Method and apparatus for logging measurements for learning models in a wireless communication system

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. The present disclosure generally relates to wireless communications, in particular, but not exclusively to a method and system for logging measurements for learning models in a communication network. The method comprises storing training data for artificial intelligence (AI) / machine learning (ML) in memory of the UE; identifying that the stored training data exceeds a buffer of the memory; and stopping measurement for the training data.

Description

METHOD AND APPARATUS FOR LOGGING MEASUREMENTS FOR LEARNING MODELS IN A WIRELESS COMMUNICATION SYSTEM

[0001] The present disclosure generally relates to wireless communications, in particular, but not exclusively to a method and system for logging measurements for learning models in a communication network.

[0002] 5G mobile communication technologies define broad frequency bands such that high transmission rates and new services are possible, and can be implemented not only in “Sub 6GHz” bands such as 3.5GHz, but also in “Above 6GHz” bands referred to as mmWave including 28GHz and 39GHz. In addition, it has been considered to implement 6G mobile communication technologies (referred to as Beyond 5G systems) in terahertz (THz) bands (for example, 95GHz to 3THz bands) in order to accomplish transmission rates fifty times faster than 5G mobile communication technologies and ultra-low latencies one-tenth of 5G mobile communication technologies.

[0003] At the beginning of the development of 5G mobile communication technologies, in order to support services and to satisfy performance requirements in connection with enhanced Mobile BroadBand (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine-Type Communications (mMTC), there has been ongoing standardization regarding beamforming and massive MIMO for mitigating radio-wave path loss and increasing radio-wave transmission distances in mmWave, supporting numerologies (for example, operating multiple subcarrier spacings) for efficiently utilizing mmWave resources and dynamic operation of slot formats, initial access technologies for supporting multi-beam transmission and broadbands, definition and operation of BWP (BandWidth Part), new channel coding methods such as a LDPC (Low Density Parity Check) code for large amount of data transmission and a polar code for highly reliable transmission of control information, L2 pre-processing, and network slicing for providing a dedicated network specialized to a specific service.

[0004] Currently, there are ongoing discussions regarding improvement and performance enhancement of initial 5G mobile communication technologies in view of services to be supported by 5G mobile communication technologies, and there has been physical layer standardization regarding technologies such as V2X (Vehicle-to-everything) for aiding driving determination by autonomous vehicles based on information regarding positions and states of vehicles transmitted by the vehicles and for enhancing user convenience, NR-U (New Radio Unlicensed) aimed at system operations conforming to various regulation-related requirements in unlicensed bands, NR UE Power Saving, Non-Terrestrial Network (NTN) which is UE-satellite direct communication for providing coverage in an area in which communication with terrestrial networks is unavailable, and positioning.

[0005] Moreover, there has been ongoing standardization in air interface architecture / protocol regarding technologies such as Industrial Internet of Things (IIoT) for supporting new services through interworking and convergence with other industries, IAB (Integrated Access and Backhaul) for providing a node for network service area expansion by supporting a wireless backhaul link and an access link in an integrated manner, mobility enhancement including conditional handover and DAPS (Dual Active Protocol Stack) handover, and two-step random access for simplifying random access procedures (2-step RACH for NR). There also has been ongoing standardization in system architecture / service regarding a 5G baseline architecture (for example, service based architecture or service based interface) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies, and Mobile Edge Computing (MEC) for receiving services based on UE positions.

[0006] As 5G mobile communication systems are commercialized, connected devices that have been exponentially increasing will be connected to communication networks, and it is accordingly expected that enhanced functions and performances of 5G mobile communication systems and integrated operations of connected devices will be necessary. To this end, new research is scheduled in connection with eXtended Reality (XR) for efficiently supporting AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality) and the like, 5G performance improvement and complexity reduction by utilizing Artificial Intelligence (AI) and Machine Learning (ML), AI service support, metaverse service support, and drone communication.

[0007] Furthermore, such development of 5G mobile communication systems will serve as a basis for developing not only new waveforms for providing coverage in terahertz bands of 6G mobile communication technologies, multi-antenna transmission technologies such as Full Dimensional MIMO (FD-MIMO), array antennas and large-scale antennas, metamaterial-based lenses and antennas for improving coverage of terahertz band signals, high-dimensional space multiplexing technology using OAM (Orbital Angular Momentum), and RIS (Reconfigurable Intelligent Surface), but also full-duplex technology for increasing frequency efficiency of 6G mobile communication technologies and improving system networks, AI-based communication technology for implementing system optimization by utilizing satellites and AI (Artificial Intelligence) from the design stage and internalizing end-to-end AI support functions, and next-generation distributed computing technology for implementing services at levels of complexity exceeding the limit of UE operation capability by utilizing ultra-high-performance communication and computing resources.

[0008] In general, Artificial Intelligence (AI) and Machine Learning (ML) models are used in a variety of fields. 3rd Generation Partnership Project (3gpp) is studying the AI / ML on an air interface. AI / ML based algorithms can be used for enhanced performance and / or reduced complexity / overhead. Enhanced performance here depends on use cases under consideration and could be such as improved throughput, robustness, accuracy or reliability, etc. The AI / ML models reside on the User Equipment (UE) or on the network entities like base station or Operation And Maintenance (OAM). It is also possible that AI / ML Models are reside on both UE and network entities side.

[0009] Data collection may be performed for different purposes in Life Cycle Management (LCM), such as model training, model inference, model monitoring, model selection, model update, etc. For all types of offline model training where the model is trained based on pre-stored data (i.e., UE- / NW- / two-sided model training), there is no latency requirement for data collection. For model inference, when required data comes from other entities, there is a latency requirement for data collection. For (real-time) performance monitoring, when required monitoring data (e.g., performance metric) comes from other entities, there is a latency requirement for data collection.

[0010] A typical usage of AI / ML on the air interface is AI / ML for beam management which includes at least a spatial-domain downlink beam prediction for Set A of beams based on measurement results of Set B of beams, and a temporal downlink beam prediction for Set A of beams based on the historic measurement results of Set B of beams.

[0011] Further, the AI / ML on air interface also may be used for positioning, such as for Direct AI / ML positioning comprising at least UE-based positioning with UE-side model, UE-assisted / (Location Management Function) LMF-based positioning with LMF-side model, and NG-RAN node assisted positioning with LMF-side model.

[0012] Further, the AI / ML on air interface also may be used for positioning, such as for AI / ML assisted positioning comprising at least UE-assisted / LMF-based positioning with UE-side model, and NG-RAN node assisted positioning with gNB-side model.

[0013] Another use case of AI / ML on air interface is for (Channel State Information) CSI feedback enhancements. This may be used for CSI prediction, and CSI compression. The AI / ML may be used for other cases such as AI / ML for mobility. The training can be online training or offline training. Core Network (CN) / Operation Administration and Maintenance (OAM) / Over the Top Server (OTT) collection of UE-sided model training (and even network sided model training) data may be supported.

[0014] Therefore, the performance of the gNB is supported using the AI / ML models residing in a gNodeB (gNB). To achieve this, the AI / ML models require specific measurements as inputs to enhance the performance of the gNB. However, in the conventional technologies, there is no proper configuration performed for the UE, for logging one or more specific measurements for the AI / ML models, residing in the gNB (or a network node with similar functionalities as the gNB).

[0015] In view of the above, there is a need for a system to address above-mentioned one or more problems.

[0016] The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

[0017] In an embodiment the present disclosure provides a method and system for logging measurements for learning models in a communication network.

[0018] In an embodiment, the method comprises transmitting, by a User Equipment (UE), to a network entity, a capability information indicating a capability of the UE and an available memory space in the UE for logging one or more measurements for one or more learning models. Further, the method includes receiving, by the UE, from the network entity, configuration data for logging the one or more measurements based on the transmitted capability information. Furthermore, the method includes logging, by the UE, the one or more measurements in the available memory space, based on the received configuration data.

[0019] Another aspect of the present disclosure includes a system for logging measurements for learning models in a communication network. The system comprises a processor, and a memory coupled with the processor. In an embodiment, the processor is configured to transmit to a network entity, a capability information indicating a capability of the UE and an available memory space in the UE for logging one or more measurements for one or more learning models. Further, the processor is configured to receive, from the network entity, configuration data for logging the one or more measurements based on the transmitted capability information. Furthermore, the processor is configured to log the one or more measurements in the available memory space, based on the received configuration data.

[0020] In an embodiment the present disclosure provides a method and system for configuring a User Equipment (UE) for logging measurements in a communication network. The method comprises receiving, by a network entity, from a UE, a capability information indicating a capability of the UE and an available memory space in the UE for logging one or more measurements for one or more learning models. Further, the method includes determining, by the network entity, a configuration data for logging the one or more measurements, based on the received capability information. Furthermore, the method includes configuring, by the network entity, the UE for logging the one or more measurements, by transmitting the determined configuration data to the UE.

[0021] Another aspect of the present disclosure includes a system for configuring a User Equipment (UE) for logging measurements in a communication network. The system comprises a processor, and a memory coupled with the processor. The processor is configured to receive, from a UE, a capability information indicating a capability of the UE and an available memory space in the UE for logging one or more measurements for one or more learning models. Further, the processor determines a configuration data for logging the one or more measurements, based on the received capability information. Furthermore, the processor configures the UE for logging the one or more measurements, by transmitting the determined configuration data to the UE.

[0022] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

[0023] Aspects of the present disclosure provide efficient communication methods in a wireless communication system.

[0024] The embodiments of the disclosure itself, as well as a preferred mode of use, further objectives, and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. One or more embodiments are now described, by way of example only, with reference to the accompanying drawings in which:

[0025] FIG. 1 illustrates an exemplary environment for logging measurements for learning models in a communication network, according to some embodiments of the present disclosure;

[0026] FIG. 2 illustrates a block diagram of a User Equipment (UE), according to some embodiments of the present disclosure;

[0027] FIG. 3 illustrates a block diagram of a network entity, according to some embodiments of the present disclosure;

[0028] FIGs. 4A-4B illustrates a scenario representation of configuration for measurement handling for the one or more learning models, according to some embodiments of the present disclosure;

[0029] FIG. 5 illustrates a scenario representation of reporting logged measurements for the one or more learning models, according to some embodiments of the present disclosure;

[0030] FIGs. 6A-6C illustrates a flowchart representation of releasing the configuration data for measuring handling for one or more learning models, according to some embodiments of the present disclosure;

[0031] FIG. 7 illustrates a flowchart representation of a method of logging one or more measurements for the one or more learning models in a communication network, according to some embodiments of the present disclosure;

[0032] FIG. 8 illustrates a flowchart representation of a method of configuring a User Equipment (UE) for logging measurements in a communication network, according to some embodiments of the present disclosure;

[0033] FIG. 9 is a block diagram of a terminal or user equipment (UE) according to an embodiment of the disclosure; and

[0034] FIG. 10 is a block diagram of a base station (BS) according to an embodiment of the disclosure.

[0035] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer-readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[0036] Hereinafter, embodiments of the disclosure will be described in detail with reference to the accompanying drawings.

[0037] In describing the embodiments, descriptions related to technical contents well-known in the art and not associated directly with the disclosure will be omitted. Such an omission of unnecessary descriptions is intended to prevent obscuring of the main idea of the disclosure and more clearly transfer the main idea.

[0038] For the same reason, in the accompanying drawings, some elements may be exaggerated, omitted, or schematically illustrated. Further, the size of each element does not completely reflect the actual size. In the drawings, identical or corresponding elements are provided with identical reference numerals or different reference numerals.

[0039] The advantages and features of the disclosure and ways to achieve them will be apparent by making reference to embodiments as described below in detail in conjunction with the accompanying drawings. However, the disclosure is not limited to the embodiments set forth below, but may be implemented in various different forms. The following embodiments are provided only to completely disclose the disclosure and inform those skilled in the art of the scope of the disclosure, and the disclosure is defined only by the scope of the appended claims. Throughout the specification, the same or like reference numerals designate the same or like elements. Furthermore, in describing the disclosure, a detailed description of known functions or constitution incorporated herein will be omitted in the case that it is determined that the description may make the subject matter of the disclosure unnecessarily unclear. The terms which will be described below are terms defined in consideration of the functions in the disclosure, and may be different according to users, intentions of the operators, or customs. Therefore, the definitions of the terms should be made based on the contents throughout the specification.

[0040] Herein, it will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, may be performed based on computer program instructions. These computer program instructions may be loaded collectively onto at least one processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which perform through any one of, or in any combination of, the at least one processor of the computer or other programmable data processing apparatus, create means for performing the functions specified in the flowchart block(s). These computer program instructions may also be stored in a non-transitory computer usable or computer-readable memory that may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer usable or computer-readable memory produce an article of manufacture including instruction means that perform the function specified in the flowchart block(s). The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable data processing apparatus to produce a computer executed process such that the instructions that perform on the computer or other programmable data processing apparatus provide steps for executing the functions specified in the flowchart block(s).

[0041] Further, each block may represent a module, segment, or portion of code, which includes one or more executable instructions for executing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of the order. For example, two blocks(or functions) shown in succession may in fact be performed substantially concurrently or the blocks may sometimes be performed in the reverse order, depending upon the functionality involved.

[0042] As used in embodiments of the disclosure, a “~unit” may refer to a software element or a hardware element, such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), which performs a predetermined function. However, the term including the word “~unit” does not always have a meaning limited to software or hardware. The “~unit” may be constructed either to be stored in an addressable storage medium or to execute one or more processors. Therefore, the “~unit” includes, for example, software elements, object-oriented software elements, components such as class elements and task elements, processes, functions, properties, procedures, sub-routines, segments of a program code, drivers, firmware, micro-codes, circuits, data, database, data structures, tables, arrays, and parameters. The components and functions provided by the “~unit” may be either combined into a smaller number of components and a “~unit,” or divided into additional components and a “~unit.” Moreover, the components and “~units” may be implemented to reproduce one or more central processing units (CPUs) within a device or a security multimedia card. Further, in the embodiments, the “~unit” may include one or more processors.

[0043] It should be appreciated that the blocks in each flowchart and combinations of the flowcharts may be performed by one or more computer programs which include instructions. The entirety of the one or more computer programs may be stored in a single memory device or the one or more computer programs may be divided with different portions stored in different multiple memory devices.

[0044] Any of the functions or operations described herein can be processed by one processor or a combination of processors. The one processor or the combination of processors is circuitry performing processing and includes circuitry like an application processor (AP, e.g. a CPU), a communication processor (CP, e.g., a modem), a graphics processing unit (GPU), a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip), a Wi-Fi chip, a Bluetooth® chip, a global positioning system (GPS) chip, a near field communication (NFC) chip, connectivity chips, a sensor controller, a touch controller, a finger-print sensor controller, a display driver integrated circuit (IC), an audio CODEC chip, a universal serial bus (USB) controller, a camera controller, an image processing IC, a microprocessor unit (MPU), a system on chip (SoC), an IC, or the like.

[0045] It will be appreciated that various embodiments of the disclosure according to the claims and description in the specification can be realized in the form of hardware, software or a combination of hardware and software.

[0046] Any such software may be stored in non-transitory computer readable storage media. The non-transitory computer readable storage media store one or more computer programs (software modules), the one or more computer programs include computer-executable instructions that, when executed by one or more processors of an electronic device individually or collectively, cause the electronic device to perform a method of the disclosure.

[0047] Any such software may be stored in the form of volatile or non-volatile storage such as, for example, a storage device like read only memory (ROM), whether erasable or rewritable or not, or in the form of memory such as, for example, random access memory (RAM), memory chips, device or integrated circuits or on an optically or magnetically readable medium such as, for example, a compact disk (CD), digital versatile disc (DVD), magnetic disk or magnetic tape or the like. It will be appreciated that the storage devices and storage media are various embodiments of non-transitory machine-readable storage that are suitable for storing a computer program or computer programs comprising instructions that, when executed, implement various embodiments of the disclosure. Accordingly, various embodiments of the present disclosure may provide a program comprising code for implementing apparatus or a method as claimed in any one of the claims of this specification and a non-transitory machine-readable storage storing such a program.

[0048] Hereinafter, the determination of priority between A and B in the present disclosure may refer to various actions such as selecting the one having a higher priority based on a predefined priority rule and performing an operation corresponding thereto, or omitting or dropping an operation corresponding to the one having a lower priority.

[0049] Hereinafter, "A or B" as described in the present disclosure may be understood as "A and / or B," which may include A, or B, or both A and B.

[0050] In addition, "at least one of A, B, and C" as described in the present disclosure may be understood to include A, or B, or C, or any combination of A, B, and C.

[0051] In addition, "at least one of A, B, or C" as described in the present disclosure may be understood to include A, or B, or C, or any combination of A, B, and C.

[0052] Furthermore, "A / B" as described in the present disclosure may be understood as "A and / or B," which may include A, or B, or both A and B.

[0053] Furthermore, "A, B" as described in the present disclosure may be understood as "A and / or B," which may include A, or B, or both A and B.

[0054] Furthermore, "A and B" as described in the present disclosure may be understood as "A and / or B," which may include A, or B, or both A and B.

[0055] Furthermore, “if condition A and condition B are satisfied,” as described in the present disclosure, may not be limited to a case where both condition A and condition B are satisfied, but may be understood to include a case where either condition A or condition B is individually satisfied, both condition A and condition B are satisfied, or one or more additional conditions are satisfied in combination.

[0056] Furthermore, throughout this disclosure, ordinal terms such as "first," "second," "third," etc., (and similar qualifiers) are used merely to distinguish between different instances, occurrences, configurations, messages, stages, or aspects of elements, operations, or information as described herein. Unless the context clearly dictates otherwise, the use of such ordinal terms does not itself require that the elements, operations, or information distinguished by these terms be structurally different, numerically distinct, or substantively dissimilar. For example, a "first signal" and a "second signal" may refer to instances of the same signal transmitted at different times or containing the same core information despite minor variations, or they may refer to signals with different content or characteristics, depending on the specific context. Similarly, a "first value" and a "second value" may represent the same magnitude but measured or applied in different circumstances, or they may represent different magnitudes. The interpretation should be guided by the specific technical context, function, and relationship described in the relevant portion of the specification and claims.

[0057] Furthermore, the terms “first ~”, “second ~”, etc., as described in the present disclosure with respect to various elements (e.g., information, objects, operation, sequences, or the like), should not limit those elements. These terms may only be intended to distinguish one element from another, and may not be intended to indicate a specific order. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element.

[0058] Furthermore, even if “first ~” and “second ~” are described in the present disclosure, it may be understood that element(s) referred to by “first ~” and “second ~” may be the same or different. For example, in case of element(s) being information, first information and second information may both be same information and, in some cases, are separate and different information.

[0059] In addition, the terms “if ~” and “in case that ~” as used in the disclosure or claims may be interpreted to include the meanings of “when (or upon) ~,” “in response to ~,” “based on ~,” or “according to ~,” and may be used interchangeably with these expressions. In addition, expressions other than those exemplified herein may also be used, as long as they have substantially the same meaning and do not impair the technical features of the present disclosure.

[0060] For example, the physical layer signaling may be referred to as Layer 1 (L1) signaling and may include downlink control information (DCI). In addition, the higher layer signaling may include a medium access control (MAC) control message, a radio resource control (RRC) signaling message, a non-access stratum (NAS) signaling message, or an application layer message. The RRC signaling message may be referred to as L3 (layer 3) signaling. It should be noted, however, that the higher layer signaling is not limited to the aforementioned examples.

[0061] In addition, the term "not perform" as used in the present disclosure or claims may, in context, be understood to mean that the corresponding step is omitted or skipped. Such a term may be replaced with other terms having the same or substantially equivalent meaning.

[0062] In addition, "transmitting a message including A and B" as described in the present disclosure, may be understood as encompassing both (i) transmitting A and B in a single message, and (ii) transmitting A and B separately via multiple messages (e.g., transmitting a first message including A and a second message including B). This interpretation may also apply to messages that include two or more items (e.g., A, B, C), transmitted either together or separately.

[0063] In addition, "transmitting a message including A and transmitting a message including B" may also be interpreted as transmitting a message including A and B in a single message.

[0064] In the specific embodiments of the present disclosure described below, terms or components included in the disclosure may be expressed in singular or plural form depending on the specific embodiments presented. However, such singular or plural expressions are selected appropriately for convenience of description, and the present disclosure is not limited to a singular or plural number of components. A component expressed in the plural form may be implemented as a single component, and a component expressed in the singular form may be implemented as multiple components.

[0065] The drawings or flowcharts described below illustrate exemplary methods that may be implemented according to the principles of the present disclosure, and various modifications may be made to the methods illustrated in the flowcharts of the present disclosure. For example, although illustrated as a series of steps, various steps in each drawing or flowchart may overlap, occur in parallel, occur in a different order, or be repeated. In other examples, any step may be omitted or replaced with another step.

[0066] The methods and apparatuses proposed in the embodiments of the present disclosure are not limited to each embodiment individually, but may also be applied in combination of all or some of the embodiments proposed in the disclosure. Therefore, the embodiments of the present disclosure may be modified and applied without significantly departing from the scope of the present disclosure, as would be understood by those skilled in the art.

[0067] In this case, even if certain wordings are described differently across embodiments, they may be used interchangeably or in substitution or in combination if their underlying concepts are equivalent. For example, for the same or equivalent concept, even if one embodiment uses the expression "A" and another embodiment uses the expression "B", such expressions may be understood interchangeably, in substitution, or in combination.

[0068] The terms used in the following description to refer to access nodes, network entities, messages, interfaces between network entities, various types of identification information, and the like, are provided merely for the convenience of explanation by way of example. Therefore, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may also be used. Such terms may also be interchangeable with terms defined in any 3rd generation partnership project　(3GPP) technical specifications (TS) where appropriate.

[0069] Hereinafter, a base station is an entity that allocates resources to terminals, and may be at least one of a gNode B, an eNode B, a Node B, a base station (BS), a wireless access unit, a BS controller, or a node on a network.

[0070] Furthermore, the base station of the present disclosure may include a split architecture comprising a central unit (CU) and a distributed unit (DU). In this structure, the CU is configured to process the higher layers of the control and user planes, while the DU is configured to process lower-layer radio resource functions. The embodiments of the present disclosure may be equally applicable to 5G base station architectures in which such CU and DU functional splits are implemented.

[0071] A terminal may include a UE, a mobile station (MS), a cellular phone, a smartphone, a computer, or a multimedia system capable of performing communication functions.

[0072] In the disclosure, a downlink (DL) refers to a radio link through which a BS transmits a signal to a UE, and an uplink (UL) refers to a radio link through which a UE transmits a signal to a BS.

[0073] Furthermore, hereinafter, 5th generation (5G) mobile communication technologies (e.g., 5G new radio (NR)), 6th generation (6G) mobile communication technologies may be described by way of example, but the embodiments of the present disclosure may also be applied to other communication systems having similar technical backgrounds or channel types. For example, newly evolved mobile communication systems developed after 5G and 6G may be included. Furthermore, based on determinations by those skilled in the art, the embodiments of the present disclosure may also be applied to other communication systems (e.g., Wi-Fi systems) through some modifications without significantly departing from the scope of the present disclosure

[0074] In the following description, the terms physical channel and signal may be used interchangeably with data or control signal. For example, the term physical downlink shared channel (PDSCH) refers to a physical channel through which data is transmitted, but the term PDSCH may also be used to refer to the data itself. That is, in the present disclosure, the expression "transmit a physical channel" may be interpreted as being equivalent to the expression "transmit data or a signal via a physical channel."

[0075] Hereinafter, in the context of the present disclosure, higher layer signaling may refer to signaling corresponding to at least one or any combination of the following: master information block (MIB), system information block (SIB) or SIB M (M = 1, 2, ...), radio resource control (RRC), or medium access control (MAC) control element (CE), or a non-access stratum (NAS) signaling message, or an application layer message. The RRC signaling message may be referred to as L3 (layer 3) signaling.

[0076] In addition, L1 signaling may refer to signaling corresponding to at least one or any combination of signaling techniques using the at least one or any combination of the following physical layer channels or signaling: physical downlink control channel (PDCCH), downlink control information (DCI), user equipment (UE)-specific DCI, group-common DCI, common DCI, scheduling DCI (e.g., DCI used for scheduling downlink or uplink data), non-scheduling DCI (e.g., DCI not used for scheduling downlink or uplink data) physical uplink control channel (PUCCH), or uplink control information (UCI). The L1 signaling message may be referred to as a physical layer signaling.

[0077] Hereinafter, the expression that information is configured by the BS, as used in the present disclosure or claims, may, in context, be understood to mean that the terminal receives the corresponding information from the BS via a physical layer signaling or a higher layer signaling. Such an expression may be replaced with other terms having the same or substantially equivalent meaning.

[0078] Hereinafter, the operational principle of the present disclosure will be described in detail with reference to the accompanying drawings.

[0079] The present disclosure relates to wireless communication, more particularly, to handling Artificial Intelligence (AI) / Machine Learning (ML) configurations.

[0080] In general, usage of AI and ML is used in a variety of fields. 3GPP is studying the AI / ML on air interface. AI / ML based algorithms can be used for enhanced performance and / or reduced complexity / overhead. Enhanced performance here depends on the use cases under consideration and could be, e.g., improved throughput, robustness, accuracy or reliability, etc. AI / ML models may reside on a User Equipment (UE) or on the network entities like base station or Operation and Maintenance (OAM). It is also possible that AI / Models are 2 sided models which reside on both UE and network entities.

[0081] Data collection may be performed for different purposes in Life Cycle Management (LCM) of AI / ML models, e.g., model training, model inference, model monitoring, model selection, model update, etc. For all types of offline model training (i.e., UE- / NW- / two-sided model training), there is no latency requirement for data collection. For model inference, when required data comes from other entities, there is a latency requirement for data collection. For (real-time) performance monitoring, when required monitoring data (e.g., performance metric) comes from other entities, there is a latency requirement for data collection. A typical usage of AI / ML on air interface is AI / ML for beam management includes

[0082] - Spatial-domain Downlink beam prediction for Set A of beams based on measurement results of Set B of beams

[0083] - Temporal Downlink beam prediction for Set A of beams based on the historic measurement results of Set B of beams.

[0084] AI / ML on air interface also may be used for positioning, such as

[0085] Direct AI / ML positioning:

[0086] UE-based positioning with UE-side model, direct AI / ML positioning

[0087] UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning

[0088] NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning

[0089] AI / ML assisted positioning:

[0090] UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning

[0091] NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning

[0092] Another use case of AI / ML on air interface is for CSI feedback enhancements. This may be used for CSI prediction, CSI compression etc. the AI / ML may be used for other cases such as AI / ML for mobility. The training can be online training or offline training. CN (Core Network) / OAM / OTT (Over the Top Server) collection of UE-sided model training (and even Network sided model training) data may be supported. The UE may be configured by the network for reporting measurements for AI / ML. These measurements may be logged and reported to the network. A variation of immediate MDT may be used for the reporting.

[0093] Minimization of Drive tests:The radio-related measurements may be collected by the network from the UE via immediate MDT in RRC_CONNECTED mode. The measurements for immediate MDT are not logged. Similarly, the measurements may be connected by the network from the UE via logged MDT. For logged MDT, UE may be configured to log the measurements in RRC_IDLE or RRC_INACTIVE states, using messages such as LoggedMeasurementConfiguration. UE may inform the network about the availability of the logged MDT measurements and the network may retrieve the measurements in RRC_CONNECTED state, for e.g. via UE information procedure. Immediate MDT may be enhanced for data collection for AI / ML, for e.g. OAM-centric data collection for the training of a network-sided model for AI / ML for beam management. Immediate MDT may be enhanced for periodic reporting and event based reporting. UE may report multiple instances of logged L1 measurement result from UE to gNB via a RRC message as configured by gNB.

[0094] Version 18.2.0 of TS 38.331, 38.321, 38.306, 37.340, 38.300 are considered as background for the present disclosure.

[0095] Thus, it is desired to address the above-mentioned disadvantages, issues, or other shortcomings or at least provide a useful alternative.

[0096] The principal object of the embodiments herein is to provide system and method for handling AI / ML configurations.

[0097] Embodiments disclosed herein provides a system and method for handling AI / ML configurations. The method includes data collection for AI / ML models, such as AI / ML for air interface for beam management, positioning and other use cases or AI / ML for mobility. Further, the present disclosure discloses a method of configuring a UE by the network for logging and / or reporting the data.

[0098] As is traditional in the field, embodiments may be described and illustrated in terms of blocks which carry out a described function or functions.　These blocks, which may be referred to herein as managers, units, modules, hardware components or the like, are physically implemented by analog and / or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like.　The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block.　Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the disclosure.　Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the disclosure.

[0099] Embodiments disclosed herein provides a system and method for handling AI / ML configurations. The method includes data collection for AI / ML models, such as AI / ML for air interface for beam management, positioning and other use cases or AI / ML for mobility. Further, the present disclosure discloses a method of configuring a UE by the network for logging and / or reporting the data.

[0100] In an embodiment, network node such as gNB (or any other Radio Access Network Node such as the base station in 6G wireless communication systems. i.e. gNB in the invention refers to the base station or radio access network in general) configures the UE for reporting the radio related measurements and related information for AI / ML. This may be used for training of a network sided model (i.e. network sided AI / ML models such as RAN centric models or OAM centric models) or other purposes.

[0101] In an embodiment, network node such as gNB (or any other Radio Access Network Node) configures the UE for reporting the radio related measurements and related information for AI / ML using Minimisation of Drive Test (MDT) Framework, for e.g. by enhancing immediate MDT framework, or alternatively by enhancing the logged measurement frame work.

[0102] In the context of this invention, configuration for measurement handling for AI / ML comprises of one or more of the following:

[0103] - Configuration for logging the measurements for AI / ML.

[0104] - Configuration for reporting the measurement for AI / ML.

[0105] - Configuration for logging and reporting the measurements for AI / ML.

[0106] - Configuration for logging the layer 1 measurements (RSRP, RSRQ, SINR etc.) for AI / ML.

[0107] - Configuration for reporting the layer 1 measurements (RSRP, RSRQ, SINR etc.) for AI / ML.

[0108] - Configuration for logging and reporting the layer 1 measurements (RSRP, RSRQ, SINR etc.) for AI / ML.

[0109] - Configuration for logging the layer 3 measurements (RSRP, RSRQ, SINR etc.) for AI / ML.

[0110] - Configuration for reporting the layer 3 measurements (RSRP, RSRQ, SINR etc.) for AI / ML.

[0111] - Configuration for logging and reporting the layer 3 measurements (RSRP, RSRQ, SINR etc.) for AI / ML.

[0112] - Configuration for logging the positioning related measurements for AI / ML.

[0113] - Configuration for reporting the positioning related measurements for AI / ML.

[0114] - Configuration for logging and reporting the positioning related measurements for AI / ML.

[0115] - Configuration for logging the location related information for AI / ML.

[0116] - Configuration for reporting the location related information for AI / ML.

[0117] - Configuration for logging and reporting the location related information for AI / ML.

[0118] In an embodiment, network node such as gNB provide the configuration for measurement handling for AI / ML based on the inputs from entities such as OAM or TCE.

[0119] In an embodiment, some of the configuration for measurement handling for AI / ML is applicable in connected state (RRC_CONNECTED) (for e.g. the configuration for logging and / or reporting the layer1 measurements for AI / ML, the configuration for logging and / or reporting the layer3 measurements for AI / ML, the configuration for logging and / or reporting the location and / or positioning related measurements for AI / ML etc.).

[0120] In an embodiment, some of the configuration for measurement handling for AI / ML is applicable in inactive state (such as RRC_INACTIVE) (for e.g. the configuration for logging / reporting the layer3 measurements for AI / ML, the configuration for logging and / or reporting the location and / or positioning related measurements for AI / ML etc.).

[0121] In an embodiment, some of the configuration for measurement handling for AI / ML is applicable in idle state (such as RRC_IDLE) (for e.g. the configuration for logging / reporting the layer3 measurements for AI / ML, the configuration for logging and / or reporting the location and / or positioning related measurements for AI / ML etc.).

[0122] In an embodiment, the network node provides the configuration for measurement handling for AI / ML to the UE while reconfiguring the UE, for e.g. in messages such as NR RRCReconfiguration.

[0123] In an embodiment, the network node provides the configuration for measurement handling for AI / ML in a new RRC message other than RRCReconfiguration.

[0124] In an embodiment, the configuration for measurement handling for AI / ML will inform the UE how to perform the measurements including the frequency where the UE need to perform the measurements, sub carrier spacing, reference signal(s) such as but not limited to SS, PBCH-DMRS, SSB, CSIRS, measurement period, measurement timing configuration, measurement bandwidth, frequency domain parameters, applicable frequency range(s), RAT(s), coverage area or geographic area, the list of cells where the measurements can be performed, the RRC states where the measurements can be performed etc.

[0125] In an embodiment, the configuration for measurement handling for AI / ML will inform the UE how to report the measurements to the network, i.e. whether the measurements need to be reported upon satisfaction of a measurement event (e.g. the measurement events used for other purpose) or based on a request from the network, or based on a time period, , thresholds including performance thresholds, accuracy thresholds, reliability thresholds, number of reported cells, number of reported RS per Cell etc.

[0126] In an embodiment, the configuration for measurement handling for AI / ML can be different for the measurements performed for different purposes.

[0127] In an embodiment, configuration for measurement handling for AI / ML will inform the UE the area where the measurements can be logged and / or reported. This area may include one or more of PLMN information such as PLMN identity, NPN information such as SNPN identity or PNI-NPN identity, a list of cells (such as CGI or PCI and NR-ARFCN combination or an identifier which groups a set of cells), registration area identifier, tracking area related information such as tracking area code, tracking area identity list. In an embodiment, the configuration for measurement handling for AI / ML may include measurement identifier for each of the measurements configured. In an embodiment, the configuration for measurement handling for AI / ML may include measurement object information such as measurement object identifier for each of the measurements configured.

[0128] In an embodiment, the UE informs the network whether it has logged measurements for AI / ML (i.e. availability of logged measurements for AI / ML) while transitioning from a RRC_IDLE state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCSetupComplete.

[0129] In an embodiment, the UE informs the network whether it has logged measurements for AI / ML (i.e. availability of logged measurements for AI / ML) during RRCReestablishment procedure. UE may include a single bit of information such as an enumerated or a flag in RRCReestablishmentComplete.

[0130] In an embodiment, the UE informs the network whether it has logged measurements for AI / ML (i.e. availability of logged measurements for AI / ML) during RRCReconfiguration procedure. UE may include a single bit of information such as an enumerated or a flag in RRCReconfigurationComplete.

[0131] In an embodiment, the UE informs the network whether it has logged L3 measurements for AI / ML (i.e. availability of logged measurements for AI / ML) while transitioning from RRC_IDLE state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCSetupComplete.

[0132] In an embodiment, the UE informs the network whether it has logged L3 measurements for AI / ML (i.e. availability of logged measurements for AI / ML) during RRCReestablishment procedure. UE may include a single bit of information such as an enumerated or a flag in RRCReestablishmentComplete.

[0133] In an embodiment, the UE informs the network whether it has logged L3 measurements for AI / ML (i.e. availability of logged measurements for AI / ML) during RRCReconfiguration procedure. UE may include a single bit of information such as an enumerated or a flag in RRCReconfigurationComplete.

[0134] In an embodiment, the UE informs the network whether it has logged L1 measurements for AI / ML (i.e. availability of logged measurements for AI / ML) while transitioning from RRC_IDLE state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCSetupComplete.

[0135] In an embodiment, the UE informs the network whether it has logged L1 measurements for AI / ML (i.e. availability of logged measurements for AI / ML) during RRCReestablishment procedure. UE may include a single bit of information such as an enumerated or a flag in RRCReestablishmentComplete.

[0136] In an embodiment, the UE informs the network whether it has logged L1 measurements for AI / ML (i.e. availability of logged measurements for AI / ML) during RRCReconfiguration procedure. UE may include a single bit of information such as an enumerated or a flag in RRCReconfigurationComplete.

[0137] In an embodiment, the UE informs the network whether it has logged location measurements for AI / ML (i.e. availability of logged measurements for AI / ML) while transitioning from RRC_IDLE state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCSetupComplete.

[0138] In an embodiment, the UE informs the network whether it has logged location measurements for AI / ML (i.e. availability of logged measurements for AI / ML) during RRCReestablishment procedure. UE may include a single bit of information such as an enumerated or a flag in RRCReestablishmentComplete.

[0139] In an embodiment, the UE informs the network whether it has logged location measurements for AI / ML (i.e. availability of logged measurements for AI / ML) during RRCReconfiguration procedure. UE may include a single bit of information such as an enumerated or a flag in RRCReconfigurationComplete.

[0140] The network receiving the information from the UE about the logged measurements for AI / ML may retrieve the logged measurements for AI / ML based on the information.

[0141] In an embodiment, the UE releases the configuration for measurement handling for AI / ML during RRCReestablishment procedure. In an embodiment, this release will be performed following cell selection while NR timer T311 (or equivalent timer in other RAT) is running. Alternatively, the release may be performed upon the reception of RRC Reestablishment message.

[0142] In an embodiment, the UE may keep the configuration for measurement handling for AI / ML following cell selection while NR timer T311 (or equivalent timer in other RAT) during RRCReestablishment procedure if the UE is configured for attempting conditional reconfiguration when the selected cell is a CHO candidate or attempting LTM cell switch when the selected cell is a LTM candidate cell. If the attempt to perform CHO or LTM cell switch, UE releases the configuration.

[0143] In an embodiment, UE maintains the configuration for measurement handling for AI / ML received from the network. If a UE which is configured with the configuration for measurement handling for AI / ML receives a RRC message which doesn't include the configuration for measurement handling for AI / ML, UE performs / logs / reports the measurements with already received configuration. UE may further release the configuration based on explicit instruction from the network.

[0144] In an embodiment, the UE releases the configuration for measurement handling for AI / ML upon performing a handover. In an embodiment, this may be applicable for the configuration for logging / reporting the layer 1 measurements. In an embodiment, the UE may also discard any stored measurements for AI / ML purpose upon performing the handover.

[0145] In an embodiment, the UE releases the configuration for measurement handling for AI / ML upon performing handover, if the UE is configured to perform measurements in a specific cell (such as in the source cell). This ensures that the UE can save the memory for storing the configuration. In an embodiment, the UE may also discard any stored measurements for AI / ML purpose upon performing the handover.

[0146] In an embodiment, the UE keeps the configuration for measurement handling for AI / ML upon performing handover, for e.g. even if the UE is configured to perform measurements in a specific cell. (Such as in the source cell). Further UE may avoid performing the measurements in the new serving cell after handover. If the UE again moves back to the old source cell (for e.g. after a handover back to the source cell), the UE may again perform measurements. This saves the signaling a there is no need for a providing the same configuration in the cell.

[0147] In an embodiment, the UE may perform measurements for AI / ML in a different frequency or in the same frequency but requiring gaps, using measurement gaps, if they are configured.

[0148] In an embodiment, the UE may report the measurements for AI / ML in RRC messages such as MeasurementReport message. In an embodiment the measurements for AI / ML may be send in SRB2. In an embodiment, while reporting the measurements for AI / ML, the UE may include the identifier corresponding for the measurement for AI / ML and the measurement results.

[0149] In an embodiment, the UE may report the measurements for AI / ML in RRC messages other than message used for sending connected mode measurements (i.e. other than in MeasurementReport). In an embodiment the measurements for AI / ML may be send in SRB4 and may be send in MeasurementReportAppLayer. In an embodiment, this may be a send in a new RRC message (such as other than MeasurementReport or MeasurementReportAppLayer).

[0150] In an embodiment, the UE releases the configuration for measurement handling for AI / ML during RRCReestablishment procedure when the purpose of the measurement is related to the logging / reporting of layer 1 measurements or positioning measurements. In an embodiment, this release will be performed following cell selection while NR timer T311 (or equivalent timer in other RAT) is running.

[0151] In an embodiment, UE keeps the configuration for measurement handling for AI / ML upon LTM cell switch.

[0152] In an embodiment, UE keeps the configuration for measurement handling for AI / ML upon LTM cell switch for MCG. In yet another embodiment, UE keeps the configuration for measurement handling for AI / ML upon LTM cell switch for SCG.

[0153] In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, (for e.g. as specified in NR TS 38.331 section 5.3.7.3), and the LTM cell switch is triggered on MCG, the UE releases / clears all current dedicated and common radio configurations which have not been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except a set of specific configurations which includes the configuration for measurement handling for AI / ML. In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, (for e.g. as specified in NR TS 38.331 section 5.3.7.3), and the LTM cell switch is triggered on SCG, the UE releases / clears all current dedicated and common radio configurations which have been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except a set of specific configurations which includes the configuration for measurement handling for AI / ML.

[0154] In an example embodiment, Upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, as specified in 5.3.7.3, the UE shall:

[0155] 1> if the LTM cell switch is triggered on the MCG:

[0156] 2> release / clear all current dedicated and common radio configurations which have not been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except for the following:

[0157] - the radio bearer configuration (configured via RadioBearerConfig)

[0158] - the logicalChannelIdentity and logicalChannelIdentityExt of RLC bearers configured in RLC-BearerConfig and the associated RLC entities, their state variables, buffers, and timers;

[0159] - the UE variables VarLTM-ServingCellNoResetID and VarLTM-ServingCellUE-MeasuredTA-ID;

[0160] - the ltm-Config;

[0161] - the MCG C-RNTI;

[0162] - the AS security configurations associated with the master key;

[0163] - the configuration for measurement handling for AI / ML;

[0164] 1> else, if the LTM cell switch is triggered on the SCG:

[0165] 2> release / clear all current dedicated and common radio configurations which have been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except for the following:

[0166] - the radio bearer configuration (configured via RadioBearerConfig IE)

[0167] - the logicalChannelIdentity and logicalChannelIdentityExt of RLC bearers configured in RLC-BearerConfig and the associated RLC entities, their state variables, buffers, and timers;

[0168] - the UE variables VarLTM-ServingCellNoResetID and VarLTM-ServingCellUE-MeasuredTA-ID;

[0169] - the ltm-Config;

[0170] - the AS security configurations associated with the secondary key;

[0171] - the configuration for measurement handling for AI / ML;

[0172] In an embodiment, UE keeps the configuration for measurement handling for AI / ML upon LTM cell switch when the configuration is for logging / reporting the L3 measurements.

[0173] In an embodiment, UE keeps the configuration for measurement handling for AI / ML for logging the L3 measurements upon LTM cell switch for MCG. In yet another embodiment, UE keeps the configuration for measurement handling for AI / ML for logging / reporting the L3 measurements upon LTM cell switch for SCG.

[0174] In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, (for e.g. as specified in NR TS 38.331 section 5.3.7.3), and the LTM cell switch is triggered on MCG, the UE releases / clears all current dedicated and common radio configurations which have not been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except a set of specific configurations which includes the configuration for measurement handling for AI / ML for logging / reporting the L3 measurements.

[0175] In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, (for e.g. as specified in NR TS 38.331 section 5.3.7.3), and the LTM cell switch is triggered on SCG, the UE releases / clears all current dedicated and common radio configurations which have been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except a set of specific configurations which includes the configuration for measurement handling for AI / ML for logging / reporting the L3 measurements.

[0176] In an embodiment, UE keeps the configuration for measurement handling for AI / ML upon LTM cell switch when the configuration is for logging / reporting the location measurements.

[0177] In an embodiment, UE keeps the configuration for measurement handling for AI / ML for logging the location measurements upon LTM cell switch for MCG. In yet another embodiment, UE keeps the configuration for measurement handling for AI / ML for logging / reporting the location measurements upon LTM cell switch for SCG.

[0178] In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, (for e.g. as specified in NR TS 38.331 section 5.3.7.3), and the LTM cell switch is triggered on MCG, the UE releases / clears all current dedicated and common radio configurations which have not been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except a set of specific configurations which includes the configuration for measurement handling for AI / ML for logging / reporting the location measurements.

[0179] In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, (for e.g. as specified in NR TS 38.331 section 5.3.7.3), and the LTM cell switch is triggered on SCG, the UE releases / clears all current dedicated and common radio configurations which have been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except a set of specific configurations which includes the configuration for measurement handling for AI / ML for logging / reporting the location measurements.

[0180] In an embodiment, UE keeps the configuration for measurement handling for AI / ML upon LTM cell switch when the configuration is for logging / reporting the layer 1 measurements.

[0181] In an embodiment, UE keeps the configuration for measurement handling for AI / ML for logging the layer 1 measurements upon LTM cell switch for MCG. In yet another embodiment, UE keeps the configuration for measurement handling for AI / ML for logging / reporting the layer 1 measurements upon LTM cell switch for SCG.

[0182] In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, (for e.g. as specified in NR TS 38.331 section 5.3.7.3), and the LTM cell switch is triggered on MCG, the UE releases / clears all current dedicated and common radio configurations which have not been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except a set of specific configurations which includes the configuration for measurement handling for AI / ML for logging / reporting the layer 1 measurements.

[0183] In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection performed while timer T311 was running, (for e.g. as specified in NR TS 38.331 section 5.3.7.3), and the LTM cell switch is triggered on SCG, the UE releases / clears all current dedicated and common radio configurations which have been received either via SRB1 within mrdc-SecondaryCellGroup, or via SRB3 except a set of specific configurations which includes the configuration for measurement handling for AI / ML for logging / reporting the layer 1 measurements.

[0184] UE may continue to perform the logging / reporting of the measurements for AI / ML based on the configuration when it keeps the configuration. If the UE releases the configuration for logging / reporting of the measurements for AI / ML, it also stops performing the measurements based on the released configuration and waits for the network to provide the configuration again before performing the logging / reporting of the measurements for AI / ML.

[0185] In an embodiment, the UE is communicated to the network, both UE, and network includes a memory, a processor, a communicator, and an AI / ML controller.

[0186] The memory is configured to store instructions to be executed by the processor. The memory can include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory may, in some examples, be considered a non-transitory storage medium. The term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” should not be interpreted that the memory is non-movable. In some examples, the memory is configured to store larger amounts of information. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache).

[0187] The processor may include one or a plurality of processors. The one or the plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-dedicated processor such as a neural processing unit (NPU). The processor may include multiple cores and is configured to execute the instructions stored in the memory.

[0188] In an embodiment, the communicator includes an electronic circuit specific to a standard that enables wired or wireless communication. The communicator is configured to communicate internally between internal hardware components of the UE and with external devices via one or more networks.

[0189] In an embodiment, the AI / ML controller handles AI / ML configurations.

[0190] The various actions, acts, blocks, steps, or the like in the method may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.

[0191] The present disclosure generally relates the field of wireless communications, and more particularly to methods and systems for performing AI / ML in New Radio.

[0192] Artificial Intelligence (AI) and Machine Learning (ML) is used in a variety of fields. 3rd Generation Partnership Project (3gpp) is studying the AI / ML on air interface. AI / ML based algorithms can be used for enhanced performance and / or reduced complexity / overhead. Enhanced performance here depends on use cases under consideration and could be, e.g., improved throughput, robustness, accuracy or reliability, etc. AI / ML models may reside on the User Equipment (UE) or on the network entities like base station or Operation And Maintenance (OAM). It is also possible that AI / Models are 2 sided models which reside on both UE and network entities.

[0193] Data collection may be performed for different purposes in Life Cycle Management (LCM), e.g., model training, model inference, model monitoring, model selection, model update, etc. For all types of offline model training (i.e., UE- / NW- / two-sided model training), there is no latency requirement for data collection. For model inference, when required data comes from other entities, there is a latency requirement for data collection. For (real-time) performance monitoring, when required monitoring data (e.g., performance metric) comes from other entities, there is a latency requirement for data collection.

[0194] A typical usage of AI / ML on air interface is AI / ML for beam management which includes:

[0195] - Spatial-domain Downlink beam prediction for Set A of beams based on measurement results of Set B of beams.

[0196] - Temporal Downlink beam prediction for Set A of beams based on the historic measurement results of Set B of beams.

[0197] AI / ML on air interface also may be used for positioning, such as

[0198] Direct AI / ML positioning:

[0199] - UE-based positioning with UE-side model, direct AI / ML positioning.

[0200] - UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning.

[0201] - NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning.

[0202] - AI / ML assisted positioning:

[0203] - UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning.

[0204] - NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning.

[0205] Another use case of AI / ML on air interface is for CSI feedback enhancements. This may be used for CSI prediction, CSI compression etc. The AI / ML may be used for other cases such as AI / ML for mobility. The training can be online training or offline training. Core Network (CN) / OAM / Over the Top Server (OTT) collection of UE-sided model training (and even network sided model training) data may be supported.

[0206] UE may be configured by the network for reporting measurements for AI / ML. These measurements may be logged and reported to the network. A variation of immediate MDT may be used for the reporting.

[0207] For minimisation of drive tests: The radio-related measurements may be collected by the network from the UE via immediate Minimization of Drive Test (MDT) in RRC_CONNECTED mode. The measurements for immediate MDT are not logged. Similarly, the measurements may be connected by the network from the UE via logged MDT. The UE may be configured to log the measurements in RRC_IDLE or RRC_INACTIVE states, using messages such as LoggedMeasurementConfiguration. The UE may inform the network about the availability of the logged MDT measurements and the network may retrieve the measurements in RRC_CONNECTED state, for e.g. via UE information procedure. Immediate MDT may be enhanced for data collection for AI / ML, for e.g. OAM-centric data collection for the training of a network-sided model for AI / ML for beam management. Immediate MDT may be enhanced for periodic reporting and event-based reporting. The UE may report multiple instances of logged L1 measurement result from UE to gNB via a RRC message as configured by gNB.

[0208] Version 18.2.0 of TS 38.331, 38.321, 38.306, 37.340, 38.300 are considered as background for this invention.

[0209] The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the disclosure and should not be taken as acknowledgment or any form of suggestion that this information forms prior art already known to a person skilled in the art.

[0210] The present disclosure relates to methods for performing Artificial Intelligence / Machine Learning AI / ML in New Radio. The present disclosure addresses data collection for AI / ML, such as AI / ML for air interface for beam management and other use cases or AI / ML for mobility. The present disclosure addresses User Equipment (UE) capability reporting, handling of various RRC procedures and full configuration along with the configuration for logging / reporting measurements for AI / ML.

[0211] In an embodiment, network node such as gNB (or any other Radio Access Network Node) configures the UE for reporting the radio related measurements for AI / ML. This may be used for training of a network sided model (i.e. network sided AI / ML models such as RAN centric models or Operation And Maintenance (OAM) centric models).

[0212] In an embodiment, network node such as gNB (or any other Radio Access Network Node) configures the UE for reporting the radio related measurements for AI / ML using Minimization of Drive Test (MDT) Framework, for e.g. by enhancing immediate MDT framework, or alternatively by enhancing the logged measurement framework.

[0213] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

[0214] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof.

[0215] Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase “in an embodiment”, “in another embodiment”, and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0216] In an embodiment, network node such as gNB (or any other Radio Access Network Node) configures the User Equipment (UE) for reporting the radio related measurements for Artificial Intelligence / Machine Learning (AI / ML). This may be used for training of a network sided model (i.e. network sided AI / ML models such as RAN centric models or Operation And Maintenance (OAM) centric models).

[0217] In an embodiment, network node such as gNB (or any other Radio Access Network Node) configures the UE for reporting the radio related measurements for AI / ML using Minimization of Drive Test (MDT) Framework, for e.g. by enhancing immediate MDT framework, or alternatively by enhancing the logged measurement framework.

[0218] In the context of the present disclosure, configuration for measurement handling for AI / ML comprises of one or more of the following:

[0219] - Configuration for logging the measurements for AI / ML.

[0220] - Configuration for reporting the measurement for AI / ML.

[0221] - Configuration for logging and reporting the measurements for AI / ML.

[0222] - Configuration for logging the layer 1 measurements (RSRP,RSRQ,SINR etc.) for AI / ML.

[0223] - Configuration for reporting the layer 1 measurements (RSRP,RSRQ,SINR etc.)for AI / ML.

[0224] - Configuration for logging and reportin33g the layer 1 measurements (RSRP,RSRQ,SINR etc.) for AI / ML.

[0225] - Configuration for logging the layer 3 measurements (RSRP,RSRQ,SINR etc.) for AI / ML.

[0226] - Configuration for reporting the layer 3 measurements (RSRP,RSRQ,SINR etc.)for AI / ML.

[0227] - Configuration for logging and reporting the layer 3 measurements (RSRP,RSRQ,SINR etc.) for AI / ML.

[0228] - Configuration for logging the positioning related measurements for AI / ML.

[0229] - Configuration for reporting the positioning related measurements for AI / ML.

[0230] - Configuration for logging and reporting the positioning related measurements for AI / ML.

[0231] - Configuration for logging the location related information for AI / ML.

[0232] - Configuration for reporting the location related information for AI / ML.

[0233] - Configuration for logging and reporting the location related information for AI / ML.

[0234] In an embodiment, the capabilities for AI / ML may be reported to the network in RRC messages such as NR UECapabilityInformation in response to a message such as NR UECapabilityEnquiry. The received capabilities for AI / ML may be used by the network for configuring the UE for performing measurements for AI / ML and logging / reporting of those measurements.

[0235] In an embodiment, the UE informs the network node such as gNB its capability for logging the measurements for AI / ML. In an embodiment, the UE informs the network node such as gNB its capability for logging the L1 measurements for AI / ML. In an embodiment, the UE informs the network node such as gNB its capability for logging the L3 measurements for AI / ML. The capabilities for logging may be reported separately for different purposes- In an embodiment, there may be a capability bit reported for logging the measurements for AI / ML for beam management and in yet another embodiment, this may be a per-FR capability. In an embodiment, there may be a capability bit reported for logging the measurements for AI / ML for location measurements and in yet another embodiment, this may be a per-UE capability. In an embodiment, there may be a capability bit reported for logging the measurements for AI / ML for CSI optimization and in yet another embodiment, this may be a per-FR capability. In an embodiment, there may be a capability bit reported for logging the measurements for AI / ML for mobility and in yet another embodiment, this may be a per-UE capability.

[0236] In an embodiment, the UE informs the network node such as gNB its capability for reporting the measurements for AI / ML. In an embodiment, the UE informs the network node such as gNB its capability for reporting the L1 measurements for AI / ML. In an embodiment, the UE informs the network node such as gNB its capability for reporting the L3 measurements for AI / ML. The capabilities for reporting may be reported separately for different purposes- In an embodiment, there may be a capability bit reported for reporting the measurements for AI / ML for beam management and in yet another embodiment, this may be a per-FR capability. In an embodiment, there may be a capability bit reported for reporting the measurements for AI / ML for location measurements and in yet another embodiment, this may be a per-UE capability. In an embodiment, there may be a capability bit reported for reporting the measurements for AI / ML for CSI optimization and in yet another embodiment, this may be a per-FR capability. In an embodiment, there may be a capability bit reported for reporting the measurements for AI / ML for mobility and in yet another embodiment, this may be a per-UE capability.

[0237] In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the measurements for AI / ML. In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the L1 measurements for AI / ML. In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the L3 measurements for AI / ML. The capabilities for logging and reporting may be reported separately for different purposes- In an embodiment, there may be a capability bit reported for logging and reporting the measurements for AI / ML for beam management and in yet another embodiment, this may be a per-FR capability. In an embodiment, there may be a capability bit reported for logging and reporting the measurements for AI / ML for location measurements and in yet another embodiment, this may be a per-UE capability. In an embodiment, there may be a capability bit reported for logging and reporting the measurements for AI / ML for CSI optimization and in yet another embodiment, this may be a per-FR capability. In an embodiment, there may be a capability bit reported for logging and reporting the measurements for AI / ML for mobility and in yet another embodiment, this may be a per-UE capability.

[0238] In an embodiment, the UE informs the network node such as gNB its capability for logging the measurements for AI / ML in RRC_IDLE mode. In an embodiment, the UE informs the network node such as gNB its capability for logging the L1 measurements for AI / ML in RRC_IDLE mode. In an embodiment, the UE informs the network node such as gNB its capability for logging the L3 measurements for AI / ML in RRC_IDLE mode. In an embodiment, this may be a per-UE capability without FDD-TDD differentiation.

[0239] In an embodiment, the UE informs the network node such as gNB its capability for reporting the measurements for AI / ML in RRC_IDLE mode. In an embodiment, the UE informs the network node such as gNB its capability for reporting the L1 measurements for AI / ML in RRC_IDLE mode. In an embodiment, the UE informs the network node such as gNB its capability for reporting the L3 measurements for AI / ML in RRC_IDLE mode. In an embodiment, this may be a per-UE capability without FDD-TDD differentiation.

[0240] In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the measurements for AI / ML in RRC_IDLE mode. In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the L1 measurements for AI / ML in RRC_IDLE mode. In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the L3 measurements for AI / ML in RRC_IDLE mode. In an embodiment, this may be a per-UE capability without FDD-TDD differentiation.

[0241] In an embodiment, the UE informs the network node such as gNB its capability for logging the measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, the UE informs the network node such as gNB its capability for logging the L1 measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, the UE informs the network node such as gNB its capability for logging the L3 measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, this may be a per-UE capability without FDD-TDD differentiation.

[0242] In an embodiment, the UE informs the network node such as gNB its capability for reporting the measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, the UE informs the network node such as gNB its capability for reporting the L1 measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, the UE informs the network node such as gNB its capability for reporting the L3 measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, this may be a per-UE capability without FDD-TDD differentiation.

[0243] In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the L1 measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, the UE informs the network node such as gNB its capability for logging and reporting the L3 measurements for AI / ML in RRC_INACTIVE mode. In an embodiment, this may be a per-UE capability without FDD-TDD differentiation.

[0244] In an embodiment, the UE may configure a memory for logging the measurements for AI / ML. The amount of memory may be fixed size or configured by the network, for e.g. from a set of values. In an embodiment, the UE may inform the network the memory capability for logging the measurements for AI / ML to the network. For e.g. UE may inform the network that it is capable of logging N kilo bytes of data. This allows the network to determine the configuration for the measurement logging, for e.g. how many cells need to be measured and what should be the periodicity for the measurement.

[0245] Logging and reporting in RRC_IDLE / RRC_INACTIVE modes mean logging in RRC_IDLE / RRC_INACTIVE and reporting while it is RRC_CONNECTED.

[0246] In an embodiment, the network node provides the configuration for measurement handling for AI / ML to the UE while resuming the UE, for e.g. in messages such as NR RRCResume. In an embodiment the network node may ask UE to apply the stored configuration for measurement handling for AI / ML. In an embodiment, the configuration for measurement handling for AI / ML for one or more of logging / reporting of L1 measurements, L3 measurements / location measurements.

[0247] In an embodiment, the configuration for measurement handling for AI / ML may inform the UE how to perform the measurements such as periodicity of measurements. The network may inform the UE how often it needs to perform the measurements for AI / ML. In an embodiment, configuration for measurement handling for AI / ML may inform the UE the area where the measurements can be logged and / or reported. This area may include one or more of PLMN information such as PLMN identity, NPN information such as SNPN identity or PNI-NPN identity, a list of cells (such as CGI or PCI and NR-ARFCN combination or an identifier which groups a set of cells), registration area identifier, tracking area related information such as tracking area code, tracking area identity list. In an embodiment, the configuration for measurement handling for AI / ML may include measurement identifier for each of the measurements configured for AI / ML.

[0248] In an embodiment, the configuration for measurement handling for AI / ML may include measurement object information such as measurement object identifier for each of the measurements configured. This configuration may be included in RRCRelease message, for the measurements for AI / ML in RRC_IDLE mode. In an embodiment, the configuration may be included in RRCRelease message within suspendConfig for the measurements for AI / ML in RRC_INACTIVE mode.

[0249] The UE applies the configuration and performs measurements, logs the measurements and reports them to network based on the configuration.

[0250] In an embodiment, handling measurements for AI / ML may include the following: In an embodiment, the UE informs the network whether it has logged measurements for AI / ML (i.e. availability of logged measurements for AI / ML) while transitioning from a non-connected (RRC_IDLE / RRC_INACTIVE) state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCResumeComplete. In an embodiment, the availability is reported for one or more of L1 measurements, L3 measurements,location measurements logged for AI / ML. In an embodiment, a separate bit may be used for indicating availability of each type of measurements such as L1 measurements, L3 measurements and location measurements.

[0251] In an embodiment, the UE sends the network the logged measurements for AI / ML in RRCResumeComplete or RRCReconfigurationComplete messages. In an embodiment, the network retrieves the information from the UE, by sending an RRC message such as UE Information Request and in the response message UE includes the logged measurements for AI / ML.

[0252] In an embodiment, the UE may report the measurements for AI / ML in RRC messages such as MeasurementReport message. In an embodiment the measurements for AI / ML may be sent in SRB2. In an embodiment, while reporting the measurements for AI / ML, the UE may include the identifier corresponding for the measurement for AI / ML and the measurement results. In an embodiment, this may be logged measurements for AI / ML.

[0253] In an embodiment, the UE may report the measurements for AI / ML in RRC messages other than legacy message used for sending connected mode measurements (i.e. other than in MeasurementReport). In an embodiment the measurements for AI / ML may be sent in SRB4 and may be sent in MeasurementReportAppLayer. In an embodiment, this may be a sent in a new RRC message (such as other than MeasurementReport or MeasurementReportAppLayer).

[0254] In an embodiment, the UE may send the (logged) measurements for AI / ML in multiple segments of a RRC message. Network may configure UE whether it is allowed to send multiple segments for the logged measurements for AI / ML. Based on the configuration, UE may send multiple segments to the network. In an embodiment, if segmentation is not allowed according to the configuration from the network, UE will discard the measurements for AI / ML which won't fit in the RRC message. In an embodiment, if segmentation is not allowed according to the configuration from the network, UE will discard the measurements for AI / ML which won't fit in the RRC message.

[0255] In an embodiment, the UE informs the configuration for logging the measurements for AI / ML to the network node such as gNB in NR. In an embodiment, the UE includes the received configuration for logging the measurements for AI / ML along with the measurements for AI / ML. In an embodiment, this may be applicable for the configuration of measurements for AI / ML to be performed in a non-connected state, such as RRC_IDLE and RRC_INACTIVE. The receiving node which receives the measurements for AI / ML may not have the configuration for the measurements at that point. So, it may use the information received from the UE to route the measurements to proper network node.

[0256] In an embodiment, the UE may configure a memory for logging the measurements for AI / ML. The amount of memory may be fixed size or configured by the network, for e.g. from a set of values. In an embodiment, the UE may log N measurements for AI / ML. In an embodiment, if the memory or the number of measurements for AI / ML exceed this capability, the UE may stop logging the measurements for AI / ML. In an embodiment, the UE may also stop performing the measurements for AI / ML. This ensures that the measurements for AI / ML are not lost in case the network is not able to retrieve the measurements immediately.

[0257] In an alternative embodiment, if the memory or the number of measurements for AI / ML exceed this capability, the UE may overwrite the old measurements for AI / ML with new measurements for AI / ML. In an embodiment, the oldest measurement for AI / ML may be overwritten first.

[0258] In an embodiment, the UE releases the measurements logged for AI / ML after a specific time, for e.g. 48 hours. In an embodiment, the UE releases the configuration for logging the measurements for AI / ML after a specific time, for e.g. 48 hours. This ensures that the network doesn't get outdated measurements for AI / ML as such measurements may lead to training errors. In an embodiment, the UE releases the measurements logged for AI / ML once they are reported to the network (once they are sent successfully to network). This avoids duplicate reporting of the logged measurements for AI / ML.

[0259] In an embodiment releasing the measurements configured for AI / ML may include, In an embodiment, the network node provides the UE with configuration for releasing the measurements configured for AI / ML, for e.g. while reconfiguring the UE or in a new RRC message. The configuration may be provided during scenarios like handover. In an embodiment, the UE releases the configuration for the measurements for AI / ML in this case.

[0260] In an embodiment, the network node provides the UE with configuration for releasing the measurements configured for AI / ML, for e.g. while reconfiguring the UE or in a new RRC message. The recon figuration may be the reconfiguration for the handover. UE releases the measurements for AI / ML, if it receives this configuration.

[0261] In an embodiment, upon receiving RRC Release with suspendConfig, the UE releases the configuration for measurement handling for AI / ML. In an embodiment, the UE stores the configuration measurement handling for AI / ML with suspend config and further releases the configurations during RRC Resume procedure (RRC Resume procedure includes the various steps as detailed in 3GPP TS 38.331 section 5.7.3). In an embodiment the aforementioned behavior is applicable when the configuration is applicable for RRC_CONNECTED state.

[0262] In an embodiment, upon receiving RRC Release with suspendConfig, the UE keeps the configuration for measurement handling for AI / ML and performs the measurements / the logging of the measurements for AI / ML. In an embodiment the aforementioned behavior is applicable when the configuration for the measurements for AI / ML is applicable for RRC_INACTIVE state.

[0263] In an embodiment, upon receiving RRC Release without suspendConfig, the UE keeps the configuration for measurement handling for AI / ML and performs the measurements for AI / ML or performs the logging of the measurements for AI / ML. In an embodiment the aforementioned behavior is applicable when the configuration for the measurements for AI / ML is applicable for RRC_IDLE state.

[0264] In an embodiment, the UE keeps the configuration for measurement handling for AI / ML upon performing full configuration. The UE releases all the dedicated configuration except specific configurations such as the configuration for measurement handling for AI / ML upon performing full configuration. In an embodiment, the configuration for measurement handling for AI / ML can be configuration for logging and reporting L1 measurements, positioning measurement or L3 measurements. In an embodiment, the configuration kept during the configuration is the configuration for RRC_INACTIVE mode or RRC_IDLE mode.

[0265] In an embodiment, the UE shall:

[0266] - release / clear all current dedicated radio configurations except for the following:

[0267] - the MCG C-RNTI;

[0268] - the AS security configurations associated with the master key;

[0269] - the SRB1 / SRB2 configurations and DRB / multicast MRB configurations as configured byradioBearerConfig or radioBearerConfig2;

[0270] - the configuration for measurement handling for AI / ML.

[0271] In an embodiment, upon receiving RRC Release without suspendConfig, the UE releases the measurement handling for AI / ML when the configuration is for logging / reporting of Layer 1 measurements.

[0272] In an embodiment, upon receiving RRC Release without suspendConfig, the UE releases the configuration for measurement handling for AI / ML when the configuration is for logging / reporting of Layer 3 measurements.

[0273] In an embodiment, upon receiving RRC Release without suspendConfig, the UE releases the configuration for measurement handling for AI / ML when the configuration is for logging / reporting of positioning measurements.

[0274] In an embodiment, the UE may release the configuration for measurement handling for AI / ML during RRCReestablishment procedure when the purpose of the measurement is related to the logging / reporting of layer 3 measurements and other layer 3 related information for AI / ML.

[0275] In an embodiment, the UE keeps the configuration for measurement handling for AI / ML during RRCReestablishment procedure and resumes the measurements once it receives RRCReestablishment from the network.

[0276] In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration”. Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0277] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure.

[0278] The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a device or system or apparatus proceeded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the device or system or apparatus.

[0279] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[0280] Embodiments described herein provide a method and system for logging measurements for learning models in a communication network. The method comprise comprises transmitting, by a User Equipment (UE), to a network entity, a capability information indicating a capability of the UE and an available memory space in the UE for logging one or more measurements for one or more learning models. Further, the method includes receiving, by the UE, from the network entity, configuration data for logging the one or more measurements based on the transmitted capability information. Furthermore, the method includes logging, by the UE, the one or more measurements in the available memory space, based on the received configuration data.

[0281] Another aspect of the present disclosure includes a system for logging measurements for learning models in a communication network. The system comprises a processor, and a memory coupled with the processor. In an embodiment, the processor is configured to transmit to a network entity, a capability information indicating a capability of the UE and an available memory space in the UE for logging one or more measurements for one or more learning models. Further, the processor is configured to receive, from the network entity, configuration data for logging the one or more measurements based on the transmitted capability information. Furthermore, the processor is configured to log the one or more measurements in the available memory space, based on the received configuration data.

[0282] UE may be configured by the network for reporting measurements for AI / ML. These measurements may be logged and reported to the network. A variation of immediate MDT may be used for the reporting.

[0283] The radio-related measurements may be collected by the network from the UE via immediate Minimization of Drive Test (MDT) in RRC_CONNECTED mode. The measurements for immediate MDT are not logged. Similarly, the measurements may be connected by the network from the UE via logged MDT. The UE may be configured to log the measurements in RRC_IDLE or RRC_INACTIVE states, using messages such as LoggedMeasurementConfiguration. The UE may inform the network about the availability of the logged MDT measurements and the network may retrieve the measurements in RRC_CONNECTED state, for e.g. via UE information procedure. Immediate MDT may be enhanced for data collection for AI / ML, for e.g. OAM-centric data collection for the training of a network-sided model for AI / ML for beam management. Immediate MDT may be enhanced for periodic reporting and event-based reporting. The UE may report multiple instances of logged L1 measurement result from UE to gNB via a RRC message as configured by gNB.

[0284] The present disclosure relates to methods for performing Artificial Intelligence / Machine Learning AI / ML in New Radio. The present disclosure addresses data collection for AI / ML, such as AI / ML for air interface for beam management and other use cases or AI / ML for mobility. The present disclosure addresses User Equipment (UE) capability reporting, handling of various RRC procedures and full configuration along with the configuration for logging / reporting measurements for AI / ML.

[0285] In an embodiment, network entity such as gNB (or any other Radio Access Network Node) configures the UE for reporting the radio related measurements for AI / ML. This may be used for training of a network sided model (i.e. network sided AI / ML models such as RAN centric models or Operation And Maintenance (OAM) centric models).

[0286] In an embodiment, network entity such as gNB (or any other Radio Access Network Node) configures the UE for reporting the radio related measurements for AI / ML using Minimization of Drive Test (MDT) Framework, for e.g. by enhancing immediate MDT framework, or alternatively by enhancing the logged measurement framework.

[0287] Embodiments disclosed herein provides a system and method for handling AI / ML configurations. The method includes data collection for AI / ML models, such as AI / ML for air interface for beam management, positioning and other use cases or AI / ML for mobility. Further, the present disclosure discloses a method of configuring a UE by the network for logging and / or reporting the data.

[0288] FIG. 1 illustrates an exemplary environment 100 for logging measurements for learning models in a communication network 101, according to some embodiments of the present disclosure.

[0289] In an embodiment, the exemplary environment may comprise a User Equipment (UE) 103, and a network entity 105. The UE 103 may transmit to the network entity 105, a capability information indicating a capability of the UE 103 and an available memory space in the UE 103 for logging one or more measurements for one or more learning models 107. In an embodiment, the one or more learning models 107 may reside in the network entity 105, as shown in FIG. 1. Further, without any limitation, the one or more learning models 107 may also reside in the UE and external to the UE 103 and the network entity 105. Further, the UE 103 may receive, from the network entity 105, configuration data for logging the one or more measurements based on the transmitted capability information.

[0290] In an embodiment, the UE 103 may include, but not limited to, a smartphone, a mobile phone, a personal digital assistant, a tablet computer, a tablet computer, a wearable device, a computer, a laptop computer, an Augmented Reality / Virtual Reality (AR / VR) device, Internet Of Things (IoT) device, a camera, any other device, and the combination thereof.

[0291] In an embodiment, the network entity 105 may include, but not limited to, a Next Generation Node B such as gNodeB (gNB), an Evolved Node B (eNB), a Distributed Unit (DU), and a Centralized Unit (CU).

[0292] In an embodiment, the UE 103 and the network entity 105 may communicate each other using the communication network 101, in which the communication network 101 may include, for example, but not limited to, a direct interconnection, a Local Area Network (LAN), a Wide Area Network (WAN), a wireless network, a point-to-point network, or another configuration.

[0293] In an embodiment, the configuration data may comprise at least one of, for example, but not limited to, frequency requirement for the one or more measurements, a network area for logging the one or more measurements, a reference signal configuration for logging the one or more measurements, list of neighbouring cells associated with the UE 103, and Radio Resource Control (RRC) states of the UE 103.

[0294] Furthermore, the UE 103 may log the one or more measurements in the available memory space of the UE 103, based on the received configuration data.

[0295] FIG. 2 illustrates a block diagram of the User Equipment (UE) 103, according to some embodiments of the present disclosure.

[0296] As shown in FIG. 2, in an embodiment, the UE 103 may include an I / O interface 203, a processor 205 and a memory 207 storing instructions, executable by the processor 205, which, on execution, may cause the UE 103 to log the one or more measurements for the one or more learning models 107. In an embodiment, the memory 207 may include data 209 and one or more modules 211. In an embodiment, each of the one or more modules 211 may be a hardware unit which may be outside the memory 207 and coupled with the UE 103.

[0297] In an embodiment, the data 209 may include for example, a capability information 213, a configuration data 215, and one or more measurements 217. In an embodiment, the capability information 213 may include indicating a capability of the UE 103 and an available memory space in the UE 103 for logging one or more measurements for one or more learning models 107. Further, the configuration data 215 comprises at least one of for example, but not limited to, frequency requirement for the one or more measurements 217, a network area for logging the one or more measurements 217, a reference signal configuration for logging the one or more measurements 217, list of neighbouring cells associated with the UE 103, and Radio Resource Control (RRC) states of the UE 103.

[0298] Further in an embodiment, the one or more modules 211 may include a capability information transmitting module 219, a configuration data receiving module 221, and a logging module 223.

[0299] In an embodiment, the capability information transmitting module 219 may be configured to transmit to the network entity 105, a capability information 213 indicating the capability of the UE 103 and the available memory space in the UE 103 for logging the one or more measurements 217 for one or more learning models 107.

[0300] In an embodiment, the configuration data receiving module 221 may be configured to receive, from the network entity 105, the configuration data 215 for logging the one or more measurements 217 based on the transmitted capability information 213.

[0301] In an embodiment, the logging module 223 may be configured to log the one or more measurements 217 in the available memory space, based on the received configuration data 215. In an embodiment, the logging module 223 may be configured to report the one or more logged measurements 217 in the available memory space to the communication network 101. In an embodiment, the available memory space may correspond to a sub-set of a total space available in the memory 207. In an embodiment, the logging module 223 may monitor memory utilization during the logging of the one or more measurements 217. Further, the logging module 223 may terminate the logging of the one or more measurements 217, when the memory utilization has reached the available memory space in the UE 103, in which the one or more measurements 217 may be used by the one or more learning models 107 present in the network entity 105, for performing network related operations.

[0302] FIG. 3 illustrates a block diagram of the network entity 105, according to some embodiments of the present disclosure.

[0303] As shown in FIG. 3, in an embodiment, the network entity 105 may include an I / O interface 303, a processor 305 and a memory 307 storing instructions, executable by the processor 305, which, on execution, may cause the network entity 105 to configure the User Equipment (UE) 103 for logging measurements (also hereafter referred as one or more measurements 217) in a communication network. In an embodiment, the memory 307 may include data 309 and one or more modules 311. In an embodiment, each of the one or more modules 311 may be a hardware unit which may be outside the memory 307 and coupled with the network entity 105.

[0304] In an embodiment, the data 309 may include for example, the capability information 213, the configuration data 215, and the one or more measurements 217.

[0305] Further in an embodiment, the one or more modules 311 may include a capability information receiving module 313, a configuration data determining module 315, and a configuration module 317.

[0306] In an embodiment, the capability information receiving module 313 may be configured to receive from the UE 103, a capability information 213 indicating a capability of the UE 103 and the available memory space in the UE 103 for logging one or more measurements 217 for one or more learning models 107.

[0307] In an embodiment, the configuration data determining module 315 may be configured to determine the configuration data 215 for logging the one or more measurements 217, based on the received capability information 213.

[0308] In an embodiment, the configuration module 317 may be configured to configure the UE 103 for logging the one or more measurements 217, by transmitting the determined configuration data 215 to the UE 103.

[0309] In an embodiment, the network entity 105 may receive the one or more logged measurements (also hereafter referred as one or more logged measurements 217) for the one or more learning models 107, from the UE 103, based on the transmitted configuration data 215. Further, the network entity 105 may train the one or more learning models 107, using the received one or more logged measurements 217.

[0310] In an embodiment, the network entity 105 may receive a ReconfigurationComplete message from the UE 103, in which the UE 103 may report availability of the one or more logged measurements 217 to the network entity 105, by including availability indication in the ReconfigurationComplete message. Further, the network entity 105 may transmit a request to receive the one or more logged measurements 217 from the UE 103, upon receiving the ReconfigurationComplete message. Furthermore, the network entity 105 may receive the one or more logged measurements 217 from the UE 103, based on the transmitted request.

[0311] FIGs. 4A-4B illustrates a scenario of configuration for measurement handling for the one or more learning models 107, according to some embodiments of the present disclosure.

[0312] In an embodiment, as shown in FIG. 4A, the network entity 105 (also hereafter referred as gNB 105) may provide the configuration data 215 for measurement handling for the one or more learning models 107, to the UE 105, while resuming the UE 103, in messages such as, for example, but not limited to, RRCResume. In an embodiment the network entity 105 may request the UE 103 to apply the stored configuration for measurement handling for the one or more learning models 105. In an embodiment, the configuration data 215 for measurement handling for the one or more learning models 107, for one or more of logging / reporting of at least, for example, but not limited to, layer 1 (L1) measurements, Layer 3 (L3) measurements, and location measurements.

[0313] In an embodiment, the configuration data 215 for measurement handling for the one or more learning models 107 may inform the UE 103 how to perform the one or more measurements 217 such as, for example, but not limited to, periodicity of the one or more measurements 217. The network entity 105 may inform the UE 103 how often the UE 103 may need to perform the one or more measurements 217 for the one or more learning models 107. Further, in an embodiment, the configuration data 215 may inform the UE 103 the area where the one or more measurements 217 may be logged and reported. For example, the area may comprise, at least, for example, but not limited to, one or more of Public Land Mobile Network (PLMN) information such as PLMN identity, Non-Public Network (NPN) information such as Standalone Non-Public Network (SNPN) identity or Public Network Integrated Non-Public Network (PNI-NPN) identity, a list of cells such as, for example, but not limited to, Cell Global Identity (CGI), Physical Cell Identity (PCI) and New Radio Absolute Radio Frequency Channel Number (NR-ARFCN) combination, a registration area identifier, a tracking area related information such as tracking area code, and a tracking area identity list.

[0314] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged the one or more measurements 217 for the one or more learning models 107. In other words, the UE 103 may inform availability of the one or more logged measurements 217 for the one or more learning models 107) while transitioning from a non-connected (RRC_IDLE / RRC_INACTIVE) state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCResumeComplete, as shown in FIG. 4A.

[0315] In an embodiment, the availability may be reported for one or more of, for example, but not limited to, L1 measurements, L3 measurements, and location measurements logged for the one or more learning models 107. In an embodiment, a separate bit may be used for indicating availability of each type of the one or more measurements 217 such as, for example, but not limited to, L1 measurements, L3 measurements, and location measurements.

[0316] In an embodiment, the UE 103 may transmit to the network entity 105, the one or more logged measurements 217 for the one or more learning models 107 in, for example, but not limited to, RRCResumeComplete message as shown in FIG. 4A, and RRCReconfigurationComplete message as shown in FIG. 4B.

[0317] In an embodiment, the network entity 105 such as, for example, but not limited to, a gNB may configure the UE 103 for reporting the one or more logged measurements 217 such, as for example, but not limited to, radio related measurements and related information for the one or more learning models 107. The one or more logged measurements 217 may be used for training of a network sided model such as, for example, but not limited to, Radio Access Network (RAN) centric models, and Operation And Maintenance (OAM) centric models. In an embodiment, the network entity 105 may configure the UE 103 for reporting the radio related measurements and related information for the one or more learning models 107 using Minimisation of Drive Test (MDT) Framework.

[0318] In an embodiment, the configuration for measurement handling for the one or more learning models 107 such as, for example, but not limited to, an Artificial Intelligence / Machine Learning (AI / ML) (also hereafter referred as AI / ML 107) may comprise one or more of the following:

[0319] - Configuration for logging the one or more measurements 217 for the AI / ML 107.

[0320] - Configuration for reporting the one or more measurements 217 for the AI / ML 107.

[0321] - Configuration for logging and reporting the one or more measurements 217 for the AI / ML 107.

[0322] - Configuration for logging the layer 1 measurements such as, for example, but not limited to, Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) for the AI / ML 107.

[0323] - Configuration for reporting the layer 1 measurements such as, for example, but not limited to, Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) for the AI / ML 107.

[0324] - Configuration for logging and reporting the layer 1 such as, for example, but not limited to, Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) for the AI / ML 107.

[0325] - Configuration for logging the layer 3 measurements such as, for example, but not limited to, Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) for the AI / ML 107.

[0326] - Configuration for reporting the layer 3 measurements such as, for example, but not limited to, Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) for the AI / ML 107.

[0327] - Configuration for logging and reporting the layer 3 measurements such as, for example, but not limited to, Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) for the AI / ML 107.

[0328] - Configuration for logging the positioning related measurements for the AI / ML 107.

[0329] - Configuration for reporting the positioning related measurements for AI / ML 107.

[0330] - Configuration for logging and reporting the positioning related measurements for AI / ML 107.

[0331] - Configuration for logging the location related information for AI / ML 107.

[0332] - Configuration for reporting the location related information for AI / ML 107.

[0333] - Configuration for logging and reporting the location related information for AI / ML 107.

[0334] In an embodiment, the network entity 105 such as the gNB 105 may provide the configuration data 215 for measurement handling for the one or more learning models 107 (hereafter also referred as the AI / ML 107) based on the inputs from entities such as, for example, but not limited to, OAM or Transmission Convergence Entity (TCE).

[0335] In an embodiment, the capabilities for the one or more learning models 107 may be reported to the network entity 105 in RRC messages such as, for example, but not limited to, New Radio (NR) UECapabilityInformation in response to a message such as NR UECapabilityEnquiry. The received capabilities for the one or more learning models 107 may be used by the network entity 105 for configuring the UE 103 for performing one or more measurements 217 for the one or more learning models 107.

[0336] In an embodiment, some of the configuration for measurement handling for the AI / ML 107 may be applicable in connected state (RRC_CONNECTED) (for e.g. the configuration for logging and / or reporting the layer1 measurements for AI / ML 107, the configuration for logging and / or reporting the layer3 measurements for the AI / ML 107, the configuration for logging and / or reporting the location and / or positioning related measurements for the AI / ML 107 etc.). In an embodiment, some of the configuration for measurement handling for AI / ML 107 may be applicable in inactive state (such as RRC_INACTIVE) (for e.g. the configuration for logging / reporting the layer3 measurements for AI / ML 107, the configuration for logging and / or reporting the location and / or positioning related measurements for the AI / ML 107 etc.).

[0337] In an embodiment, some of the configuration for measurement handling for the AI / ML 107 may be applicable in idle state (such as RRC_IDLE) (for e.g. the configuration for logging / reporting the layer3 measurements for the AI / ML 107, the configuration for logging and / or reporting the location and / or positioning related measurements for the AI / ML 107 etc.). In an embodiment, the network entity 105 may provide the configuration data 215 for the measurement handling for the AI / ML 105 to the UE 103 while reconfiguring the UE 103, in messages such as for example, but not limited to, NR RRCReconfiguration.

[0338] In an embodiment, the configuration data 215 for measurement handling for the AI / ML 107 may inform the UE 103 how to perform the one or more measurements 217 including the frequency where the UE 103 need to perform the one or more measurements 217, sub carrier spacing, reference signal such as, for example, but not limited to but not limited to Synchronization Signal (SS), Physical Broadcast Channel (PBCH), Demodulation Reference Signal (DMRS), Synchronization Signal Block (SSB), Channel State Information Reference Signal (CSIRS), measurement period, measurement timing configuration, measurement bandwidth, frequency domain parameters, applicable frequency range, Radio Access Technology (RAT), coverage area, geographic area, the list of cells where the one or more measurements 217 may be performed, and the RRC states where the one or more measurements 217 may be performed.

[0339] In an embodiment, the configuration data 215 for measurement handling for the AI / ML 107 may inform the UE 103 how to report the one or more measurements 217 to the network entity 105, i.e. whether the one or more measurements 217 need to be reported upon satisfaction of a measurement event (e.g. the measurement events used for other purpose) or based on a request from the network entity 105, or based on such as for example, but not limited to, a time period, thresholds including performance thresholds, accuracy thresholds, reliability thresholds, number of reported cells, and number of reported Reference Signal (RS) per Cell.

[0340] FIG. 5 illustrates a scenario representation of reporting the one or more logged measurements 217 for the one or more learning models 107, according to some embodiments of the present disclosure.

[0341] In an embodiment, the network entity 105 may retrieve the one or more logged measurements 217 from the UE 103, by sending an RRC message such as UE 103 Information Request and in the response message, the UE 103 may include the one or more logged measurements 217 for the one or more learning models 107.

[0342] In an embodiment, the UE 103 may report the one or more logged measurements 217 for the one or more learning models 107, in RRC messages such as MeasurementReport message. In an embodiment the one or more measurements 217 for the one or more learning models 107 may be sent in Signaling Radio Bearer 2 (SRB2). In an embodiment, while reporting the one or more logged measurements 217 for the one or more learning models 107, the UE 103 may include the identifier corresponding for the one or more measurements 217 for the one or more learning models 107 and the measurement results. In an embodiment, the UE 103 may report the one or more logged measurements 217 for the one or more learning models 107 in RRC messages.

[0343] In an embodiment the one or more measurements 217 for the one or more learning models 107 may be sent in Signaling Radio Bearer 4 (SRB4) and may be sent in MeasurementReportAppLayer. In an embodiment, the UE 103 may send the one or more logged measurements 217 for the one or more learning models 107 in multiple segments of a RRC message. Further, the network entity 105 may configure the UE 103 whether the UE 103 is allowed to send multiple segments for the one or more logged measurements 217 for the one or more learning models 107 on the configuration.

[0344] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged the one or more measurements 217 for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) while transitioning from a RRC_IDLE state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCSetupComplete.

[0345] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged the one or more measurements 217 for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) during RRCReestablishment procedure. The UE 103 may include a single bit of information such as an enumerated or a flag in RRCReestablishmentComplete.

[0346] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged the one or more measurements 217 for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) during RRCReconfiguration procedure. The UE 103 may include a single bit of information such as an enumerated or a flag in RRCReconfigurationComplete.

[0347] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged L3 measurements for AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) while transitioning from RRC_IDLE state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCSetupComplete.

[0348] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged L3 measurements for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) during RRCReestablishment procedure. The UE 103 may include a single bit of information such as an enumerated or a flag in RRCReestablishmentComplete.

[0349] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged L3 measurements for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) during RRCReconfiguration procedure. The UE 103 may include a single bit of information such as an enumerated or a flag in RRCReconfigurationComplete.

[0350] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged L1 measurements for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) while transitioning from RRC_IDLE state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCSetupComplete.

[0351] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged L1 measurements for AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) during RRCReestablishment procedure. The UE 103 may include a single bit of information such as an enumerated or a flag in RRCReestablishmentComplete.

[0352] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged L1 measurements for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) during RRCReconfiguration procedure. The UE 103 may include a single bit of information such as an enumerated or a flag in RRCReconfigurationComplete.

[0353] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged location measurements for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) while transitioning from RRC_IDLE state to connected (RRC_CONNECTED) state. In an embodiment, this may be achieved by including a single bit of information such as an enumerated or a flag in messages such as RRCSetupComplete.

[0354] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged location measurements for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) during RRCReestablishment procedure. The UE 103 may include a single bit of information such as an enumerated or a flag in RRCReestablishmentComplete.

[0355] In an embodiment, the UE 103 may inform the network entity 105 whether the UE 103 has logged location measurements for the AI / ML 107 (i.e. availability of the one or more logged measurements 217 for the AI / ML 107) during RRCReconfiguration procedure. The UE 103 may include a single bit of information such as an enumerated or a flag in RRCReconfigurationComplete.

[0356] In an embodiment, the network entity 105 may retrieve the logged measurements from the UE 103 based on the received information.

[0357] FIGs. 6A-6C illustrates a flowchart representation of releasing the configuration data 215 for measuring handling for one or more learning models 107, according to some embodiments of the present disclosure.

[0358] In an embodiment, the UE 103 may release the one or more logged measurements 217, after the pre-defined threshold time period, once the one or more measurements 217 are logged in the available memory space of the UE 103. For example, the UE 103 may release the one or more measurements 217 logged for the AI / ML 107 after the pre-defined threshold time period such as for example, but not limited to, 48 hours. In an embodiment, the UE 103 may release the configuration data 215 for logging the one or more measurements 217 for the AI / ML 107 after the pre-defined threshold time period such as for example, but not limited to, 48 hours. In an alternative embodiment, the UE 103 may release the one or more measurements 217 logged for the AI / ML 107 once the one or more logged measurements 217 are reported to the network entity 105.

[0359] In an embodiment, the network entity 105 may provide the configuration data 215 for the UE 103 for releasing the one or more measurements 217 configured for the AI / ML 107, while reconfiguring the UE 103 or in a new RRC message. In an embodiment, the configuration data 215 may be provided during handover. The UE 103 may release the one or more measurements 217 based on the configuration data 215.

[0360] In an embodiment, as shown in FIG. 6A, at block 601, the UE 103 may receive a Radio Resource Control (RRC) release message comprising a suspend configuration. Further, at block 602, the UE 103 may release the configuration data 215, based on the received RRC release message.

[0361] In an embodiment, upon receiving RRC Release with suspendConfig, the UE 103 may release the configuration data 215 for measurement handling for the AI / ML 107. In an embodiment, the UE 103 may store the configuration data 215 for measurement handling for the AI / ML 107 with suspend config and may further release the configuration data 215 during RRC Resume procedure. In an embodiment the aforementioned behaviour may be applicable, for example, but not limited to, when the configuration data 215 is applicable for RRC_CONNECTED state.

[0362] In an embodiment, upon receiving the RRC Release with suspendConfig, the UE 103 may keep the configuration data 215 for measurement handling for the AI / ML 107 and may perform the logging of the one or more measurements 217 for the AI / ML 107. In an embodiment the aforementioned behaviour may be applicable, for example, but not limited to, when the configuration data 215 is applicable for RRC_INACTIVE state.

[0363] In an embodiment, the UE 103 may also receive RRC Release without suspendConfig, upon which the UE 103 may keep the configuration data 215 for measurement handling for the AI / ML 107. Further, the UE 103 may perform the logging of the one or more measurements 217 for the AI / ML 107. In an embodiment the aforementioned behaviour may be applicable, for example, but not limited to, when the configuration data 215 is applicable for RRC_IDLE state.

[0364] In an embodiment, as shown in FIG. 6B, the UE 103 may keep the configuration data 215 for measurement handling for the AI / ML 107 upon performing full configuration. In an embodiment, the full configuration corresponds to a setup where the network entity 105 provides the UE 103 with all necessary parameters to establish and maintain communication with the UE 103. That is, at block 603, the UE 103 may receive a RRCReconfiguration message including full configuration. Further, at block 604, the UE 103 may release all the dedicated configuration except specific configurations such as the configuration data 215 for measurement handling for AI / ML 107 upon performing full configuration. In an embodiment, the configuration data 215 for measurement handling for AI / ML 107 may include, for example, but not limited to, configuration for logging and reporting L1 measurements, positioning measurements, and L3 measurements. In an embodiment, the dedicated configurations corresponds to one or more configurations not received either via at least, for example, but not limited to, SRB1 within mrdc-SecondaryCellGroup, and via SRB3. In other words, the dedicated configuration corresponds to settings or resources that are customized for a particular UE or service.

[0365] In an embodiment, the UE 103 may release all current dedicated radio configurations except the following:

[0366] - Master Cell Group Cell Radio Network Temporary Identifier (MCG C-RNTI);

[0367] - the Access Stratum (AS) security configurations associated with the master key;

[0368] - the SRB1 / SRB2 configurations and Data Radio Bearer / Multicast Radio Bearer (DRB / multicast MRB) configurations as configured by radioBearerConfig or radioBearerConfig2;

[0369] - the configuration for measurement handling for AI / ML 107.

[0370] In an embodiment, upon receiving the RRC Release without suspendConfig, the UE 103 may release the measurement handling for the AI / ML 107 when the configuration data 215 is for logging / reporting of Layer 1 measurements.

[0371] In an embodiment, upon receiving RRC Release without suspendConfig, the UE 103 may release the configuration data 215 for measurement handling for the AI / ML 107 when the configuration is for logging / reporting of Layer 3 measurements.

[0372] In an embodiment, upon receiving RRC Release without suspendConfig, the UE 103 may release the configuration data 215 for measurement handling for the AI / ML 105 when the configuration data 215 is for logging / reporting of positioning measurements.

[0373] In an embodiment, the configuration data 215 for measurement handling for the one or more learning models 107 may include measurement identifier for each of the one or more measurements 217 configured for the one or more learning models 107. Further, in an embodiment, the configuration data 215 for measurement handling for the one or more learning models 107 may include measurement object information such as, for example, but not limited to, a measurement object identifier for each of the one or more measurements 217. This configuration may be included in RRCRelease message. In an embodiment, the configuration 215 may be included in RRCRelease message within suspendConfig for the one or more measurements 217 for the AI / ML 107 in RRC_INACTIVE mode.

[0374] In an embodiment, as shown in FIG. 6C, at block 605, the UE 103 may determine, an initiation of RRCReestablishment procedure during logging of the one or more measurements 217. Further, at block 606, the UE 103 may release the configuration data 215, based on the determined initiation.

[0375] In an embodiment, the UE 103 may release the configuration data 215 for measurement handling for the AI / ML 107 during RRCReestablishment procedure when the purpose of the measurement is related to the, for example, but not limited to, logging / reporting of layer 3 measurements and other layer related information for the AI / ML 107.

[0376] In an embodiment, the UE 103 may keep the configuration data 215 for measurement handling for the AI / ML 107 during RRCReestablishment procedure and may resume the one or more measurements 217 once the UE 103 receives RRCReestablishment from the network entity 105.

[0377] In an embodiment, the UE 103 may release the configuration data 215 for measurement handling for the AI / ML 107 during RRCReestablishment procedure. In an embodiment, the release may be performed following cell selection while, for example, but not limited to, NR timer T311 is running. Alternatively, the release may be performed upon the reception of RRC Reestablishment message.

[0378] In an embodiment, the UE 103 may keep the configuration data 215 for measurement handling for the AI / ML 107 following cell selection while NR timer T311 is running during RRCReestablishment procedure if the UE 103 may be configured for attempting conditional reconfiguration when the selected cell is a Conditional Handover (CHO) candidate or attempting LTM cell switch when the selected cell is an LTM candidate cell. If the attempt to perform CHO or LTM cell switch, the UE 103 may release the configuration data 215.

[0379] In an embodiment, UE 103 may maintain the configuration data 215 for measurement handling for the AI / ML 107 received from the network entity 105. For example, if the UE 103 which may be configured with the configuration data 215 for measurement handling for the AI / ML 107 receives a RRC message without including the configuration for measurement handling for AI / ML 107, the UE 103 may perform / log / report the one or more measurements 217 with already received configuration.

[0380] In an embodiment, the UE 103 may release the configuration data 215 for measurement handling for the AI / ML 107 upon performing a handover. In an embodiment, this may be applicable for example, but not limited to, the configuration data 215 for logging / reporting the layer 1 measurements. In an embodiment, the UE 103 may also discard any stored one or more measurements 217 for the AI / ML 107 purpose upon performing the handover.

[0381] In an embodiment, the UE 103 may release the configuration data 215 for measurement handling for the AI / ML 107 upon performing handover, if the UE 103 is configured to perform measurements in a specific cell such as, for example, but not limited to, a source cell. This may ensure that the UE 103 may save the memory 207 for storing the configuration data 215.

[0382] Further, in an embodiment, the UE 103 may avoid re-performing the one or more measurements 217 in the new serving cell after handover. For example, if the UE 103 again moves back to the old source cell (for e.g. after a handover back to the source cell), the UE 103 may again perform the one or more measurements 217. This may save the signalling, as there may be no need for a providing the same configuration in the cell.

[0383] In an embodiment, the UE 103 may perform the one or more measurements 217 for the AI / ML 107 in a different frequency or in the same frequency but requiring gaps, using measurement gaps, if they are configured.

[0384] In an embodiment, the UE 103 may release the configuration data 215 for measurement handling for the AI / ML 107 during RRCReestablishment procedure when the purpose of the one or more measurements 217 is related to the logging / reporting of layer 1 measurements or positioning measurements. In an embodiment, this release may be performed following cell selection while NR timer T311 (or equivalent timer in other RAT) is running.

[0385] In an embodiment, the UE 103 may keep the configuration data 215 for measurement handling for the AI / ML 107 upon LTM cell switch. In an embodiment, the UE 103 may keep the configuration data 215 for measurement handling for AI / ML upon LTM cell switch for Master Cell Group (MCG). In yet another embodiment, the UE 103 may keep the configuration data 215 for measurement handling for the AI / ML 107 upon LTM cell switch for Slave Cell Group (SCG).

[0386] In an embodiment, upon the indication by lower layers that an LTM cell switch procedure is triggered, or upon performing LTM cell switch following cell selection process performed while timer T311 is running, and the LTM cell switch is triggered on MCG, the UE 103 may release all current dedicated and common radio configurations which have not been received either via SRB1 within mrdc-SecondaryCellGroup. Further, the UE 103 may release all current dedicated and common radio configurations which have not been received viaSRB3 except a set of specific configurations comprising configuration data 215 for measurement handling for AI / ML 107.

[0387] FIG. 7 illustrates a flowchart representation of a method 700 of logging one or more measurements 217 for the one or more learning models 107 in a communication network 101, according to some embodiments of the present disclosure.

[0388] At step 701, the method 700 includes transmitting, by the User Equipment (UE) 103, to the network entity 105, a capability information 213 indicating a capability of the UE 103 and an available memory space in the UE 103 for logging one or more measurements 217 for one or more learning models 107.

[0389] At step 702, the method 700 includes receiving, by the UE 103, from the network entity 105, configuration data 215 for logging the one or more measurements 217 based on the transmitted capability information 213.

[0390] At step 703, the method 700 includes logging, by the UE 103, the one or more measurements 217 in the available memory space, based on the received configuration data 215. In an embodiment, the logging module 223 may be configured to log the one or more measurements 217 in the available memory space, based on the received configuration data 215. In an embodiment, the available memory space may correspond to a sub-set of a total space available in the memory 207. In an embodiment, the logging module 223 may monitor memory 207 utilization during the logging of the one or more measurements 217. Further, the logging module 223 may terminate the logging of the one or more measurements 217, when the memory 207 utilization has reached the available memory space in the UE 103, in which the one or more measurements 217 may be used by the one or more learning models 107 present in the network entity 105, for performing network related operations.

[0391] In an embodiment, the UE 103 may report to the network entity 105, availability of the one or more logged measurements 217 by including availability indication in a ReconfigurationComplete message. Further, the UE 103 may receive, from the network entity 105, a request to transmit the one or more logged measurements 217. Furthermore, the UE 103 may transmit, the one or more logged measurements 217 to the network entity 105, based on the received request.

[0392] FIG. 8 illustrates a flowchart representation of a method 800 of configuring the User Equipment (UE) 103 for logging one or more measurements 217 in the communication network 101, according to some embodiments of the present disclosure.

[0393] At step 801, the method 800 includes receiving, by the network entity 105, from the UE 103, a capability information 213 indicating a capability of the UE 103 and an available memory space in the UE 103 for logging one or more measurements 217 for the one or more learning models 107.

[0394] At step 802, the method 800 includes determining, by the network entity 105, the configuration data 215 for logging the one or more measurements 217, based on the received capability information 213.

[0395] At step 803, the method 800 includes configuring, by the network entity 105, the UE 103 for logging the one or more measurements 217, by transmitting the determined configuration data to the UE 103.

[0396] FIG. 9 is a block diagram of a terminal or user equipment (UE) 900 according to an embodiment of the disclosure.

[0397] The terminal is an electronic device capable of wireless communication, may include a User Equipment (UE), a portable phone, a smartphone, a tablet, an Internet of things (IoT) device, etc., having various form factors, and may perform wireless communication with a base station (BS) through a wireless channel.

[0398] Referring to FIG. 9, the UE 900 may include at least one transceiver (hereinafter, referred to as simply “transceiver”) 901, at least one processor (hereinafter, referred to as simply “processor”) 902, and at least one memory (hereinafter, referred to as simply “memory”) 903. According to at least one or a combination of methods corresponding to the embodiments described in the present disclosure, the transceiver 901, the processor 902, and the memory 903 of the UE 900 may operate. However, components of the UE 900 are not limited to the exemplary components illustrated in FIG. 9. In another embodiment, the UE 900 may further include additional components in addition to the above-mentioned components, or some components may be omitted. Further, in some embodiments, any combination of the transceiver 901, the processor 902, or the memory 903 may be integrated in the form of one component.

[0399] The transceiver 901 may be a communication circuit or communication circuitry that enables the UE 900 to perform wireless communication with a node or an entity of a network. For example, the transceiver 901 may enable the UE 900 to transmit or receive a signal to or from a BS through cellular communication, or to transmit or receive a signal to or from another UE through cellular communication. For example, the transceiver 901 may support at least one of various cellular communication technologies including 3rd generation (3G), 4th generation (4G), long term evolution (LTE), 5th generation (5G) NR, 6th generation (6G), and various cellular wireless communication technologies supported by the transceiver (901) may include all subsequent generations of evolved wireless communications.

[0400] According to an embodiment, the UE 900 may include a plurality of transceivers. For example, in the case of supporting evolved-universal terrestrial radio access-new radio (E-UTRA-NR) sual connectivity (EN-DC), the UE 900 may include a first transceiver supporting the 4G LTE wireless communication and a second transceiver supporting the 5G NR wireless communication. According to another embodiment, in the case of supporting NR-dual connectivity (NR-DC), the UE 900 may include a plurality of transceivers supporting the 5G NR wireless communication. According to still another embodiment, in the case of supporting near field wireless communication, the UE 900 may separately include a transceiver supporting at least one standard in the group of wireless communication protocol standards as defined in the protocol standards for Bluetooth®, wireless local area network (WLAN) network (including institute of electrical and electronics engineers (IEEE) 802.11-2016 standard or its amendments, e.g., 802.11ah, 802.11ad, 802.11ay, 802.11ax, 802.11az, 802.11ba, and 802.11be, without being limited thereto).

[0401] According to an embodiment, the transceiver 901 may include various circuit structures used to transmit or receive signals to or from a BS through a wireless channel. The signals may include control information and data. For example, the transceiver 901 may include a radio frequency (RF) transmitter for up-converting and amplifying the frequency of a transmitted signal and an RF receiver for low-noise-amplifying a received signal and down-converting the frequency thereof. The transceiver 901 may output a signal received through a wireless channel to the processor 902 and may transmit, through a wireless channel, a signal output from the processor 902.

[0402] The processor 902 may control general operations of the UE 900 according to embodiments of the disclosure. The processor 902 may be implemented by one or more integrated circuit (or circuitry) (IC) chips and may execute various data processings. The processor 902 may include at least one electric circuit, and may execute instructions (or a program, codes, data, etc.) stored in the memory 903, individually, collectively or in any combination thereof. Further, the processor 902 may include a single-core processor or multi-core processor, and may include a processor assembly including a plurality of processing circuits (circuitry) according to a specific implementation scheme.

[0403] The processor 902 may be electrically, operatively, or communicatively coupled to the transceiver 901 to control the transceiver 901.

[0404] The processor 902 may include at least one processor (or processing circuitry), and the at least one processor may perform the following operations individually, collectively or in any combination thereof. For example, the processor 902 may include a communication processor (CP) configured to control communication operations and an application processor (AP) configured to control execution of an upper layer (for example, an application layer). In a specific embodiment, at least a part of the processor 902 may be included in one chip and the other part of the processor 902 may be included in another chip. Otherwise, at least one processor may be included in another component, for example, the transceiver 901 or the memory 903.

[0405] The processor 902 may perform or control or cause an operation of the UE 900 for executing at least one or a combination of methods according to embodiments of the disclosure. For example, the processor 902 may control operations of the UE 900 for processing a downlink signal received from a BS or generating and transmitting an uplink signal to a BS. To this end, the processor 902 may execute a computer program, codes, or instructions stored in the memory 903, so as to control other components of the UE 900 to enable execution of various operations.

[0406] The memory 903 corresponds to a hardware storage device capable of temporarily or permanently storing information and may include one or more storage media. For example, the memory 903 may include a memory assembly including one or more storage media. For example, the one or more storage media may include permanent memory, such as a hard drive, flash memory, or read-only memory (ROM), semipermanent memory, such as random access memory (RAM), cache memory, or a combination thereof.

[0407] The memory 903 may be electrically, operatively, or communicatively coupled to the processor 902 and may be accessed by the processor 902.

[0408] The memory 903 may store a computer program, codes, or instructions executable by the processor 902. According to an embodiment, a computer program, codes, or instructions executable by the processor 902 may be either stored in a single memory device or separated and distributedly stored in two or more memory devices. By executing the instructions stored in the memory 903, the processor 902 may perform various functions according to an embodiment of the disclosure.

[0409] According to an embodiment of the disclosure, operations of the UE 900 may be caused to be performed based on execution of instructions (or a computer program or codes) stored in the memory 903 by at least one processor (or processing circuitry) configured to execute the same individually, collectively, or in any combination thereof, based on processing circuitry that is not configured to execute instructions, and / or based on components of processing circuitry that is not configured to execute instructions.

[0410] FIG. 10 is a block diagram of a base station (BS) 1000 according to an embodiment of the disclosure.

[0411] The BS 1000 may perform wireless communication with at least one user equipment (UE) located within the area of the BS 1000 through a wireless channel.

[0412] Referring to FIG. 10, the BS 1000 may include at least one transceiver (hereinafter, referred to as simply “transceiver”) 1001, at least one processor (hereinafter, referred to as simply “processor”) 1002, and at least one memory (hereinafter, referred to as simply “memory”) 1003. According to at least one or a combination of methods corresponding to the embodiments described in the present disclosure, the transceiver 1001, the processor 1002, and the memory 1003 of the BS 1000 may operate. However, components of the BS 1000 are not limited to the exemplary components illustrated in FIG. 10. In another embodiment, the BS 1000 may further include additional components in addition to the above-mentioned components, or some components may be omitted. Further, in some embodiments, any combination of the transceiver 1001, the processor 1002, or the memory 1003 may be integrated in the form of one component.

[0413] The transceiver 1001 may be a communication circuit or communication circuitry that enables the BS 1000 to perform wireless communication with a node or an entity of a network. For example, the transceiver 1001 may enable the BS 1000 to transmit or receive a signal to or from the UE 900 through cellular communication, or to transmit or receive a signal to or from another network entity through wireless communication. For example, the transceiver 1001 may support various cellular communication technologies including 3rd generation (3G), 4th generation (4G), long term evolution (LTE), 5th generation (5G) NR, 6th generation (6G), and various cellular wireless communication technologies supported by the transceiver (1001) may include all subsequent generations of evolved wireless communications. According to an embodiment, the transceiver 1001 may include various circuit structures used to transmit or receive signals to or from a UE through a wireless channel. The signals may include control information and data. For example, the transceiver 1001 may include a radio frequency (RF) transmitter for up-converting and amplifying the frequency of a transmitted signal and an RF receiver for low-noise-amplifying a received signal and down-converting the frequency thereof. The transceiver 1001 may output a signal received through a wireless channel to the processor 1002 and may transmit, through a wireless channel, a signal output from the processor 1002.

[0414] Meanwhile, according to an embodiment of the present disclosure, the BS 1000 may perform communication with a node or an entity of a network through wired or wireless communication. For example, the BS 1000 may perform wired or wireless communication with an adjacent BS, or a node or an entity of a core network through a backhaul network. Although not illustrated in FIG. 10, when the BS 1000 performs wired communication, the BS 1000 may further include a separate network interface for wired communication in addition to the transceiver 1001. The network interface may be referred to as network interface circuitry or communication interface circuitry.

[0415] The processor 1002 may control general operations of the BS 1000 according to embodiments of the disclosure. The processor 1002 may be implemented by one or more integrated circuit (or circuitry) (IC) chips and may execute various data processings. The processor 1002 may include at least one electric circuit, and may execute instructions (or a program, codes, data, etc.) stored in the memory 1003, individually, collectively or in any combination thereof. Further, the processor 1002 may include a single-core processor or multi-core processor, and may include a processor assembly including a plurality of processing circuits (circuitry) according to a specific implementation scheme.

[0416] The processor 1002 may be electrically, operatively, or communicatively coupled to the transceiver 1001 to control the transceiver 1001.

[0417] The processor 1002 may include at least one processor (or processing circuitry), and the at least one processor may perform the following operations individually, collectively or in any combination thereof. In a specific embodiment, at least a part of the processor 1002 may be included in one chip and the other part of the processor 1002 may be included in another chip. Otherwise, at least one processor may be included in another component, for example, the transceiver 1001 or the memory 1003.

[0418] The processor 1002 may perform or control or cause an operation of the BS 1000 for executing at least one or a combination of methods according to embodiments of the disclosure. For example, the processor 1002 may control operations of the BS 1000 for generating and transmitting a downlink signal to a UE or processing an uplink signal received from a UE. Otherwise, the BS 1000 may transmit or receive a signal to or from a neighboring BS, transfer a signal received from a UE to an upper node of the network, or transmit a signal transferred from an upper node of the network to a UE. To this end, the processor 1002 may execute a computer program, codes, or instructions stored in the memory 1003, so as to control other components of the BS 1000 to enable execution of various operations.

[0419] The memory 1003 corresponds to a hardware storage device capable of temporarily or permanently storing information and may include one or more storage media. For example, the memory 1003 may include a memory assembly including one or more storage media. For example, the one or more storage media may include permanent memory, such as a hard drive, flash memory, or read-only memory (ROM), semipermanent memory, such as random access memory (RAM), cache memory, or a combination thereof.

[0420] The memory 1003 may be electrically, operatively, or communicatively coupled to the processor 1002 and may be accessed by the processor 1002.

[0421] The memory 1003 may store a computer program, codes, or instructions executable by the processor 1002. According to an embodiment, a computer program, codes, or instructions executable by the processor 1002 may be either stored in a single memory device or separated and distributedly stored in two or more memory devices. By executing the instructions stored in the memory 1003, the processor 1002 may perform various functions according to an embodiment of the disclosure.

[0422] According to an embodiment of the disclosure, operations of the BS 1000 may be caused to be performed based on execution of instructions (or a computer program or codes) stored in the memory 1003 by at least one processor (or processing circuitry) configured to execute the same individually, collectively, or in any combination thereof, based on processing circuitry that is not configured to execute instructions, and / or based on components of processing circuitry that is not configured to execute instructions.

[0423] While various aspects and embodiments have been disclosed herein, other aspects and embodiments may be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

[0424] Meanwhile, although specific embodiments of the present disclosure have been described in detail, various modifications may be made without departing from the scope of the present disclosure. Therefore, the scope of the present disclosure should not be limited to the described embodiments, but should be defined by the claims and equivalents thereof.

Claims

1.A method performed by a user equipment (UE) comprising:storing training data for artificial intelligence (AI) / machine learning (ML) in memory of the UE;identifying that the stored training data exceeds a buffer of the memory; andstopping measurement for the training data.2.The method of claim 1, further comprising:transmitting, to a base station, capability information including first information for a size of the memory.3.The method of claim 2, wherein the capability information includes second information for reporting the measurement.4.The method of claim 1, further comprising:receiving, from a base station, a configuration for configuring the measurement.5.The method of claim 4, wherein the stored training data is released 48 hours after a release of the configuration for configuring the measurement.6.The method of claim 1, further comprising:receiving, from a base station, a configuration for releasing the measurement.7.The method of claim 1, wherein the configuration for configuring the measurement is released upon receiving a radio resource control (RRC) release message from the base station.8.A user equipment (UE) comprising:a transceiver; anda controller coupled with the transceiver, and configured to:store training data for artificial intelligence (AI) / machine learning (ML) in memory of the UE,identify that the stored training data exceeds a buffer of the memory, andstop measurement for the training data.9.The UE of claim 8, wherein the controller is further configured to:transmit, to a base station, capability information including first information for a size of the memory.10.The UE of claim 9, wherein the capability information includes second information for reporting the measurement.11.The UE of claim 8, wherein the controller is further configured to:receive, from a base station, a configuration for configuring the measurement.12.The UE of claim 11, wherein the stored training data is released 48 hours after a release of the configuration for configuring the measurement.13.The UE of claim 8, wherein the controller is further configured to:receive, from a base station, a configuration for releasing the measurement.14.The UE of claim 8, wherein the configuration for configuring the measurement is released upon receiving a radio resource control (RRC) release message from the base station.

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