a channel distribution map is shared between a base station and a terminal or between terminals, the terminal evaluates the out-of-distribution (OOD) probability of an expected channel point based on velocity and acceleration vectors, and responds with online learning before encountering an out-of-distribution point, thereby reducing the out-of-sync probability of a channel task in the physical layer.

[0171] Basic principles of channel point mapping

[0172] A channel point map can be created by the following procedure. In the following description, λ x is the average density for the physical location of the reference channel point, λ Z is the density of the channel point map corresponding to the reference channel point, ρ X is the average density λ X and λ Z The average distance between the physical distances of the corresponding reference channel points, ρ Z refers to the average distance between points on a channel point map. The channel point map can be created by a base station or other device.

[0173] Step 1) The initial set of channel measurement points is an empty set. The target channel point density λ X and λ Z To generate a channel point map with higher density, terminals conduct channel point measurement campaigns, and base stations collect channel point measurement data. Simultaneously, the measurement data is collected, and the embedding space Z can be completed through contrastive learning.

[0174] Step-2) Once sufficient measurement data is collected, the average distance between any two points x1 and x2 in area X is This is to be and / or the distance on the channel distribution map Clustering is performed to achieve this. The center point of each region is defined as the reference channel point. During the clustering process, a location with a relatively large amount of terminal measurement data or where channel measurement is easy can be selected as the reference channel point.

[0175] Step-3) A reference channel point area is selected centered on the reference channel point of the cluster.

[0176] Step 4) The steps described above are repeated.

[0177] Procedure for creating a channel point map

[0178] The following describes the operation of devices according to various embodiments of the present disclosure. The following describes a procedure between a base station and a terminal, in which a terminal performs measurements and a base station creates a channel point map. However, the embodiments described below can be applied not only to a base station and a terminal, but also to a terminal and a 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 a V2X master terminal.

[0179] FIG. 23 illustrates an example of a procedure for assisting in the generation of a channel point map in a wireless communication system according to one embodiment of the present disclosure. FIG. 23 illustrates a method performed by a terminal that provides information for generating a channel point map.

[0180] Referring to FIG. 23, in step S2301, the terminal receives configuration information related to measurement. The configuration information may include at least one of information related to reference signals transmitted for measurement (e.g., resources, sequences, etc.), information related to required operations (e.g., measurement, learning, training, etc.), and information related to feedback (e.g., format, resources, number of feedbacks, reporting cycles, etc.). In addition, according to various embodiments, the configuration information may further include at least one of information related to a model for generating and interpreting channel points (e.g., metric model identifier, feature model identifier, etc.), and information related to a channel point map (e.g., distance metric, minimum distance between measurement points, number of reference channel points, reference channel point identifier, context information, information related to embedding space, etc.).

[0181] In step S2303, the terminal receives signals for measurement. The terminal receives signals (e.g., reference signals) based on the configuration information. That is, the terminal can receive signals based on the sequence indicated by the configuration information through the resources indicated by the configuration information. That is, according to various embodiments, the terminal receives signals transmitted for measurement to create a channel point map.

[0182] In step S2305, the terminal performs measurement based on the signal. That is, the terminal measures the channel with the base station based on the received signal. At this time, according to one embodiment, the terminal may check contextual information, such as the terminal's current location and current context, and record the measurement results and contextual information together. In other words, the terminal links the measurement results with contextual information.

[0183] In step S2307, the terminal transmits a report on the measurement results. That is, the terminal transmits feedback information generated based on the received signals. For example, the report may include a precoding matrix indicator (PMI), a rank indicator (RI), a channel quality indicator (CQI), etc. Furthermore, according to one embodiment, the report may include information regarding the context of the terminal during the measurement. The information included in the report may be used by the base station to generate a channel point map. Therefore, the information transmitted in this step may be understood as information for generating a channel point map.

[0184] FIG. 24 illustrates an example of a procedure for generating a channel point map in a wireless communication system according to one embodiment of the present disclosure. FIG. 24 illustrates a method performed by a base station for generating a channel point map.

[0185] Referring to FIG. 24, in step S2401, the base station transmits configuration information related to measurement. The configuration information may include at least one of information related to reference signals transmitted for measurement (e.g., resources, sequences, etc.), information related to required operations (e.g., measurement, learning, training, etc.), and information related to feedback (e.g., format, resources, number of feedbacks, reporting cycles, etc.). In addition, according to various embodiments, the configuration information may further include at least one of information related to a model for generating and interpreting channel points (e.g., metric model identifier, feature model identifier, etc.), and information related to a channel point map (e.g., distance metric, minimum distance between measurement points, number of reference channel points, reference channel point identifier, context information, information related to embedding space, etc.).

[0186] In step S2403, the base station transmits signals for measurement. The base station transmits signals (e.g., reference signals) based on the configuration information. That is, the base station can transmit signals based on the sequence indicated by the configuration information through the resources indicated by the configuration information. That is, according to various embodiments, the base station transmits signals so that at least one terminal performs measurements for creating a channel point map.

[0187] In step S2405, the base station receives a report on the measurement results. That is, the base station transmits feedback information generated based on the received signals. For example, the report may include a precoding matrix indicator (PMI), a rank indicator (RI), a channel quality indicator (CQI), etc. Furthermore, according to one embodiment, the report may include information regarding the context of the terminal during the measurement. The information included in the report may be used by the base station to generate a channel point map. Therefore, the information transmitted in this step may be understood as information for generating a channel point map.

[0188] In step S2407, the base station generates a channel point map based on the measurement results. For example, the base station may add channel information contained in the measurement results to the embedding space. To this end, the base station may determine an embedding vector from the channel information contained in the measurement results, determine the distances to other channel points through contrastive learning, and place the embedding vector within the embedding space based on the distances.

[0189] In the embodiment described with reference to FIG. 24, the channel point map is described as being generated by the base station. However, in other embodiments, the channel point map may be generated by a device other than the base station. Specifically, the other device may receive information collected from the base station (e.g., measurement results) and generate a channel point map. In this case, the operation of generating the channel point map in step S2407 may be understood as an operation in which the base station transmits the collected information to another device and receives data including the channel point map from the other device.

[0190] Signaling for creating channel point maps

[0191] According to the aforementioned embodiments, a channel point map for online learning can be generated. Here, the specific channel point map generation operation may vary depending on the terminal's operating mode. The channel point map generation operation according to the terminal's operating mode (e.g., idle mode, connected mode, etc.) is as follows.

[0192] In idle mode, at least one base station and at least one terminal cooperate during actual operation to create a channel point map, specifically, to update relevant information in real time. The following describes the operation for creating a channel point map in idle mode.

[0193] The base station transmits channel point measurement operation and reporting setting information to the terminal via a broadcast channel. For example, the information transmitted via the broadcast channel may include at least one of the items listed in [Table 3] below.

[0194] IE (information element) description Measurement period n-DRX. k candidate channel points are measured every n DRX cycles. Reference signal: Reference signal for channel measurement. Based on the current cell, in idle mode, it can be PSCH, SSCH, CRS, etc. Channel distance metric Distance metric. Average filter coefficient. Metric model. Identifier of the metric model (e.g., f(v)). Feature model. Identifier of the feature model (e.g., e(H)). Report period. The reporting period. The n-DRX period, and if the value is 0, it is transmitted at cell update. Minimum distance. The minimum distance between measurement points. Max K-neighborhood. The maximum number of measurable neighbor distances.

[0195] If existing channel point information exists, the information transmitted through the broadcast channel may further include at least one of the items listed in [Table 4] below.

[0196] IE(information element)DescriptionNrcpNumber of reference channel points.RCP_info(1, …, Nrcp)Array of reference channel point information.> Reference channel point IDReference channel point identifier.> Reference channel point contextContext vector values ​​such as terminal position and velocity at the reference channel point.> Reference channel point distributionProbability mass function having the property of empirical data distribution of information z as a reference channel point.

[0197] Accordingly, the terminal can perform measurements on channel points at each measurement cycle based on the received information. For example, the terminal can identify a reference channel point corresponding to the current location at each measurement cycle, and if the distance between the identified reference channel point and the reference channel point corresponding to the previous measurement is greater than the minimum distance between the measurement points, the terminal can perform measurements. Then, the terminal reports the measurement results on the channel points at each reporting cycle based on the received information. The reported information can include at least one of the items listed in [Table 5] below.

[0198] IE(information element)Description(description)Reference channel point IDReference channel point identifier.Channel point distributionA probability mass function that has the empirical data distribution properties of the current channel point, and is a differential vector value from the reference channel point distribution.Channel point contextA context vector value such as terminal position and speed at the current channel point.

[0199] Thereafter, the base station generates a channel distribution map based on the reported information. The reported information or information about the channel distribution map may be shared with at least one neighboring base station.

[0200] The base station is any two context points in area X. and The average distance of This and / or the distance on the channel distribution map The reporting cycle can be adjusted to achieve this. To achieve this, the base station can update configuration information for related measurement operations and reporting, and transmit the updated configuration information to the terminal via a broadcast channel. By shortening or lengthening the measurement cycle for channel points, the channel point density can be indirectly controlled.

[0201] To create a channel distribution map in connected mode, the following actions can be performed for measurement setup and reporting.

[0202] The base station transmits a message (e.g., a CP_MEAS_SETUP_REQ message) to the terminal requesting measurement for channel point measurement. In response, the terminal transmits a message indicating positive (true) response to the request (e.g., a CP_MEAS_SETUP_CNF message). Subsequently, the base station transmits a message (e.g., a CP_MEAS_CONFIG_REQ message) containing configuration information related to the measurement and requests a report. Here, the configuration information for the corresponding transmission point may further include at least one of the items listed in [Table 6] below.

[0203] IE (information element) Description Measurement period: Instructs reporting in N slot cycles. Reference signal: Reference signal for channel measurement. Based on the current cell, it can be CSI-RS. Channel distance metric Distance metric. Average filter coefficient. Metric model. Identifier of the metric model (e.g., f(H)). Feature model. Identifier of the feature model (e.g., e(H)). Report method. Indicates whether the report is an event or periodic report. Report period. In case of periodic reports, the valid reporting period. Report event. In case of event-based reports, the reporting conditions. (e.g., (Reported when the average distance metric measurement is greater than a certain value) Minimum distance The minimum distance between measurement points. Max K-neighborhood The maximum number of measurable neighboring distances.

[0204] If there is existing reference channel point information, the setting information may further include at least one of the items listed in [Table 7] below.

[0205] IE(information element)DescriptionNrcpNumber of reference channel points.RCP_info(1, …, Nrcp)Array of reference channel point information.> Reference channel point IDReference channel point identifier.> Reference channel point contextContext vector values ​​such as terminal position and velocity at the reference channel point.> Reference channel point distributionProbability mass function having the property of empirical data distribution of information z as a reference channel point.

[0206] The terminal transmits a message (e.g., CP_MEAS_CONFIG_CNF message) to the base station to confirm receipt of configuration information for measurement. Based on the configuration information, measurements can be performed on channel points for each measurement cycle, and the distribution of channel points can be estimated. The base station and terminal can terminate the measurement process through a message requesting release of measurement (e.g., CP_MEAS_RELEASE_REQ) and a message confirming release of measurement (e.g., CP_MEAS_RELEASE_CNF).

[0207] Measurement reporting for channel distribution map creation is performed through the following operations. The base station transmits a message (e.g., CP_REPORT_SETUP_REQ message) requesting measurement results for channel points to the terminal. The terminal transmits a response message indicating positive response (e.g., CP_REPORT_SETUP_CNF message), and the terminal transmits a message (e.g., CP_REPORT_MEAS message) containing measurement results for channel points at each reporting cycle. The information reported through the message containing the measurement results includes at least one measurement result data set. Each measurement result data set corresponds to one reference channel point and may include at least one of the items listed in [Table 8] below.

[0208] IE (information element) Description Reference channel point ID Reference channel point identifier. Channel point distribution A probability mass function that has the empirical data distribution properties of the current channel point, and a differential vector value from the reference channel point distribution. Channel point context Context vector values ​​such as terminal position and speed at the current channel point

[0209] The base station generates a channel distribution map based on the reported information. The reported information or information related to the channel distribution map may be shared with neighboring base stations.

[0210] The base station is any two context points in area X. and The average distance of This and / or the distance on the channel distribution map The reporting cycle can be adjusted to achieve this. To this end, the base station can update the configuration information for the related measurement operation and report, and transmit the updated configuration information to the terminal via a message. By adjusting the measurement cycle for the channel point to be short or long, the channel point density can be indirectly controlled. In addition, the base station and the terminal can terminate the reporting procedure via a message requesting the release of the report (e.g., a CP_REPORT_RELEASE_REQ message) and a message confirming the release of the report (e.g., a CP_REPORT_RELEASE_CNF message).

[0211] Querying online learning data

[0212] Once a channel point map has been generated through the aforementioned procedures, the terminal can request training data for online learning from the base station. In other words, the terminal or base station can request training data for online learning of an AI / ML model. The query procedure for training data for online learning can be performed as follows.

[0213] To request online training data for a channel point, the terminal transmits a message requesting training data (e.g., a CP_TR_DATA_REQ message). The message may include at least one request information set corresponding to at least one reference channel point, and each request information set may include at least one of the items listed in [Table 9] below.

[0214] IE(information element)Description(description)Reference channel point IDChannel point identifier.Channel point contextContext vector values ​​such as terminal position and speed at the current channel point.

[0215] In response, the base station may transmit a message (e.g., a CP_TR_DATA_RES message) containing training data. The message may include at least one training data set corresponding to at least one reference channel point, and each training data set may include at least one of the items listed in [Table 10] below.

[0216] IE(information element)Description(description)Reference channel point IDReference channel point identifier.Channel representative data setRepresentative training data set for reference channel point location.Channel point contextContext vector values ​​such as terminal location and speed at the current channel point.

[0217] FIG. 25 illustrates an example of a procedure for setting information for generating a channel point map in a wireless communication system according to one embodiment of the present disclosure. FIG. 25 illustrates signaling between a terminal (2510) and a base station (2520).

[0218] Referring to FIG. 25, in step S2501, the base station (2520) transmits a CP_MEAS_SETUP_REQ message to the terminal (2510). In step S2503, the terminal (2510) checks its measurement capability. In the present embodiment, the terminal (2510) has the capability for measurement to generate a channel point map. Accordingly, in step S2505, the terminal (2510) transmits a CP_MEAS_SETUP_CNF message to the base station (2520).

[0219] Thereafter, in step S2507, the base station (2520) transmits a CP-MEAS_CONFIG_REQ message to the terminal (2510). Through this, the terminal (2510) obtains configuration information for measurement. In step S2509, the terminal (2510) prepares measurement for a channel point. That is, the terminal (2510) sets internal variables for measurement based on the obtained configuration information. In step S2511, the terminal (2510) transmits a CP_MEAS_CONFIG_CNF message to the base station (2520). Accordingly, the base station (2520) identifies that the terminal (2510) has completed the configuration for measurement. Then, in step S2513, the base station (2520) prepares to build a channel measurement map.

[0220] Thereafter, in step S2515, the base station (2520) transmits a CP_MEAS_RELEASE_REQ message to the terminal (2510). In response, in step S2517, the terminal (2510) terminates measurement for the channel point. Then, in step S2519, the terminal (2510) transmits a CP_MEAS_RELEASE_CNF message to the base station (2520).

[0221] FIG. 26 illustrates an example of a procedure for performing measurements to generate a channel point map in a wireless communication system according to one embodiment of the present disclosure. FIG. 26 illustrates signaling between a terminal (2610) and a base station (2620).

[0222] Referring to FIG. 26, in step S2601, the country_ transmits a CP_REPORT_SETUP_REQ message to the country_. Through this, the country_ can obtain setup information for reporting and set internal variables for reporting. Then, in step S2603, the country_ transmits a CP_REPORT_SETUP_CNF message to the country_. After this, the country_ performs measurements for the channel points.

[0223] Afterwards, in step S2607, the horse transmits a CP_REPORT_MEAS message to the station. Through this, the station can obtain the measurement results from the horse. In step S2609, the station builds a channel point map. That is, the station creates a channel point map based on the measurement results received from the horse. In step S2611, the horse transmits a CP_REPORT_MEAS message to the station. Accordingly, although not shown in FIG. 26, the station can continuously build a channel point map. In addition, steps S2607 and S2609 may be performed repeatedly.

[0224] Thereafter, in step S2613, the base station (2620) transmits a CP_REPORT_RELEASE_REQ message to the terminal (2610). In response, in step S2615, the terminal (2610) terminates the measurement for the channel point. In addition, the terminal also terminates reporting on the measurement results. Then, in step S2617, the terminal (2610) transmits a CP_REPORT_RELEASE_CNF message to the base station (2620).

[0225] As described above, a channel point map is generated, and online learning can be performed using the channel point map. An example of a channel point map generated according to the proposed technology is shown in FIG. 27 below. FIG. 27 illustrates an example of a channel point map in a wireless communication system according to an embodiment of the present disclosure. As shown in FIG. 27, a channel point map with a specific density can be generated through measurement operations of a terminal at channel points.

[0226] Additionally, a channel point map can be generated that includes heterogeneous areas, such as areas with high and low channel point densities. For example, if a rural area with little topographic variation and a dense urban area with much topographic variation are mixed, a channel point map with various densities can be generated. Fig. 28 illustrates another example of a channel point map in a wireless communication system according to an embodiment of the present disclosure. As shown in Fig. 28, the central area has a high density, and the outer areas have a low density. This is because the channel distribution in areas with little topographic variation is similar at almost all points, while the channel distribution in areas with little topographic variation varies. As a result, areas with different densities may exist in the channel point area map.

[0227] Differences in the density of areas in a channel point map can be caused by controlling the distance average distribution. In order to track, it is possible to control the density to suit the channel data characteristics of the field by adaptively adjusting the measurement cycle to be small or large.

[0228] As described above, the present disclosure provides a framework capable of proactively detecting changes in channel distribution as a terminal moves in online learning. This is because when a terminal enters a specific area, the success probability of a channel task is determined based on conditional probability in situations where the channel distribution is internally distributed.

[0229] When a terminal encounters a new channel environment during online continuous learning, communication interruptions can occur due to learning delays. This phenomenon stems from the causality of learning, which relies on past and current channel inputs. However, by applying the proposed technology, which utilizes a non-causal framework, the base station can provide sample information about the channel the terminal will experience in the future.

[0230] When performing online learning, encountering an unfamiliar channel can lead to the emergence of a new input distribution class, allowing the task to be treated as a distinct task from the previous one. The line where the task changes in this way is called the task boundary. Recently, AI / ML learning that recognizes the task boundary has been shown to outperform learning that does not. This proposal allows the terminal to consistently maintain AI / ML performance by preemptively announcing the task boundary point and providing samples of the channels it will encounter in the future.

[0231] Increasing the density of reference channel points will improve online continuous learning performance by enabling faster acquisition of sample information about future channels. This allows for more accurate prediction of future channel distributions. However, higher density also increases terminal power consumption and signaling overhead. The proposed technology provides a mechanism to manage this tradeoff.

[0232] According to the proposed technology, channel points can be determined based on an empirical probability distribution based on terminal context, including the terminal's location, acceleration, velocity, and sensor information, and CSI measured dependent on the terminal context. Furthermore, low-dimensional mapping of channel information can be performed based on the similarity between the empirical probability distribution distances between multiple channel points.

[0233] In idle mode or connected mode, the terminal may be provided with configuration information necessary to measure channel points according to a method designated by the base station. Signaling procedures and messages for providing the configuration information are proposed. Accordingly, the terminal may measure channel points according to the designated method and report them to the base station. Furthermore, the base station may receive channel point information from the terminal and provide corresponding training data to the terminal to support online learning. Furthermore, signaling procedures and messages are proposed to support online learning between the base station and the terminal, allowing the terminal to measure and report channels using a generative AI / ML model at channel points according to channel point density.

[0234] Additionally, to support online learning for base stations and terminals, AI / ML models and latent space can be generated based on channel point measurements, generating channel point maps. To provide online learning data for base stations and terminals, terminals can provide information related to channel distribution and distances at channel points, regardless of task type.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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 receiving setup information related to measurement; A step of receiving at least one signal for measurement; A step of performing a measurement based on at least one signal; Comprising a step of transmitting a report related to the results of the above measurement, A method in which the above setting information includes information related to a feature model and a metric model used to generate a channel point map, which is information required for online learning of a model for performing a channel task.

2. In claim 1, The above measurement comprises a measurement for at least one channel point included in the channel point map, A method wherein the report includes at least one of information related to a difference between the at least one channel point and a reference channel point and information related to a context of the terminal at the at least one channel point.

3. In claim 1, A method wherein said measurement is initiated in response to receiving said setup information and is terminated in response to receiving a release request for measurement or reporting.

4. In claim 1, The above setting information includes information related to the cycle of the above measurement, The above cycle is set to a multiple of a DRX (discoutinous reception) cycle when the terminal operates in idle mode, and is set to a multiple of a slot when the terminal operates in connected mode.

5. In claim 1, A method in which the above measurement is performed based on information related to the minimum distance between measurement points included in the above setting information.

6. In claim 1, A method wherein said reporting is initiated in response to receiving a message requesting said reporting and is terminated in response to receiving a release request for said reporting.

7. In claim 1, A step of receiving sample data of channel points generated based on the above channel point map; and A method further comprising the step of performing online learning for a channel task model based on the above sample data.

8. In a wireless communication system, at the terminal, Transmitter and receiver; and comprising a processor connected to the above transceiver, The above processor, Receive setup information related to the measurement, Receive at least one signal for measurement, Performing a measurement based on at least one signal above, Controls transmission of reports related to the results of the above measurements, The above setting information is a terminal including information related to a feature model and a metric model used to generate a channel point map, which is information required for online learning of a model for performing a channel task.

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 receiving setup information related to measurement; A step of receiving at least one signal for measurement; A step of performing a measurement based on at least one signal; Comprising a step of transmitting a report related to the results of the above measurement, The above setting information is a communication device including information related to a feature model and a metric model used to generate a channel point map, which is information required for online learning of a model for performing a channel task.

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: Receive setup information related to the measurement, Receive at least one signal for measurement, Performing a measurement based on at least one signal above, Controls transmission of reports related to the results of the above measurements, The above setting information is a non-transitory computer-readable medium including information related to a feature model and a metric model used to generate a channel point map, which is information necessary for online learning of a model for performing a channel task.

Images