Apparatus and method for collecting information related to channel in wireless communication system

Abstract

The present disclosure relates to an apparatus and a method for collecting information related to a channel in a wireless communication system. The method performed by a terminal in a wireless communication system comprises the steps of: receiving configuration information related to measuring; receiving at least one signal for measuring; performing measuring on the basis of the at least one signal; and transmitting a report related to the results of measuring, wherein the report related to the results of measuring can include information related to a reliability upper limit function value used to select a set of terminals performing measuring in order to collect the probability distribution information of the channel.

Description

Device and method for collecting channel-related information in a wireless communication system

[0001] The following description relates to a wireless communication system, and to a device and method for collecting information related to a channel 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 relates to a device and method for effectively collecting information related to a communication channel in a wireless communication system.

[0005] The present disclosure relates to a device and method for collecting information on the distribution of communication channels within a base station site in a wireless communication system.

[0006] The present disclosure relates to a device and method for collecting downlink channel distribution information in a wireless communication system.

[0007] The present disclosure relates to a device and method for estimating a channel distribution based on a downlink reference signal in a wireless communication system.

[0008] The present disclosure relates to a device and method for collecting uplink channel distribution information in a wireless communication system.

[0009] The present disclosure relates to a device and method for estimating a channel distribution based on an uplink reference signal in a wireless communication system.

[0010] The present disclosure relates to a device and method for collecting channel distribution information with a minimum budget in a wireless communication system.

[0011] The present disclosure relates to a device and method for obtaining statistical information on channel distribution within the total number of transmissions allowed for a reference signal in a wireless communication system.

[0012] The present disclosure relates to a device and method for determining the number of reference signal transmissions to multiple terminals within a base station area for collecting channel distribution information in a wireless communication system.

[0013] The present disclosure relates to a device and method for selecting at least one terminal to transmit a reference signal based on an upper confidence bound (UCB) value for a channel distribution in a wireless communication system.

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

[0015] As an example of the present disclosure, a method performed by a terminal in a wireless communication system includes 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, wherein the report related to the result of the measurement may include information related to an upper confidence bound (UCB) function value used to select a set of terminals performing measurement to collect probability distribution information of a channel.

[0016] As an example of the present disclosure, a method performed by a base station in a wireless communication system includes the steps of transmitting configuration information related to measurement, transmitting at least one signal for measurement, and receiving a report related to a result of measurement based on the at least one signal, wherein the report related to the result of measurement may include information related to a confidence upper bound function value used to select a set of terminals performing measurement to collect probability distribution information of a channel.

[0017] As an example of the present disclosure, in a wireless communication system, a terminal includes a transceiver and a processor connected to the transceiver, wherein the processor controls the terminal to receive configuration information related to measurement, receive at least one signal for measurement, perform measurement based on the at least one signal, and transmit a report related to a result of the measurement, wherein the report related to the result of the measurement may include information related to a confidence upper bound function value used to select a set of terminals performing measurement to collect probability distribution information of a channel.

[0018] As an example of the present disclosure, in a wireless communication system, a base station includes a transceiver and a processor connected to the transceiver, wherein the processor controls to transmit configuration information related to measurement, to transmit at least one signal for measurement, and to receive a report related to a result of measurement based on the at least one signal, wherein the report related to the result of measurement may include information related to a confidence upper bound function value used to select a set of terminals performing measurement to collect probability distribution information of a channel.

[0019] As an example of the present disclosure, a communication device includes at least one processor, and at least one computer memory coupled to the at least one processor and storing instructions that direct operations when executed by the at least one processor, the operations including: 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, wherein the report related to the result of the measurement may include information related to a confidence upper bound function value used to select a set of terminals performing the measurement to collect probability distribution information of a channel.

[0020] 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 measurement based on the at least one signal, and transmit a report related to a result of the measurement, wherein the report related to the result of the measurement may include information related to a confidence upper bound function value used to select a set of terminals performing measurement to collect probability distribution information of a channel.

[0021] 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.

[0022] The following effects may be achieved by embodiments based on the present disclosure.

[0023] According to the present disclosure, information related to a communication channel can be effectively collected.

[0024] 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.

[0025] 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.

[0026] Figure 1 illustrates an example of a communication system applicable to the present disclosure.

[0027] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0028] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure.

[0029] Figure 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure.

[0030] FIG. 5 illustrates an example of a communication structure that can be provided in a 6G (6th generation) system applicable to the present disclosure.

[0031] Figure 6 illustrates an electromagnetic spectrum applicable to the present disclosure.

[0032] Figure 7 illustrates a transmitter structure applicable to the present disclosure.

[0033] Figure 8 illustrates an example of a functional framework for application of artificial intelligence technology applicable to the present disclosure.

[0034] Figure 9 illustrates an example of a procedure for utilizing an artificial intelligence model applicable to the present disclosure.

[0035] Figure 10 illustrates a communication procedure based on AI (artificial intelligence) technology applicable to the present disclosure.

[0036] Figure 11 illustrates an example of the result of low-dimensional conversion of channel information in a wireless communication system.

[0037] Figure 12 illustrates an example of a channel environment of a base station and a terminal in a wireless communication system.

[0038] Figure 13 illustrates an example of the concept of base station area beamforming based on channel charting in a wireless communication system.

[0039] Figure 14 illustrates an example of beam performance based on channel charting in a wireless communication system.

[0040] FIG. 15 illustrates an example of a model for generating a channel point map in a wireless communication system applicable to the present disclosure.

[0041] FIG. 16 illustrates an example of a channel distribution distance according to an embodiment of the present disclosure.

[0042] FIG. 17 illustrates an example of a generative network model for classifying channel classes according to one embodiment of the present disclosure.

[0043] FIG. 18 illustrates an example of channel probability distribution collection in a wireless communication system according to one embodiment of the present disclosure.

[0044] FIG. 19 illustrates an example of a procedure for reporting channel measurement results in a wireless communication system according to one embodiment of the present disclosure.

[0045] FIG. 20 illustrates an example of a procedure for receiving channel measurement results in a wireless communication system according to one embodiment of the present disclosure.

[0046] FIG. 21 illustrates an example of a procedure for collecting channel-related data in a wireless communication system according to one embodiment of the present disclosure.

[0047] FIG. 22 illustrates an example of a procedure for collecting data related to a downlink channel in a wireless communication system according to one embodiment of the present disclosure.

[0048] FIG. 23 illustrates an example of a procedure for collecting data related to an uplink channel according to one embodiment of the present disclosure.

[0049] FIG. 24 illustrates an example of collecting probability distributions in a wireless communication system according to one embodiment of the present disclosure.

[0050] Figure 25 illustrates an example of a wireless device applicable to the present disclosure.

[0051] Figure 26 illustrates an example of a portable device applicable to the present disclosure.

[0052] FIG. 27 illustrates an example of a vehicle or autonomous vehicle applicable to the present disclosure.

[0053] Figure 28 illustrates an example of a vehicle applicable to the present disclosure.

[0054] FIG. 29 illustrates an example of an extended reality (XR) device applicable to the present disclosure.

[0055] Figure 30 illustrates an example of a robot applicable to the present disclosure.

[0056] Figure 31 illustrates an example of an AI device applicable to the present disclosure.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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).

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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).

[0070] 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.

[0071] 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.

[0072] Communication system applicable to the present disclosure

[0073] 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.

[0074] 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.

[0075] Figure 1 illustrates an example of a communication system applied to the present disclosure.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] Devices applicable to the present disclosure

[0080] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 scramblers (310), modulators (320), a layer mapper (330), a precoder (340), resource mappers (350), and signal generators (360). At this time, for example, the operation / function of FIG. 3 may be performed in the processor (202) and / or the transceiver (206) of FIG. 2. Furthermore, for example, the hardware elements of FIG. 3 may be implemented in the processor (202) and / or the transceiver (206) of FIG. 2. For 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.

[0096] 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., a UL-SCH transport block, a DL-SCH transport block). Here, the information block may include data related to AI (e.g., training data, AI model data, input data, output data, etc.), and the codeword may be an encoded bit sequence corresponding to the data related to AI. The wireless signal may be transmitted through various physical channels (e.g., a PUSCH, a PDSCH). Specifically, the codeword may be converted into a bit sequence scrambled by scramblers (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 may be modulated into a modulation symbol sequence by modulators (320). Modulation schemes may include pi / 2-BPSK (pi / 2-binary phase shift keying), m-PSK (m-phase shift keying), m-QAM (m-quadrature amplitude modulation), etc.

[0097] A complex modulation symbol sequence can be mapped to at least one transport layer by a layer mapper (330). Here, a transport layer is a logical resource unit for mapping a signal or data transmitted through spatial resources to antenna ports, and one transport layer can correspond to one stream or one antenna port. Each of the complex modulation symbols included in the complex modulation symbol sequence is mapped to at least one transport layer, thereby determining which antenna port it will be transmitted through. The modulation symbols of each transport layer can be mapped to the 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 a precoding matrix W of NХM. Here, N is the number of antenna ports, and M is the number of transport layers. Here, the precoder (340) may perform precoding after performing transform precoding (e.g., discrete Fourier transform (DFT) transform) on complex modulation symbols. Additionally, the precoder (340) may perform precoding without performing transform precoding.

[0098] Resource mappers (350) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources may include a plurality of symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and a plurality of subcarriers in the frequency domain. Signal generators (360) generate wireless signals from the mapped modulation symbols, and the generated wireless signals can be transmitted to other devices through each antenna. To this end, each of the signal generators (360) may include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, etc.

[0099] 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.

[0100] The signal processing circuit (300) described with reference to FIG. 3 is exemplified as including a plurality of scramblers (310), modulators (320), a plurality of resource mappers (350), and a plurality of signal generators (360). However, at least one of the scramblers, modulators, resource mappers, and signal generators may be implemented as a single integrated structure. That is, the number of at least one of the scramblers, modulators, resource mappers, and signal generators may be smaller than the number of layers. Furthermore, at least one of the components exemplified in FIG. 3 may be omitted.

[0101] 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.

[0102] 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).

[0103] 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 by 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. The system information may include information related to an AI function. For example, the system information may include at least one of information related to an AI model, information related to training, and information related to inference / prediction, as information required for operations performed based on AI. However, the request and provision of the system information may be performed after a random access procedure described below.

[0104] 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.

[0105] 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. In addition, the signaling of the control information may be performed to convey information related to an AI function. For example, the information related to an AI function is information necessary for an operation performed based on AI, and may include at least one of information related to an AI model, information related to training, and information related to inference / prediction. More specifically, information related to the AI ​​function signaled in step 407 may be combined and / or linked with information related to the AI ​​function signaled in step 403, and the two may be defined in a hierarchical, mutually complementary, or substitutive structure.

[0106] 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. Here, the transmitted data is data related to AI, and may include, for example, data for AI-based operations or data generated by AI-based operations.

[0107] Steps 401 to 409 illustrated with reference to FIG. 4 do not necessarily have to be performed in the order illustrated in FIG. 4, and the order of at least some of the steps may vary. Furthermore, at least some of steps 401 to 409 may be combined into a single step or omitted. That is, the steps illustrated in FIG. 4 may be performed in various modified forms.

[0108] 6G communication systems and core implementation technologies of 6G systems

[0109] 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.

[0110] 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.

[0111] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100 bps / HzMobility supportup to 1000 km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0112] 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.

[0113] Figure 5 illustrates an example of a communication architecture that can be provided in a 6G system applicable to the present disclosure. Referring to Figure 5, a 6G system is expected to have 50 times higher simultaneous wireless communication connectivity than a 5G wireless communication system. URLLC, a key feature of 5G, is expected to become an even more important technology in 6G communications by providing end-to-end latency of less than 1 ms. Furthermore, 6G systems will have significantly superior volumetric spectral efficiency, unlike the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, so mobile devices in 6G systems may not need to be separately charged. New network characteristics in 6G may include the following:

[0114] - Satellite integrated network: 6G is expected to integrate with satellites to provide a global mobile network. The integration of terrestrial, satellite, and airborne networks into a single wireless communications system is crucial for 6G.

[0115] Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is revolutionary, upgrading the wireless evolution from "connected objects" to "connected intelligence." AI can be applied at every stage of the communication process (or at every signal processing step, as described below).

[0116] - Seamless integration of wireless information and energy transfer: 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

[0117] - Ubiquitous super 3D connectivity: Access to networks and core network functions of drones and very low Earth orbit satellites will create super 3D connectivity in 6G ubiquitous.

[0118] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0119] - Small cell networks: The concept of small cell networks was introduced to improve received signal quality in cellular systems by increasing throughput, energy efficiency, and spectral efficiency. Consequently, small cell networks are essential for 5G and beyond-5G (5GB) communication systems. Accordingly, 6G communication systems also adopt the characteristics of small cell networks.

[0120] Ultra-dense heterogeneous networks: Ultra-dense heterogeneous networks will be another key feature of 6G communication systems. Multi-tier networks comprised of heterogeneous networks improve overall QoS and reduce costs.

[0121] High-capacity backhaul: Backhaul connections are characterized by high-capacity backhaul networks to support high-volume traffic. High-speed fiber optics and free-space optics (FSO) systems may be potential solutions to this problem.

[0122] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communications is a key feature of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0123] - Softwarization and virtualization: Softwarization and virtualization are two critical features that form the foundation of the design process for 5GB networks to ensure flexibility, reconfigurability, and programmability. Furthermore, billions of devices can be shared on a shared physical infrastructure.

[0124] To satisfy the above-mentioned characteristics, the core implementation technologies of the 6G system may include 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).

[0125] For example, THz communication can be utilized in 6G systems. THz communication is a communication that utilizes a spectrum in a frequency band between 0.3 THz and 3 THz with a corresponding wavelength in the range of 0.1 mm to 1 mm, as shown in FIG. 6. Referring to FIG. 6, the frequency band of THz waves is located in the middle region between the infrared band and the millimeter wave band, and therefore, THz waves can be understood as radio waves with the shortest wavelength and light waves with the longest wavelength. Therefore, THz waves share some of the characteristics of infrared and microwave waves, and specifically, they can simultaneously have the transparency of electromagnetic waves and the straightness of light waves.

[0126] Figure 7 illustrates a transmitter structure applicable to the present disclosure.

[0127] Referring to Figure 7, in order to modulate data into an optical signal, an optical source of a laser can be passed through an optical wave guide to change the phase of the signal, etc. At this time, data is loaded by changing the electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform.

[0128] Data may be provided from a data signal generator. Here, the data may include various user data, configuration information, control information, etc. transmitted through a channel. Furthermore, the data may include data related to AI-based operations, such as information for configuring an AI model, input / output data for tasks of the AI ​​model, etc. To this end, components related to AI functions (e.g., an AI processing unit) may be included in the data signal generator or may be linked to the data signal generator.

[0129] An optical / electronic converter (O / E converter) can generate THz pulses by optical rectification using a nonlinear crystal, photoelectric conversion using a photoconductive antenna, or emission from a bunch of relativistic electrons. The THz pulse generated in the above manner can have a length in the range of femtoseconds to picoseconds. The optical / electronic converter (O / E converter) performs down conversion by utilizing the nonlinearity of the device.

[0130] Considering the THz spectrum usage, it is likely that multiple contiguous GHz bands will be used for THz systems, either fixed or for mobile services. For an outdoor scenario, the available bandwidth can be categorized based on an oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is divided into multiple band chunks can be considered. As an example of this framework, if the THz pulse length for a single carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0131] Effective down-conversion from the infrared band to the THz band depends on how to utilize the nonlinearity of the optical / electrical converter (O / E converter). In other words, to down-convert to the desired THz band, it is necessary to design an O / E converter with the most ideal non-linearity for transferring to the THz band. If an O / E converter that is not suitable for the target frequency band is used, errors in the amplitude and phase of the pulse are likely to occur.

[0132] A THz transmission and reception system can be implemented using a single optical-to-electrical converter in a single-carrier system. Depending on the channel environment, optical-to-electrical converters may be required as many as the number of carriers in a multi-carrier system. This phenomenon will be particularly noticeable in a multi-carrier system that utilizes multiple broadbands according to the aforementioned spectrum usage plan. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-frequency converted based on an optical-to-electrical converter may be transmitted in a specific resource region (e.g., a specific frame). The frequency region of the specific resource region may include multiple chunks. Each chunk may be composed of at least one component carrier (CC).

[0133] 6G systems may introduce AI technology. Efficient resource management and optimization are required to maintain connectivity between various services and devices. AI technology may include technologies that perform data analysis, pattern recognition, and predictive modeling using AI / ML (artificial intelligence / machine learning) models. Here, an AI / ML model can be understood as a set of parameter values ​​and / or weight values ​​related to mathematical formulas or algorithms generated through learning to discover patterns in input data or make predictions. To create such an AI / ML model, an AI / ML model learning process is required, which builds an AI / ML model by learning the relationship between inputs and outputs in a data-driven manner. Various learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, can be utilized as learning algorithms. To generate output, a user can input specific data into a trained AI / ML model, and the process of obtaining output data by inputting input data into an AI / ML model can be referred to as AI / ML "inference" or "prediction."

[0134] Network control parameters can be obtained as output through AI / ML inference using trained AI / ML models. Users can utilize the output parameter values ​​to improve network efficiency. For example, AI technology can be utilized in various fields, such as wireless network resource allocation, traffic management, fault prediction, and quality of service (QoS) management. In particular, machine learning can efficiently allocate resources even in dynamically changing network environments based on real-time data. Therefore, AI technology can be utilized to provide hyper-connectivity and ultra-low latency.

[0135] At this time, AI / ML inference can be performed based on a combination of various devices. For example, the UE and the network can jointly perform AI / ML inference, and such an AI / ML model can be referred to as a two-sided AI / ML model or a two-sided model. In this case, the UE can perform the first part of the inference first, and the base station can perform the remaining inference, or vice versa. Alternatively, inference can be performed entirely on the UE, and such an AI / ML model can be referred to as a UE-side AI / ML model or a UE-side model.

[0136] Additionally, life cycle management (LCM) can be performed for AI / ML models. Life cycle management can include model training, model deployment, model inference, model monitoring, and model updates. This may require support for data collection, model training, functional / model identification, model delivery / transfer, model inference operations, functional / model selection / activation / deactivation / fallback, functional / model monitoring, model updates, and UE capabilities.

[0137] For example, AI / ML technology can be operated based on a functional framework such as FIG. 8. FIG. 8 illustrates an example of a functional framework for application of AI / ML technology applicable to the present disclosure. First, a data collection function (810) performs data preparation on input data collected from objects (e.g., UE, RAN node, network node, etc.) to generate training data (801), monitoring data (803), and / or inference data (805) including processed input data. A model training function (820), which receives training data (801) from the data collection function (810), performs training on an AI / ML model using the training data (801) and provides a trained / updated model (813) to a model repository (840). The model repository (840) can store and retain the received trained / updated model (813).

[0138] A management function (830) may be used to control AI / ML model training. The management function (830) may control the operation of the AI / ML model or AI / ML functions, or supervise their performance. To this end, the management function (830) may receive monitoring data (830) from the data collection function (810) and inference output (809) from the inference function (840). The management function (830) manages the data received from the data collection function (810) and the inference function (840) so that the inference task can be performed efficiently. That is, the management function (830) may transmit performance feedback or a retraining request (807) to the model training function (820) to improve the inference task. Here, the performance feedback may be used to indicate a learning goal or as a reward for reinforcement learning. Additionally, the management function (830) can transmit management instructions (811) that instruct the inference function (840) to select AI / ML models or AL / ML-based functions to be used, activate / deactivate them, or switch to non-AI / ML operation.

[0139] The inference function (840) generates an inference output (809) by performing inference and / or prediction using the inference data (805) received by the data collection function (810). Here, the inference output (809) refers to the inference output of the AI / ML model used by the inference function (840), and the details of the inference output may vary depending on the use case. The AI / ML model used by the inference function (840) can be controlled by the management function (830). That is, the management function (830) can transmit a model transfer / forward request signal (815) to request a necessary AI / ML model to the model repository function (850), and the model repository function (850) can transmit the corresponding AI / ML model to the inference function (840) via a model transfer / forward signal (817). Therefore, the inference function (840) can perform inference using the AI / ML model (817) according to the received management instruction (811).

[0140] Additionally, the management function (830) may trigger or perform a designated task / action based on the inference output (809). Accordingly, the management function (830) may trigger a task / action for another entity (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 exemplified in FIG. 8 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.

[0141] Not all of the functions (810 to 850) illustrated in FIG. 8 need to be used to utilize the AI / ML model, and the method of combining them is not limited to a specific method. Accordingly, the functions (810 to 850) may be operated in an integrated manner, or some functions may be omitted. Furthermore, the functions (810 to 850) illustrated in FIG. 8 are not necessarily limited to being implemented as separate devices or apparatuses. For example, some or all of the functions (810 to 850) may be included in the processor (202) of FIG. 2. Furthermore, the model storage function (850) may be included in the memory (204) of FIG. 2.

[0142] FIG. 9 illustrates an example of a procedure for utilizing an AI model applicable to the present disclosure. FIG. 9 illustrates a case where a model training function (820) is included in a network node and a model inference function (840) is included in a RAN node. Referring to FIG. 9, 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 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, speed, etc.) together 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 also 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 UE and RAN node 2. In step 5, RAN node 1 performs AI model-based inference using the received inference data to generate output data (e.g., prediction or decision). In step 6, if applicable, RAN node 1 may transmit 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 network nodes.

[0143] Network nodes can manage AI models based on feedback information regarding the AI ​​model's inference results. For example, the network node can perform additional training on the AI ​​model or generate additional information about the AI ​​model (e.g., performance information, accuracy information, etc.). If additional training is performed on the AI ​​model, the network node can distribute the updated AI model to RAN node 1.

[0144] As described with reference to Figure 9, model training can be performed by network nodes, and inference using the model can be performed by RAN node 1. In other words, the model training and inference functions can be distributed. Typically, model training requires significant computational resources because it utilizes large amounts of data and complex algorithms for optimization. In contrast, inference, which uses a trained model to derive conclusions about new data, requires relatively fewer computational resources compared to model training. Therefore, using the procedure of Figure 9, model training can be performed through network nodes when the computational resources of the UE or RAN node are insufficient. Furthermore, security can be ensured for the AI ​​model because the AI ​​model is not disclosed to the UE.

[0145] FIG. 9 illustrates a case where a model training function (820) is included in a network node and a model inference function (840) is included in a RAN node, but the present disclosure is not limited thereto. For example, if the computational resources of the RAN node are sufficient, both the model training function (820) and the model inference function (840) may be included in RAN node 1. In this case, RAN node 1 receives training data for training an AI model from the UE and RAN node 2. RAN node 1 trains the AI ​​model using the received training data. Thereafter, RAN node 1 receives inference data for AI model inference from the UE and RAN node 2. RAN node 1 performs inference based on the AI ​​model using the received inference data to generate output data. Based on the output data, the UE, RAN node 1, and RAN node 2 may perform operations related to communication (e.g., handover, cell change). Thereafter, the UE and RAN node 2 may transmit feedback regarding the operations to RAN node 1. Therefore, RAN node 1 can learn the AI ​​model and update its own AI model using feedback information regarding the AI ​​model's inference results. According to the aforementioned method, signaling with the network is not required for AI model learning and inference, thereby reducing network load and delays until the AI ​​model is trained or inference results are received. Furthermore, since the UE performs inference using the AI ​​model, its personal information is not transmitted to network nodes, etc., thereby enhancing the security of personal information.

[0146] As another example, the model training function (820) may be included in the RAN node, and the model inference function (840) may be included in the UE. The RAN node receives training data for training an AI model from the UE, and trains the AI ​​model using the received training data. The RAN node distributes the trained AI model to the UE. The UE may perform inference based on the received AI model to generate output data. At this time, the data for inference may be received from the RAN node, or the UE may use data acquired on its own. The UE and the RAN node may perform communication-related operations based on the output data generated by inference. Thereafter, the UE may transmit feedback regarding the operation to the RAN node. Therefore, the RAN node may train the AI ​​model and distribute the updated AI model to the UE through feedback information regarding the inference result of the AI ​​model. According to the above-described method, the model training function (820) may be included in the RAN node, and the model inference function (840) may be included in the UE to perform inference, thereby reducing the load on the RAN node. Additionally, if the UE uses data acquired on its own to make inferences, even if the UE loses connection with the RAN node after receiving the AI ​​model, the UE can continue to infer the distributed AI model and perform operations based on the inference results.

[0147] According to the framework and procedures described above, an AI model can be trained and utilized in a wireless communication system. The model training function (820) and the model inference function (840) can be combined in various ways and are not necessarily limited to the case of FIG. 9. In the framework and procedures described above, various types of data, such as input data, training data, and inference data, are introduced, and the specific content of the data described above 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 data described above.

[0148] Figure 10 illustrates an AI technology-based communication procedure applicable to the present disclosure. The detailed procedures illustrated in Figure 10 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 10. As another example, the results of the inference illustrated in Figure 10 can be used to transmit and / or receive data according to various embodiments of the present disclosure.

[0149] Referring to FIG. 10, in step S1001, at least one of the UE (1010), the RAN node (1020), and the network node (1030) 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 S1003, at least one of the UE (1010), the RAN node (1020), and the network node (1030) performs a configuration procedure. Through the configuration procedure, parameters, resources, connections, and / or entities necessary for performing subsequent procedures in layers between the UE (1010) and the RAN node (1020) and / or in at least one layer between the UE (1010) and the network node (1030) 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.

[0150] In step S1005, at least one of the UE (1010), the RAN node (1020), and the network node (1030) performs a model training procedure. At least one of the UE (1010), the RAN node (1020), and the network node (1030) 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.

[0151] In step S1007, at least one of the UE (1010), the RAN node (1020), and the network node (1030) 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.).

[0152] In step S1009, at least one of the UE (1010), the RAN node (1020), and the network node (1030) transmits and / or receives data. At this time, the result of the task performed in step 1007 may be used. In some cases, the task performed in step 1007 may include transmitting and / or receiving data, in which case this step may be omitted as it is part of step 1007.

[0153] Specific embodiments of the present disclosure

[0154] The present disclosure relates to collecting channel-related information in a wireless communication system, and more specifically, to a technique for measuring and collecting channel distribution within a base station site with a minimum budget. The minimum budget refers to the total number of transmissions allowed for a reference signal. The base station site refers to the base station coverage area or multiple cells managed by the base station.

[0155] Heavy tail distribution characteristics of communication channel environments

[0156] Communication channel environments can exhibit the characteristic of a thick-tailed distribution, meaning that the channel distribution is not concentrated in a specific environment. As frequencies increase, such as millimeter-wave or terahertz waves, in addition to the propagation straightness, radio waves can exhibit unique distributions based on region, combining with diverse local environments and diffraction, scattering, and reflection characteristics. Therefore, the characteristic of a thick-tailed distribution is more likely to appear in 6G.

[0157] Propagation environments can be categorized based on the influence of reference signals in the millimeter-wave or terahertz bands on channel tasks. High-frequency radio waves in the millimeter-wave or terahertz bands, unlike those in lower-frequency bands, are more linear. Therefore, the distribution of high-frequency propagation paths, which undergo different reflections, diffraction, and refraction, can vary widely, depending on not only static buildings and terrain, but also moving objects, antenna placement, and terminal movement. For example, the following various propagation channels are presented in the TR (technical report) 38.901 standard.

[0158] a) Urban microcells (UMi) with outdoor-to-outdoor (O2O) and outdoor-to-indoor (O2I) (e.g. streets and building forests, open areas)

[0159] b) UMa (urban macrocell) with O2O and I2I

[0160] c) Indoor: Environment, shopping malls. Typical office environments include open cubicle areas and walled spaces.

[0161] d) Backhaul, including outdoor rooftop backhaul and street canyon scenarios with small cells in urban areas.

[0162] e) D2D / V2V. Device-to-device access in open spaces, canyons, and indoor scenarios. V2V is vehicle-to-vehicle communication.

[0163] f) Other scenarios such as stadiums (e.g., open roof) and gymnasiums (e.g., closed roof).

[0164] g) Indoor industry scenario

[0165] Locality refers to the more regionally dependent nature of the communication channel probability distribution in the radio environment a terminal faces. The geographical distribution of a specific terminal and its dynamic state have the greatest impact on the communication channel probability distribution. In other words, locality implies that the communication channel probability distribution can be associated with a specific region. In addition to this locality, various terminal contexts, such as the terminal's speed, acceleration, and the movement of surrounding objects, further diversify the channel probability distribution. Consequently, the data distribution of AI / ML (artificial intelligence / machine learning) models can vary significantly.

[0166] In this way, the types or classes of channels will increase according to various channel probability distributions, and accordingly, the base station can manage various classes of channels to reflect the regional characteristics of the base station area.

[0167] Channel charting

[0168] Channel charting refers to mapping high-dimensional information, such as channel state information (CSI), into easier-to-manage low-dimensional spatial geometry. In other words, channel charting is the process of transforming the representation of CSI, which describes a channel, and can be understood as a structural simplification of the data. The output of channel charting is a channel chart. The proximity between points in the channel chart represents their proximity in real space. Specifically, using AI / ML technologies, it is possible to train channel charts to derive spatial or positional 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.

[0169] 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.

[0170] 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.

[0171] Figure 11 illustrates an example of the result of low-dimensional transformation of channel information in a wireless communication system. Referring to Figure 11, a base station (1120) generates a low-dimensional chart (1104) which is a result of learning on a specific plane by low-dimensionally transforming channel data (1102) measured in a two-dimensional space. It is confirmed that a path expressed as VIP in an actual physical space is mapped to the low-dimensional chart (1104). The physical path expressed as VIP in the channel data (1102) is also displayed in the channel chart (1104). Although the path in the channel chart (1104) is not explicitly expressed as VIP, it includes distance information locally.

[0172] Site-specific beamforming using channel charting

[0173] By leveraging the concept of channel charting, beamforming vectors specific to a base station's region can be derived using an AI / ML model. Figure 12 illustrates an example of a channel environment for a base station and a terminal in a wireless communication system. Referring to Figure 12, terminals (1210-1 to 1210-4) are located within the site of a first base station (1220-1) and / or a second base station (1220-2). In Figure 12, h denotes the channel between the base station and the terminal, g denotes the beamforming vector of the base station, and l denotes the location of the terminal within the base station's site.

[0174] Figure 13 illustrates an example of the concept of base station area beamforming based on channel charting in a wireless communication system. Referring to Figure 13, the i-th base station (BS i ) is the jth channel h through channel charting (CC), i.e., h i,j is projected into the low-dimensional space z. Then, the kth base station (BS k) performs location-based beamforming (LBB) based on low-dimensional spatial projection results. An example of beam performance based on channel charting is shown in Fig. 14.

[0175] Figure 14 illustrates an example of beam performance based on channel charting in a wireless communication system. Figure 14 illustrates beam performance based on channel charting, which was created by collecting data using the method illustrated in Figure 13 in an environment similar to Figure 12. In Figure 14, the correlation distance between the expected and beamforming vectors is shaded, and referring to this, it can be seen that the performance of the beamforming vector system with nonlinear characteristics is well reflected.

[0176] Channel map for offline and online learning support

[0177] Physical layer channel tasks can be divided into regression and classification. Regression-related channel tasks may include channel estimation, acquisition and tracking, beam tracking, and CSI prediction. Furthermore, classification-related channel tasks may include class-determining tasks 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 tasks may vary slightly depending on the requirements and design of the communication system.

[0178] Unlike existing mathematical model-based approaches, the approach to performing channel tasks using AI / ML models 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 the CSI-RS and utilizes the characteristics of the radio channel obtained based on the received data corresponding to the CSI-RS to train an AI model (e.g., a deep neural network) and utilize the trained AI model.

[0179] However, if a terminal moves and encounters a communication channel environment that it has not trained on, the performance of the trained AI model will degrade. In particular, terminals with small memory and computational capacity have AI models of limited complexity, so there is a high probability that they will encounter new channel distributions that have not been trained before due to movement. When the AI ​​model encounters these new channels, communication quality degradation, such as data truncation, may occur. These problems can be detected as a decrease in SNR (signal-to-noise ratio), BER (bit error rate), SER (symbol error rate), and BER (block error rate) while performing physical layer tasks. The aforementioned problems may occur when the AI ​​model performs communication tasks.

[0180] Therefore, to solve the aforementioned problems, a channel point map can be utilized to support online or offline learning. Here, a channel point map is a type of channel chart learned to project onto a map information about the context of a terminal, such as its location, velocity, acceleration, and position, as well as the probability distribution of empirical channels and the probability distribution distance between two points. In other words, a channel point map is a data set that defines channel points specified by the context and physical points of a terminal, and expresses the distance between channel points in terms of the probability distribution of channel characteristics. Here, the channel characteristics can be defined in various ways, such as at least one or more of an instantaneous channel value, a probability value for each channel class, a value related to signal timing, a value related to signal quality, a value related to signal strength, a value related to a beam index, and a value related to a beam precoder. A channel point map can be used to train an artificial intelligence model for channel-related tasks. A channel point map can be generated through the collaboration of terminals and base stations with various contexts.

[0181] FIG. 15 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. 15, a feature encoder (1502) converts high-dimensional CSI into low-dimensional CSI and extracts channel features. In addition, the feature encoder (1502) determines an empirical probability distribution v through accumulation and normalization of feature information. A metric model (1504) is trained through similarity-based contrastive learning and maps the input empirical probability distribution v to an embedding space Z. Through this, a channel point map can be generated.

[0182] As described above, the generated channel point map can serve as a foundation for assisting terminals or base stations in predicting and providing future channel information for online learning. For example, a terminal can recognize the channel class distribution at its current location through the channel point map and receive information on the channel class distribution and distance of the channel distribution for the area it is expected to move to. If the distance information for the channel distribution is large, the terminal can check whether the current AI / ML model covers the channel classes in the area it is expected to move to. If not, it can perform online learning in advance.

[0183] Fig. 16 illustrates an example of a channel distribution distance according to an embodiment of the present disclosure. Referring to Fig. 16, a terminal (1610) currently has a channel distribution At this time, the terminal (1610) is expected to move through the channel point map to determine the channel distribution at the location. and current channel distribution The difference between the two can be confirmed. The terminal (1610) is expected to move to a different location, and the channel distribution is and current channel distribution If the gap is large, you can perform online learning in advance before moving to the expected location.

[0184] Channel probability distribution

[0185] A channel probability distribution defined as follows may be used as a probability distribution handled in a channel point map. The present disclosure below describes a procedure related to a channel point map using the channel probability distribution described below, but a channel point map to which a probability distribution according to another definition is applied may be applied to the proposed technology.

[0186] FIG. 17 illustrates an example of a generative network model for classifying channel classes according to an embodiment of the present disclosure. Referring to FIG. 17, a neural network (1702) e(H) for classifying channel classes generates a softmax output. The ith terminal may have N+1 channels in the antenna port, frequency, and / or time axis for its own context (e.g., dynamic information of the terminal such as position, speed, and attitude). The channel probability distribution is defined as a probability distribution estimated using the measurement model e(H) based on these channel values. The types of the channel probability distribution may include a channel class distribution, a beam index distribution, a time synchronization distribution, an RSSI distribution, an RI, a CQI, or a PMI distribution, depending on the type of the reference signal. The channel classes may be classified into urban macrocells (UMa), rural macrocells (RMa), urban microcells (UMi), etc., and may have a set of channel distribution classes within the base station area in system operation. When the softmax output of the neural network is normalized, a probability vector with L classes is obtained. In other words, the softmax output of the neural network (1702) e(H) can be treated as a probability distribution, which is a discrete distribution probability P i can be expressed as . Such channel probability distribution can be reflected as information in a channel chart or channel map.

[0187] Collect channel probability distributions

[0188] According to one embodiment of the present disclosure, a base station collects a channel probability distribution from terminals within a base station area. FIG. 18 illustrates an example of channel probability distribution collection in a wireless communication system according to one embodiment of the present disclosure. Referring to FIG. 18, a base station (1820-1) and a plurality of terminals (1810-1 to 1810-9) existing within a plurality of cells corresponding to the base station area interact to measure and / or collect a channel probability distribution at each point. That is, the base station (1820-1) and each of the plurality of terminals (1810-1 to 1810-9) can collect samples for determining the channel probability distribution through interaction based on transmission and reception of reference signals and signaling. For example, the base station can schedule K terminals existing in a channel map to transmit a Sounding Reference Signal (SRS) via an uplink. As another example, the base station can collect samples of channel classes via a downlink CSI-RS and reflect the collected samples in a channel point map. In the present disclosure, the channel probability distribution may be referred to as a channel distribution, or another term having a corresponding technical meaning.

[0189] Among multiple terminals The measurement problem for the target channel distribution of a dog's terminal can be defined as follows. The th terminal is the channel distribution vector has. Here, is the number of classes of the channel, which corresponds to the size of the channel distribution. At this time, Satisfying Assume that the number of reference signal transmissions is a given budget. The number of transmissions given to the th terminal If you say so, This is satisfied. The target distribution of the actual population within the given budget and the sample distribution obtained by sampling The difference should be minimized. This can be expressed as [Mathematical Formula 1] below.

[0190]

[0191] In [Equation 1], is the target distribution of the actual population, is the sample distribution obtained by sampling, is the distance function, Is The number of transmissions given to the th terminal, is the number of reference signal transmissions, means the number of terminals.

[0192] In [Mathematical Formula 1], the objective function is a function for determining the difference or distance between the target distribution and the sample distribution, for example,

[0193] , , or , which can be defined as L2 distance, L1 distance, and KL (Kullback-Leibler divergence) distance, respectively.

[0194] In other words, it is desirable for the difference between the target distribution and the sample distribution, i.e., the average distance, to have a minimum value. To achieve this, the number of signal transmissions for measurement must be appropriately distributed to each terminal.

[0195] The base station is To collect channel distributions corresponding to the context of a set of terminals, a reference signal can be randomly transmitted, and random scheduling can be performed to collect channel distribution information. However, by utilizing statistical properties during sampling, it is possible to reduce the number of samplings or the number of scheduling times for reference signal transmissions compared to performing random scheduling while allowing for the same error. Therefore, solving this optimization problem is meaningful. In statistics, when trying to minimize the difference between the estimated value of a sample distribution and the distribution of the population, the variance of the estimate must be minimized online along with the sampling. The variance of the sample estimate determines the confidence interval in terms of hypothesis testing. Here, the confidence interval represents the degree of uncertainty in the distribution value estimated by the sample, and is specified by the upper confidence bound and the lower confidence bound. The smaller the confidence bound, the greater the confidence in the sample distribution. Scheduling based on the upper bound of the confidence bound is referred to as "upper confidence bound (UCB)-based sampling" or "upper confidence bound sampling." It is known that confidence bound sampling can be used to estimate the probability distribution of multiple users. To solve the aforementioned optimization problem, an appropriate scheduling protocol between the base station and the terminal is required. Note that sampling is a statistical term, and in this disclosure, sampling can be referred to as channel distribution data collection.

[0196] FIG. 19 illustrates an example of a procedure for reporting channel measurement results in a wireless communication system according to one embodiment of the present disclosure. FIG. 19 illustrates a method performed by a terminal reporting channel measurement results.

[0197] Referring to FIG. 19, in step S1901, the terminal transmits capability information related to measurement. That is, the terminal can transmit capability information related to channel measurement of the terminal to the base station. The capability information related to channel measurement is information indicating whether measurement and / or collection of channel distribution is possible, and may include at least one of information regarding whether channel distribution measurement is possible and information regarding the channel measurement model in use.

[0198] In step S1903, 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 configuration information related to channel distribution measurement. The configuration information related to channel distribution measurement may include at least one of the number of terminals participating in channel distribution measurement, the total number of transmissions of reference signals for channel distribution measurement, the type of channel distribution, the size of the channel distribution, the number of simultaneous transmissions (e.g., the number of simultaneously available reference signal resources), an allowable range of a channel estimation error, an objective function for the estimation error, an upper confidence bound function, information on a model for channel measurement (e.g., a model identifier), and context information.

[0199] In step S1905, 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. In other words, the terminal receives reference signals transmitted for channel distribution measurement.

[0200] In step S1907, 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 situation information, for example, the current location of the terminal and the current context of the terminal, and record the measurement result and the situation information together. In other words, the terminal links the measurement result and the situation information. At this time, the current context of the terminal may be based on the received configuration information. The channel measurement result may include information about the channel distribution. For example, the channel measurement result may include at least one of a channel class distribution, a beam index distribution, a time synchronization distribution, an RSSI distribution, a PMI distribution, an RI distribution, and a CQI distribution. According to one embodiment, the terminal may calculate a confidence upper bound function value based on the channel distribution.

[0201] In step S1909, the terminal transmits a report on the measurement results. That is, the terminal transmits feedback information generated based on the received signals. According to various embodiments, the report may include information related to a confidence upper bound function value used to select a set of terminals performing measurements to collect channel probability distribution information. For example, the report may include at least one of a channel distribution and a confidence upper bound function value. Furthermore, according to one embodiment, the report may include information about 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.

[0202] FIG. 20 illustrates an example of a procedure for receiving channel measurement results in a wireless communication system according to one embodiment of the present disclosure. FIG. 28 illustrates a method performed by a base station for receiving channel measurement results.

[0203] Referring to FIG. 20, in step S2001, the base station receives capability information related to measurement. That is, the base station can transmit capability information related to channel measurement of the base station to the terminal. The capability information related to channel measurement is information indicating whether measurement and / or collection of channel distribution is possible, and may include at least one of information regarding whether channel distribution measurement is possible and information regarding the channel measurement model in use.

[0204] In step S2003, 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 configuration information related to channel distribution measurement. The configuration information related to channel distribution measurement may include at least one of the number of terminals participating in channel distribution measurement, the total number of transmissions of reference signals for channel distribution measurement, the type of channel distribution, the size of the channel distribution, the number of simultaneous transmissions (e.g., the number of simultaneously available resources), an allowable range of channel estimation error, an objective function for estimation error, an upper confidence bound function, information on a model for channel measurement (e.g., a model identifier), and context information.

[0205] In step S2005, the base station transmits signals for measurement. The base station transmits signals (e.g., reference signals) based on configuration information. That is, the base station can transmit signals based on a sequence indicated by the configuration information through resources indicated by the configuration information. According to various embodiments, the base station transmits reference signals so that at least one terminal performs channel distribution measurements for creating a channel point map.

[0206] In step S2007, the base station receives a report on the measurement results. That is, the base station receives feedback information generated based on the received signals. According to various embodiments, the report may include information related to a confidence upper bound function value used to select a set of terminals performing measurements to collect probability distribution information of the channel. For example, the report may include at least one of channel distribution information and a confidence upper bound function value. For example, the feedback information may include at least one of a channel class distribution, a beam index distribution, a time synchronization distribution, an RSSI distribution, a PMI distribution, an RI distribution, and a CQI distribution. Furthermore, according to one embodiment, the report may include information on 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.

[0207] In step S2009, 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 distances to other channel points through learning, and place the embedding vector within the embedding space based on the distances.

[0208] In the embodiments described with reference to FIGS. 19 and 20, the terminal receives reference signals from the base station and measures the channel distribution using the received reference signals. However, according to another embodiment, the terminal may transmit reference signals to the base station, and the base station may measure the channel distribution using the reference signals. In this case, the operation of the terminal reporting feedback information to the base station may be omitted.

[0209] Capability Procedure

[0210] According to an embodiment of the present disclosure, a base station and a terminal can collect channel-related data after mutually confirming their ability to perform channel-related data collection functions. The channel-related data includes channel distribution information.

[0211] FIG. 21 illustrates an example of a procedure for collecting channel-related data in a wireless communication system according to one embodiment of the present disclosure. FIG. 21 illustrates the procedure of a terminal (2110) and a base station (2120).

[0212] Referring to FIG. 21, in step S2101, the terminal (2110) and the base station (2120) perform a data collection capability verification procedure. The data collection capability verification procedure is a procedure for mutually verifying whether the terminal (2110) and the base station (2120) support a data collection function related to a channel. That is, the base station (2120) may transmit a message to the terminal (2110) inquiring whether the terminal (2110) supports a data collection function related to a channel, and the terminal (2110) may transmit a response message to the base station (2120). At this time, the inquiry message may include information indicating that the base station (2120) supports a data collection function related to a channel. In addition, the response message may include information indicating that the terminal (2110) supports a data collection function related to a channel.

[0213] In step S2103, the terminal (2110) and the base station (2120) perform a channel-related data collection procedure. That is, after mutually confirming that the terminal (2110) and the base station (2120) support a channel-related data collection function, they perform a channel-related data collection procedure. Specifically, the terminal (2110) and the base station (2120) can collect data related to a downlink channel or data related to an uplink channel.

[0214] If data related to a downlink channel is collected, the base station (2120) collects data from among multiple terminals within the base station area. After selecting a terminal, a reference signal is transmitted to the selected terminal set, and the terminal (2110) measures the channel distribution based on the reference signal received from the base station (2120) and transmits data related to the channel including the channel distribution measurement result to the base station (2120).

[0215] On the other hand, when collecting data related to an uplink channel, the base station (2120) collects data from among multiple terminals within the base station area. After selecting a terminal, a request is made to transmit a reference signal to the selected terminal set, and the terminal (2110) transmits the reference signal to the base station (2120). At this time, the base station (2120) can measure the channel distribution based on the reference signal transmitted from the terminal (2110), and collect and / or update data related to the channel based on the channel distribution measurement result.

[0216] FIG. 22 illustrates an example of a procedure for collecting data related to a downlink channel in a wireless communication system according to one embodiment of the present disclosure. FIG. 22 illustrates signaling between a terminal (2210) and a base station (2220) for collecting data related to a downlink channel. At least one operation of FIG. 22 may be performed after a procedure for verifying channel-related data collection capabilities between the terminal (2210) and the base station (2220) is performed.

[0217] Referring to FIG. 22, in step S2201, the base station (2220) transmits a data collection request message to the terminal (2210). That is, the base station (2220) transmits a message requesting the collection of channel-related data to multiple terminals within the base station area. The channel-related data may include channel distribution or data obtained based on the channel distribution (e.g., a confidence upper bound function value). The data collection request message includes configuration information related to channel distribution measurement. The configuration information related to channel distribution measurement may include at least one of the information elements exemplified in [Table 2] below.

[0218] Information elementsVariablesDescriptionnumber of UEs Number of terminals participating in channel distribution estimation transmission budget The total number of downlink reference signal transmissions allowed until the base station completes the channel distribution measurement, i.e., budget channel distribution type Types of channel distributions: class distribution, timing distribution (e.g., TA distribution, time synchronization probability distribution for CSI-RS for time synchronization tracking, etc.), quality distribution, signal strength distribution, beam index distribution, beam precoder distribution, and are scalable because they are determined by arbitrary models and reference signals. channel distribution size Size of channel distribution number of simultaneous transmissions Confidence budget: the number of reference signal resources or terminals that can be used simultaneously when transmitting a base station downlink reference signal Confidence interval budget, indicating the acceptable range of estimation error. objective function It is passed in the form of an identifier as an objective function, and can be set according to L1, L2, KL, which are the objective or loss functions for the estimation error. UCB function As a confidence upper bound function, it is a function that depends on the sum of the estimated value and the confidence interval, the number of times the reference signal has been transmitted so far, the number of terminals participating in the channel distribution estimation, and the confidence error tolerance range, and is determined according to the objective function. Measurement model It is passed as an identifier (ID) for the model for channel measurement. It is expressed as a model function e(H) that uses the channel H as input. For example, this function can be an average function, a low-dimensional transformation function (e.g., PCA, IsoMap, SVD, etc.), the output of a neural network classification softmax, or the latent space output function of an autoencoder. The output of this function is a multidimensional output.

[0219] Although not included in [Table 2], the data collection request message may further include context information elements. Here, the context may include dynamic information of the terminal (e.g., location, mobility level, movement speed, etc.) for measuring channel distribution.

[0220] Furthermore, according to one embodiment, the data collection request message may be a message requesting transmission of capability information related to channel measurement of the terminal.

[0221] In step S2203, the terminal (2210) transmits a data collection response message to the base station (2220). The data collection response message is a response to the data collection request and may indicate whether the terminal (3010) is capable of performing data collection. According to one embodiment, the data collection response message may include capability information of the terminal related to channel measurement. According to one embodiment, if the terminal (2210) does not have data collection capability related to the channel, the terminal (2210) may not transmit the data collection response message to the base station (2220).

[0222] In step S2205, the base station (2220) transmits a channel-related data collection configuration message to the terminal (2210). That is, the base station (2220) transmits a data collection response message to one of the multiple terminals. Select the terminal of the dog and select A channel-related data configuration message including configuration information for channel-related data collection is transmitted to terminals. The configuration information for channel-related data collection may include at least one of information related to reference signals transmitted for measurement (e.g., resources, sequences, etc.) and information related to feedback (e.g., format, resources, number of feedbacks, reporting cycles, etc.). According to one embodiment, the configuration information for channel-related data collection may further include at least one information element among the information elements listed in Table 2.

[0223] In step S2207, the base station (2220) determines a set of terminals. For example, the base station (2220) Among the terminals of the dog Select the terminals of the dog and select By including the terminals of the dog in the terminal set, the terminal set can be determined. Specifically, the base station (2220) transmits a counter After initializing to 1, the terminal set Initializes the base station (2220) A set of terminals among the terminals of a dog Not belonging to Select the terminal of the dog and select A collection of terminals of a dog can be included in. Here, the transmission counter Indicates the number of times the reference signal is transmitted. In this embodiment, terminal (2210) is included in the terminal set.

[0224] In step S2209, the base station (2220) transmits a reference signal to the terminal (2210). At this time, the base station (2220) is a terminal set including the terminal (2210). A downlink reference signal is transmitted to terminals belonging to the base station (2220). At this time, although not shown in Fig. 22, the base station (2220) can further transmit a DL grant. The DL grant is a transmission counter indicating the number of reference signal transmissions up to the present. and may include at least one of information related to a downlink reference signal.

[0225] In step S2211, the terminal (2210) updates channel distribution information. The terminal (301) can measure the channel distribution for the reference signal based on the configuration information related to channel distribution measurement. The terminal (2210) updates the channel distribution based on the downlink reference signal received from the base station (2220). and measure, Upper confidence limit at the point in time can be calculated. For example, the terminal (2210) can determine a probability variable representing a distance metric between a target probability distribution and a currently estimated probability distribution, and determine an upper confidence limit value by checking the upper limit of the confidence interval of the determined probability variable. That is, an error or an estimated value of a distance from the target probability distribution can be determined using the measured channel distribution. Here, since the estimated value of the distance is a probability variable, a confidence interval of the estimated value exists. An upper confidence limit value can be determined as a result of reflecting this confidence interval. And, can be updated, i.e., increased, based on the number of reference signal transmissions. In addition, the terminal (2210) can calculate a confidence upper bound function value based on the calculated confidence upper bound value. In other words, the terminal (2210) can calculate the confidence upper bound value based on the calculated confidence upper bound value. And the number of times the terminal (2210) has received a downlink reference signal up to now Confidence upper bound function value based on can be calculated.

[0226] In step S2213, the terminal (2210) transmits data related to the channel. For example, the data related to the channel may be channel distribution. or upper confidence limit , and the upper confidence limit function value may include at least one of the following: According to one embodiment, the terminal (2210) may include a transmission counter Total number of transmissions allowed Channel distribution depending on whether it is less than or equal to , upper confidence limit , and the upper confidence limit function value At least one of them can be selected and transmitted to the base station (2220). For example, a transmission counter Total number of transmissions allowed If less than or equal to, the terminal (2210) is the upper confidence limit value , and the upper confidence limit function value Data related to a channel including at least one of the above can be transmitted to the base station (2220). On the other hand, the transmission counter Total number of transmissions allowed If larger, the terminal (2210) Channel distribution at the point in time can be transmitted to the base station (2220).

[0227] Thereafter, the base station (2220) may repeatedly perform at least some of the steps S2207 to S2213, or may generate a channel point map based on data related to a channel received from the terminal (2210).

[0228] Specifically, the base station (2220) transmits a counter Based on this, steps S2207 to S2213 may be repeated, or steps S2209 to S2213 may be repeated.

[0229] For example, the transmission counter The number of terminals participating in channel distribution estimation If less than or equal to, the base station (2220) To collect data related to the downlink channel for the terminals, steps S2207 to S2213 may be repeated.

[0230] On the other hand, the transmission counter The number of terminals participating in channel distribution estimation If larger, the base station (2220) It is determined that data related to the downlink channel has been collected from the terminals of the dog, Based on the upper trust limit value of each terminal You can select at least one terminal among the terminals.

[0231] Afterwards, the base station (2220) can perform steps S2209 to S2213 for at least one selected terminal. That is, the base station (2220) can additionally collect data related to the downlink channel of at least one terminal having a relatively large upper limit of confidence. In other words, the base station (2220) The upper confidence limit function value of each terminal is checked, and at least one terminal having a relatively large checked upper confidence limit function value is selected, and then a downlink reference signal can be additionally transmitted to at least one selected terminal. To this end, the base station (2220) transmits a transmission counter A set of terminals corresponding to The upper trust limit function value of each terminal belonging to The terminals can be sorted based on the upper confidence bound function value. That is, the base station The terminals can be sorted in descending order. The base station (2220) selects the uppermost terminal among the sorted terminals. Select the terminals of the dog and select A downlink reference signal can be additionally transmitted to the terminals of the dog. Here, the selected It additionally transmits downlink reference signals to the terminals of the dog. This means that the number of reference signal transmissions is additionally distributed to the terminals.

[0232] The terminals of the dog distribute the channel based on the received downlink reference signal. and update, Upper confidence limit at the point in time and confidence upper bound function values can be recalculated. At this time, the transmission counter Total number of transmissions allowed If less than or equal to, The dog's terminals Upper confidence limit at the point in time and confidence upper bound function values Transmit at least one of the signals to the base station (2220), and the transmission counter Total number of transmissions allowed If it is larger than The dog's terminals Channel distribution at the point in time can be transmitted to the base station (2220).

[0233] Through the repetitive movements described above, When data related to a channel including channel distribution is received from terminals, the base station (2220) can generate a channel point map based on the data related to the received channel.

[0234] In the embodiment described with reference to FIG. 22, steps S2201, S2203, and S2205 may be included in a data collection setup procedure or a collection capability verification procedure related to a channel (e.g., step S2101 of FIG. 21), and steps S2207, S2209, S2211, and S2213 may be included in a data collection activation procedure related to a channel (e.g., step S2103 of FIG. 21). The data collection activation procedure related to a channel may be repeatedly performed as needed.

[0235] In the embodiment described with reference to Fig. 22, Additional transmission of a reference signal to at least one terminal among the terminals with a relatively large upper limit of trust, and additional collection of channel-related data for this, is performed by setting a preset budget, i.e., the total number of transmissions of the downlink reference signal for which the number of transmissions of the reference signal is specified. The purpose is to minimize the difference between the target distribution and the sample distribution within a range that does not exceed .

[0236] The embodiment described with reference to FIG. 22 is a data collection procedure related to a downlink channel, in which a base station transmits a downlink reference signal to a terminal and collects channel-related data including channel distribution information from the terminal. However, the embodiments of the present disclosure are not limited to the data collection procedure related to a downlink channel. That is, according to various embodiments, a base station may receive an uplink reference signal and collect channel-related data including channel distribution information based thereon. Such a data collection procedure related to an uplink channel may be performed as illustrated in FIG. 23.

[0237] FIG. 23 illustrates an example of a procedure for collecting data related to an uplink channel according to one embodiment of the present disclosure. FIG. 23 illustrates signaling between a terminal (2310) and a base station (2320) for collecting data related to an uplink channel. At least one operation of FIG. 23 may be performed after a procedure for verifying channel-related data collection capabilities between the terminal (2310) and the base station (2320) is performed. The base station (2320) may independently manage at least one of the information elements listed in [Table 3] below for collecting data related to an uplink channel.

[0238] Information elementsVariablesDescriptionnumber of UEs Number of terminals participating in channel distribution estimation transmission budget Total number of allowed uplink reference signal transmissions (budget) until the base station completes channel distribution measurement. channel distribution type Types of channel distribution: class distribution, timing distribution, quality distribution, signal strength distribution, beam index distribution, and beam precoder distribution. It is scalable because it is determined by an arbitrary model and reference signal. channel distribution size Size of channel distribution number of simultaneous transmissions The number of RS resources available simultaneously when transmitting an uplink reference signal or the number of terminals transmitting an uplink reference signal simultaneously is the confidence budget. Confidence interval budget, indicating the acceptable range of estimation error. objective function It is passed in the form of an identifier as an objective function, and can be set according to L1, L2, KL, which are the objective or loss functions for the estimation error. UCB function As a confidence upper bound function, it is a function that depends on the sum of the estimated value and the confidence interval, the number of times the reference signal has been transmitted so far, the number of terminals participating in the channel distribution estimation, and the confidence error tolerance range, and is determined according to the objective function. Measurement model It is passed as an identifier (ID) for the model for channel measurement. It is expressed as a model function e(H) that uses the channel H as input. For example, this function can be an average function, a low-dimensional transformation function (e.g., PCA, IsoMap, SVD, etc.), the output of a neural network classification softmax, or the latent space output function of an autoencoder. The output of this function is a multidimensional output.

[0239] Although not included in [Table 3], information elements independently managed at the base station (2320) for data collection related to the uplink channel may further include context information elements.

[0240] Referring to FIG. 23, in step S2301, the base station (2320) transmits a data collection request message to the terminal (2310). That is, the base station (2320) transmits a message requesting the collection of channel-related data to multiple terminals within the base station area. The channel-related data may include channel distribution or data obtained based on the channel distribution (e.g., a confidence upper bound function value). According to one embodiment, the data collection request message may include at least one of the information elements listed in [Table 3].

[0241] In step S2303, the terminal (2310) transmits a data collection response message to the base station (2320). The data collection response message is a response message to a data collection request. According to one embodiment, if the terminal (2310) does not have data collection capabilities related to the channel, the terminal (2310) may not transmit the data collection response message to the base station (2320).

[0242] In step S2305, the base station (2320) transmits a channel-related data collection configuration message to the terminal (2310). That is, the base station (2320) transmits a data collection response message to one of the multiple terminals. Select the terminal of the dog and select A channel-related data configuration message containing configuration information for channel-related data collection is transmitted to the terminals. The configuration information for null-related data collection may include information related to reference signals transmitted for measurement (e.g., resources, sequences, etc.). The configuration information for null-related data collection may further include at least one of the information elements listed in Table 3.

[0243] In step S2307, the base station (2320) determines a set of terminals. That is, the base station (2320) Among the terminals of the dog Select the terminals of the dog and select By including the terminals of the dog in the terminal set, the terminal set is determined. Specifically, the base station (2320) transmits a counter After initializing to 1, the terminal set Initializes the base station (2320) A set of terminals among the terminals of a dog Not belonging to Select the terminal of the dog and select A collection of terminals of a dog can be included in the transmission counter Indicates the number of times the reference signal is transmitted.

[0244] In step S2309, the base station (2320) transmits a reference signal transmission request message to the terminal (2310). At this time, the terminal (2310) may be a terminal included in the terminal set. That is, the base station (2320) is a terminal set. Requests transmission of an uplink reference signal to terminals belonging to the terminal. At this time, the uplink reference signal transmission request message may include scheduling information (e.g., transmission resources, sequence, etc.) for transmission of the uplink reference signal of the terminal (2310).

[0245] In step S2311, the terminal (2310) transmits an uplink reference signal. That is, the terminal (2310) can transmit an uplink reference signal based on scheduling information included in the reference signal transmission request message.

[0246] In step S2313, the base station (2320) updates the channel distribution information. That is, the base station (2320) updates the channel distribution based on the uplink reference signal received from the terminal (2310). and update, Upper confidence limit at the point in time can be calculated. At this time, can be updated based on the number of reference signal transmissions. That is, can be increased by the number of times the reference signal is transmitted.

[0247] Thereafter, the base station (2320) may repeatedly perform at least some of the steps S2307 to S2313, or generate a channel point map based on channel distribution information.

[0248] Specifically, the base station (2320) transmits a counter Based on this, steps S2307 to S2313 may be repeated, or steps S2309 to S2313 may be repeated.

[0249] for example, go If less than or equal to, the base station (2320) To collect data related to the uplink channel for the terminals, steps S2307 to S2313 may be repeated.

[0250] On the other hand, the updated go If larger, the base station (2320) It is confirmed that data related to the uplink channel for the terminals of the dog has been collected and based on the upper confidence limit value. After selecting at least one terminal among the terminals, steps S2309 to S2313 may be repeated for at least one selected terminal. That is, the base station (2320) may additionally collect data related to the uplink channel of at least one terminal having a high confidence upper bound function value. In other words, the base station (2320) After calculating the upper confidence limit function value of each terminal, and selecting at least one terminal having a relatively large calculated upper confidence limit function value, an additional request for uplink reference signal transmission can be made to at least one selected terminal. That is, the base station (2320) transmits a transmission counter A set of terminals corresponding to About, The upper limit of trust transmitted by the th terminal and The number of times the th terminal has received the uplink reference signal so far Confidence upper bound function value based on and calculate the calculated confidence upper bound function value A set of terminals based on The terminals belonging to can be sorted. That is, the base station has a confidence upper bound function value The terminals can be sorted in this order. The base station (2320) is the uppermost of the sorted terminals. Select the terminals of the dog and select The terminals of the dog can be requested to transmit an uplink reference signal. Here, the selected Requesting the uplink reference signal transmission to the terminals of the dog is This means that the number of reference signal transmissions is additionally distributed to the terminals.

[0251] The terminals of the dog retransmit the uplink reference signal, and the base station (2320) retransmits the uplink reference signal based on the uplink reference signal. Channel distribution for dog terminals can be renewed. At this time, go If less than or equal to, the base station (2320) About the terminals of the dog Upper confidence limit at the point in time can be calculated. On the other hand, go If it is larger than About the terminals of the dog Channel distribution at the point in time Based on this, a channel point map can be created.

[0252] In the embodiment described with reference to FIG. 22, the upper confidence bound function value is calculated at the terminal, and in the embodiment described with reference to FIG. 23, the upper confidence bound function value is calculated at the base station. However, when collecting data related to an uplink channel, the upper confidence bound function value may also be calculated at the terminal. In this case, the terminal may obtain at least one of the values ​​required for calculating the upper confidence bound function value (e.g., a target probability distribution, a currently estimated probability distribution, a probability variable, an upper confidence bound value, etc.) from the base station, and may calculate the upper confidence bound function value based on the obtained value.

[0253] In the data collection process related to the uplink channel or the downlink channel as described above, the objective function and the upper confidence bound function (UCB function) can be defined in various ways. The objective function is a function that determines the distance between distributions (e.g., the target distribution and the sample distribution) and may include the expected value of the sum of the values ​​related to the differences between the elements of each distribution. The upper confidence bound function is a function that determines the upper confidence bound value. The lower the number of reference signal transmissions assigned to the corresponding terminal and the larger the sum of the estimated value and the confidence interval, the larger the upper confidence bound value can be determined. For example, the objective function and upper confidence bound function defined as in [Table 4] below can be used.

[0254] objective functionUCB function

[0255] In [Table 4], is the target distribution of the actual population, is the sample distribution obtained by sampling, is the distance function, Is At this point The upper trust limit of the th terminal, Is At this point The number of transmissions given to the th terminal, is the tolerance, is the total number of terminals, is the number of channel classes, means the current reference signal transmission count.

[0256] The objective function and upper confidence bound function in [Table 4] are merely examples to aid understanding, and the embodiments of the present disclosure are not limited thereto. As the theory develops, the upper confidence bound function may be modified in various ways.

[0257] The embodiment described with reference to FIG. 22 relates to a procedure for collecting data related to a downlink channel, and the embodiment described with reference to FIG. 23 relates to a procedure for collecting data related to an uplink channel. However, the proposed technology is not limited thereto, and according to other embodiments, data related to an uplink channel and data related to a downlink channel can be collected simultaneously under base station control. In other words, a procedure for collecting data related to a bidirectional link channel can also be performed under base station control.

[0258] The characteristics of the data collection procedure related to the downlink channel are that the terminal has a probability distribution , and when the estimation procedure is completed, is transmitted to the base station. At this time, the base station receives the upper confidence limit value and the upper confidence limit function value calculated by the terminal for scheduling. On the other hand, the characteristic of the data collection procedure related to the uplink channel is that the base station obtains the probability distribution It estimates and manages it until the estimation process is completed. In addition, the upper confidence limit and the lower confidence limit can be unilaterally calculated and managed at the base station.

[0259] The data collection procedure related to the bidirectional link channel may be changed as follows, taking into account the characteristics of the data collection procedure related to the downlink channel and the characteristics of the data collection procedure related to the uplink channel as described above.

[0260] Recent transmissions If it is a downlink transmission, the next transmission can be uplink collection. At this time, the base station can obtain the probability distribution of the terminals secured so far through UL grant or upper signaling message. can be controlled to be transmitted to the base station. Afterwards, the base station can perform scheduling for the next downlink or uplink based on the trust upper bound function value of each terminal.

[0261] Conversely, recent transmissions If it is an uplink transmission, the next transmission can be downlink collection. At this time, the base station can obtain the probability distribution obtained from the base station so far through UL grant or upper signaling message. can be controlled to be transmitted to the terminal. Thereafter, the terminal can calculate a confidence upper bound function value based on the received probability distribution and transmit the calculated confidence upper bound function value to the base station so that the base station can reflect it in the next downlink or uplink scheduling.

[0262] FIG. 24 illustrates an example of collecting probability distributions in a wireless communication system according to one embodiment of the present disclosure. FIG. 24 assumes that there are five terminals (2410-1 to 2410-5) within the coverage area of ​​a base station (2420-1).

[0263] Referring to Fig. 24, the base station (2420-1) can collect the channel probability distribution, i.e., the distribution of the wireless radio channel class, at each point where five terminals (2410-1 to 2410-5) are located. CSI-RS can be used to collect data related to the downlink channel. As a system setting value of the base station (2420-1), a kind of tolerance between the probability distribution of the population and the sample probability distribution estimation can be set. By the upper confidence bound theory. Estimated value for the sample probability distribution of the th terminal and population probability distribution go The probability of one day is can be expressed as the total number of transmissions allowed by all terminals until the channel probability distribution collection is completed. Send as much as you want, In this case, 1000, the total number of transmissions of the five terminals (2410-1 to 2410-5) is This becomes the transmission counter. until The total number of transmissions of the th terminal is , and the measurement probability distribution is For channel point maps or channel charting, the measurement model transmitted by the base station to the terminal is e(H), which can be a classification neural network model that classifies the channel class. In this case, the channel class includes three classes such as urban, rural, and office. The probability distribution simply has three distributions for each context including the locations of the three terminals. In addition, the base station can transmit an objective function to the terminal. As an example, the L2 objective function Is , and the corresponding upper confidence bound function is Here, , , and, is a terminal of is the estimated value of the second class.

[0264] In order to evaluate the confidence upper bound function of the channel distribution, all terminals (2410-1 to 2410-5) must receive the downlink reference signal at least once. That is, each of the five terminals (2410-1 to 2410-5) must receive the channel distribution by transmitting the downlink reference signal a total of five times. , and in this case, the upper confidence limit function can be evaluated. That is, when the base station (2420-1) performs a total of five downlink reference signal transmissions, each terminal (2410-1 to 2410-5) can distribute the channel based on the downlink reference signal. After calculating the upper confidence limit function value, the upper confidence limit function value is calculated based on this. Each terminal (2410-1 to 2410-5) can transmit the upper confidence limit function value to the base station. Thereafter, the base station (2420-1) schedules the downlink reference signal based on the upper confidence limit function value. This operation is performed by a transmission counter for the reference signal transmission. Total number of transmissions allowed = can be repeated until it reaches 1000.

[0265] That is, looking at the scheduling aspect based on the upper confidence limit function, when collecting channel distribution information based on the downlink reference signal, the upper confidence limit function value is calculated at the terminal, and the base station can perform scheduling for the downlink reference signal based on the upper confidence limit function value calculated at the terminal. Conversely, when collecting channel distribution information based on the uplink reference signal, the base station can directly calculate the upper confidence limit function value and perform scheduling for the uplink reference signal of the terminal based on this.

[0266] In terms of the diversity aspect of channel distribution according to one embodiment, channel distribution information may vary depending on the purpose of channel charting or channel point map collection. For example, when beam CSI-RS is used, the beam index distribution may be measured and reflected in the channel point map. When synchronous CSI-RS is used, the probability distribution of signal frequency and time synchronization may be measured. Furthermore, in the case of CSI-RS for channel quality measurement, the distribution of SNR and / or RSSI distribution in the current terminal context may be measured and utilized. Such measurement of channel distribution may be determined by a model and / or reference signal, and the present disclosure is not limited thereto.

[0267] As described above, the present disclosure presents a method for minimizing the transmission of reference signals according to the terminal context when collecting data for generating channel charting or channel point maps, i.e., channel probability distributions. Furthermore, the present disclosure enables the effective use of the upper confidence bound theory by mutually negotiating measurement models between the terminal and the base station and communicating related settings. When scheduling is performed using an upper bound on the difference between the population probability distribution and the sample probability distribution estimates according to the upper confidence bound theory, the number of reference signal transmissions can be reduced compared to when terminals are randomly scheduled. In particular, the present disclosure presents a protocol that enables the upper confidence bound theory to be effectively incorporated into the channel point map.

[0268] When performing channel tasks using AI / ML models, building channel charts or channel point maps helps improve system performance from the perspective of base stations with unique local channel environments. That is, base stations are local natives of the channel environment, while terminals are visitors, making building channel charts or channel point maps extremely useful. Therefore, the present disclosure proposes a protocol between terminals and base stations for various channel distribution types, such as class distribution, timing distribution, quality distribution, signal strength distribution, beam index distribution, and beam precoder distribution, to enhance the diversity of channel charts or channel point maps.

[0269] Below, examples of wireless device utilization to which various embodiments of the present disclosure are applied are described.

[0270] Figure 25 illustrates an example of a wireless device applicable to the present disclosure. The wireless device may be implemented in various forms depending on the use case / service (see Figure 1).

[0271] Referring to FIG. 25, the wireless device (200) corresponds to the wireless device (200) of FIG. 2 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (200) may include a communication unit (210), a control unit (220), a memory unit (230), and additional elements (240). The communication unit may include a communication circuit (212) and a transceiver(s) (214). For example, the communication circuit (212) may include one or more processors (202) and / or one or more memories (204) of FIG. 2. For example, the transceiver(s) (214) may include one or more transceivers (206) and / or one or more antennas (208) of FIG. 2. The control unit (220) is electrically connected to the communication unit (210), the memory unit (230), and the additional elements (240) and controls the overall operations of the wireless device. For example, the control unit (220) can control the electrical / mechanical operations of the wireless device based on the program / code / command / information stored in the memory unit (230). In addition, the control unit (220) can transmit information stored in the memory unit (230) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (210), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (230).

[0272] The additional element (240) may be configured in various ways depending on the type of the wireless device. For example, the additional element (240) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 1, 100a), a vehicle (Fig. 1, 100b-1, 100b-2), an XR device (Fig. 1, 100c), a portable device (Fig. 1, 100d), a home appliance (Fig. 1, 100e), an IoT device (Fig. 1, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 1, 400), a base station (Fig. 1, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0273] In FIG. 25, various elements, components, units / parts, and / or modules within the wireless device (200) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (210). For example, within the wireless device (200), the control unit (220) and the communication unit (210) may be wired, and the control unit (220) and a first unit (e.g., 230, 240) may be wirelessly connected via the communication unit (210). In addition, each element, component, unit / part, and / or module within the wireless device (200) may further include one or more elements. For example, the control unit (220) may be composed of a set of one or more processors. For example, the control unit (220) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0274] Below, the implementation example of Fig. 25 is described in more detail with reference to the drawings.

[0275] Figure 26 illustrates examples of portable devices applicable to the present disclosure. Portable devices may include smartphones, smart pads, wearable devices (e.g., smartwatches, smartglasses), and portable computers (e.g., laptops, etc.). Portable devices may be referred to as Mobile Stations (MS), User Terminals (UT), Mobile Subscriber Stations (MSS), Subscriber Stations (SS), Advanced Mobile Stations (AMS), or Wireless Terminals (WT).

[0276] Referring to FIG. 26, the portable device (200) may include an antenna unit (208), a communication unit (210), a control unit (220), a memory unit (230), a power supply unit (240a), an interface unit (240b), and an input / output unit (240c). The antenna unit (208) may be configured as a part of the communication unit (210). Blocks 210 to 230 / 240a to 240c of FIG. 26 correspond to blocks 210 to 230 / 240 of FIG. 25, respectively.

[0277] The communication unit (210) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (220) can control components of the mobile device (200) to perform various operations. The control unit (220) can include an AP (Application Processor). The memory unit (230) can store data / parameters / programs / codes / commands required for operating the mobile device (200). In addition, the memory unit (230) can store input / output data / information, etc. The power supply unit (240a) supplies power to the mobile device (200) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (240b) can support connection between the mobile device (200) and other external devices. The interface unit (240b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (240c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (240c) may include a camera, a microphone, a user input unit, a display unit (240d), a speaker, and / or a haptic module.

[0278] For example, in the case of data communication, the input / output unit (240c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (230). The communication unit (210) converts the information / signals stored in the memory into wireless signals, and can directly transmit the converted wireless signals to other wireless devices or to a base station. In addition, the communication unit (210) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (230) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (240c).

[0279] Figure 27 illustrates examples of vehicles or autonomous vehicles applicable to the present disclosure. The vehicles or autonomous vehicles may be implemented as mobile robots, cars, trains, manned / unmanned aerial vehicles (AVs), ships, etc.

[0280] Referring to FIG. 27, a vehicle or autonomous vehicle (200-1) may include an antenna unit (208-1), a communication unit (210-1), a control unit (220-1), a driving unit (240a-1), a power supply unit (240b-1), a sensor unit (240c-1), and an autonomous driving unit (240d-1). The antenna unit (208-1) may be configured as a part of the communication unit (210-1). Blocks 210-1 / 230-1 / 240a-1 to 240d-1 of FIG. 27 correspond to blocks 210 / 230 / 240 of FIG. 25, respectively.

[0281] The communication unit (210-1) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations (ROS), etc.), and servers. The control unit (220-1) can control elements of the vehicle or autonomous vehicle (200-1) to perform various operations. The control unit (220-1) may include an ECU (Electronic Control Unit). The drive unit (240a-1) can drive the vehicle or autonomous vehicle (200-1) on the ground. The drive unit (240a-1) may include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (240b-1) supplies power to the vehicle or autonomous vehicle (200-1) and may include a wired / wireless charging circuit, a battery, etc. The sensor unit (240c-1) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (240c-1) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an incline sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (240d-1) may implement a technology for maintaining a driving lane, a technology for automatically controlling speed such as adaptive cruise control, a technology for automatically driving along a set path, a technology for automatically setting a path and driving when a destination is set, etc.

[0282] For example, the communication unit (210-1) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (240d-1) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (220-1) can control the drive unit (240a-1) so that the vehicle or autonomous vehicle (200-1) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (210-1) can irregularly / periodically acquire the latest traffic information data from an external server and can acquire surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit (240c-1) can acquire vehicle status and surrounding environment information. The autonomous driving unit (240d-1) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (210-1) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to an external server. The external server can predict traffic information data in advance using AI technology, etc. based on information collected from the vehicle or autonomous vehicles, and provide the predicted traffic information data to the vehicle or autonomous vehicles. If the device (220-2) is an autonomous vehicle, it can perform the same procedure as the vehicle or autonomous vehicle (200-1). In addition, if the device (220-2) is a base station or a roadside base station, the device (220-2) can transmit data, control signals, etc. to the vehicle or autonomous vehicle (200-1) through the communication unit (210-2).

[0283] Figure 28 illustrates an example of a vehicle applicable to the present disclosure. The vehicle may also be implemented as a means of transportation, a train, an aircraft, a ship, etc. Referring to Figure 28, the vehicle (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), and a position measurement unit (240b). Here, blocks 210 to 230 / 240a to 240b correspond to blocks 210 to 230 / 240 of Figure 25, respectively.

[0284] The communication unit (210) can transmit and receive signals (e.g., data, control signals, etc.) with other vehicles or external devices such as base stations. The control unit (220) can control components of the vehicle (200) to perform various operations. The memory unit (230) can store data / parameters / programs / codes / commands that support various functions of the vehicle (100). The input / output unit (240a) can output AR / VR objects based on information in the memory unit (230). The input / output unit (240a) can include a HUD. The position measurement unit (240b) can obtain position information of the vehicle (200). The position information can include absolute position information of the vehicle (200), position information within a driving line, acceleration information, position information with respect to surrounding vehicles, etc. The position measurement unit (240b) can include GPS and various sensors.

[0285] For example, the communication unit (210) of the vehicle (200) can receive map information, traffic information, etc. from an external server and store them in the memory unit (230). The location measurement unit (240b) can obtain vehicle location information through GPS and various sensors and store the information in the memory unit (230). The control unit (220) can create a virtual object based on the map information, traffic information, and vehicle location information, and the input / output unit (240a) can display the created virtual object on the vehicle window (240a-1, 240a-2). In addition, the control unit (220) can determine whether the vehicle (200) is being driven normally within the driving line based on the vehicle location information. If the vehicle (200) abnormally deviates from the driving line, the control unit (220) can display a warning on the vehicle window through the input / output unit (240a). Additionally, the control unit (220) can broadcast a warning message regarding driving abnormalities to surrounding vehicles through the communication unit (210). Depending on the situation, the control unit (220) can transmit vehicle location information and information regarding driving / vehicle abnormalities to relevant authorities through the communication unit (210).

[0286] Figure 29 illustrates examples of XR devices applicable to the present disclosure. The XR devices may be implemented as HMDs, head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, and the like.

[0287] Referring to FIG. 29, the XR device (200a) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), a sensor unit (240b), and a power supply unit (240c). Here, blocks 210 to 230 / 240a to 240c of FIG. 29 correspond to blocks 210 to 230 / 240 of FIG. 25, respectively.

[0288] The communication unit (210) can transmit and receive signals (e.g., media data, control signals, etc.) with external devices such as other wireless devices, portable devices, or media servers. The media data can include videos, images, sounds, etc. The control unit (220) can control components of the XR device (200a) to perform various operations. For example, the control unit (220) can be configured to control and / or perform procedures such as video / image acquisition, (video / image) encoding, metadata generation and processing, etc. The memory unit (230) can store data / parameters / programs / codes / commands required for driving the XR device (200a) / generating XR objects. The input / output unit (240a) can obtain control information, data, etc. from the outside, and output the generated XR object. The input / output unit (240a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module. The sensor unit (240b) can obtain the XR device status, surrounding environment information, user information, etc. The sensor unit (240b) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar. The power supply unit (240c) supplies power to the XR device (200a) and may include a wired / wireless charging circuit, a battery, etc.

[0289] For example, the memory unit (230) of the XR device (200a) may include information (e.g., data, etc.) required for creating an XR object (e.g., AR / VR / MR object). The input / output unit (240a) may obtain a command to operate the XR device (200a) from the user, and the control unit (220) may operate the XR device (200a) according to the user's operating command. For example, when the user attempts to watch a movie, news, etc. through the XR device (200a), the control unit (220) may transmit content request information to another device (e.g., a mobile device (200b)) or a media server through the communication unit (230). The communication unit (230) may download / stream content such as movies and news from another device (e.g., a mobile device (200b)) or a media server to the memory unit (230). The control unit (220) controls and / or performs procedures such as video / image acquisition, (video / image) encoding, and metadata generation / processing for content, and can generate / output an XR object based on information about surrounding space or real objects acquired through the input / output unit (240a) / sensor unit (240b).

[0290] In addition, the XR device (200a) is wirelessly connected to the mobile device (200b) through the communication unit (210), and the operation of the XR device (200a) can be controlled by the mobile device (200b). For example, the mobile device (200b) can act as a controller for the XR device (200a). To this end, the XR device (200a) can obtain 3D location information of the mobile device (200b), and then generate and output an XR object corresponding to the mobile device (200b).

[0291] Figure 30 illustrates examples of robots applicable to the present disclosure. Robots can be classified into industrial, medical, household, and military types, depending on their intended use or field.

[0292] Referring to FIG. 30, the robot (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), a sensor unit (240b), and a driving unit (240c). Here, blocks 210 to 230 / 240a to 240c of FIG. 30 correspond to blocks 210 to 230 / 240 of FIG. 25, respectively.

[0293] The communication unit (210) can transmit and receive signals (e.g., driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers. The control unit (220) can control components of the robot (200) to perform various operations. The memory unit (230) can store data / parameters / programs / codes / commands that support various functions of the robot (200). The input / output unit (240a) can obtain information from the outside of the robot (200) and output information to the outside of the robot (200). The input / output unit (240a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module. The sensor unit (240b) can obtain internal information of the robot (200), surrounding environment information, user information, etc. The sensor unit (240b) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, a radar, etc. The driving unit (240c) may perform various physical operations, such as moving the robot joints. In addition, the driving unit (240c) may enable the robot (200) to drive on the ground or fly in the air. The driving unit (240c) may include an actuator, a motor, wheels, brakes, propellers, etc.

[0294] Figure 31 illustrates an example of an AI device applicable to the present disclosure.

[0295] AI devices can be implemented as fixed or mobile devices, such as TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, and vehicles.

[0296] Referring to FIG. 31, the AI ​​device (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a / 240b), a learning processor unit (240c), and a sensor unit (240d). Blocks 210 to 230 / 240a to 240d of FIG. 31 correspond to blocks 210 to 230 / 140 of FIG. 25, respectively.

[0297] The communication unit (210) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning models, control signals, etc.) with external devices such as other AI devices (e.g., 100a to 100f, 120 of FIG. 1) or AI servers (e.g., 100g of FIG. 1) using wired and wireless communication technology. To this end, the communication unit (210) can transmit information within the memory unit (230) to the external device or transfer a signal received from the external device to the memory unit (230).

[0298] The control unit (220) may determine at least one executable operation of the AI ​​device (200) based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the control unit (220) may control components of the AI ​​device (200) to perform the determined operation. For example, the control unit (220) may request, search, receive, or utilize data from the learning processor unit (240c) or the memory unit (230), and may control components of the AI ​​device (200) to perform at least one executable operation, a predicted operation, or an operation determined to be desirable. In addition, the control unit (220) may collect history information including the operation contents of the AI ​​device (200) or user feedback on the operation, and store the collected history information in the memory unit (230) or the learning processor unit (240c), or transmit the collected history information to an external device such as an AI server (FIG. 1, 100g). The collected history information may be used to update a learning model.

[0299] The memory unit (230) can store data that supports various functions of the AI ​​device (200). For example, the memory unit (230) can store data obtained from the input unit (240a), data obtained from the communication unit (210), output data of the learning processor unit (240c), and data obtained from the sensing unit (140). In addition, the memory unit (230) can store control information and / or software codes necessary for the operation / execution of the control unit (220).

[0300] The input unit (240a) can obtain various types of data from the outside of the AI ​​device (200). For example, the input unit (220) can obtain learning data for model learning, input data to which the learning model will be applied, etc. The input unit (240a) may include a camera, a microphone, and / or a user input unit. The output unit (240b) may generate output related to vision, hearing, or touch. The output unit (240b) may include a display unit, a speaker, and / or a haptic module, etc. The sensing unit (140d) can obtain at least one of internal information of the AI ​​device (200), information about the surrounding environment of the AI ​​device (200), and user information using various sensors. The sensing unit (140d) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar, etc.

[0301] The learning processor unit (240c) can train a model composed of an artificial neural network using learning data. The learning processor unit (240c) can perform AI processing together with the learning processor unit of the AI ​​server (Fig. 1, 100g). The learning processor unit (240c) can process information received from an external device via the communication unit (210) and / or information stored in the memory unit (230). In addition, the output value of the learning processor unit (240c) can be transmitted to an external device via the communication unit (210) and / or stored in the memory unit (230).

[0302] 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).

[0303] 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.

[0304] 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.

[0305] 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.

[0306] Additionally, embodiments of the present disclosure can be applied to various applications such as autonomous vehicles and drones.

Claims

1. In a method performed by a terminal in a wireless communication system, A step of receiving setting information related to measurement; A step of receiving at least one signal for measurement; performing a measurement based on at least one signal; and A step of transmitting a report related to the results of the above measurement is included, A method for reporting the results of the above measurement, wherein the reporting includes information related to an upper confidence bound (UCB) function value used to select a set of terminals performing measurements to collect probability distribution information of the channel.

2. In claim 1, A method in which the above configuration information includes at least one of information related to the total number of transmissions allowed for the at least one signal for collecting data related to the channel, the number of terminals participating in channel distribution measurement for collecting data related to the channel, the type of channel distribution, the size of the channel distribution, the number of simultaneous transmissions of the at least one signal, an allowable range of a channel estimation error, an objective function for the estimation error, an upper confidence bound function, information on a model for channel measurement, and context information.

3. In claim 1, A method in which a report related to the results of the above measurement comprises at least one of a measured channel distribution based on the at least one signal, or an obtained upper confidence limit value based on the channel distribution, or a value of the upper confidence limit function.

4. In claim 3, Reports related to the results of the above measurements are as follows: If the transmission counter of said at least one signal is less than or equal to the total number of transmissions allowed for said at least one signal, at least one of said upper confidence limit value or said upper confidence limit function value is included, A method comprising the channel distribution value, wherein the transmission counter of at least one signal is greater than the total number of transmissions allowed for the at least one signal.

5. In claim 3, A step of additionally receiving at least one signal for the above measurement; A step of performing a measurement based on at least one additionally received signal; and Further comprising a step of transmitting a report related to the results of the above measurement, A method wherein the at least one additionally received signal is additionally received based on the total number of allowed transmissions for the at least one signal and the upper confidence limit function value.

6. In claim 1, A step of receiving a first message requesting collection of data related to a channel; and A method further comprising a transmitting step of transmitting, in response to the first message, a second message including capability information for collecting data related to the channel.

7. In a method performed by a base station in a wireless communication system, A step of transmitting setting information related to measurement; a step of transmitting at least one signal for measurement; and A step of receiving a report related to the result of a measurement based on at least one signal, A method for reporting the results of the above measurements, wherein the reporting includes information related to a confidence upper bound function value used to select a set of terminals performing measurements to collect probability distribution information of the channel.

8. In claim 7, A method in which the above-mentioned setting information further includes at least one of the number of terminals participating in channel distribution measurement for collecting data related to the channel, the type of channel distribution, the size of the channel distribution, the number of simultaneous transmissions of the at least one signal, the allowable range of channel estimation error, an objective function for the estimation error, an upper confidence limit function, information on a model for channel measurement, and context information.

9. In claim 8, The step of transmitting at least one signal for the above measurement is: A step of selecting a plurality of terminals based on the number of simultaneous transmissions of at least one signal; A step of transmitting at least one signal to the plurality of terminals, A method in which the number of the above-mentioned selected multiple terminals is less than or equal to the number of terminals participating in the channel distribution measurement.

10. In claim 1, A method in which a report related to the results of the above measurement comprises at least one of a measured channel distribution based on the at least one signal, or an obtained upper confidence limit value based on the channel distribution, or a value of the upper confidence limit function.

11. In claim 10, A step of selecting at least one terminal based on the total number of allowed transmissions for at least one signal and the confidence upper bound function value; a step of additionally transmitting at least one signal for the measurement to at least one selected terminal; and A method comprising the step of receiving a report related to the results of a measurement based on at least one signal additionally received from at least one selected terminal.

12. In a wireless communication system, at a terminal, Transmitter and receiver; and A processor connected to the above transmitter and receiver is included, 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, Control to transmit a report related to the results of the above measurement, A report relating to the results of the above measurement includes information relating to a confidence upper bound function value used to select a set of terminals performing measurements to collect probability distribution information of the channel.

13. In a wireless communication system, at a base station, Transmitter and receiver; and A processor connected to the above transmitter and receiver is included, The above processor, Transmits setup information related to measurement, Transmit at least one signal for measurement, Control to receive a report related to the result of a measurement based on at least one signal, A report related to the results of the above measurement, wherein the base station includes information related to the confidence upper bound function value used to select a set of terminals performing measurements to collect probability distribution information of the channel.

14. In communication devices, At least one processor; At least one computer memory connected 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 setting information related to measurement; A step of receiving at least one signal for measurement; performing a measurement based on at least one signal; and A step of transmitting a report related to the results of the above measurement is included, A communication device comprising a report relating to the results of the above measurement, wherein the report comprises information relating to a confidence upper bound function value used to select a set of terminals performing measurements to collect probability distribution information of the channel.

15. 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, Control to transmit a report related to the results of the above measurement, A report relating to the results of the above measurement, a non-transitory computer-readable medium including information relating to a confidence upper bound function value used to select a set of terminals performing measurements to collect probability distribution information of the channel.

Images