Hybrid type measurement reporting in wireless communication system

Abstract

The invention relates to hybrid type measurement reporting in wireless communications. According to an embodiment of the present disclosure, a method performed by a user equipment (UE) configured to operate in a wireless communication system comprises: receiving, from a network, a first measurement configuration relating to generating a measurement result for a measurement object based on a measurement of a reference signal, and a second measurement configuration relating to generating a measurement result for the measurement object based on the measurement of the reference signal; and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; obtaining a first measurement result by measuring the reference signal based on the first measurement configuration; obtaining a second measurement result by providing input to the ML model based on the second measurement configuration; and transmitting at least one of the first measurement result or the second measurement result to a network.

Description

Technical Field

[0001] The present disclosure relates to mixed types of measurement reporting in wireless communications. Background Art

[0002] The 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is a technology that allows high-speed packet communications. Many solutions have been proposed for the LTE goal, including those aimed at reducing user and supplier costs, improving service quality, and expanding and improving coverage and system capacity. As upper layer requirements, 3GPP LTE requires reduced cost per bit, increased service availability, flexible use of frequency bands, simple structure, open interfaces, and appropriate power consumption of terminals.

[0003] The International Telecommunication Union (ITU) and 3GPP have begun work on developing requirements and specifications for New Radio (NR) systems. 3GPP must identify and develop the technical components required for successful standardization of new RATs that will meet both urgent market needs and longer-term requirements set forth by the ITU Radiocommunication Sector (ITU-R) International Mobile Telecommunications (IMT)-2020 process in a timely manner. In addition, NR should be able to use any spectrum band up to at least the 100 GHz range that can be used for wireless communications even in the more distant future.

[0004] The goal of NR is to be a single technology framework that addresses all use cases, requirements and deployment scenarios, including enhanced mobile broadband (eMBB), massive machine type communications (mMTC), ultra-reliable and low latency communications (URLLC), etc. NR should be inherently forward compatible.

[0005] In wireless communications, a user equipment (UE) may be required to provide measurement results to the network. The UE may perform actual measurements on reference signals related to the measurement target to obtain the measurement results. The UE may also obtain the measurement results by using an artificial intelligence (AI) / machine learning (ML) model. The UE may apply a machine learning algorithm related to the AI / ML model to the model input and obtain AI / ML-based measurement results. Summary of the invention

[0006] Solution to the problem

[0007] An aspect of the present disclosure is to provide a method and apparatus for mixed-type measurement reporting in a wireless communication system.

[0008] Another aspect of the present disclosure is to provide a method and apparatus for actual measurement and AI / ML-based measurement reporting in a wireless communication system.

[0009] According to an embodiment of the present disclosure, a method performed by a user equipment (UE) configured to operate in a wireless communication system includes: receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; based on the first measurement configuration, obtaining a first measurement result by measuring a reference signal; based on the second measurement configuration, obtaining a second measurement result by providing an input to the ML model; and sending at least one of the first measurement result or the second measurement result to the network.

[0010] According to an embodiment of the present disclosure, a user equipment (UE) configured to operate in a wireless communication system includes: at least one transceiver; at least one processor; and at least one memory, which is operatively connected to the at least one processor and stores instructions, and the instructions perform operations based on being executed by the at least one processor, and the operations include: receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; based on the second measurement configuration, obtaining a second measurement result by providing input to the ML model; and sending at least one of the first measurement result or the second measurement result to the network.

[0011] According to an embodiment of the present disclosure, a network node configured to operate in a wireless communication system includes: at least one transceiver; at least one processor; and at least one memory, wherein the at least one memory is operatively connected to the at least one processor and stores instructions, and the instructions perform operations based on being executed by the at least one processor, wherein the operations include: sending a first measurement configuration and a second measurement configuration to a user equipment (UE), wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; receiving a first measurement result and a second measurement result from the UE, wherein the first measurement result is obtained by measuring a reference signal based on the first measurement configuration, and the second measurement result is obtained by providing an input to the ML model based on the second measurement configuration; determining whether to continue or stop reporting a configuration of a measurement result derived according to the ML model based on the first measurement result and the second measurement result; and sending the configuration to the UE.

[0012] According to an embodiment of the present disclosure, a method performed by a network node configured to operate in a wireless communication system includes: sending a first measurement configuration and a second measurement configuration to a user equipment (UE), wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; receiving a first measurement result and a second measurement result from the UE, wherein the first measurement result is obtained by measuring a reference signal based on the first measurement configuration, and the second measurement result is obtained by providing input to the ML model based on the second measurement configuration; determining whether to continue or stop reporting a configuration of a measurement result derived from the ML model based on the first measurement result and the second measurement result; and sending the configuration to the UE.

[0013] According to an embodiment of the present disclosure, a device suitable for operating in a wireless communication system includes: at least one processor; and at least one memory, which is operatively connected to the at least one processor and stores instructions, and the instructions perform operations based on being executed by the at least one processor, and the operations include: receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; based on the second measurement configuration, obtaining a second measurement result by providing input to the ML model; and sending at least one of the first measurement result or the second measurement result to the network.

[0014] According to an embodiment of the present disclosure, a non-transitory computer-readable medium (CRM) stores a program code, which implements instructions. These instructions perform operations based on being executed by at least one processor, and the operations include: receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; based on the second measurement configuration, obtaining a second measurement result by providing input to the ML model; and sending at least one of the first measurement result or the second measurement result to the network.

[0015] The present disclosure may have various beneficial effects.

[0016] For example, the network can verify the accuracy of the current model and take actions such as model changes or fallback to non-AI / ML based operations.

[0017] The beneficial effects that can be obtained by the specific embodiments of the present disclosure are not limited to the beneficial effects listed above. For example, there may be various technical effects that can be understood and / or derived from the present disclosure by a person of ordinary skill in the relevant field. Therefore, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that can be understood or derived from the technical features of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 An example of a communication system to which an implementation of the present disclosure is applied is shown.

[0019] Figure 2 An example of a wireless device to which an implementation of the present disclosure is applied is shown.

[0020] Figure 3 An example of a UE to which an implementation of the present disclosure is applied is shown.

[0021] Figure 4 and Figure 5 An example of a protocol stack in a 3GPP-based wireless communication system to which an implementation of the present disclosure is applied is shown.

[0022] Figure 6 The frame structure in a 3GPP-based wireless communication system to which the implementation of the present disclosure is applied is shown.

[0023] Figure 7 An example of data flow in a 3GPP NR system to which an implementation of the present disclosure is applied is shown.

[0024] Figure 8 An example of a functional framework of AI / ML according to an embodiment of the present disclosure is shown.

[0025] Fig. 9 An example of a normal measurement process according to an embodiment of the present disclosure is shown.

[0026] Fig.10 An example of an ML-based / assisted measurement process according to an embodiment of the present disclosure is shown.

[0027] Fig.11 An example of a method performed by a UE according to an embodiment of the present disclosure is shown.

[0028] Fig.12 An example of a method performed by a network node according to an embodiment of the present disclosure is shown.

[0029] Fig.13 An example of hybrid reporting of normal measurements and ML-based / assisted measurements according to an embodiment of the present disclosure is shown.

[0030] Fig.14AAn example of hybrid periodic reporting with substitution according to an embodiment of the present disclosure is shown.

[0031] Fig. 14B An example of hybrid periodic reporting without replacement according to an embodiment of the present disclosure is shown.

[0032] Fig.15 An example of dynamic reporting based on a report trigger indicating a report type identifier according to an embodiment of the present disclosure is shown.

[0033] Fig.16 An example of compressed CSI reporting based on a report trigger indicating a report type identifier according to an embodiment of the present disclosure is shown.

[0034] Fig.17 An example of event-triggered reporting according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0035] The following techniques, devices and systems can be applied to various wireless multiple access systems. Examples of multiple access systems include code division multiple access (CDMA) systems, frequency division multiple access (FDMA) systems, time division multiple access (TDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, and multi-carrier frequency division multiple access (MC-FDMA) systems. CDMA can be implemented by radio technologies such as Universal Terrestrial Radio Access (UTRA) or CDMA2000. TDMA can be implemented by radio technologies such as Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), or Enhanced Data Rates for GSM Evolution (EDGE). OFDMA can be implemented by radio technologies such as Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, or Evolved UTRA (E-UTRA). UTRA is part of Universal Mobile Telecommunications System (UMTS). 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) is part of Evolved UMTS (E-UMTS) using E-UTRA. 3GPP LTE adopts OFDMA in the downlink (DL) and SC-FDMA in the uplink (UL). Evolutions of 3GPP LTE include LTE-Advanced (LTE-A), LTE-A Pro and / or 5G New Radio (NR).

[0036] For ease of description, the implementation of the present disclosure is mainly described with respect to a 3GPP-based wireless communication system. However, the technical features of the present disclosure are not limited thereto. For example, although the following detailed description is given based on a mobile communication system corresponding to a 3GPP-based wireless communication system, various aspects of the present disclosure that are not limited to a 3GPP-based wireless communication system are applicable to other mobile communication systems.

[0037] For terms and techniques not specifically described among the terms and techniques adopted in the present disclosure, reference may be made to wireless communication standard documents published prior to the present disclosure.

[0038] In the present disclosure, "A or B" may mean "only A", "only B", or "both A and B". In other words, in the present disclosure, "A or B" may be interpreted as "A and / or B". For example, in the present disclosure, "A, B or C" may mean "only A", "only B", "only C", or "any combination of A, B, and C".

[0039] In the present disclosure, a slash ( / ) or a comma (,) may mean "and / or". For example, "A / B" may mean "A and / or B". Thus, "A / B" may mean "only A", "only B", or "both A and B". For example, "A, B, C" may mean "A, B, or C".

[0040] In the present disclosure, "at least one of A and B" may mean "only A", "only B", or "both A and B". In addition, the expression "at least one of A or B" or "at least one of A and / or B" in the present disclosure may be interpreted as being the same as "at least one of A and B".

[0041] In addition, in the present disclosure, "at least one of A, B, and C" may mean "only A", "only B", "only C", or "any combination of A, B, and C". In addition, "at least one of A, B, or C" or "at least one of A, B and / or C" may mean "at least one of A, B, and C".

[0042] In addition, the brackets used in the present disclosure may mean "for example". In detail, when it is shown as "control information (PDCCH)", "PDCCH" may be cited as an example of "control information". In other words, in the present disclosure, "control information" is not limited to "PDCCH", and "PDCCH" may be cited as an example of "control information". In addition, even when it is shown as "control information (ie, PDCCH)", "PDCCH" may be cited as an example of "control information".

[0043] The technical features described separately in one figure in the present disclosure can be implemented separately or simultaneously.

[0044] Although not limited to this, the various descriptions, functions, processes, suggestions, methods and / or operational flowcharts of the present disclosure disclosed herein can be applied to various fields that require wireless communication and / or connection between devices (e.g., 5G).

[0045] Hereinafter, the present disclosure will be described in more detail with reference to the accompanying drawings. Unless otherwise specified, the same reference numerals in the following drawings and / or descriptions may refer to the same and / or corresponding hardware blocks, software blocks and / or functional blocks.

[0046] Figure 1 An example of a communication system to which an implementation of the present disclosure is applied is shown.

[0047] Figure 1 The 5G usage scenarios shown are only exemplary, and the technical features of the present disclosure can be applied to Figure 1 Other 5G usage scenarios not shown.

[0048] The three main demand categories for 5G include: (1) enhanced mobile broadband (eMBB) category, (2) massive machine type communication (mMTC) category, and (3) ultra-reliable and low-latency communication (URLLC) category.

[0049] Reference Figure 1 , the communication system 1 includes wireless devices 100a to 100f, a base station (BS) 200, and a network 300. Figure 1 A 5G network is illustrated as an example of a network of the communication system 1, but implementations of the present disclosure are not limited to the 5G system and may be applied to future communication systems other than the 5G system.

[0050] BS 200 and network 300 may be implemented as wireless devices, and a specific wireless device may operate as a BS / network node relative to other wireless devices.

[0051] The wireless devices 100a to 100f represent devices that perform communication using a radio access technology (RAT) (e.g., 5G NR or LTE) and may be referred to as communication / radio / 5G devices. The wireless devices 100a to 100f may include, but are not limited to, a robot 100a, a vehicle 100b-1 and 100b-2, an extended reality (XR) device 100c, a handheld device 100d, a home appliance 100e, an Internet of Things (IoT) device 100f, and an artificial intelligence (AI) device / server 400. For example, a vehicle may include a vehicle with a wireless communication function, an autonomous vehicle, and a vehicle capable of performing communication between vehicles. A vehicle may include an unmanned aerial vehicle (UAV) (e.g., a drone). The XR device may include an augmented reality (AR) / virtual reality (VR) / mixed reality (MR) device and may be implemented in the form of a head-mounted device (HMD), a head-up display (HUD) installed in a vehicle, a television, a smart phone, a computer, a wearable device, a home appliance device, a digital signage, a vehicle, a robot, and the like. Handheld devices may include smart phones, smart tablets, wearable devices (e.g., smart watches or smart glasses), and computers (e.g., notebooks). Home appliances may include TVs, refrigerators, and washing machines. IoT devices may include sensors and smart meters.

[0052] In the present disclosure, the wireless devices 100a to 100f may be referred to as user equipment (UE). UE may include, for example, a cellular phone, a smart phone, a laptop computer, a digital broadcast terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation system, a plate-shaped personal computer (PC), a tablet PC, an ultrabook, a vehicle, a vehicle with an autonomous driving function, a connected car, a UAV, an AI module, a robot, an AR device, a VR device, an MR device, a hologram device, a public safety device, an MTC device, an IoT device, a medical device, a FinTech device (or a financial device), a security device, a weather / environmental device, a device related to 5G services, or a device related to the field of the fourth industrial revolution.

[0053] The wireless devices 100a to 100f may be connected to the network 300 via the BS 200. The AI ​​technology may be applied to the wireless devices 100a to 100f, and the wireless devices 100a to 100f may be connected to the AI ​​server 400 via the network 300. The network 300 may be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, and a super 5G network. Although the wireless devices 100a to 100f may communicate with each other via the BS 200 / network 300, the wireless devices 100a to 100f may perform direct communication (e.g., side link communication) with each other without passing through the BS 200 / network 300. For example, the vehicles 100b-1 and 100b-2 may perform direct communication (e.g., vehicle-to-vehicle (V2V) / vehicle-to-everything (V2X) communication). An IoT device (e.g., a sensor) may perform direct communication with other IoT devices (e.g., sensors) or other wireless devices 100a to 100f.

[0054] Wireless communication / connection 150a, 150b, and 150c may be established between wireless devices 100a to 100f and / or between wireless devices 100a to 100f and BS 200 and / or between BS 200. Here, wireless communication / connection may be established through various RATs (e.g., 5G NR) such as uplink / downlink communication 150a, sidelink communication (or device-to-device (D2D) communication) 150b, inter-base station communication 150c (e.g., relay, integrated access and backhaul (IAB)), etc. The wireless devices 100a to 100f and BS 200 / wireless devices 100a to 100f may send / receive radio signals to / from each other through wireless communication / connection 150a, 150b, and 150c. For example, wireless communication / connection 150a, 150b, and 150c may send / receive signals through various physical channels. To this end, various configuration information configuration processes for sending / receiving radio signals, various signal processing processes (e.g., channel coding / decoding, modulation / demodulation, and resource mapping / demapping), and at least a portion of the resource allocation process can be performed based on various proposals of the present disclosure.

[0055] NR supports multiple numerologies (and / or multiple subcarrier spacings (SCS)) to support various 5G services. For example, if the SCS is 15kHz, wide areas can be supported in traditional cellular bands, while if the SCS is 30kHz / 60kHz, dense cities, lower latency, and wider carrier bandwidths can be supported. If the SCS is 60kHz or higher, bandwidths greater than 24.25GHz can be supported to overcome phase noise.

[0056] The NR frequency band may be defined as two types of frequency ranges, namely, frequency range 1 (FR1) and frequency range 2 (FR2). The numerical values ​​of the frequency ranges may be changed. For example, the two types of frequency ranges (FR1 and FR2) may be as shown in Table 1 below. For ease of explanation, in the frequency range used in the NR system, FR1 may mean "a range below 6 GHz", FR2 may mean "a range above 6 GHz", and may be referred to as millimeter wave (mmW).

[0057] [Table 1]

[0058] Frequency range name Corresponding frequency range Subcarrier spacing FR1 450MHz-6000MHz 15, 30, 60kHz FR2 24250MH-52600MHz 60, 120, 240kHz

[0059] As described above, the numerical value of the frequency range of the NR system can be changed. For example, FR1 may include a frequency band of 410 MHz to 7125 MHz as shown in Table 2 below. That is, FR1 may include a frequency band of 6 GHz (or 5850 MHz, 5900 MHz, 5925 MHz, etc.) or higher. For example, a frequency band of 6 GHz (or 5850 MHz, 5900 MHz, 5925 MHz, etc.) or more included in FR1 may include an unlicensed frequency band. The unlicensed frequency band may be used for various purposes, for example, for communication of vehicles (e.g., autonomous driving).

[0060] [Table 2]

[0061] Frequency range name Corresponding frequency range Subcarrier spacing FR1 410MHz-7125MHz 15, 30, 60kHz FR2 24250MHz-52600MHz 60, 120, 240kHz

[0062] Here, the radio communication technology implemented in the wireless device in the present disclosure may include narrowband Internet of Things (NB-IoT) technology for low power communication and LTE, NR and 6G. For example, NB-IoT technology may be an example of low power wide area network (LPWAN) technology, which may be implemented in specifications such as LTE Cat NB1 and / or LTE Cat NB2, and may not be limited to the above names. Additionally and / or alternatively, the radio communication technology implemented in the wireless device in the present disclosure may communicate based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology, and may be called by various names such as enhanced machine type communication (eMTC). For example, LTE-M technology may be implemented in at least one of various specifications, such as 1) LTE Cat 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-bandwidth limited (non-BL), 5) LTE-MTC, 6) LTE machine type communication and / or 7) LTE M, and may not be limited to the above names. Additionally and / or alternatively, the radio communication technology implemented in the wireless device in the present disclosure may include at least one of ZigBee, Bluetooth, and / or LPWAN considering low power communication, and may not be limited to the above names. For example, ZigBee technology may generate a personal area network (PAN) associated with small / low power digital communication based on various specifications (such as IEEE 802.15.4), and may be referred to as various names. Figure 2 An example of a wireless device to which an implementation of the present disclosure is applied is shown.

[0063] exist Figure 2 In the embodiment, the first wireless device 100 and / or the second wireless device 200 may be implemented in various forms according to the usage / service. For example, {the first wireless device 100 and the second wireless device 200} may correspond to Figure 1 At least one of {wireless devices 100a to 100f and BS200}, {wireless devices 100a to 100f and wireless devices 100a to 100f} and / or {BS200 and BS200}. The first wireless device 100 and / or the second wireless device 200 may be configured by various elements, devices / components and / or modules.

[0064] The first wireless device 100 may include at least one transceiver (eg, transceiver 106 ), at least one processing chip (eg, processing chip 101 ), and / or one or more antennas 108 .

[0065] The processing chip 101 may include at least one processor (eg, the processor 102 ) and at least one memory (eg, the memory 104 ). Additionally and / or alternatively, the memory 104 may be placed outside the processing chip 101 .

[0066] The processor 102 may control the memory 104 and / or the transceiver 106, and may be adapted to implement the descriptions, functions, processes, suggestions, methods, and / or operational flow charts described in the present disclosure. For example, the processor 102 may process the information in the memory 104 to generate first information / signals, and then send a radio signal including the first information / signals through the transceiver 106. The processor 102 may receive a radio signal including second information / signals through the transceiver 106, and then store information obtained by processing the second information / signals in the memory 104.

[0067] The memory 104 may be operatively connected to the processor 102. The memory 104 may store various types of information and / or instructions. The memory 104 may store firmware and / or software code 105 that implements codes, commands, and / or command sets that, when executed by the processor 102, perform the descriptions, functions, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure. For example, the firmware and / or software code 105 may implement instructions that, when executed by the processor 102, perform the descriptions, functions, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure. For example, the firmware and / or software code 105 may control the processor 102 to execute one or more protocols. For example, the firmware and / or software code 105 may control the processor 102 to execute one or more layers of a wireless interface protocol.

[0068] In this document, the processor 102 and the memory 104 may be part of a communication modem / circuit / chip designed to implement a RAT (e.g., LTE or NR). The transceiver 106 may be connected to the processor 102 and transmit and / or receive radio signals through one or more antennas 108. Each transceiver 106 may include a transmitter and / or a receiver. The transceiver 106 may be used interchangeably with a radio frequency (RF) unit. In the present disclosure, the first wireless device 100 may represent a communication modem / circuit / chip.

[0069] The second wireless device 200 may include at least one transceiver (eg, transceiver 206 ), at least one processing chip (eg, processing chip 201 ), and / or one or more antennas 208 .

[0070] The processing chip 201 may include at least one processor (eg, the processor 202 ) and at least one memory (eg, the memory 204 ). Additionally and / or alternatively, the memory 204 may be placed outside the processing chip 201 .

[0071] The processor 202 may control the memory 204 and / or the transceiver 206, and may be adapted to implement the descriptions, functions, processes, suggestions, methods, and / or operational flow charts described in the present disclosure. For example, the processor 202 may process the information in the memory 204 to generate third information / signals, and then send a radio signal including the third information / signals through the transceiver 206. The processor 202 may receive a radio signal including fourth information / signals through the transceiver 106, and then store information obtained by processing the fourth information / signals in the memory 204.

[0072] The memory 204 may be operatively connected to the processor 202. The memory 204 may store various types of information and / or instructions. The memory 204 may store firmware and / or software code 205 that implements codes, commands, and / or command sets that, when executed by the processor 202, perform the descriptions, functions, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure. For example, the firmware and / or software code 205 may implement instructions that, when executed by the processor 202, perform the descriptions, functions, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure. For example, the firmware and / or software code 205 may control the processor 202 to execute one or more protocols. For example, the firmware and / or software code 205 may control the processor 202 to execute one or more layers of a wireless interface protocol.

[0073] In this article, the processor 202 and the memory 204 may be part of a communication modem / circuit / chip designed to implement a RAT (e.g., LTE or NR). The transceiver 206 may be connected to the processor 202 and transmit and / or receive radio signals through one or more antennas 208. Each of the transceivers 206 may include a transmitter and / or a receiver. The transceiver 206 may be used interchangeably with an RF unit. In the present disclosure, the second wireless device 200 may represent a communication modem / circuit / chip.

[0074] In the following, the hardware elements of the wireless devices 100 and 200 will be described in more detail. One or more protocol layers may be implemented by, but not limited to, one or more processors 102 and 202. For example, one or more processors 102 and 202 may implement one or more layers (e.g., functional layers such as a physical (PHY) layer, a medium access control (MAC) layer, a radio link control (RLC) layer, a packet data convergence protocol (PDCP) layer, a radio resource control (RRC) layer, and a service data adaptation protocol (SDAP) layer). One or more processors 102 and 202 may generate one or more protocol data units (PDUs), one or more service data units (SDUs), messages, control information, data, or information according to the descriptions, functions, processes, suggestions, methods, and / or operation flow charts disclosed in the present disclosure. One or more processors 102 and 202 may generate a signal (e.g., a baseband signal) including a PDU, SDU, message, control information, data, or information according to the description, functions, procedures, suggestions, methods, and / or operational flowcharts disclosed in the present disclosure and provide the generated signal to one or more transceivers 106 and 206. One or more processors 102 and 202 may receive a signal (e.g., a baseband signal) from one or more transceivers 106 and 206 and obtain the PDU, SDU, message, control information, data, or information according to the description, functions, procedures, suggestions, methods, and / or operational flowcharts disclosed in the present disclosure.

[0075] One or more processors 102 and 202 may be referred to as controllers, microcontrollers, microprocessors, or microcomputers. One or more processors 102 and 202 may be implemented by hardware, firmware, software, or a combination thereof. As an example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), or one or more field programmable gate arrays (FPGAs) may be included in one or more processors 102 and 202. For example, one or more processors 102 and 202 may be configured by a group of communication control processors, application processors (APs), electronic control units (ECUs), central processing units (CPUs), graphics processing units (GPUs), and memory control processors.

[0076] One or more memories 104 and 204 may be connected to one or more processors 102 and 202 and store various types of data, signals, messages, information, programs, codes, instructions, and / or commands. One or more memories 104 and 204 may be configured by random access memory (RAM), dynamic RAM (DRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EPROM), flash memory, volatile memory, non-volatile memory, hard drive, register, cache memory, computer-readable storage medium, and / or a combination thereof. One or more memories 104 and 204 may be located inside and / or outside of one or more processors 102 and 202. One or more memories 104 and 204 may be connected to one or more processors 102 and 202 by various technologies such as wired connection or wireless connection.

[0077] One or more transceivers 106 and 206 can send user data, control information and / or radio signals / channels mentioned in the description, function, process, suggestion, method and / or operation flow chart disclosed in the present disclosure to one or more other devices. One or more transceivers 106 and 206 can receive user data, control information and / or radio signals / channels mentioned in the description, function, process, suggestion, method and / or operation flow chart disclosed in the present disclosure from one or more other devices. For example, one or more transceivers 106 and 206 can be connected to one or more processors 102 and 202 and send and receive radio signals. For example, one or more processors 102 and 202 can perform control so that one or more transceivers 106 and 206 can send user data, control information or radio signals to one or more other devices. One or more processors 102 and 202 can perform control so that one or more transceivers 106 and 206 can receive user data, control information or radio signals from one or more other devices.

[0078] One or more transceivers 106 and 206 may be connected to one or more antennas 108 and 208. Additionally or alternatively, one or more transceivers 106 and 206 may include one or more antennas 108 and 208. One or more transceivers 106 and 206 may be adapted to transmit and receive user data, control information, and / or radio signals / channels mentioned in the description, functions, processes, suggestions, methods, and / or operational flow charts disclosed in the present disclosure through one or more antennas 108 and 208. In the present disclosure, one or more antennas 108 and 208 may be multiple physical antennas or multiple logical antennas (e.g., antenna ports).

[0079] One or more transceivers 106 and 206 may convert received user data, control information, radio signals / channels, etc. from RF band signals to baseband signals, so as to process the received user data, control information, radio signals / channels, etc. using one or more processors 102 and 202. One or more transceivers 106 and 206 may convert user data, control information, radio signals / channels, etc. processed using one or more processors 102 and 202 from baseband signals to RF band signals. To this end, one or more transceivers 106 and 206 may include (analog) oscillators and / or filters. For example, one or more transceivers 106 and 206 may up-convert OFDM baseband signals to OFDM signals through their (analog) oscillators and / or filters under the control of one or more processors 102 and 202 and transmit the up-converted OFDM signals at the carrier frequency. One or more transceivers 106 and 206 may receive an OFDM signal at a carrier frequency and down-convert the OFDM signal to an OFDM baseband signal through their (analog) oscillators and / or filters under the control of one or more processors 102 and 202 .

[0080] although Figure 2 140 . Although not shown in the figure, the wireless devices 100 and 200 may also include additional components. The additional components 140 may be configured differently depending on the type of the wireless devices 100 and 200. For example, the additional components 140 may include at least one of a power supply unit / battery, an input / output (I / O) device (e.g., an audio I / O port, a video I / O port), a drive device, and a computing device. The additional components 140 may be connected to one or more processors 102 and 202 via various technologies such as a wired or wireless connection.

[0081] In an implementation of the present disclosure, a UE may be used as a transmitting device in an uplink (UL) and a receiving device in a downlink (DL). In an implementation of the present disclosure, a BS may be used as a receiving device in the UL and a transmitting device in the DL. Hereinafter, for the convenience of description, it is mainly assumed that the first wireless device 100 is used as a UE and the second wireless device 200 is used as a BS. For example, a processor 102 connected to the first wireless device 100, installed on the first wireless device 100, or started in the first wireless device 100 may be adapted to perform UE behavior according to an implementation of the present disclosure or control the transceiver 106 to perform UE behavior according to an implementation of the present disclosure. A processor 202 connected to the second wireless device 200, installed on the second wireless device 200, or started in the second wireless device 200 may be adapted to perform BS behavior according to an implementation of the present disclosure or control the transceiver 206 to perform BS behavior according to an implementation of the present disclosure.

[0082] In this disclosure, a BS is also referred to as a Node B (NB), an eNode B (eNB), or a gNB.

[0083] Figure 3 An example of a UE to which an implementation of the present disclosure is applied is shown.

[0084] Reference Figure 3 , UE 100 may correspond to Figure 2 A first wireless device 100 is provided.

[0085] UE 100 includes a processor 102 , a memory 104 , a transceiver 106 , one or more antennas 108 , a power management module 141 , a battery 142 , a display 143 , a keypad 144 , a subscriber identity module (SIM) card 145 , a speaker 146 , and a microphone 147 .

[0086] The processor 102 may be adapted to implement the descriptions, functions, processes, suggestions, methods and / or operational flow charts disclosed in the present disclosure. The processor 102 may be adapted to control one or more other components of the UE 100 to implement the descriptions, functions, processes, suggestions, methods and / or operational flow charts disclosed in the present disclosure. The radio interface protocol layer may be implemented in the processor 102. The processor 102 may include an ASIC, other chipsets, logic circuits and / or data processing devices. The processor 102 may be an application processor. The processor 102 may include at least one of a digital signal processor (DSP), a central processing unit (CPU), a graphics processing unit (GPU), a modem (modulator and demodulator). Examples of the processor 102 may be MANUFACTURED BY SNAPDRAGON TM Series processors, Manufactured by EXYNOS TM Series processors, A series of processors manufactured by Made by HELIO TM Series processors, ATOM manufactured TM series processors or the corresponding next-generation processors.

[0087] The memory 104 is connected to the processor 102 when in operation and stores various information to operate the processor 102. The memory 104 may include ROM, RAM, flash memory, memory card, storage medium and / or other storage devices. When the embodiment is implemented in software, the technology described herein can be implemented using modules (e.g., processes, functions, etc.) that execute the descriptions, functions, processes, suggestions, methods and / or operation flow charts disclosed in this disclosure. The modules can be stored in the memory 104 and implemented by the processor 102. The memory 104 can be implemented within the processor 102 or outside the processor 102 (in this case, the memory can be communicatively connected to the processor 102 via various means known in the art).

[0088] The transceiver 106 is connected to the processor 102 during operation and sends and / or receives radio signals. The transceiver 106 includes a transmitter and a receiver. The transceiver 106 may include a baseband circuit to process radio frequency signals. The transceiver 106 controls one or more antennas 108 to send and / or receive radio signals.

[0089] The power management module 141 manages the power of the processor 102 and / or the transceiver 106. The battery 142 supplies power to the power management module 141.

[0090] The display 143 outputs a result processed by the processor 102. The keypad 144 receives an input to be used by the processor 102. The keypad 144 may be displayed on the display 143.

[0091] The SIM card 145 is an integrated circuit designed to securely store an International Mobile Subscriber Identity (IMSI) number and its associated keys, which are used to identify and authenticate subscribers on mobile telephony devices such as mobile phones and computers. Contact information can also be stored on many SIM cards.

[0092] The speaker 146 outputs sound related results processed by the processor 102. The microphone 147 receives sound related input to be used by the processor 102.

[0093] Figure 4 and Figure 5 An example of a protocol stack in a 3GPP-based wireless communication system to which an implementation of the present disclosure is applied is shown.

[0094] Specifically, Figure 4 An example of a radio interface user plane protocol stack between a UE and a BS is illustrated, and Figure 5 An example of a radio interface control plane protocol stack between a UE and a BS is illustrated. The control plane refers to a path for transmitting control messages used to manage calls by the UE and the network. The user plane refers to a path for transmitting data generated in the application layer (for example, voice data or Internet packet data). Figure 4 , the user plane protocol stack can be divided into layer 1 (ie, PHY layer) and layer 2. Figure 5 , the control plane protocol stack can be divided into layer 1 (ie, PHY layer), layer 2, layer 3 (eg, RRC layer) and non-access stratum (NAS) layer. Layer 1, layer 2 and layer 3 are called access stratum (AS).

[0095] In the 3GPP LTE system, Layer 2 is separated into the following sublayers: MAC, RLC, and PDCP. In the 3GPP NR system, Layer 2 is separated into the following sublayers: MAC, RLC, PDCP, and SDAP. The PHY layer provides transport channels to the MAC sublayer, the MAC sublayer provides logical channels to the RLC sublayer, the RLC sublayer provides RLC channels to the PDCP sublayer, and the PDCP sublayer provides radio bearers to the SDAP sublayer. The SDAP sublayer provides Quality of Service (QoS) flows to the 5G core network.

[0096] In the 3GPP NR system, the main services and functions of the MAC sublayer include: mapping between logical channels and transport channels; multiplexing MAC SDUs belonging to one or different logical channels to / demultiplexing transport blocks (TBs) delivered to / from the physical layer on the transport channel; scheduling information reporting; error correction through hybrid automatic repeat request (HARQ) (one HARQ entity per cell in the case of carrier aggregation (CA); priority handling between UEs with dynamic scheduling; priority handling between logical channels of one UE with logical channel prioritization; padding. A single MAC entity can support multiple parameter sets, transmission timings, and cells. Mapping restrictions in logical channel prioritization control which parameter set(s), cell, and transmission timing a logical channel can use.

[0097] MAC provides different types of data transmission services. In order to adapt to different types of data transmission services, multiple types of logical channels are defined, that is, each logical channel supports the transmission of a specific type of information. Each logical channel type is defined by what type of information is transmitted. Logical channels are divided into two groups: control channels and traffic channels. Control channels are only used for the transmission of control plane information, and traffic channels are only used for the transmission of user plane information. The broadcast control channel (BCCH) is a downlink logical channel for broadcasting system control information, the paging control channel (PCCH) is a downlink logical channel for transmitting paging information, system information change notifications, and indications of ongoing public warning services (PWS) broadcasts, the common control channel (CCCH) is a logical channel for sending control information between the UE and the network and for UEs that do not have an RRC connection with the network, and the dedicated control channel (DCCH) is a point-to-point bidirectional logical channel that sends dedicated control information between the UE and the network and is used by UEs with an RRC connection. The dedicated traffic channel (DTCH) is a point-to-point logical channel dedicated to one UE, which is used to transmit user information. DTCH can exist in both the uplink and downlink. In the downlink, there are the following connections between logical channels and transport channels: BCCH can be mapped to the broadcast channel (BCH); BCCH can be mapped to the downlink shared channel (DL-SCH); PCCH can be mapped to the paging channel (PCH); CCCH can be mapped to DL-SCH; DCCH can be mapped to DL-SCH; and DTCH can be mapped to DL-SCH. In the uplink, there are the following connections between logical channels and transport channels: CCCH can be mapped to the uplink shared channel (UL-SCH); DCCH can be mapped to UL-SCH; and DTCH can be mapped to UL-SCH.

[0098] The RLC sublayer supports three transmission modes: transparent mode (TM), unacknowledged mode (UM) and acknowledged mode (AM). RLC configuration is for each logical channel and does not depend on parameter sets and / or transmission duration. In 3GPP NR systems, the main services and functions of the RLC sublayer depend on the transmission mode and include: delivery of upper layer PDUs; sequence numbering independent of one of PDCP (UM and AM); error correction through ARQ (AM only); segmentation (AM and UM) and re-segmentation (AM only) of RLC SDUs; reassembly of SDUs (AM and UM); duplicate detection (AM only); RLC SDU discard (AM and UM); RLC re-establishment; protocol error detection (AM only).

[0099] In the 3GPP NR system, the main services and functions of the PDCP sublayer for the user plane include: sequence numbering; header compression and decompression using robust header compression (ROHC); transmission of user data; reordering and duplicate detection; in-sequence delivery; PDCP PDU routing (in the case of split bearers); retransmission of PDCP SDU; ciphering, deciphering and integrity protection; PDCP SDU discard; PDCP re-establishment and data recovery for RLC AM; PDCP status report for RLC AM; duplication of PDCP PDU and duplicate discard indication to lower layers. The main services and functions of the PDCP sublayer for the control plane include: sequence numbering; ciphering, deciphering and integrity protection; transmission of control plane data; reordering and duplicate detection; in-sequence delivery; duplication of PDCP PDU and duplicate discard indication to lower layers.

[0100] In 3GPP NR system, the main services and functions of SDAP include: mapping between QoS flows and data radio bearers; marking QoS flow ID (QFI) in both DL and UL packets. A single SDAP protocol entity is configured for each individual PDU session.

[0101] In the 3GPP NR system, the main services and functions of the RRC sublayer include: broadcast of system information related to AS and NAS; paging initiated by 5GC or NG-RAN; establishment, maintenance and release of RRC connection between UE and NG-RAN; security functions including key management; establishment, configuration, maintenance and release of signaling radio bearers (SRBs) and data radio bearers (DRBs); mobility functions (including: handover and context transfer; UE cell selection and reselection and control of cell selection and reselection; inter-RAT mobility); QoS management functions; UE measurement reporting and control of reporting; detection and repair of radio link failure; transmission of NAS messages from UE to NAS / from NAS to UE.

[0102] Figure 6 The frame structure in a 3GPP-based wireless communication system to which the implementation of the present disclosure is applied is shown.

[0103] Figure 6The frame structure shown is only exemplary, and the number of subframes, the number of time slots, and / or the number of symbols in a frame may vary. In a 3GPP-based wireless communication system, OFDM parameter sets (e.g., subcarrier spacing (SCS), transmission time interval (TTI) duration) may be configured differently between multiple cells aggregated for one UE. For example, if the UE is configured with different SCSs for cells aggregated for the cells, the (absolute time) duration of time resources (e.g., subframes, time slots, or TTIs) including the same number of symbols may be different among the aggregated cells. In this article, the symbol may include an OFDM symbol (or CP-OFDM symbol), a SC-FDMA symbol (or a discrete Fourier transform-spread-OFDM (DF%s-OFDM) symbol).

[0104] Reference Figure 6 , downlink and uplink transmissions are organized into frames. Each frame has T f =10ms duration. Each frame is divided into two half-frames, where each half-frame has a duration of 5ms. Each half-frame includes 5 sub-frames, where the duration of each sub-frame is T sf is 1 ms. Each subframe is divided into slots, and the number of slots in a subframe depends on the subcarrier spacing. Each slot includes 14 or 12 OFDM symbols based on a cyclic prefix (CP). In a normal CP, each slot includes 14 OFDM symbols, and in an extended CP, each slot includes 12 OFDM symbols. The parameter set is based on an exponentially scalable subcarrier spacing βf=2 u *15kHz.

[0105] Table 3 shows the subcarrier spacing βf=2 u *N is the number of OFDM symbols per slot for normal CP of 15kHz slot symb , the number of time slots per frame N frame，u slot And the number of time slots N in each subframe subframe，u slot .

[0106] [Table 3]

[0107] u <![CDATA[N slot symb ]]> <![CDATA[N frame，u slot ]]> <![CDATA[N subframe，u slot ]]> 0 14 10 1 1 14 20 2 2 14 40 4 3 14 80 8 4 14 1 60 16

[0108] Table 4 shows the subcarrier spacing βf=2 u *N number of OFDM symbols per slot for extended CP of 15kHz slot symb , the number of time slots per frame N frame，u slo，t And the number of time slots N in each subframesubfrane,u slot .

[0109] [Table 4]

[0110] u <![CDATA[N slot symb ]]> <![CDATA[N frame，u slot ]]> <![CDATA[N subframe，u slot ]]> 2 1 2 40 4

[0111] A slot includes a plurality of symbols (e.g., 14 or 12 symbols) in the time domain. For each parameter set (e.g., subcarrier spacing) and carrier, a common resource block (CRB) N indicated by higher layer signaling (e.g., RRC signaling) is defined. start,u grid Starting N size，u grid *N RB sc subcarriers and N subframe，u symb OFDM symbol resource grid, where N size,u grid,x N is the number of resource blocks (RBs) in the resource grid, and the subscript x is DL for the downlink and UL for the uplink. RB sc is the number of subcarriers per RB. In 3GPP-based wireless communication systems, N RB sc Typically 12. For a given antenna port p, subcarrier spacing configuration u, and transmission direction (DL or UL), there is one resource grid. The carrier bandwidth N for subcarrier spacing configuration u size,u grid Given by high-level parameters (e.g., RRC parameters). Each element in the resource grid for antenna port p and subcarrier spacing configuration u is called a resource element (RE), and one complex symbol can be mapped to each RE. Each RE in the resource grid is uniquely identified by an index k in the frequency domain and an index 1 in the time domain that represents the symbol position relative to a reference point. In a 3GPP-based wireless communication system, an RB is defined by 12 consecutive subcarriers in the frequency domain. Figure 6As shown, as the SCS doubles, the slot length and symbol length are halved. For example, when the SCS is 15kHz, the slot length is 1ms, which is the same as the subframe length. When the SCS is 30kHz, the slot length is 0.5ms (=500us), and the symbol length is half of the symbol length when the SCS is 15kHz. When the SCS is 60kHz, the slot length is 0.25ms (=250us), and the symbol length is half of the symbol length when the SCS is 30kHz. When the SCS is 120kHz, the slot length is 0.125ms (=125us), and the symbol length is half of the symbol length when the SCS is 60kHz. When the SCS is 240kHz, the slot length is 0.0625ms (=62.5us), and the symbol length is half of the symbol length when the SCS is 120kHz.

[0112] In 3GPP NR systems, RBs are classified into CRBs and physical resource blocks (PRBs). For subcarrier spacing configuration u, CRBs are numbered from 0 upwards in the frequency domain. The center of subcarrier 0 of CRB0 for subcarrier spacing configuration u coincides with "point A" used as a common reference point for the resource block grid. In 3GPP NR systems, PRBs are defined within bandwidth parts (BWPs) and are numbered from 0 to N. size BwP,i-1 where i is the number of bandwidth parts. The number of physical resource blocks n in bandwidth part i PRB With common resource block n CRB The relationship between is as follows: PRB =n CRB +N size BwP,i , where N size BWP,i is a common resource block where the bandwidth part starts relative to CRB0. A BWP consists of multiple consecutive RBs. A carrier may include up to N (e.g., 5) BWPs. A UE may be configured with one or more BWPs on a given component carrier. Only one BWP out of the multiple BWPs configured for a UE may be activated at a time. The active BWP defines the operating bandwidth of the UE within the operating bandwidth of the cell.

[0113] In the present disclosure, the term "cell" may refer to a geographical area where one or more nodes provide a communication system or to a radio resource. A "cell" as a geographical area may be understood as a coverage area within which a node can provide services using a carrier, and a "cell" as a radio resource (e.g., a time-frequency resource) is associated with a bandwidth as a frequency range configured by a carrier. A "cell" associated with a radio resource is defined by a combination of downlink resources and uplink resources (e.g., a combination of a DL component carrier (CC) and a ULCC). A cell may be configured only by downlink resources, or may be configured by downlink resources and uplink resources. Since the DL coverage (which is the range within which a node can send a valid signal) and the UL coverage (which is the range within which a node can receive a valid signal from a UE) depend on the carrier carrying the signal, the coverage of a node may be associated with the coverage of a "cell" of a radio resource used by the node. Therefore, the term "cell" may sometimes be used to represent the service coverage of a node, to represent a radio resource at other times, or to represent a range within which a signal using a radio resource can reach with effective strength at other times.

[0114] In CA, two or more CCs are aggregated. The UE can receive or transmit on one or more CCs simultaneously according to its capabilities. CA is supported for both continuous CCs and non-contiguous CCs. When CA is configured, the UE has only one RRC connection with the network. When the RRC connection is established / reestablished / switched, one serving cell provides NAS mobility information, and when the RRC connection is reestablished / switched, one serving cell provides security input. This cell is called the primary cell (PCell). PCell is a cell operating on the primary frequency, where the UE performs an initial connection establishment process or initiates a connection reestablishment process. Depending on the UE capabilities, the secondary cell (SCell) can be configured to form a set of serving cells together with the PCell. SCell is a cell that provides additional radio resources on top of a special cell (SpCell). Therefore, the set of configured serving cells for the UE always consists of one PCell and one or more SCells. For dual connection (DC) operation, the term SpCell refers to the PCell of the primary cell group (MCG) or the primary SCell (PSCell) of the secondary cell group (SCG). SpCell supports PUCCH transmission and contention-based random access and is always activated. MCG is a group of serving cells associated with a master node, which includes SpCell (PCell) and optionally one or more SCells. For UEs configured with DC, SCG is a subset of serving cells associated with a secondary node, which includes PSCell and zero or more SCells. For UEs in RRC_CONNECTED that are not configured with CA / DC, there is only one serving cell consisting of PCell. For UEs in RRC_CONNECTED that are configured with CA / DC, the term "serving cell" is used to refer to a set of cells consisting of SpCell and all SCells. In DC, two MAC entities are configured in the UE: one for MCG and one for SCG.

[0115] Figure 7 An example of data flow in a 3GPP NR system to which an implementation of the present disclosure is applied is shown.

[0116] Reference Figure 7 , "RB" means radio bearer, and "H" means header. Radio bearers are classified into two groups: DRB for user plane data and SRB for control plane data. MAC PDU is transmitted / received to / from an external device through the PHY layer using radio resources. MAC PDU arrives at the PHY layer in the form of a transport block.

[0117] In the PHY layer, uplink transport channels UL-SCH and RACH are mapped to their physical channels, physical uplink shared channel (PUSCH) and physical random access channel (PRACH), respectively, and downlink transport channels DL-SCH, BCH and PCH are mapped to physical downlink shared channel (PDSCH), physical broadcast channel (PBCH) and PDSCH, respectively. In the PHY layer, uplink control information (UCI) is mapped to physical uplink control channel (PUCCH), and downlink control information (DCI) is mapped to physical downlink control channel (PDCCH). The UE sends MAC PDU related to UL-SCH via PUSCH based on UL grant, and the BS sends MAC PDU related to DL-SCH via PDSCH based on DL assignment.

[0118] The following describes artificial intelligence (AI) / machine learning (ML) related features.

[0119] Figure 8 An example of a functional framework of AI / ML according to an embodiment of the present disclosure is shown.

[0120] exist Figure 8 In , data collection is the function of providing input data for model training, management, and inference functions. Input data can include at least one of the following items:

[0121] -Serves as training data required as input for AI / ML model training functions;

[0122] - Monitoring data required as input to AI / ML models or management of AI / ML functions; or

[0123] -Serves as the inference data required as input for AI / ML inference functions.

[0124] For example, the monitoring data may include at least one of the following:

[0125] - Beam prediction accuracy related KPIs, for example, Top-K / 1 beam prediction accuracy;

[0126] Link quality related KPIs, such as throughput, L1-RSRP, L1-SINR, and assumed BLER;

[0127] -Performance metrics based on AI / ML input / output data distribution; or

[0128] -L1-RSRP difference evaluated by comparing measured RSRP and predicted RSRP. The model training function performs training, validation, and testing of the AI / ML model, which can generate model performance metrics that can be used as part of the model testing process. If necessary, the model training function is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and conversion) based on the training data passed by the data collection function. In the case of a model storage function, the model training function is used to pass the trained, validated, and tested AI / ML model (i.e., the trained model) to the model storage function, or to pass an updated version of the model (i.e., the updated model) to the model storage function.

[0129] Management is the function of overseeing the operation (e.g., selection / (de)activation / switching / fallback) and monitoring of AI / ML models or AI / ML functions. This function is also responsible for making decisions based on data received from the data collection function and the reasoning function to ensure correct reasoning operation. Selection / (de)activation / switching / fallback is the information required as input for the management reasoning function. Relevant information may include selection / (de)activation / switching of AI / ML models or AI / ML-based functions, falling back to non-AI / ML operation (i.e., not relying on the reasoning process). Model transfer / delivery requests are used to request models from the model storage function. Performance feedback / retraining requests are information required as input for the model training function, for example for model (re)training or updating purposes.

[0130] Inference is the function that provides the output from the process of applying an AI / ML model or AI / ML function to new data (i.e., inference data). If necessary, the inference function is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on the inference data passed by the data collection function. Inference output is the data used by the management function to monitor the performance of the AI / ML model or AI / ML function.

[0131] Model storage is the function responsible for storing trained / updated models that can be used to perform inference processes. The model storage function (if any) is primarily used as a reference point for protocol termination, model delivery / transfer, and related processing (when applicable). It should be emphasized that its purpose does not include limiting the actual storage location of the model. Model delivery / transfer is used to pass the AI / ML model to the inference function.

[0132] The UE may perform normal measurement, where normal measurement is a measurement process based on actual measurement of a known reference signal and possibly post-processing the measurement result of the reference signal (e.g., filtering based on linear average or exponential moving average, etc.). In the present disclosure, this type of normal measurement may be referred to as a first type of measurement.

[0133] Fig. 9An example of a normal measurement process according to an embodiment of the present disclosure is shown.

[0134] Reference Fig. 9 In step S901, the UE may receive a measurement configuration from the network. The measurement configuration may include a measurement object list (measObject), a report configuration list (reportConfig), and a measurement identifier ID list (measID). The measurement ID may be related to / correspond to a combination of a measurement object and a report configuration. The measurement object may indicate information about an object that the UE should measure. For example, the object information may include a measurement frequency and / or a cell list including a serving cell / neighboring cell. The report configuration may include a condition for performing an action corresponding to a report type in the report configuration. For example, the condition may include a reporting condition that the UE should satisfy when sending a measurement report.

[0135] In step S903, the UE may perform measurement based on the measurement configuration. For example, the UE may measure the reference signal received from the serving cell and / or the neighboring cell at the measurement frequency specified by the measurement object to obtain the measurement result of the serving cell and / or the neighboring cell. The measurement result may include the cell quality / signal strength / signal quality / channel quality / channel state / reference signal received power (RSRP) / reference signal received quality (RSRQ) of the serving cell and / or the neighboring cell. The UE may perform actual measurement on the reference signal and derive the measurement result based on the actual measurement (the measurement result of the reference signal may be post-processed, for example, filtering based on linear average or exponential moving average, etc.).

[0136] In step S905, the UE may send a measurement report to the network. The UE may send a measurement report including measurement results of the serving cell and / or neighbor cells to the network based on the report configuration (eg, when the reporting condition is met).

[0137] In addition, the UE may perform ML-based / assisted measurements, where ML-based / assisted measurements are measurement processes that derive measurement results with the aid of ML (based on certain measurements of known reference signals). ML-based / assisted measurements may be associated with the size of compressed measurement results, thereby reducing reporting overhead. For example, machine learning based on autoencoders / DNNs / CNNs may be used to implement compressed CSI reporting by generating compressed CSI information on the UE side and reconstructing the desired CSI information on the network side (i.e., a two-sided ML algorithm may be used). ML-based / assisted measurements may be associated with predicting measurement results at future times. Measurement predictions may be based on machine learning models that use current and / or past measurement results and other local / environmental / useful information available on the UE side and provide available measurement results and possible available information as input. In the present disclosure, this type of ML-based / assisted measurements may be referred to as second type measurements.

[0138] Fig.10 An example of an ML-based / assisted measurement process according to an embodiment of the present disclosure is shown.

[0139] Reference Fig.10 In step S1001, the UE may receive a measurement configuration from the network. The measurement configuration may include: Fig. 9 The information elements described in step 901.

[0140] In step S1003, the UE may receive an ML model configuration from the network. In order to derive an ML-assisted result, the UE may be provided with the ML model configuration. The ML model configuration may include prediction model structure information.

[0141] In some implementations, the order of steps S1001 and S1003 may be changed, or steps S1001 and S1003 may be performed simultaneously. For example, the measurement configuration may include an ML model configuration.

[0142] In some implementations, the ML model information can include a machine learning type, such as reinforcement learning, supervised learning, or unsupervised learning.

[0143] In some implementations, the network may configure a machine learning model to be used by the UE. The ML model information may include machine learning models such as deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning (DRL).

[0144] For example, the configured ML model can be a pre-trained ML model that the network has been pre-trained. The configured ML model can be described by model description information including model structure and parameters. For example, a neural network-based model may include an input layer, an output layer, and a hidden layer, each of which includes one or more neurons (equivalent to nodes). Different layers are connected based on the connections between neurons in different layers. Each connection between two different neurons in two different layers can be directed (for example, neuron A to neuron B, meaning that the output of neuron A is fed into neuron B). Each neuron can provide input to one or more connected neurons (1 to N connections). For the connection between two neurons (neuron A to neuron B), the output of one neuron A is scaled by a weight, and the other neuron uses the scaled output as its input. Each neuron can get input from one or more connected neurons (N to 1 connection), combine the inputs from the connected neurons, and generate outputs based on activation functions.

[0145] For example, the configured ML model may be an ML model to be trained. The configured ML model is described by model description information including a model structure and initial parameters to be trained. When the network configures the ML model to be trained, it may also configure training parameters such as optimization targets and optimization-related configuration parameters.

[0146] In some implementations, the ML model configuration may include machine learning input parameters of the machine learning model, such as UE location information, radio measurements related to the serving cell and neighbor cells, and UE mobility history.

[0147] In some implementations, the ML model configuration may include learning outputs such as UE trajectory prediction, predicted target cell, predicted time of handover, and UE traffic prediction.

[0148] In step S1005, the UE may perform training, validation, and testing of the machine learning model, which may generate model performance indicators based on the prediction model configuration / ML model configuration. The UE may perform model training using machine learning input parameters. In some implementations, step S1005 may be performed when the UE is configured with an ML model to be trained, i.e., when the UE is configured with a pre-trained ML model that has been pre-trained by the network, step S1005 may be skipped.

[0149] In step S1007, the UE may perform ML tasks, such as measurement prediction based on the configured / trained ML model. The UE may derive machine learning outputs, which may be measurement results. In some implementations, the UE may infer measurement results from the outputs and use these outputs as feedback for the machine learning model.

[0150] In step S1009 , the UE may send a measurement report including measurement results derived based on the configured / trained ML model to the network.

[0151] If ML is used for information compression / reconstruction, using ML-based / assisted measurement reporting can help reduce reporting overhead. If ML is used to estimate or predict measurement results that exceed current / past available measurements in time and / or frequency, ML-based / assisted measurement reporting can assist the network in optimizing data scheduling and mobility of related UEs. However, there may be a risk of low accuracy in ML-based / assisted measurements. Low accuracy in ML-based / assisted measurements may be caused by several factors, which may include invalid model selection, insufficient or incomplete model training, invalid auxiliary information used as input to the ML algorithm, etc. If the network uses inaccurate ML-based / assisted measurements, it will result in waste of radio resources, degradation of data communication quality, degradation of mobility performance, etc. Therefore, it is very important to ensure that the network can verify whether the reported ML-based / assisted measurements are sufficiently valid or accurate.

[0152] To ensure that the network can verify whether the reported ML-based / assisted measurements are sufficiently valid or accurate, hybrid reporting of normal measurements and ML-based / assisted measurements is proposed.

[0153] Fig.11 An example of a method performed by a UE according to an embodiment of the present disclosure is shown. The method may also be performed by a wireless device.

[0154] Reference Fig.11 In step S1101, the UE may receive a first measurement configuration and a second measurement configuration from the network. The first measurement configuration may be related to generating a measurement result for a measurement object based on measuring a reference signal. The second measurement configuration may be related to deriving a measurement result for a measurement object based on an ML model.

[0155] In step S1103, the UE may obtain a first measurement result by measuring a reference signal based on the first measurement configuration.

[0156] In step S1105 , the UE may obtain a second measurement result by providing an input to the ML model based on the second measurement configuration.

[0157] In step S1107, the UE may send at least one of the first measurement result or the second measurement result to the network.

[0158] According to various embodiments, the input may include at least one of the following: one or more ML input parameters received from the network; one or more reference signals; measurement values ​​of one or more reference signals; or past measurement results. The second measurement result may include at least one of the following: an output of the ML model; a compressed measurement result; a predicted measurement result derived from a measurement result including past measurement results; a predicted measurement result of a reference signal derived from measurement results of other reference signals; or a beam index of one or more beams in descending order of beam quality according to a beam with the highest beam quality. The one or more ML input parameters may include at least one of the following: location information of the UE, a measurement result for at least one reference signal configured for the UE, a measurement result for at least one of a serving cell or one or more neighbor cells, or mobility history information of the UE.

[0159] According to various embodiments, the ML model may include at least one of the following: a deep neural network (DNN) model, a convolutional neural network (CNN) model, a recurrent neural network (RNN) model, or a deep reinforcement learning (DRL) model.

[0160] According to various embodiments, the UE may apply an ML algorithm corresponding to the ML model to the input to obtain a second measurement result as an output of the ML model.

[0161] According to various embodiments, the first measurement configuration may include a measurement identification (ID) list, a measurement object list, and a report configuration list. Each measurement ID in the measurement ID list may identify a corresponding measurement object in the measurement object list and a corresponding report configuration in the report configuration list. The second measurement configuration may include one or more measurement IDs in the measurement ID list.

[0162] According to various embodiments, the first measurement configuration may include a first periodicity for sending the first measurement result. The second measurement configuration may include a second periodicity for sending the second measurement result. The first periodicity may be longer than the second periodicity.

[0163] According to various embodiments, the UE may determine a first timing for sending a first measurement result based on a first periodicity. The UE may determine a second timing for sending a second measurement result based on a second periodicity. Based on the first timing overlapping with the second timing, the UE may send the first measurement result at the first timing without sending the second measurement result.

[0164] According to various embodiments, the UE may determine a first timing for sending a first measurement result based on a first periodicity. The UE may determine a second timing for sending a second measurement result based on a second periodicity. Based on the first timing overlapping the second timing, the UE may send both the first measurement result and the second measurement result at the first timing.

[0165] According to various embodiments, the UE may receive a report request from the network. The UE may determine the type of measurement result to be reported in a first type and a second type based on the report request. The UE may send a measurement result related to the measurement result type to the network. Based on the measurement result type being the first type, a first measurement report may be sent; based on the measurement result type being the second type, a second measurement report may be sent. The report request may be received via at least one of downlink control information (DCI), medium access control (MAC) control element (CE) signaling, or radio resource control (RRC) signaling.

[0166] According to various embodiments, based on the report request including the measurement result type set to the first type, the measurement result type may be determined to be the first type.Based on the report request including the measurement result type set to the second type, the measurement result type may be determined to be the second type.

[0167] According to various embodiments, the UE may receive a configuration of a default type of the first type and the second type from the network. Based on the default type being the first type or the report request including the measurement result type set to the first type, it may be determined that the measurement result type is the first type. Based on the default type being the second type or the report request including the measurement result type set to the second type, it may be determined that the measurement result type is the second type.

[0168] According to various embodiments, the first measurement configuration may include a first duration for sending the first measurement result. The second measurement configuration may include a second duration for sending the second measurement result. Based on satisfying a reporting event during the first duration, the first measurement result may be sent. Based on satisfying a reporting event during the second duration, the second measurement result may be sent.

[0169] According to various embodiments, the UE may receive a first measurement configuration and a second measurement configuration for a common measurement target. The first measurement configuration may be related to a measurement based on a known reference signal associated with the measurement target. The second measurement configuration may be related to a measurement based on a task-oriented machine learning model. The UE may report a first measurement result derived based on the first measurement configuration for the measurement target. The UE may report a second measurement result derived based on the second measurement configuration for the measurement target.

[0170] Fig.12 An example of a method performed by a network node according to an embodiment of the present disclosure is shown.The network node may include a base station (BS).

[0171] Reference Fig.12 In step S1201, the network node may send a first measurement configuration and a second measurement configuration to the UE. The first measurement configuration may be related to generating a measurement result for a measurement object based on measuring a reference signal. The second measurement configuration may be related to deriving a measurement result for a measurement object based on an ML model.

[0172] In step S1203, the network node may receive a first measurement result and a second measurement result from the UE. The first measurement result may be obtained by measuring a reference signal based on a first measurement configuration. The second measurement result may be obtained by providing an input to an ML model based on a second measurement configuration.

[0173] In step S1205 , the network node may determine whether to continue or stop reporting the configuration of the measurement result derived according to the ML model based on the first measurement result and the second measurement result.

[0174] In step S1207, the network node may send the configuration to the UE.

[0175] Fig.13An example of hybrid reporting of normal measurements and ML-based / assisted measurements according to an embodiment of the present disclosure is shown.

[0176] Reference Fig.13 In step S1301, the UE may receive a first measurement configuration and a second measurement configuration.

[0177] The UE may be configured with a first measurement configuration. The first measurement configuration may be related to measurement and / or reporting of a first type of measurement. That is, the first measurement configuration may be used to perform normal measurement based on reference signal measurement. The first measurement configuration may be used to perform reporting of measurement results of normal measurement.

[0178] The first measurement configuration may include one or more measurement target / object configurations.

[0179] The first measurement configuration may include one or more measurement reporting configurations.

[0180] The first measurement configuration may include measurement (time) resource information for the first type of measurement.

[0181] The first measurement configuration may include measurement reporting resource information for reporting the first type of measurement.

[0182] The UE may be configured with a second measurement configuration. The second measurement configuration may be related to measurement and / or reporting of the second type of measurement. The second measurement configuration may be used to perform ML-based / assisted measurement for the measurement target / object included in the first measurement configuration. The second measurement configuration may be used to perform ML-based / assisted measurement and report ML-based / assisted measurement results.

[0183] The second measurement configuration may include measurement (time) resource information.

[0184] The second measurement configuration may include measurement reporting resource information for reporting the second type of measurement.

[0185] For configuration of the second measurement configuration, the second measurement configuration may be configured separately from the first measurement configuration. In another example, the second measurement configuration may be configured as part of the first measurement configuration. The second measurement configuration may include information indicating which measurement targets / objects in the first measurement are related to the second measurement configuration. That is, the second measurement configuration may include one or more measurement IDs that identify the related measurement targets / objects and the reporting configuration.

[0186] In step S1303, the UE may perform the first type measurement according to / based on the first measurement configuration. The UE may perform the first type measurement in the resources indicated by the measurement resource information of the first type measurement. The UE may derive / generate a measurement result based on the first type measurement.

[0187] In step S1305, the UE may perform the second type measurement according to / based on the second measurement configuration. The UE may perform the second type measurement in the resources indicated by the measurement resource information of the second type measurement. The UE may derive the measurement result based on the second type measurement.

[0188] The resources for the first type of measurement and the resources for the second type of measurement may be time division multiplexed (TDMed), that is, at a certain moment, the UE may perform the first type of measurement or the second type of measurement.

[0189] The resources for the first type of measurement and the resources for the second type of measurement may overlap. For example, the resource set for the second type of measurement may be a subset of the resource set for the first type of measurement, and vice versa.

[0190] The UE may be configured with time information indicating when to perform the first type measurement / second type measurement.

[0191] The time information may indicate the length of the duration and the periodicity of the periodically occurring duration.

[0192] The time information may indicate the time interval between two consecutive measurements of the measurement type of interest (first type or second type). If a minimum (MIN) time interval is configured, the UE may need to perform at most one measurement of the measurement type of interest during the MIN time interval. If a maximum (MAX) time interval is configured, the UE may need to perform at least one measurement of the measurement type of interest during the MAX time interval.

[0193] In some implementations, the execution order of steps S1303 and S1305 can be interchanged. In another implementation, steps S1303 and S1305 can be executed simultaneously.

[0194] In step S1307, the UE may send a measurement report including one or more measurement results of the first type measurement and / or the second type measurement.

[0195] The UE may send the measurement result of the first type measurement according to / based on the first measurement configuration.

[0196] The report may be based on the measurement report resource for the first type of measurement. If the measurement report resource for the first type of measurement has been configured, the UE may send the measurement result of the first type of measurement in the resource indicated by the measurement report resource information. The measurement report resource information may include time information indicating when to report the first type of measurement.

[0197] The time information may include a time pattern. The time pattern may indicate the periodicity of the report.

[0198] The time information may indicate a time interval between two consecutive measurements of the first type. If a MIN time interval is configured, the UE may need to perform at most one measurement of the first measurement type during the MIN time interval. If a MAX time interval is configured, the UE may need to perform at least one measurement of the first measurement type during the MAX time interval.

[0199] The reporting may be based on the measurement (time) resources for the first measurement type.The UE may send a measurement report for the first type of measurement if the measurement (time) resources for the first type of measurement have been configured and reporting is triggered during the measurement (time) resources for the first type of measurement.

[0200] The UE may send the measurement result of the second type measurement according to / based on the second measurement configuration.

[0201] The report may be based on the measurement report resource for the second type of measurement. If the measurement report resource for the second type of measurement has been configured, the UE may send the measurement result of the second type of measurement in the resource indicated by the measurement report resource information. The measurement report resource information may include time information indicating when to report the second type of measurement.

[0202] The time information may include a time pattern. The time pattern may indicate the periodicity of the report.

[0203] The time information may indicate a time interval between two consecutive measurements of the second type. If a MIN time interval is configured, the UE may need to perform at most one measurement of the second measurement type during the MIN time interval. If a MAX time interval is configured, the UE may need to perform at least one measurement of the second measurement type during the MAX time interval.

[0204] The reporting may be based on the measurement (time) resources for the second measurement type.If the measurement (time) resources for the second type of measurement have been configured and reporting is triggered during the measurement (time) resources for the second type of measurement, the UE may send a measurement report for the second type of measurement.

[0205] Reports can be triggered periodically (however configured to do so).

[0206] If a reporting event has been configured and is met, a report can be triggered.

[0207] The report can be triggered dynamically by the network. The network can dynamically request the report via DCI / MAC CE / RRC signaling.

[0208] The measurement report may include measurement type information of the measurement result currently being reported.

[0209] The measurement report may include both the first type of measurements and the second type of measurements (if the first measurement configuration and / or the second measurement configuration configures such a combined report).

[0210] Fig.14A An example of hybrid periodic reporting with substitution according to an embodiment of the present disclosure is shown.

[0211] Reference Fig.14A , by default, type 2 measurement results (ie, measurement results of the second type measurement) may be periodically reported, and type 1 measurement results (ie, measurement results of the first type measurement) may be sparsely periodically reported.

[0212] When the reporting time of the type 1 measurement result conflicts with the reporting time of the type 2 measurement result, the type 1 measurement result may replace the type 2 measurement result. That is, the UE may send a measurement report including the type 1 measurement result but not including the type 2 measurement result to the network at the reporting time.

[0213] Fig. 14B An example of hybrid periodic reporting without replacement according to an embodiment of the present disclosure is shown.

[0214] Reference Fig. 14B , by default, type 2 measurement results (ie, measurement results of the second type measurement) may be periodically reported, and type 1 measurement results (ie, measurement results of the first type measurement) may be sparsely periodically reported.

[0215] Even if the reporting time of the type 1 measurement result conflicts with the reporting time of the type 2 measurement result, the type 1 measurement result will not replace the type 2 measurement result. That is, the UE can send a measurement report including both the type 1 measurement result and the type 2 measurement result to the network at the reporting time.

[0216] In some implementations, measurement reporting may be triggered by a network command, such as Fig.15 and Fig.16 shown.

[0217] Fig.15 An example of dynamic reporting based on a report trigger indicating a report type identifier according to an embodiment of the present disclosure is shown.

[0218] Reference Fig.15 , the UE may receive a report command / request from the network and perform measurement reporting to the network based on the report command / request. The report command / request may include a measurement report type indicator. The UE may receive a report command / request from the network via DCI, MAC CE, and / or RRC signaling.

[0219] For example, when the reporting command / request includes a measurement report type indicator set to type 2 (ie, measurement report type indicator = type 2), the UE may send a measurement report including type 2 measurement results to the network.

[0220] For another example, when the reporting command / request includes a measurement report type indicator set to type 1 (ie, measurement report type indicator = type 1), the UE may send a measurement report including type 1 measurement results to the network.

[0221] In some implementations, a specific type among type 1 and type 2 may be configured as a default type through RRC signaling. In this case, when requesting a measurement result of the default type, the report command / request may not include a measurement report type indicator.

[0222] Fig.16 An example of compressed CSI reporting based on a report trigger indicating a report type identifier according to an embodiment of the present disclosure is shown.

[0223] Reference Fig.16 , the UE may receive a CSI reporting command / request from the network and perform CSI reporting to the network based on the CSI reporting command / request. The CSI reporting command / request may include a CSI measurement report type indicator. The UE may receive a CSI reporting command / request from the network via DCI, MAC CE, and / or RRC signaling.

[0224] For example, when the CSI reporting command / request includes a CSI measurement report type indicator set to type 2 (ie, CSI measurement report type indicator = type 2), the UE may send a measurement report including type 2 CSI measurement results to the network.

[0225] For another example, when the CSI reporting command / request includes a CSI measurement report type indicator set to Type 1 (ie, CSI measurement report type indicator = Type 1), the UE may send a measurement report including Type 1 CSI measurement results to the network.

[0226] Type 1 CSI measurement results (i.e., CSI measurement results based on the first type of measurement) may be normal CSI measurement results derived based on actual measurements of the CSI-RS. Type 2 CSI measurement results (i.e., CSI measurement results based on the second type of measurement) may be compressed CSI measurement results derived on the UE side based on some machine learning algorithms / neural networks (such as, autoencoder-based or CNN-based models).

[0227] After receiving the compressed CSI measurement result, the network needs to decompress the received CSI measurement result (i.e., the compressed CSI measurement result) by using the network-side machine learning algorithm / neural network corresponding to the UE-side machine learning algorithm / neural network to obtain the desired CSI measurement result.

[0228] Fig.17 An example of event-triggered reporting according to an embodiment of the present disclosure is shown.

[0229] Reference Fig.17 , the UE may be configured with a duration for type 1 measurement (i.e., first type measurement) and a duration for type 2 measurement (i.e., second type measurement). If a measurement reporting event is satisfied during the duration for type 1 measurement, the UE may send a measurement result of type 1 measurement. If a measurement reporting event is satisfied during the duration for type 2 measurement, the UE may send a measurement result of type 2 measurement.

[0230] By jointly considering the first type of measurement results and the second type of measurement results received from the UE, the network can evaluate the accuracy of the type of measurement of interest (e.g., the accuracy of the measurement results based on / assisted ML) and take appropriate actions, i.e., continue to use the measurement results based on / assisted ML when the accuracy is sufficient, or stop using the measurement results based on / assisted ML when the accuracy is insufficient. The network can reconfigure the UE to stop reporting inaccurate measurement results.

[0231] In addition, the method described in the present disclosure from the perspective of the UE (for example, in Fig.11 (in Chinese) can be Figure 2 The first wireless device 100 and / or Figure 3 The UE 100 shown in FIG. 1 performs.

[0232] More specifically, the UE includes at least one transceiver, at least one processor, and at least one computer memory that is operatively connected to the at least one processor and stores instructions that perform operations based on being executed by the at least one processor.

[0233] These operations include: receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; based on the second measurement configuration, obtaining a second measurement result by providing an input to the ML model; and sending at least one of the first measurement result or the second measurement result to the network.

[0234] In addition, the method described in the present disclosure from the perspective of the UE (for example, in Fig.11 in) can be stored in Figure 2 The software code 105 in the memory 104 included in the first wireless device 100 shown in FIG. 1 is executed.

[0235] More specifically, at least one computer readable medium (CRM) stores instructions that perform operations based on being executed by at least one processor, and the operations include: receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; based on the first measurement configuration, obtaining a first measurement result by measuring a reference signal; based on the second measurement configuration, obtaining a second measurement result by providing input to the ML model; and sending at least one of the first measurement result or the second measurement result to the network.

[0236] In addition, the method described in the present disclosure from the perspective of the UE (for example, in Fig.11 (in Chinese) can be obtained through Figure 2 The control and / or Figure 3 The control execution is performed by the processor 102 included in the UE 100 shown in FIG.

[0237] More specifically, an apparatus (e.g., wireless device / UE) configured / adapted to operate in a wireless communication system includes at least one processor and at least one computer memory that can be operatively connected to the at least one processor. The at least one processor is configured / adapted to perform operations, the operations including: receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result of the measurement object based on a machine learning (ML) model; based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; based on the second measurement configuration, obtaining a second measurement result by providing an input to the ML model; and sending at least one of the first measurement result or the second measurement result to the network.

[0238] In addition, the method described in the present disclosure from the perspective of a network node associated with the first cell (e.g., Fig.12 (in Chinese) can be Figure 2 The second wireless device 200 is shown to perform.

[0239] More specifically, the network node includes at least one transceiver, at least one processor, and at least one computer memory operatively connected to the at least one processor and storing instructions that perform operations upon execution by the at least one processor.

[0240] These operations include: sending a first measurement configuration and a second measurement configuration to a user equipment (UE), wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning (ML) model; receiving a first measurement result and a second measurement result from the UE, wherein the first measurement result is obtained by measuring the reference signal based on the first measurement configuration, and the second measurement result is obtained by providing input to the ML model based on the second measurement configuration; determining whether to continue or stop reporting a configuration of a measurement result derived according to the ML model based on the first measurement result and the second measurement result; and sending the configuration to the UE.

[0241] The present disclosure may have various beneficial effects.

[0242] For example, the network can verify the accuracy of the current model and take actions such as model changes or fallback to non-AI / ML based operations.

[0243] The beneficial effects that can be obtained by the specific embodiments of the present disclosure are not limited to the beneficial effects listed above. For example, there may be various technical effects that can be understood and / or derived from the present disclosure by a person of ordinary skill in the relevant field. Therefore, the specific effects of the present disclosure are not limited to those explicitly described herein, but may include various effects that can be understood or derived from the technical features of the present disclosure.

[0244] The claims in this disclosure may be combined in various ways. For example, the technical features in the method claims of this disclosure may be combined to be implemented or performed in a device, and the technical features in the device claims may be combined to be implemented or performed in a method. In addition, the technical features in the method claims and the device claims may be combined to be implemented or performed in a device. In addition, the technical features in the method claims and the device claims may be combined to be implemented or performed in a method. Other implementations are within the scope of the appended claims.

Claims

1. A method performed by a user equipment (UE) configured to operate in a wireless communication system, the method comprising: Receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning ML model; Based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; Based on the second measurement configuration, obtaining a second measurement result by providing an input to the ML model; and At least one of the first measurement result or the second measurement result is sent to the network.

2. The method according to claim 1, wherein: The input includes at least one of the following: one or more ML input parameters received from the network; one or more reference signals; measurements of the one or more reference signals; or Past measurement results, The second measurement result includes at least one of the following items: The output of the ML model; Compression measurements; predicted measurement results derived from measurement results including said past measurement results; Predicted measurements of a reference signal derived from measurements of other reference signals; or beam indices of one or more beams in descending order of beam quality from the beam with the highest beam quality, and The one or more ML input parameters include at least one of the following items: location information of the UE, measurement results of at least one reference signal configured for the UE, measurement results of at least one of the serving cell or one or more neighboring cells, or mobility history information of the UE.

3. The method according to claim 1, wherein: The ML model includes at least one of the following items: a deep neural network DNN model, a convolutional neural network CNN model, a recurrent neural network RNN ​​model, or a deep reinforcement learning DRL model.

4. The method according to claim 1, wherein: The step of obtaining the second measurement result includes applying an ML algorithm corresponding to the ML model to the input to obtain the second measurement result as an output of the ML model.

5. The method according to claim 1, wherein: The first measurement configuration includes: a measurement identification ID list, a measurement object list and a report configuration list, Each measurement ID in the measurement ID list identifies a corresponding measurement object in the measurement object list and a corresponding report configuration in the report configuration list, and The second measurement configuration includes one or more measurement IDs in the measurement ID list.

6. The method according to claim 1, wherein: The first measurement configuration comprises a first periodicity for sending the first measurement result, The second measurement configuration includes a second periodicity for sending the second measurement result, and The first periodicity is longer than the second periodicity.

7. The method according to claim 6, further comprising: determining a first timing for sending the first measurement result based on the first periodicity; determining a second timing for sending the second measurement result based on the second periodicity; as well as Based on the overlap between the first timing and the second timing, the first measurement result is sent at the first timing without sending the second measurement result.

8. The method according to claim 6, further comprising: determining a first timing for sending the first measurement result based on the first periodicity; determining a second timing for sending the second measurement result based on the second periodicity; as well as Based on the overlap of the first timing and the second timing, both the first measurement result and the second measurement result are transmitted at the first timing.

9. The method according to claim 1, further comprising: receiving a report request from the network; Based on the report request, determining a measurement result type to be reported from a first type and a second type; as well as sending a measurement result associated with the measurement result type to the network, wherein, based on the measurement result type being the first type, sending the first measurement report; wherein, based on the measurement result type being the second type, sending the second measurement report, and The report request is received via at least one of the following: downlink control information DCI, medium access control MAC control element CE signaling, or radio resource control RRC signaling.

10. The method according to claim 9, wherein: determining that the measurement result type is the first type based on the report request including the measurement result type being set to the first type, and The measurement result type is determined to be the second type based on the report request including the measurement result type set to the second type.

11. The method according to claim 9, further comprising: receiving, from the network, a configuration for a default type of the first type and the second type, wherein, based on the default type being the first type or the report request including the measurement result type set to the first type, determining that the measurement result type is the first type, and The measurement result type is determined to be the second type based on the fact that the default type is the second type or the report request includes the measurement result type set to the second type.

12. The method according to claim 1, wherein: The first measurement configuration comprises a first duration for sending the first measurement result, The second measurement configuration includes a second duration for sending the second measurement result, wherein the first measurement result is sent based on a reporting event being satisfied during the first duration, and Wherein, based on satisfying the reporting event during the second duration, the second measurement result is sent.

13. The method according to claims 1 to 12, wherein: The UE communicates with at least one of a mobile device, a network, or an autonomous vehicle.

14. A user equipment (UE) configured to operate in a wireless communication system, the UE comprising: at least one transceiver; at least one processor; as well as at least one memory operatively coupled to the at least one processor and storing instructions, the instructions performing operations upon being executed by the at least one processor, the operations comprising: Receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning ML model; Based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; Based on the second measurement configuration, obtaining a second measurement result by providing an input to the ML model; and At least one of the first measurement result or the second measurement result is sent to the network.

15. The UE according to claim 14, wherein: The UE is arranged to implement the method according to one of claims 2 to 13.

16. A network node configured to operate in a wireless communication system, the network node comprising: at least one transceiver; at least one processor; as well as at least one memory operatively coupled to the at least one processor and storing instructions, the instructions performing operations upon being executed by the at least one processor, the operations comprising: Sending a first measurement configuration and a second measurement configuration to a user equipment UE, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning ML model; receiving a first measurement result and a second measurement result from the UE, wherein the first measurement result is obtained by measuring the reference signal based on the first measurement configuration, and the second measurement result is obtained by providing an input to the ML model based on the second measurement configuration; determining whether to continue or stop reporting a configuration of measurement results derived according to the ML model based on the first measurement result and the second measurement result; and The configuration is sent to the UE.

17. A method performed by a network node configured to operate in a wireless communication system, the method comprising: Sending a first measurement configuration and a second measurement configuration to a user equipment UE, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning ML model; receiving a first measurement result and a second measurement result from the UE, wherein the first measurement result is obtained by measuring the reference signal based on the first measurement configuration, and the second measurement result is obtained by providing an input to the ML model based on the second measurement configuration; determining whether to continue or stop reporting a configuration of measurement results derived according to the ML model based on the first measurement result and the second measurement result; and The configuration is sent to the UE.

18. The method according to claim 17, wherein: The UE is arranged to implement the method according to one of claims 1 to 13.

19. A device adapted to operate in a wireless communication system, the device comprising: at least one processor; as well as at least one memory operatively coupled to the at least one processor and storing instructions, the instructions performing operations upon being executed by the at least one processor, the operations comprising: Receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning ML model; Based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; Based on the second measurement configuration, obtaining a second measurement result by providing an input to the ML model; and At least one of the first measurement result or the second measurement result is sent to the network.

20. A non-transitory computer readable medium (CRM) having program code stored thereon, the program code implementing instructions, the instructions performing operations upon being executed by at least one processor, the operations comprising: Receiving a first measurement configuration and a second measurement configuration from a network, wherein the first measurement configuration is related to generating a measurement result for a measurement object based on measuring a reference signal, and the second measurement configuration is related to deriving a measurement result for the measurement object based on a machine learning ML model; Based on the first measurement configuration, obtaining a first measurement result by measuring the reference signal; Based on the second measurement configuration, obtaining a second measurement result by providing an input to the ML model; and At least one of the first measurement result or the second measurement result is sent to the network.

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