Method and device for monitoring model or functionality of artificial intelligence and machine learning in wireless communication system

Abstract

Provided are a method and device for monitoring a model or functionality of artificial intelligence (AI) / machine learning (ML) in a wireless communication system. A terminal receives, from a base station, configuration information for performance monitoring of a model or functionality of AI / ML. Here, the configuration information includes at least one of monitoring period information and monitoring gap information. In addition, the terminal performs the performance monitoring of the model or functionality of AI / ML on the basis of the configuration information that has been received, and transmits, to the base station, a report on a result of the performance monitoring of the model or functionality of AI / ML.

Description

Method and device for monitoring artificial intelligence and machine learning functions or models in wireless communication systems

[0001] This specification relates to wireless communications applicable to 5G NR, 5G-Advanced and 6G.

[0002] As more and more communication devices demand ever-increasing communication traffic, the need for next-generation 5G systems, which offer enhanced wireless broadband communication capabilities over existing LTE systems, is growing. This next-generation 5G system, known as NewRAT, differentiates communication scenarios into Enhanced Mobile BroadBand (eMBB), Ultra-reliability and low-latency communication (URLLC), and Massive Machine-Type Communications (mMTC).

[0003] Here, eMBB is a next-generation mobile communication scenario with characteristics such as High Spectrum Efficiency, High User Experienced Data Rate, and High Peak Data Rate; URLLC is a next-generation mobile communication scenario with characteristics such as Ultra Reliable, Ultra Low Latency, and Ultra High Availability (e.g., V2X, Emergency Service, and Remote Control); and mMTC is a next-generation mobile communication scenario with characteristics such as Low Cost, Low Energy, Short Packet, and Massive Connectivity (e.g., IoT).

[0004] An object of the present specification is to provide a method and device for a terminal to monitor the performance of an AI / ML function or model in a wireless communication system that performs communication using AI / ML.

[0005] One embodiment of the present specification provides a method for a wireless communication system in which a terminal receives configuration information for performance monitoring of an artificial intelligence (AI) / machine learning (ML) functionality or model from a base station. The configuration information includes at least one of monitoring cycle information and monitoring gap information. In addition, the terminal performs performance monitoring of the AI / ML functionality or model based on the received configuration information, and transmits a report of the results of the performance monitoring of the performed AI / ML functionality or model to the base station.

[0006] In addition, one embodiment of the present specification provides a method in which, in a wireless communication system, a base station transmits configuration information for performance monitoring of an AI / ML function or model to a terminal. The configuration information includes at least one of monitoring period information and monitoring gap information. Thereafter, the base station provides a method for receiving, from the terminal, a report on the results of performance monitoring of the AI / ML function or model based on the transmitted configuration information.

[0007] In addition, one embodiment of the present specification provides a wireless communication system, comprising at least one processor, and at least one memory storing instructions and being operably electrically connected to the at least one processor, wherein an operation performed based on the instructions being executed by the at least one processor comprises: receiving configuration information for performance monitoring of an AI / ML function or model from a base station, wherein the configuration information includes at least one of monitoring period information and monitoring gap information. In addition, a terminal is provided that performs performance monitoring of the AI / ML function or model based on the received configuration information, and transmits a report of the result of the performance monitoring of the performed AI / ML function or model to the base station.

[0008] In addition, one embodiment of the present specification provides a wireless communication system, comprising at least one processor, and at least one memory storing instructions and being operably electrically connected to the at least one processor, wherein an operation performed based on the instructions being executed by the at least one processor is: transmitting configuration information for performance monitoring of an AI / ML function or model to a terminal, wherein the configuration information includes at least one of monitoring period information and monitoring gap information. Thereafter, a base station is provided that receives a report of the result of performance monitoring of the AI / ML function or model based on the transmitted configuration information from the terminal.

[0009] According to the disclosure of this specification, there is an effect that the terminal can efficiently monitor the performance of AI / ML functions or models and operate based thereon, thereby maximizing the overall system performance.

[0010] Figure 1 is a diagram illustrating a wireless communication system.

[0011] Figure 2 illustrates the structure of a radio frame used in NR.

[0012] Figures 3a to 3c are exemplary diagrams showing exemplary architectures for wireless communication services.

[0013] Figure 4 illustrates the slot structure of an NR frame.

[0014] Figure 5 illustrates an example of subframe types in NR.

[0015] Figure 6 illustrates the structure of a self-contained slot.

[0016] Figure 7 illustrates a procedure of a terminal and a base station according to one embodiment of the present specification.

[0017] FIG. 8 illustrates a periodic AI / ML function / model monitoring / evaluation setup according to one embodiment of the present specification.

[0018] Figure 9 illustrates an operation method of a terminal according to one embodiment of the present specification.

[0019] Figure 10 illustrates an operation method of a base station according to one embodiment of the present specification.

[0020] Figure 11 illustrates a device according to one embodiment of the present specification.

[0021] Fig. 12 is a block diagram showing the configuration of a terminal according to one embodiment of the present specification.

[0022] Figure 13 shows a block diagram of a processor in which the disclosure of this specification is implemented.

[0023] FIG. 14 is a block diagram showing in detail the transmitter / receiver of the first device illustrated in FIG. 11 or the transmitter / receiver unit of the device illustrated in FIG. 12.

[0024] It should be noted that the technical terms used in this specification are used merely to describe specific embodiments and are not intended to limit the contents of this specification. In addition, unless specifically defined otherwise herein, the technical terms used in this specification should be interpreted as having a meaning generally understood by those skilled in the art to which this specification pertains, and should not be interpreted in an excessively broad or narrow sense. In addition, if a technical term used in this specification is an incorrect technical term that does not accurately express the contents and ideas of this specification, it should be replaced with a technical term that can be correctly understood by a person skilled in the art. In addition, general terms used in this specification should be interpreted according to their dictionary definitions or according to the preceding and following context, and should not be interpreted in an excessively narrow sense.

[0025] Additionally, the singular expressions used herein include plural expressions unless the context clearly dictates otherwise. In this application, terms such as "consist of" or "have" should not be construed to necessarily include all of the components or steps described in the specification, and should be construed to mean that some of the components or steps may not be included, or that additional components or steps may be included.

[0026] Additionally, terms including ordinal numbers, such as "first" and "second," used herein may be used to describe various components, but these components should not be limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present invention, a first component could be referred to as a second component, and similarly, a second component could also be referred to as a first component.

[0027] When a component is referred to as being connected or connected to another component, it may be directly connected or connected to that other component, but there may also be other components intervening. Conversely, when a component is referred to as being directly connected or connected to another component, it should be understood that there are no other components intervening.

[0028] Hereinafter, embodiments will be described in detail with reference to the attached drawings. Regardless of the drawing reference numerals, identical or similar components will be given the same reference numerals, and redundant descriptions thereof will be omitted. In addition, when describing the contents of this specification, if a detailed description of a related known technology is judged to obscure the gist of this specification, the detailed description thereof will be omitted. In addition, it should be noted that the attached drawings are only intended to make the contents and ideas of this specification easily understandable, and should not be construed as limiting the contents and ideas of this specification by the attached drawings. The contents and ideas of this specification should be construed to extend to all changes, equivalents, and substitutes other than the attached drawings.

[0029] In this specification, “A or B” can mean “only A,” “only B,” or “both A and B.” In other words, “A or B” in this specification can be interpreted as “A and / or B.” For example, “A, B or C” in this specification can mean “only A,” “only B,” “only C,” or “any combination of A, B, and C.”

[0030] As used herein, a slash ( / ) or a comma can mean "and / or." For example, "A / B" can mean "A and / or B." Accordingly, "A / B" can mean "only A," "only B," or "both A and B." For example, "A, B, C" can mean "A, B, or C."

[0031] In this specification, “at least one of A and B” may mean “only A,” “only B,” or “both A and B.” Additionally, in this specification, the expressions “at least one of A or B” or “at least one of A and / or B” may be interpreted identically to “at least one of A and B.”

[0032] Additionally, in this specification, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0033] Additionally, parentheses used in this specification may mean “for example.” Specifically, when “control information (PDCCH)” is indicated, “PDCCH (Physical Downlink Control Channel)” may be suggested as an example of “control information.” In other words, “control information” in this specification is not limited to “PDCCH,” and “PDDCH” may be suggested as an example of “control information.” Furthermore, even when indicated as “control information (i.e., PDCCH),” “PDCCH” may be suggested as an example of “control information.”

[0034] Technical features individually described in a single drawing in this specification may be implemented individually or simultaneously.

[0035] Although the attached drawing illustrates a UE (User Equipment) as an example, the illustrated UE may also be referred to as a terminal, ME (Mobile Equipment), etc. In addition, the UE may be a portable device such as a laptop, mobile phone, PDA, smart phone, multimedia device, etc., or a non-portable device such as a PC or vehicle-mounted device.

[0036] Hereinafter, the term "UE" is used as an example of a device capable of wireless communication (e.g., a wireless communication device, a wireless device, or a wireless device). The operations performed by the UE can be performed by any device capable of wireless communication. A device capable of wireless communication may also be referred to as a wireless communication device, a wireless device, or a wireless device.

[0037] The term base station used below generally refers to a fixed station that communicates with wireless devices, and can be used as a comprehensive term that includes eNodeB (evolved-NodeB), eNB (evolved-NodeB), BTS (Base Transceiver System), Access Point, gNB (Next generation NodeB), RRH (remote radio head), TP (transmission point), RP (reception point), relay, etc.

[0038] Although this specification describes embodiments using LTE systems, LTE-A systems, and NR systems, these embodiments may be applied to any communication system falling within the above definitions.

[0039] Wireless Communication System

[0040] Building on the success of LTE (long term evolution) / LTE-Advanced (LTE-A) for 4th generation mobile communications, commercialization of the next generation, or 5th generation (so-called 5G) mobile communications, and follow-up research are also ongoing.

[0041] The International Telecommunication Union (ITU) defines 5G mobile communications as providing data transfer speeds of up to 20 Gbps and a perceived transmission speed of at least 100 Mbps everywhere. Its official name is "IMT-2020."

[0042] ITU proposes three usage scenarios: eMBB (enhanced Mobile BroadBand), mMTC (massive Machine Type Communication), and URLLC (Ultra Reliable and Low Latency Communications).

[0043] URLLC addresses usage scenarios that require high reliability and low latency. For example, services such as autonomous driving, factory automation, and augmented reality require high reliability and low latency (e.g., sub-1ms). Current 4G (LTE) latency is statistically 21-43ms (best 10%) and 33-75ms (median). This is insufficient to support services requiring sub-1ms latency. Next, eMBB usage scenarios address usage scenarios that require mobile ultra-wideband.

[0044] In other words, the 5th generation mobile communication system can support higher capacity than the current 4G LTE, increase the density of mobile broadband users, and support D2D (Device to Device), high reliability, and MTC (Machine-type communication). 5G research and development also aims for lower latency and lower battery consumption than 4G mobile communication systems to better implement the Internet of Things. For this 5G mobile communication, a new radio access technology (New RAT or NR) may be proposed.

[0045] The NR frequency band can be defined by two types of frequency ranges (FR1, FR2). The numerical values ​​of the frequency ranges can be changed, and for example, the two types of frequency ranges (FR1, FR2) can be as shown in Table 1 below. For convenience of explanation, among the frequency ranges used in the NR system, FR1 can mean the “sub 6 GHz range”, and FR2 can mean the “above 6 GHz range” and can be called millimeter wave (mmW).

[0046] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0047] The numerical value of the frequency range of the NR system can be changed. For example, FR1 can include a band from 410 MHz to 7125 MHz, as shown in Table 1. That is, FR1 can include frequency bands above 6 GHz (or 5850, 5900, 5925 MHz, etc.). For example, the frequency bands above 6 GHz (or 5850, 5900, 5925 MHz, etc.) included within FR1 can include unlicensed bands. Unlicensed bands can be used for various purposes, such as for communications for vehicles (e.g., autonomous driving).

[0048] Meanwhile, 3GPP-based communication standards define downlink physical channels corresponding to resource elements that carry information originating from upper layers, and downlink physical signals corresponding to resource elements that are used by the physical layer but do not carry information originating from upper layers. For example, the physical downlink shared channel (PDSCH), physical broadcast channel (PBCH), physical multicast channel (PMCH), physical control format indicator channel (PCFICH), physical downlink control channel (PDCCH), and physical hybrid ARQ indicator channel (PHICH) are defined as downlink physical channels, and reference signals and synchronization signals are defined as downlink physical signals. A reference signal (RS), also referred to as a pilot, is a signal with a special predefined waveform known to the gNB and the UE. For example, cell specific RS, UE-specific RS (UE-RS), positioning RS (PRS), and channel state information RS (CSI-RS) are defined as downlink reference signals. The 3GPP LTE / LTE-A standard defines uplink physical channels corresponding to resource elements carrying information originating from higher layers, and uplink physical signals corresponding to resource elements used by the physical layer but not carrying information originating from higher layers.For example, a physical uplink shared channel (PUSCH), a physical uplink control channel (PUCCH), and a physical random access channel (PRACH) are defined as uplink physical channels, and a demodulation reference signal (DMRS) for uplink control / data signals and a sounding reference signal (SRS) used for uplink channel measurement are defined.

[0049] In this specification, PDCCH (Physical Downlink Control CHannel) / PCFICH (Physical Control Format Indicator CHannel) / PHICH ((Physical Hybrid automatic retransmit request Indicator CHannel) / PDSCH (Physical Downlink Shared CHannel) mean a set of time-frequency resources or a set of resource elements that carry DCI (Downlink Control Information) / CFI (Control Format Indicator) / downlink ACK / NACK (ACKnowlegement / Negative ACK) / downlink data, respectively. In addition, PUCCH (Physical Uplink Control CHannel) / PUSCH (Physical Uplink Shared CHannel) / PRACH (Physical Random Access CHannel) mean a set of time-frequency resources or a set of resource elements that carry UCI (Uplink Control Information) / uplink data / random access signals, respectively.

[0050] Figure 1 is a diagram illustrating a wireless communication system.

[0051] As can be seen from FIG. 1, the wireless communication system includes at least one base station (BS). The BS is divided into a gNodeB (or gNB) (20a) and an eNodeB (or eNB) (20b). The gNB (20a) supports 5th generation mobile communications. The eNB (20b) supports 4th generation mobile communications, i.e., long term evolution (LTE).

[0052] Each base station (20a and 20b) provides communication services for a specific geographic area (commonly referred to as a cell) (20-1, 20-2, 20-3). The cell may be further divided into multiple areas (referred to as sectors).

[0053] A UE (user equipment) typically belongs to a single cell, and the cell to which the UE belongs is called a serving cell. The base station that provides communication services for the serving cell is called a serving base station (BS). Since the wireless communication system is a cellular system, there are other cells adjacent to the serving cell. These other cells adjacent to the serving cell are called neighbor cells. The base station that provides communication services to the neighbor cell is called a neighbor BS. The serving cell and neighbor cells are determined relative to the UE.

[0054] Hereinafter, downlink refers to communication from a base station (20) to a UE (10), and uplink refers to communication from a UE (10) to a base station (20). In downlink, the transmitter may be part of the base station (20), and the receiver may be part of the UE (10). In uplink, the transmitter may be part of the UE (10), and the receiver may be part of the base station (20).

[0055] Meanwhile, wireless communication systems can be broadly divided into frequency division duplex (FDD) and time division duplex (TDD). In FDD, uplink and downlink transmissions occupy different frequency bands and occur at different times. In TDD, uplink and downlink transmissions occupy the same frequency band but occur at different times. The channel response in TDD is essentially reciprocal, meaning that the downlink and uplink channel responses are nearly identical in a given frequency range. Therefore, in TDD-based wireless communication systems, the downlink channel response can be derived from the uplink channel response. In TDD, uplink and downlink transmissions are time-divided across the entire frequency band, so downlink transmission by the base station and uplink transmission by the UE cannot be performed simultaneously. In TDD systems, where uplink and downlink transmissions are divided into subframes, uplink and downlink transmissions are performed in different subframes.

[0056] Figure 2 illustrates the structure of a radio frame used in NR.

[0057] In NR, uplink and downlink transmissions are structured as frames. A radio frame is 10ms long and is defined by two 5ms half-frames (HF). Each half-frame is defined by five 1ms subframes (SF). A subframe is divided into one or more slots, and the number of slots in a subframe depends on the subcarrier spacing (SCS). Each slot contains 12 or 14 OFDM(A) symbols, depending on the cyclic prefix (CP). When a normal CP is used, each slot contains 14 symbols. When an extended CP is used, each slot contains 12 symbols. Here, the symbols may include OFDM symbols (or CP-OFDM symbols), SC-FDMA symbols (or DFT-s-OFDM symbols).

[0058] Support for various numerologies

[0059] In NR systems, multiple numerologies may be provided to terminals as wireless communication technologies advance. For example, an SCS of 15 kHz supports a wide area in traditional cellular bands. An SCS of 30 kHz / 60 kHz supports dense urban environments, lower latency, and wider carrier bandwidth. An SCS of 60 kHz or higher supports a bandwidth greater than 24.25 GHz to overcome phase noise.

[0060] The above numerology can be defined by the cycle prefix (CP) length and subcarrier spacing (SCS). A single cell can provide multiple numerologies to a terminal. When the numerology index is represented by μ, each subcarrier spacing and the corresponding CP length can be as shown in the table below.

[0061] μ△f=2 μ 15 [kHz]CP015 General 130 General 260 General, Extended 3120 General 4240 General 5480 General 6960 General

[0062] For general CP, when the index of the numerology is represented by μ, the number of OFDM symbols per slot (N slot symb ), number of slots per frame (N frame,μ slot ) and the number of slots per subframe (N subframe,μ slot ) is as shown in the table below.

[0063] μ△f=2 μ 15 [kHz]N slot symb N frame,μ slot N subframe,μ slot 015141011301420226014404312014808424014160165480143203269601464064

[0064] For extended CP, when the index of the numerology is represented by μ, the number of OFDM symbols per slot (N slot symb ), number of slots per frame (N frame,μ slot ) and the number of slots per subframe (N subframe,μ slot ) is as shown in the table below.

[0065] μSCS (15*2 u )N slot symb N frame,μ slot N subframe,μslot 260KHz (u=2)12404

[0066] In an NR system, OFDM(A) numerology (e.g., SCS, CP length, etc.) may be set differently between multiple cells that are merged into a single terminal. Accordingly, the (absolute time) interval of a time resource (e.g., SF, slot, or TTI) (conveniently referred to as TU (Time Unit)) consisting of the same number of symbols may be set differently between the merged cells.

[0067] Figures 3a to 3c are exemplary diagrams showing exemplary architectures for wireless communication services.

[0068] Referring to FIG. 3a, the UE is connected to an LTE / LTE-A-based cell and an NR-based cell in a DC (dual connectivity) manner.

[0069] The above NR-based cell is connected to the core network for existing 4th generation mobile communication, i.e. Evolved Packet Core (EPC).

[0070] Referring to FIG. 3b, unlike FIG. 3a, the LTE / LTE-A-based cell is connected to a core network for 5th generation mobile communication, i.e., a 5G core network.

[0071] A service method based on an architecture as illustrated in Figures 3a and 3b above is called NSA (non-standalone).

[0072] Referring to Figure 3c, the UE is connected only to NR-based cells. A service method based on this architecture is called SA (standalone).

[0073] Meanwhile, in the above NR, it may be considered that reception from the base station utilizes a downlink subframe, and transmission to the base station utilizes an uplink subframe. This method can be applied to paired and unpaired spectrums. A pair of spectrums means that two carrier spectrums are included for downlink and uplink operations. For example, in a pair of spectrums, one carrier may include a downlink band and an uplink band that are paired with each other.

[0074] Figure 4 illustrates the slot structure of an NR frame.

[0075] A slot contains multiple symbols in the time domain. For example, in the case of a normal CP, one slot contains 14 symbols, but in the case of an extended CP, one slot contains 12 symbols. A carrier contains multiple subcarriers in the frequency domain. An RB (Resource Block) is defined as multiple (e.g., 12) consecutive subcarriers in the frequency domain. A BWP (Bandwidth Part) is defined as multiple consecutive (physical, P)RBs in the frequency domain, and can correspond to a single numerology (e.g., SCS, CP length, etc.). A terminal can be configured with up to N (e.g., 4) BWPs in the downlink and uplink, respectively. Downlink or uplink transmission is performed through an activated BWP, and at a given time, only one BWP among the BWPs configured for the terminal can be activated. In the resource grid, each element is referred to as a Resource Element (RE), to which one complex symbol can be mapped.

[0076] Figure 5 illustrates an example of subframe types in NR.

[0077] The transmission time interval (TTI) illustrated in FIG. 5 may be referred to as a subframe or slot for NR (or new RAT). The subframe (or slot) of FIG. 5 may be used in a TDD system of NR (or new RAT) to minimize data transmission delay. As illustrated in FIG. 5, a subframe (or slot) includes 14 symbols. The symbols in the front of the subframe (or slot) may be used for a downlink (DL) control channel, and the symbols in the back of the subframe (or slot) may be used for an uplink (UL) control channel. The remaining symbols may be used for DL ​​data transmission or UL data transmission. According to this subframe (or slot) structure, downlink transmission and uplink transmission may be sequentially performed in one subframe (or slot). Therefore, downlink data may be received within a subframe (or slot), and an uplink acknowledgment (ACK / NACK) may be transmitted within the subframe (or slot).

[0078] The structure of these subframes (or slots) can be called self-contained subframes (or slots).

[0079] Specifically, the first N symbols in a slot are used to transmit a DL control channel (hereinafter, DL control region), and the last M symbols in the slot can be used to transmit a UL control channel (hereinafter, UL control region). N and M are each integers greater than or equal to 0. A resource region (hereinafter, data region) between the DL control region and the UL control region can be used for DL ​​data transmission or UL data transmission. For example, a physical downlink control channel (PDCCH) can be transmitted in the DL control region, and a physical downlink shared channel (PDSCH) can be transmitted in the DL data region. A physical uplink control channel (PUCCH) can be transmitted in the UL control region, and a physical uplink shared channel (PUSCH) can be transmitted in the UL data region.

[0080] Using this subframe (or slot) structure has the advantage of minimizing the final data transmission latency by reducing the time required to retransmit data that has experienced reception errors. In this self-contained subframe (or slot) structure, a time gap may be required during the transition from transmit mode to receive mode or from receive mode to transmit mode. To this end, some OFDM symbols during the transition from DL to UL in the subframe structure can be designated as a guard period (GP).

[0081] Figure 6 illustrates the structure of a self-contained slot.

[0082] In an NR system, a frame is characterized by a self-contained structure in which a DL control channel, DL or UL data, and a UL control channel can all be included within a single slot. For example, the first N symbols within a slot can be used to transmit a DL control channel (hereinafter, referred to as a DL control region), and the last M symbols within a slot can be used to transmit a UL control channel (hereinafter, referred to as a UL control region). N and M are each integers greater than or equal to 0. The resource region (hereinafter, referred to as a data region) between the DL control region and the UL control region can be used for DL ​​data transmission or UL data transmission. As an example, the following configuration can be considered. Each section is listed in chronological order.

[0083] 1. DL only configuration

[0084] 2. UL only configuration

[0085] 3. Mixed UL-DL configuration

[0086] - DL area + GP (Guard Period) + UL control area

[0087] - DL control area + GP + UL area

[0088] DL area: (i) DL data area, (ii) DL control area + DL data area

[0089] UL domain: (i) UL data domain, (ii) UL data domain + UL control domain

[0090] In the DL control region, a PDCCH can be transmitted, and in the DL data region, a PDSCH can be transmitted. In the UL control region, a PUCCH can be transmitted, and in the UL data region, a PUSCH can be transmitted. In the PDCCH, downlink control information (DCI), for example, DL data scheduling information, UL data scheduling information, etc., can be transmitted. In the PUCCH, uplink control information (UCI), for example, ACK / NACK (Positive Acknowledgement / Negative Acknowledgement) information for DL ​​data, CSI (Channel State Information) information, SR (Scheduling Request), etc., can be transmitted. GP provides a time gap when a base station and a terminal switch from transmission mode to reception mode or when switching from reception mode to transmission mode. Some symbols at the time of switching from DL to UL within a subframe can be set as GP.

[0091] <Artificial Intelligence (AI) / Machine Learning (ML)>

[0092] Currently, 3GPP is conducting research to improve the performance of wireless communication systems by applying AI / ML, starting with Release 18. Specifically, the following aspects are being studied regarding the life cycle management (LCM) of AI / ML models.

[0093] - Data collection

[0094] - Model training

[0095] - Functionality / model identification

[0096] - Model delivery / transfer

[0097] - Model inference operation

[0098] - Functionality / model selection, activation, deactivation, switching, and fallback operations.

[0099] - Functionality / model monitoring

[0100] - Model update

[0101] - UE capability

[0102] Meanwhile, the list of terms applied to AI / ML is discussed as shown in Table 5 below.

[0103] Terminology Description Data collection A process of collecting data by the network nodes, management entity, or UE for the purpose of AI / ML model training, data analytics and inference AI / ML Model A data driven algorithm that applies AI / ML techniques to generate a set of outputs based on a set of inputs.)AI / ML model trainingA process to train an AI / ML Model [by learning the input / output relationship] in a data driven manner and obtain the trained AI / ML Model for inference. AI / ML model inferenceA process of using a trained AI / ML model to produce a set of outputs based on a set of inputs. AI / ML model validationA subprocess of training to evaluate the quality of an AI / ML model using a dataset different from the one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training. AI / ML model testing Testing is a sub-process of training that evaluates the performance of the final AI / ML model using a dataset different from that used for model training and validation. Unlike AI / ML model validation, testing does not assume subsequent adjustments to the model.(A subprocess of training, to evaluate the performance of a final AI / ML model using a dataset different from one used for model training and validation. Differently from AI / ML model validation, testing does not assume subsequent tuning of the model.)UE-side (AI / ML) modelAn AI / ML Model whose inference is performed entirely at the UENetwork-side (AI / ML) modelAn AI / ML Model whose inference is performed entirely at the networkOne-sided (AI / ML) modelA UE-side (AI / ML) model or a Network-side (AI / ML) modelTwo-sided (AI / ML) modelA pair of AI / ML model(s) on which joint inference is performed. Here, joint inference is AI / ML inference where inference is performed jointly across the UE and the network, i.e., the first part of the inference is performed first by the UE and the remaining part by the gNB, or vice versa.(A paired AI / ML Model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e., the first part of inference is firstly performed by the UE and then the remaining part is performed by gNB, or vice versa.)AI / ML model transferTransfer of an AI / ML model over a wireless interface with parameters of a model structure known to the receiving end or a new model having parameters. Delivery of an AI / ML model over the air interface, either parameters of a model structure known at the receiving end or a new model with parameters. Delivery may contain a full model or a partial model. Model download: Model transfer from the network to UE. Model upload: Model transfer from UE to the network. Federated learning / federated training: A machine learning technique for training an AI / ML model on multiple distributed edge nodes (e.g., UEs, gNBs), each performing local model training using local data samples. This technique requires multiple interactions of the model but does not require the exchange of local data samples.(A machine learning technique that trains an AI / ML model across multiple decentralized edge nodes (e.g., UEs, gNBs) each performing local model training using local data samples. The technique requires multiple interactions of the model, but no exchange of local data samples.) Offline field data: The data collected from the field and used for offline training of the AI / ML model. Online field data: The data collected from the field and used for online training of the AI / ML model. Model monitoring: A procedure that monitors the inference performance of the AI / ML model. Supervised learning: A process of training a model from input and its corresponding labels. Unsupervised learning: A process of training a model without labeled data.)Semi-supervised learning: A process of training a model with a mix of labeled and unlabeled data. Reinforcement Learning (RL): A process of training an AI / ML model from input (aka state) and a feedback signal (aka reward) resulting from the model's output (aka action) in an environment the model is interacting with.. Model activation: Enable an AI / ML model for a specific function. Model deactivation: Disable an AI / ML model for a specific function. Model switching: Deactivating a currently active AI / ML model and activating another AI / ML model for a specific function. a different AI / ML model for a specific function.

[0104] Currently, 3GPP is considering the following two methods as a way for base stations to manage models in terminals.

[0105] - Functionality-based LCM

[0106] - Model-based LCM

[0107] Here, feature-based LCM means that the AI / ML model of the terminal is not recognized by the base station, but the terminal performs model-based LCM. In other words, the base station performs LCM of the terminal based on functionality rather than AI / ML models, but a single functionality may actually have one or more AI / ML models mapped to it. Switching between models belonging to the same functionality may occur internally depending on the terminal implementation. However, model-based LCM, in contrast, means that the base station and the terminal recognize each other and perform LCM based on AI / ML models.

[0108] The terminal can receive configured / identified functionality(es) / model(s) from the base station through a functionality / model identification procedure.

[0109] Figure 7 illustrates a procedure of a terminal and a base station according to one embodiment of the present specification.

[0110] Referring to Figure 7, when a terminal (for the first time) connects to a cell (or base station), it transmits AI / ML model or function-related capability information supported by the terminal to the base station (S701). Based on the capability information received from the terminal, the base station sets one or more AI / ML functionality / models and transmits them to the terminal via high-layer signaling (S702).

[0111] The terminal or base station configures an applicable functionality / model set consisting of one or more AI / ML functionality / models based on the configured AI / ML functionality / model (S703). The applicable functionality / model set may be configured by the terminal based on the configured AI / ML functionality / model and reported to the base station. Alternatively, candidate AI / ML functionality / models for configuring the applicable functionality / model set may be configured by the terminal and reported to the base station, and the base station may configure an applicable functionality / model set based on the candidate AI / ML functionality / models reported by the terminal and transmit configuration / reconfiguration information therefor to the terminal. At this time, the applicable functionality / model configuration / reconfiguration information can be transmitted via RRC (radio resource control) signaling.

[0112] The base station can activate, deactivate or switch one or more AI / ML functionality / models based on configuration information of a set of applicable functionality / models for an arbitrary purpose. At this time, the arbitrary purpose may be beam management, CSI (channel state information) enhancement or positioning enhancement. Or, it may be another additional new purpose (e.g., mobility management, etc.), and the contents of the present invention can be applied without being limited by the purpose of AI / ML application. Alternatively, the base station can configure an AI / ML functionality / model group composed of one or more AI / ML functionality / models based on an arbitrary purpose, and activate, deactivate or switch the AI / ML functionality / model group unit. At this time, activation / deactivation information or switching information for each AI / ML functionality / model or AI / ML functionality / model group is instructed (or transmitted) to the terminal by the base station (S704). This can be instructed (or transmitted) to the terminal via RRC signaling, MAC (medium access control) CE (control element) signaling, or DCI (downlink control information).

[0113] Meanwhile, in order to select and activate an appropriate AI / ML functionality / model or a group of AI / ML functionality / models for an arbitrary purpose / use case / sub-use case at a base station and / or a terminal, monitoring / assessment of the performance of the activated AI / ML functionality / model or group of AI / ML functionality / models or the performance of each AI / ML functionality / model or group of AI / ML functionality / models included in the applicable (or configured) set of AI / ML functionality / models that is deactivated is required.

[0114] FIG. 8 illustrates a periodic AI / ML function / model monitoring / evaluation setup according to one embodiment of the present specification.

[0115] The present invention proposes a periodic monitoring / assessment method for monitoring / assessment of AI / ML functionality / models or groups of AI / ML functionality / models.

[0116] The base station may set a common periodic monitoring / assessment interval or gap to be applied to all AI / ML functionality / models constituting the applicable (or configured) AI / ML functionality / model set for the terminal for AI / ML functionality / model monitoring / assessment of the terminal. Alternatively, the base station may set a separate periodic monitoring / assessment interval or gap for each AI / ML functionality / model or group of AI / ML functionality / models constituting the applicable (or configured) AI / ML functionality / model set for AI / ML functionality / model monitoring / assessment. Alternatively, you can set a periodic monitoring / assessment interval or gap for an enabled AI / ML functionality / model or group of AI / ML functionality / models for AI / ML functionality / model monitoring / assessment and a monitoring / assessment interval or gap for an disabled AI / ML functionality / model or group of AI / ML functionality / models.Alternatively, a separate periodic monitoring / assessment interval or gap may be set for each of the use cases or sub-use cases described above. For example, for a terminal, a periodic monitoring / assessment interval or gap may be set for AI / ML functionality / models for beam management among the use cases described above, a periodic monitoring / assessment interval or gap may be set for AI / ML functionality / models for CSI enhancement, and a periodic monitoring / assessment interval or gap may be set for positioning enhancement, respectively.

[0117] As described above, when a periodic monitoring / assessment interval or gap is set for a terminal, the setting information may include information on setting an activated AI / ML-based operation interval and a monitoring / assessment operation interval, as illustrated in FIG. 8. For example, the setting information may include information on a period (P) in which the AI / ML-based operation interval and the monitoring / assessment operation interval are repeated, an offset value for each AI / ML-based operation interval within the period P, an offset value at which the monitoring / assessment operation interval starts, or at least one of a length value of an AI / ML-based operation interval or a length value of a monitoring / assessment operation interval. Additionally, the periodic monitoring / assessment interval or gap setting information may include AI / ML functionality / model ID information, AI / ML functionality / model group ID information, use case ID information, sub-use case ID information, or active / inactive AI / ML functionality / model instruction information that are the target of assessment / monitoring through the periodic monitoring / assessment interval or gap according to the aforementioned method.

[0118] In the AI / ML-based operation section, the terminal and base station perform wireless transmission and reception operations based on the AI / ML functionality / model or AI / ML functionality / model activated for the use case or sub-use case.

[0119] During the monitoring / assessment operation period, the terminal and base station may perform wireless transmission / reception operations based on the legacy operation method (i.e., fallback operation) defined for the use case or sub-use case, rather than the operation based on the AI / ML functionality / model activated for the use case or sub-use case. In addition, during the monitoring / assessment operation period, the terminal and base station may additionally perform monitoring / assessment on the AI / ML functionality / model that is the target of the monitoring / assessment (for example, if a common periodic monitoring / assessment interval is set for all applicable AI / ML functionality / model sets, all AI / ML functionality / models that constitute the applicable AI / ML functionality / model set) according to the preceding settings.

[0120] Alternatively, the terminal and base station may perform only reception operations for reference signals for monitoring / assessment of AI / ML functionality / models for any use case or sub-use case, without performing reception operations for downlink channels from the base station, such as during the DRX (discontinuous reception) off period, during the monitoring / assessment operation period. In addition, during the monitoring / assessment operation period, the terminal and base station may additionally perform monitoring / assessment for the AI / ML functionality / model (for example, if a common periodic monitoring / assessment interval is set for all applicable AI / ML functionality / model sets, all AI / ML functionality / models constituting the applicable AI / ML functionality / model set) that are the targets of the monitoring / assessment / assessment according to the above settings.

[0121] Additionally, the terminal may receive AI / ML functionality / model monitoring / assessment report configuration information for reporting the assessment result (or monitoring result) along with the periodic AI / ML functionality / model monitoring / assessment interval or gap configuration information from the base station. The AI / ML functionality / model monitoring / assessment report configuration information may include uplink radio resource allocation information for the report (e.g., PUCCH resource configuration information or PUSCH resource configuration information), reporting method configuration information, reporting quantity configuration information, etc. Alternatively, the reporting method or reporting quantity may be implicitly determined by the AI / ML functionality / model or use case / sub-use case that is the target of the monitoring / assessment.

[0122] Figure 9 illustrates an operation method of a terminal according to one embodiment of the present specification.

[0123] Referring to FIG. 9, a terminal receives configuration information for performance monitoring of an AI / ML functionality or model from a base station (S901). Here, the configuration information includes at least one of monitoring cycle information and monitoring gap information. In addition, the terminal performs performance monitoring of the AI / ML functionality or model based on the received configuration information (S902), and transmits a report of the results of the performance monitoring of the performed AI / ML functionality or model to the base station (S903).

[0124] The configuration information for performance monitoring of AI / ML functionality or models can be applied commonly to all AI / ML functionality or models set to be supported on the terminal.

[0125] The configuration information for performance monitoring of AI / ML functionality or models can be applied commonly to all AI / ML functionality or models applicable to the terminal.

[0126] The configuration information for performance monitoring of AI / ML functionality or models can be applied commonly to all AI / ML functionality or models activated on the terminal.

[0127] The configuration information for performance monitoring of AI / ML functionality or models can be applied commonly to all AI / ML functionality or models set to be supported on the terminal.

[0128] Configuration information for performance monitoring of AI / ML functionality or models can be applied on the terminal i) by AI / ML functionality or model, or ii) by AI / ML functionality group or model group.

[0129] Configuration information for performance monitoring of AI / ML functionality or models is configured by use case or sub-use case, and a use case or sub-use case may include at least one of beam management, CSI (channel state information) feedback, and positioning.

[0130] The configuration information for performance monitoring of AI / ML functionality or models may further include at least one of offset information and monitoring interval information.

[0131] Figure 10 illustrates an operation method of a base station according to one embodiment of the present specification.

[0132] Referring to FIG. 10, the base station transmits configuration information for performance monitoring of an AI / ML function or model to the terminal (S1001). Here, the configuration information includes at least one of monitoring cycle information and monitoring gap information. Thereafter, the base station receives a report on the results of performance monitoring of the AI / ML function or model based on the transmitted configuration information from the terminal (S1002).

[0133] The configuration information for performance monitoring of AI / ML functionality or models can be applied commonly to all AI / ML functionality or models set to be supported on the terminal.

[0134] The configuration information for performance monitoring of AI / ML functionality or models can be applied commonly to all AI / ML functionality or models applicable to the terminal.

[0135] The configuration information for performance monitoring of AI / ML functionality or models can be applied commonly to all AI / ML functionality or models activated on the terminal.

[0136] The configuration information for performance monitoring of AI / ML functionality or models can be applied commonly to all AI / ML functionality or models set to be supported on the terminal.

[0137] Configuration information for performance monitoring of AI / ML functionality or models can be applied on the terminal i) by AI / ML functionality or model, or ii) by AI / ML functionality group or model group.

[0138] Configuration information for performance monitoring of AI / ML functionality or models is configured by use case or sub-use case, and a use case or sub-use case may include at least one of beam management, CSI (channel state information) feedback, and positioning.

[0139] Figure 11 illustrates a device according to one embodiment of the present specification.

[0140] Referring to FIG. 11, a wireless communication system may include a first device (100a) and a second device (100b).

[0141] The first device (100a) may be a base station, a network node, a transmitting terminal, a receiving terminal, a wireless device, a wireless communication device, a vehicle, a vehicle equipped with an autonomous driving function, a connected car, a drone (Unmanned Aerial Vehicle, UAV), an AI (Artificial Intelligence) module, a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, an MR (Mixed Reality) 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 climate / environmental device, a device related to 5G services, or any other device related to the 4th industrial revolution field.

[0142] The second device (100b) may be a base station, a network node, a transmitting terminal, a receiving terminal, a wireless device, a wireless communication device, a vehicle, a vehicle equipped with an autonomous driving function, a connected car, a drone (Unmanned Aerial Vehicle, UAV), an AI (Artificial Intelligence) module, a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, an MR (Mixed Reality) 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 climate / environmental device, a device related to 5G services, or any other device related to the 4th industrial revolution field.

[0143] The first device (100a) may include at least one processor, such as a processor (1020a), at least one memory, such as a memory (1010a), and at least one transceiver, such as a transceiver (1031a). The processor (1020a) may perform the functions, procedures, and / or methods described above. The processor (1020a) may perform one or more protocols. For example, the processor (1020a) may perform one or more layers of a wireless interface protocol. The memory (1010a) may be connected to the processor (1020a) and may store various types of information and / or commands. The transceiver (1031a) may be connected to the processor (1020a) and may be controlled to transmit and receive wireless signals.

[0144] The second device (100b) may include at least one processor, such as a processor (1020b), at least one memory device, such as a memory (1010b), and at least one transceiver, such as a transceiver (1031b). The processor (1020b) may perform the functions, procedures, and / or methods described above. The processor (1020b) may implement one or more protocols. For example, the processor (1020b) may implement one or more layers of a wireless interface protocol. The memory (1010b) may be connected to the processor (1020b) and may store various types of information and / or commands. The transceiver (1031b) may be connected to the processor (1020b) and may be controlled to transmit and receive wireless signals.

[0145] The memory (1010a) and / or the memory (1010b) may be connected internally or externally to the processor (1020a) and / or the processor (1020b), or may be connected to another processor via various technologies such as a wired or wireless connection.

[0146] The first device (100a) and / or the second device (100b) may have one or more antennas. For example, the antenna (1036a) and / or the antenna (1036b) may be configured to transmit and receive wireless signals.

[0147] Fig. 12 is a block diagram showing the configuration of a terminal according to one embodiment of the present specification.

[0148] In particular, FIG. 12 is a drawing illustrating the device of FIG. 11 in more detail.

[0149] The device includes a memory (1010), a processor (1020), a transceiver (1031), a power management module (1091), a battery (1092), a display (1041), an input unit (1053), a speaker (1042), and a microphone (1052), a subscriber identification module (SIM) card, and one or more antennas.

[0150] The processor (1020) may be configured to implement the proposed functions, procedures, and / or methods described herein. Layers of a radio interface protocol may be implemented in the processor (1020). The processor (1020) may include an application-specific integrated circuit (ASIC), other chipsets, logic circuits, and / or data processing devices. The processor (1020) may be an application processor (AP). The processor (1020) may include at least one of a digital signal processor (DSP), a central processing unit (CPU), a graphics processing unit (GPU), and a modem (modulator and demodulator). Examples of the processor (1020) may be a SNAPDRAGON™ series processor manufactured by Qualcomm®, an EXYNOSTM series processor manufactured by Samsung®, an A series processor manufactured by Apple®, a HELIO™ series processor manufactured by MediaTek®, an ATOM™ series processor manufactured by INTEL®, a KIRINTM series processor manufactured by HiSilicon®, or a corresponding next-generation processor.

[0151] The power management module (1091) manages power to the processor (1020) and / or the transceiver (1031). The battery (1092) supplies power to the power management module (1091). The display (1041) outputs the results processed by the processor (1020). The input unit (1053) receives input to be used by the processor (1020). The input unit (1053) can be displayed on the display (1041). A SIM card is an integrated circuit used to securely store an international mobile subscriber identity (IMSI) and its associated keys, which are used to identify and authenticate subscribers in mobile devices such as mobile phones and computers. Contact information can also be stored on many SIM cards.

[0152] The memory (1010) is operably coupled to the processor (1020) and stores various information for operating the processor (610). The memory (1010) may include a read-only memory (ROM), a random access memory (RAM), flash memory, a memory card, a storage medium, and / or other storage devices. When the embodiment is implemented in software, the techniques described herein may be implemented as modules (e.g., procedures, functions, etc.) that perform the functions described herein. The modules may be stored in the memory (1010) and executed by the processor (1020). The memory (1010) may be implemented within the processor (1020). Alternatively, the memory (1010) may be implemented external to the processor (1020) and communicatively connected to the processor (1020) via various means known in the art.

[0153] The transceiver (1031) is operably coupled to the processor (1020) and transmits and / or receives a radio signal. The transceiver (1031) includes a transmitter and a receiver. The transceiver (1031) may include baseband circuitry for processing a radio frequency signal. The transceiver controls one or more antennas to transmit and / or receive a radio signal. The processor (1020) transmits command information to the transceiver (1031) to initiate communication, for example, to transmit a radio signal constituting voice communication data. The antenna functions to transmit and receive radio signals. Upon receiving a radio signal, the transceiver (1031) may transmit the signal to the processor (1020) for processing and convert the signal to baseband. The processed signal may be converted into audible or readable information output through the speaker (1042).

[0154] The speaker (1042) outputs sound-related results processed by the processor (1020). The microphone (1052) receives sound-related input to be used by the processor (1020).

[0155] A user inputs command information, such as a phone number, for example, by pressing (or touching) a button on an input unit (1053) or by voice activation using a microphone (1052). The processor (1020) receives this command information and processes it to perform an appropriate function, such as dialing a phone number. Operational data can be extracted from a SIM card or memory (1010). In addition, the processor (1020) can display command information or operation information on a display (1041) for the user's recognition and convenience.

[0156] Figure 13 shows a block diagram of a processor in which the disclosure of this specification is implemented.

[0157] As can be seen from FIG. 13, the processor (1020) implementing the disclosure of the present specification may include multiple circuits to implement the proposed functions, procedures, and / or methods described herein. For example, the processor (1020) may include a first circuit (1020-1), a second circuit (1020-2), and a third circuit (1020-3). Furthermore, although not shown, the processor (1020) may include more circuits. Each circuit may include multiple transistors.

[0158] The above processor (1020) may be called an application-specific integrated circuit (ASIC) or an application processor (AP), and may include at least one of a digital signal processor (DSP), a central processing unit (CPU), and a graphics processing unit (GPU).

[0159] FIG. 14 is a block diagram showing in detail the transmitter / receiver of the first device illustrated in FIG. 11 or the transmitter / receiver unit of the device illustrated in FIG. 12.

[0160] Referring to FIG. 14, the transceiver unit (1031) includes a transmitter (1031-1) and a receiver (1031-2). The transmitter (1031-1) includes a Discrete Fourier Transform (DFT) unit (1031-11), a subcarrier mapper (1031-12), an IFFT unit (1031-13), a CP insertion unit (1031-14), and a wireless transmitter (1031-15). The transmitter (1031-1) may further include a modulator. In addition, for example, the transmitter may further include a scramble unit (not shown), a modulation mapper (not shown), a layer mapper (not shown), and a layer permutator (not shown), which may be arranged before the DFT unit (1031-11). That is, in order to prevent an increase in PAPR (peak-to-average power ratio), the transmitter (1031-1) first passes the information through a DFT (1031-11) before mapping the signal to a subcarrier. The signal spread (or precoded in the same sense) by the DFT unit (1031-11) is mapped to a subcarrier through a subcarrier mapper (1031-12) and then passes through an IFFT (Inverse Fast Fourier Transform) unit (1031-13) to be converted into a signal on the time axis.

[0161] The DFT unit (1031-11) performs DFT on the input symbols and outputs complex-valued symbols. For example, if Ntx symbols are input (where Ntx is a natural number), the DFT size is Ntx. The DFT unit (1031-11) may be called a transform precoder. The subcarrier mapper (1031-12) maps the complex symbols to each subcarrier in the frequency domain. The complex symbols may be mapped to resource elements corresponding to resource blocks allocated for data transmission. The subcarrier mapper (1031-12) may be called a resource element mapper. The IFFT unit (1031-13) performs IFFT on the input symbols and outputs a baseband signal for data, which is a time-domain signal. The CP insertion unit (1031-14) copies a portion of the rear portion of the baseband signal for data and inserts it into the front portion of the baseband signal for data. CP insertion prevents ISI (Inter-Symbol Interference) and ICI (Inter-Carrier Interference), thereby maintaining orthogonality even in multipath channels.

[0162] On the other hand, the receiver (1031-2) includes a wireless reception unit (1031-21), a CP removal unit (1031-22), an FFT unit (1031-23), and an equalization unit (1031-24). The wireless reception unit (1031-21), the CP removal unit (1031-22), and the FFT unit (1031-23) of the receiver (1031-2) perform the inverse functions of the wireless transmission unit (1031-15), the CP insertion unit (1031-14), and the IFF unit (1031-13) of the transmitter (1031-1). The receiver (1031-2) may further include a demodulator.

[0163] Although the preferred embodiments have been described above by way of example, the disclosure of this specification is not limited to such specific embodiments, and may be modified, changed, or improved in various forms within the scope described in the spirit and claims of this specification.

[0164] In the exemplary system described above, the methods are described based on a flowchart as a series of steps or blocks. However, the order of the steps described is not limited, and some steps may occur in a different order or simultaneously with other steps described above. Furthermore, those skilled in the art will understand that the steps depicted in the flowchart are not exclusive, and other steps may be included, or one or more steps in the flowchart may be deleted without affecting the scope of the invention.

[0165] The claims set forth in this specification may be combined in various ways. For example, the technical features of the method claims of this specification may be combined and implemented as a device, and the technical features of the device claims of this specification may be combined and implemented as a method. Furthermore, the technical features of the method claims and the technical features of the device claims of this specification may be combined and implemented as a device, and the technical features of the method claims and the technical features of the device claims of this specification may be combined and implemented as a method.

Claims

1. In a method of operating a terminal in a wireless communication system, A step of receiving configuration information for performance monitoring of an AI (artificial intelligence) / ML (machine learning) functionality or model, wherein the configuration information includes at least one of monitoring cycle information and monitoring gap information; A step of performing performance monitoring for the AI / ML function or model based on the received setting information; and A method comprising the step of transmitting a report of the results of performance monitoring for the AI / ML function or model performed above.

2. In paragraph 1, The above setting information is applied commonly to all AI / ML functions or models set to be supported by the terminal.

3. In paragraph 1, The above setting information is a method that is commonly applied to all AI / ML functions or models applicable to the terminal.

4. In paragraph 1, The above setting information is applied commonly to all AI / ML functions or models activated in the terminal.

5. In paragraph 1, The above setting information is applied commonly to all AI / ML functions or models set to be supported by the terminal.

6. In paragraph 1, A method in which the above setting information is applied i) by AI / ML function or model, or ii) by AI / ML function group or model group in the terminal.

7. In paragraph 1, A method wherein the above configuration information is set for each use case or each sub-use case, and the use case or the sub-use case includes at least one of beam management, CSI (channel state information) feedback, and positioning.

8. In paragraph 1, A method wherein the above setting information further includes at least one of offset information and monitoring interval information.

9. In paragraph 1, The above setting information further includes identity (ID) information for the AI / ML function or model, A method wherein the above recognizer information includes at least one of i) AI / ML function ID or model ID information, ii) AI / ML function group ID or model group ID information, and iii) use case ID or sub-use case ID information.

10. In paragraph 1, Further comprising a step of receiving reporting setting information for reporting the results of performance monitoring for the above AI / ML function or model, A method wherein the above report setting information includes at least one of uplink resource information, report type information, and report quantity information.

11. In a method of operating a base station in a wireless communication system, A step of transmitting configuration information for performance monitoring of an AI (artificial intelligence) / ML (machine learning) functionality or model, wherein the configuration information includes at least one of monitoring cycle information and monitoring gap information; and A method comprising a step of receiving a report of the results of performance monitoring for the AI / ML function or model based on the transmitted setting information.

12. In paragraph 11, The above setting information is applied commonly to all AI / ML functions or models set to be supported by the terminal.

13. In paragraph 11, The above setting information is a method that is commonly applied to all AI / ML functions or models applicable to the terminal.

14. In paragraph 11, The above setting information is applied commonly to all AI / ML functions or models activated in the terminal.

15. In paragraph 11, The above setting information is applied commonly to all AI / ML functions or models set to be supported by the terminal.

16. In paragraph 11, A method in which the above setting information is applied i) by AI / ML function or model, or ii) by AI / ML function group or model group in the terminal.

17. In paragraph 11, A method wherein the above configuration information is set for each use case or each sub-use case, and the use case or the sub-use case includes at least one of beam management, CSI (channel state information) feedback, and positioning.

18. In paragraph 11, A method wherein the above setting information further includes at least one of offset information and monitoring interval information.

19. In paragraph 11, The above setting information further includes identity (ID) information for the AI / ML function or model, A method wherein the above recognizer information includes at least one of i) AI / ML function ID or model ID information, ii) AI / ML function group ID or model group ID information, and iii) use case ID or sub-use case ID information.

20. As a terminal in a wireless communication system, at least one processor; and At least one memory storing instructions and being operably electrically connectable to the at least one processor, wherein the operations performed based on the instructions being executed by the at least one processor are: A step of receiving configuration information for performance monitoring of an AI (artificial intelligence) / ML (machine learning) functionality or model, wherein the configuration information includes at least one of monitoring cycle information and monitoring gap information, A step of performing performance monitoring for the AI / ML function or model based on the received setting information, and A terminal comprising a step of transmitting a report of the results of performance monitoring for the AI / ML function or model performed above.

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