Method performed by user equipment, method performed by network node, user equipment and network node

Abstract

In one implementation, a User Equipment (UE) 3, network node 5 and method determining performance metric of measurement event prediction in a wireless communication network is disclosed. The method comprises receiving configuration parameters for the measurement event prediction from network node 5 to initialize Evaluation Time Window (ETW) timer and setting of counters to track measurement event prediction. Measurement event prediction is performed over one or more evaluation windows, based on the initialized ETW timer and set counters, using a prediction model. Time instance for the predicted measurement event is compared with time instance received from network node 5 to determine accuracy of predicted measurement event. Time instance received from the network node 5 is within defined Maximum Event Time Difference (ETDmax) range. Counters are updated based on determined accuracy of predicted measurement event and the updated counters are used to evaluate performance metrics of the measurement event prediction.

Description

METHOD PERFORMED BY USER EQUIPMENT, METHOD PERFORMED BY NETWORK NODE, USER EQUIPMENT AND NETWORK NODE　　The present disclosure generally relates to wireless communication systems and more particularly relates to techniques for determining performance metric and performance monitoring of measurement event prediction in a wireless communication network.　　3GPP RP-240082 focuses on mobility enhancement in RRC_CONNECTED mode over air interface by following existing mobility framework. RP-240082 describes study and evaluate potential benefits and gains of AI / ML aided mobility for network triggered L3-based handover, considering the following aspects: AI / ML based RRM measurement and measurement event prediction, Cell-level measurement prediction including intra and inter-frequency (UE sided and NW sided model) [RAN2], Inter-cell Beam-level measurement prediction for L3 Mobility (UE sided and NW sided model) [RAN2], HO failure / RLF prediction (UE sided model) [RAN2], Measurement events prediction (UE sided model) [RAN2], Study the need / benefits of any other UE assistance information for the network side model [RAN2].　　In addition, 3GPP TR38.744 V0.0.4 (2024-10) describes that the use cases in this study focus on RRC_CONNECTED mode and cover RRM measurement prediction, measurement event prediction and RLF / HOF prediction for PCell change procedure in standalone NR scenario. TR38.744 also describes that the study of the use cases is driven mainly by two study goals, the 1st study goal is to reduce measurement efforts in temporal, spatial or frequency domain by using predicted measurements, and the 2nd study goal is to improve the handover performance (e.g., Ping-pong HO, HOF / RLF, short time of stay, Handover interruption).　　Hence, there is a need to consider the measurement event prediction and improved handover performance.　　The following presents a simplified summary of the disclosure in order to provide a basic understanding of some of the aspects of disclosure embodiments. This summary is not an extensive overview of the disclosure. It is not intended to identify key / critical elements of the embodiments or to delineate the scope of the disclosure. Its sole purpose to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.In one aspect, the implementations of the present disclosure provide a method for a User Equipment (UE) for determining performance metric of measurement event prediction. The method comprises receiving, from a network node, configuration parameters for the measurement event prediction, initializing Evaluation Time Window (ETW) timer and setting one or more counters to track measurement event prediction based on the configuration parameters and performing the measurement event prediction over one or more evaluation windows, based on the initialized ETW timer and set one or more counters, using at least one prediction model. The method further comprises comparing a time instance for the predicted measurement event with a time instance received from the network node to determine an accuracy of the predicted measurement event. The time instance received from the network node is within a defined Maximum Event Time Difference (ETDmax) range. The method further comprises updating the one or more counters based on the determined accuracy of the predicted measurement event, wherein the updated one or more counters are used to evaluate the performance metrics of the measurement event prediction.　　In another aspect, the implementations of the present disclosure provide a method performed by a network node for performance monitoring of measurement event prediction is provided. The method comprises transmitting, to at least one UE, configuration parameters for measurement event prediction and receiving, from the at least one UE a report comprising a performance metrics of the measurement event prediction, wherein the performance metrics comprise one or more counters providing an accuracy of the prediction of the measurement event.　　In another aspect, the implementations of the present disclosure provide a User Equipment (UE) for determining performance metric of measurement event prediction. The UE comprises processing circuitry and at least one memory storing instructions that, when executed by the processing circuitry, cause the UE at least to: receive, from a network node, configuration parameters for the measurement event prediction, initialize Evaluation Time Window (ETW) timer and setting one or more counters to track measurement event prediction based on the configuration parameters and perform the measurement event prediction over one or more evaluation windows, based on the initialized ETW timer and set one or more counters, using at least one prediction model. The processing circuitry is further configured to compare a time instance for the predicted measurement event with a time instance received from the network node to determine an accuracy of the predicted measurement event. The time instance received from the network node is within a defined Maximum Event Time Difference (ETDmax) range and update the one or more counters based on the determined accuracy of the predicted measurement event, wherein the updated one or more counters are used to evaluate the performance metrics of the measurement event prediction.　　In another aspect, the implementations of the present disclosure provide a network node for performance monitoring of measurement event prediction. The network node comprises at processing circuitry and at least one memory storing instructions that, when executed by the processing circuitry, cause the network node at least to: transmit, to at least one UE, configuration parameters for measurement event prediction and receive, from the at least one UE a report comprising a performance metrics of the measurement event prediction, wherein the performance metrics comprises one or more counters providing an accuracy of the prediction of the measurement event.　　In one or more implementations, the present disclosure provides at least one of the following exemplary advantages:-　　Implementation of Maximum Event Timing Difference (ETDmax) may provide consistency between predicted and actual events.-　　The proposed disclosure provides evaluation of prediction performance using precision, recall and F1-score and it's reporting over an evaluation window / multiple evaluation windows allowing prediction accuracy improvements over a period of time through adaptive measurement configuration for measurement event prediction.-　　Multiple evaluation window-based reporting reduces frequency of metric reporting to network thus lowering signaling overhead.-　　The proposed disclosure provides optimization of the measurement event prediction system by enabling dynamic switching of the prediction model and it's configuration parameters based on performance metrics / F1 score.-　　The proposed disclosure proposes a modified F1 score minimizes false event predictions which prioritizes valid predictions and penalizes invalid ones and thus prevents unwanted measurement event triggers thereby improving radio resource utilization.　　The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description, drawings, and claims.　　The foregoing and further objects, features, and advantages of the present disclosure will become apparent from the following description of exemplary embodiments with reference to the accompanying drawings, wherein like numerals are used to represent like elements.　　It is to be noted, however, that the appended drawings illustrate only typical embodiments of the present disclosure, and are, therefore, not to be considered for limiting of its scope, for the disclosure may admit to other equally effective embodiments.　　For a better understanding of the present disclosure, reference is made to the following description of an exemplary embodiment thereof, considered in conjunction with the accompanying drawings, in which:Fig. 1 schematically illustrates a telecommunication system, in accordance with an embodiment of the present disclosure.Fig. 2 illustrates a block diagram illustrating main components of a user equipment (UE), in accordance with an embodiment of the present disclosure.Fig. 3 illustrates a block diagram illustrating main components of an exemplary (R)AN node, in accordance with an embodiment of the present disclosure.Fig. 4 illustrates a (R)AN node based on O-RAN architecture in accordance with an embodiment of the present disclosure.Fig. 5 illustrates a block diagram of a radio unit, in accordance with an embodiment of the present disclosure.Fig. 6 illustrates a block diagram of a distributed unit, in accordance with one embodiment of the present disclosure.Fig. 7 illustrates a block diagram of a centralized unit, in accordance with an embodiment of the present disclosure.Fig. 8 illustrates a block diagram illustrating main components of Access Management Function (AMF), in accordance with an embodiment of the present disclosure.Fig. 9 illustrates flow diagram for the UE for determining performance metric of measurement event prediction in a wireless communication network, in accordance with an embodiment of the present disclosure.Fig. 10 illustrates a flowchart of a method for a network node for performance monitoring of measurement event prediction in the wireless communication network, in accordance with an embodiment of the present disclosure.Fig. 11 illustrates a signaling flow for the UE for determining performance metric of measurement event prediction, in accordance with an embodiment of the present disclosure.Fig. 12 illustrates a signaling flow for the network node for determining performance metric of measurement event prediction for a single cell scenario, in accordance with an embodiment of the present disclosure.Fig. 13 illustrates a signaling flow for the network node for determining performance metric of measurement event prediction for a multiple-cell scenario, in accordance with an embodiment of the present disclosure.Fig. 14 illustrates a signaling flow for the UE and the network node for determining performance metric of measurement event prediction, in accordance with an embodiment of the present disclosure.Fig. 15 illustrates a flow of measurement event prediction using an indirect prediction model, in accordance with an embodiment of the present disclosure.Fig. 16 illustrates a reporting framework of performance metric of measurement event prediction, in accordance with an embodiment of the present disclosure.Fig. 17 illustrates a block diagram for the UE, in accordance with an embodiment of the present disclosure.　　Although specific features of various embodiments may be shown in some drawings and not in others, this is for convenience only. Any feature of any drawing may be referenced and / or claimed in combination with any feature of any other drawing.　　Wireless communication systems, such as LTE and 5G networks, have revolutionised connectivity by enabling high-speed reliable data transfer. These wireless communication systems are designed to support diverse user scenarios, from stationary users to highly mobile users. One critical aspect of maintaining seamless connectivity is ability to manage handovers, where a User Equipment (UE) transitions from one cell or node to another cell or node as the UE moves through the network. To facilitate this, the UE may perform radio measurements and report to the network, enabling intelligent handover decisions.　　Traditional handover procedures are typically based on measurement events triggered by signal quality thresholds, such as events like A1, A2 or A3. While effective in many scenarios, these handover methods may lack adaptability to rapidly changing environments or dynamic UE behaviour.　　Traditional systems use fixed signal thresholds to trigger measurement events, which do not adapt dynamically to the UE's movement patterns or environmental conditions (such as urban areas, or high-speed trains). Such lack of adaptability may lead to inefficiencies, such as unnecessary handovers (ping-pong effect), resource wastage, or degraded user experience due to inaccurate or outdated measurements. Additionally, inefficient target node selection may result in unnecessary resource allocation, reducing overall network efficiency.　　Furthermore, predicting handover events and preparing resources at the target node may be challenging without an efficient prediction mechanism. Current handover mechanisms often lack predictive intelligence and rely heavily on reactive responses to signal degradation. These methods limit the network's ability to anticipate and optimize handover scenarios.　　Hence, there is a need to consider effective handover management for improving handover reliability and resource utilization.　　The embodiments of the present disclosure are described in detail with reference to the accompanying drawings. However, the present disclosure is not limited to these embodiments which are only provided to explain more clearly the present disclosure to the ordinarily skilled in the art of the present disclosure. In the accompanying drawings, like reference numerals are used to indicate like components.　　This disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of "including," "comprising," or "having," "containing," "involving," and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.　　Various aspects of the proposed apparatus and method are described fully hereinafter with reference to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. The teachings disclosed may, however, be embodied in many different models with variations and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be understood that any aspect disclosed herein may be embodied by one or more elements of a claim, and also that the following detailed description does not limit the claims.　　Also, all logical units described and depicted in the figures include the software and / or hardware components required for the unit to function. Further, each unit may comprise within itself one or more components which are implicitly understood. These components may be operatively coupled to each other and be configured to communicate with each other to perform the function of the said unit.　　In an overview, the present disclosure relates to mobility management in 5G / beyond 5G / 6G or future networks using AI / ML aided measurement event prediction for network-triggered handovers.　　Traditional handover procedures are typically based on measurement events triggered by signal quality thresholds, such as events A1, A2 or A3. While effective in many scenarios, these handover methods may lack adaptability to rapidly changing environments or dynamic UE behaviour.　　Traditional systems use fixed signal thresholds to trigger measurement events, which do not adapt dynamically to the UE's movement patterns or environmental conditions (such as urban areas, or high-speed trains). Such lack of adaptability may lead to inefficiencies, such as unnecessary handovers (ping-pong effect), resource wastage, or degraded user experience due to inaccurate or outdated measurements. Additionally, inefficient target node selection may result in unnecessary resource allocation, reducing overall network efficiency.　　Furthermore, predicting handover events and preparing resources at the target node may be challenging without an efficient prediction mechanism. Current handover mechanisms often lack predictive intelligence and rely heavily on reactive responses to signal degradation. These methods limit the network's ability to anticipate and optimize handover scenarios.　　The present disclosure relates to method(s) where the UE and the network coordinate to predict measurement events, such as signal quality thresholds and radio link failure (RLF) to optimize handover decisions. The present disclosure reduces UE measurement efforts, signaling overhead, and handover interruptions while improving reliability and efficiency using prediction models. The present disclosure discloses method(s) for configuring observation prediction windows, selecting target nodes based on predicted probabilities, and enabling proactive handover preparation, resulting in smoother transitions and enhanced user experience.　　Fig. 1 schematically illustrates a telecommunication system 1 for a mobile (cellular or wireless) to which example embodiments disclosed herein and / or the above aspects are applicable. The telecommunication system 1 represents a system overview in which an end to end communication is possible. For example, UE (or user equipment, 'mobile device') communicates with other UEs or service servers in the data network 20 via respective (R)AN (Radio Access Network) nodes 5 and a core network 7 (also referred as network node in subsequent paragraphs). The (R)AN node 5 supports any radio accesses including a 5G radio access technology (RAT), an E-UTRA (Evolved Universal Terrestrial Radio Access ) Radio Access Technology (RAT), a beyond 5G RAT, a 6G RAT and non-3GPP RAT including wireless local area network (WLAN) technology as defined by the Institute of Electrical and Electronics Engineers (IEEE).　　The (R)AN node 5 may split into a Radio Unit (RU), Distributed Unit (DU) and Centralized Unit (CU). In some aspects, each of the units may be connected to each other and structure the (R)AN node 5 by adopting an architecture as defined by the Open RAN (O-RAN) Alliance, where the units above are referred to as O-RU, O-DU and O-CU respectively. The (R)AN node 5 may be split into control plane function and user plane function. Further, multiple user plane functions can be allocated to support a communication. In some aspects, user traffic may be distributed to multiple user plane functions and user traffic over each user plane functions are aggregated in both the UE 3 and the (R)AN node 5. This split architecture may be called as 'dual connectivity' or 'Multi connectivity'. The (R)AN node 5 can also support a communication using the satellite access. In some aspects, the (R)AN node 5 may support a satellite access and a terrestrial access. In addition, the (R)AN node 5 can also be referred as an access node for a non-wireless access. The non-wireless access includes a fixed line access as defined by the Broadband Forum (BBF) and an optical access as defined by the Innovative Optical and Wireless Network (IOWN).　　The core network 7 may include logical nodes (or 'functions') for supporting a communication in the telecommunication system 1. For example, the core network 7 may be 5G Core Network (5GC) that includes, amongst other functions, control plane functions and user plane functions. Each function in a logical nodes can be considered as a network function. The network function may be provided to another node by adapting the Service Based Architecture (SBA). A Network Function can be deployed as distributed, redundant, stateless, and scalable that provides the services from several locations and several execution instances in each location by adapting the network virtualization technology as defined by the European Telecommunications Standards Institute, Network Functions Virtualization (ETSI NFV). The core network 7 may support the Non-Public Network (NPN). The NPN may be a Stand-alone Non-Public Network (SNPN) or a Public Network Integrated NPN (PNI-NPN).　　As is well known, a UE 3 may enter and leave the areas (i.e. radio cells) served by the (R)AN node 5 as the UE 3 is moving around in the geographical area covered by the telecommunication system 1. In order to keep track of the UE 3 and to facilitate movement between the different (R)AN nodes 5, the core network 7 comprises at least one access and mobility management function (AMF) 70. The AMF 70 is in communication with the (R)AN node 5 coupled to the core network 7. In some core networks, a mobility management entity (MME) or a mobility management node for beyond 5G or a mobility management node for 6G may be used instead of the AMF 70.　　The core network 7 also includes, amongst others, a Session Management Function (SMF) 71, a User Plane Function (UPF) 72, a Policy Control Function (PCF) 73, a Network Exposure Function (NEF) 74, a Unified Data Management (UDM) 75, a Network Data Analytics Function (NWDAF) 76, a Network Slice Selection Function (NSSF) 77 and a Network Repository Function (NRF) 78. When the UE 3 is roaming to a visited Public Land Mobile Network (VPLMN), a home Public Land Mobile Network (HPLMN) of the UE 3 provides the UDM 75 and at least some of the functionalities of the SMF 71, UPF 72, and PCF 73 for the roaming-out UE 3.　　The UE 3 and a respective serving (R)AN node 5 are connected via an appropriate air interface (for example the so-called "Uu" interface and / or the like). Neighboring (R)AN node 5 are connected to each other via an appropriate (R)AN node 5 to (R)AN node interface (such as the so-called "Xn" interface and / or the like). Each (R)AN node 5 is also connected to nodes in the core network 7 (such as the so-called core network nodes) via an appropriate interface (such as the so-called "N2" / "N3" interface(s) and / or the like). From the core network 7, connection to a data network 20 is also provided. The data network 20 can be an internet, a public network, an external network, a private network or an internal network of the PLMN. In case that the data network 20 is provided by a PLMN operator or Mobile Virtual Network Operator (MVNO), the IP Multimedia Subsystem (IMS) service may be provided by that data network 20. The UE 3 can be connected to the data network 20 using IPv4, IPv6, IPv4v6, Ethernet or unstructured data type. The "Uu" interface may include a Control plane of Uu interface and User plane of Uu interface.　　The User plane of Uu interface is responsible to convey user traffic between the UE 3 and a serving (R)AN node 5. The User plane of Uu interface may have a layered structure with SDAP, PDCP, RLC and MAC sublayer over the physical connection. The Control plane of Uu interface is responsible to establish, modify and release a connection between the UE 3 and a serving (R)AN node 5. The Control plane of Uu interface may have a layered structure with RRC, PDCP, RLC and MAC sublayers over the physical connection.　　For example, the following messages are communicated over the RRC layer to support AS signaling.-　　RRC Setup Request message: This message is sent from the UE 3 to the (R)AN node 5. In addition to the parameters that are disclosed by Aspects in this disclosure, following parameters may be included together in the RRC Setup Request message.　　>> establishmentCause and ue-Identity. The ue-Identity may have a value of ng-5G-S-TMSI-Part1 or randomValue.-　　RRC Setup message: This message is sent from the (R)AN node 5 to the UE 3. In addi-tion to the parameters that are disclosed by Aspects in this disclosure, following parame-ters may be included together in the RRC Setup message.　　>> masterCellGroup and radioBearerConfig.-　　RRC Setup Complete message: This message is sent from the UE 3 to the (R)AN node 5. In addition to the parameters that are disclosed by Aspects in this disclosure, following parameters may be included together in the RRC Setup Complete message.　　>> guami-Type, iab-NodeIndication, idleMeasAvailable, mobilityState, ng-5G-S-TMSI-Part2, registeredAMF, selectedPLMN-Identity.

[0055] The UE 3 and the AMF 70 are connected via an appropriate interface (for example the so-called N1 interface and / or the like). The N1 interface is responsible to provide a communication between the UE 3 and the AMF 70 to support NAS signaling. The N1 interface may be established over a 3GPP access and over a non-3GPP access. For example, the following messages are communicated over the N1 interface.-　　Registration Request message: This message is sent from the UE 3 to the AMF 70. In addition to the parameters that are disclosed by Aspects in this disclosure, following pa-rameters may be included together in the Registration Request message.　　>> 5GS registration type, ngKSI, 5GS mobile identity, Non-current native NAS key set identifier, 5GMM capability, UE security capability, Requested NSSAI, Last visited registered TAI, S1 UE network capability, Uplink data status, PDU session status, MICO indication, UE status, Additional GUTI, Allowed PDU session status, UE's usage setting, Requested DRX parameters, EPS NAS message container, LADN in-dication, Payload container type, Payload container, Network slicing indication, 5GS update type, Mobile station classmark 2, Supported codecs, NAS message container, EPS bearer context status, Requested extended DRX parameters, T3324 value, UE radio capability ID, Requested mapped NSSAI, Additional information requested, Requested WUS assistance information, N5GC indication and Requested NB-N1 mode DRX parameters.-　　Registration Accept message: This message is sent from the AMF 70 to the UE 3. In ad-dition to the parameters that are disclosed by Aspects in this disclosure, following param-eters may be included together in the Registration Accept message.　　>> 5GS registration result, 5G-GUTI, Equivalent PLMNs, TAI list, Allowed NSSAI, Rejected NSSAI, configure NSSAI, 5GS network feature support, PDU session sta-tus, PDU session reactivation result, PDU session reactivation result error cause, LADN information, MICO indication, Network slicing indication, Service area list, T3512 value, Non-3GPP de-registration timer value, T3502 value, Emergency num-ber list, Extended emergency number list, SOR transparent container, EAP message, NSSAI inclusion mode, Operator-defined access category definitions, Negotiated DRX parameters, Non-3GPP NW policies, EPS bearer context status, Negotiated ex-tended DRX parameters, T3447 value, T3448 value, T3324 value, UE radio capabil-ity ID, UE radio capability ID deletion indication, Pending NSSAI, Ciphering key data, CAG information list, Truncated 5G-S-TMSI configuration, Negotiated WUS assistance information, Negotiated NB-N1 mode DRX parameters and Extended re-jected NSSAI.-　　Registration Complete message: This message is sent from the UE 3 to the AMF 70. In addition to the parameters that are disclosed by Aspects in this disclosure, following pa-rameters may be included together in the Registration Complete message.　　>> SOR transparent container.-　　Authentication Request message: This message is sent from the AMF 70 to the UE 3. In addition to the parameters that are disclosed by Aspects in this disclosure, following pa-rameters may be included together in the Authentication Request message.　　>> ngKSI,ABBA, Authentication parameter RAND (5G authentication challenge), Au-thentication parameter AUTN (5G authentication challenge) and EAP message.-　　Authentication Response message: This message is sent from the UE 3 to the AMF 70. In addition to the parameters that are disclosed by Aspects in this disclosure, following pa-rameters may be populated together in the Authentication Response message.　　>> Authentication response message identity, Authentication response parameter and EAP message.-　　Authentication Result message: This message is sent from the AMF 70 to the UE 3. In addition to the parameters that are disclosed by Aspects in this disclosure, following pa-rameters may be populated together in the Authentication Result message.　　>> ngKSI, EAP message and ABBA.-　　Authentication Failure message: This message is sent from the UE 3 to the AMF 70. In addition to the parameters that are disclosed by Aspects in this disclosure, following pa-rameters may be populated together in the Authentication Failure message.　　>> Authentication failure message identity, 5GMM cause and Authentication failure pa-rameter.-　　Authentication Reject message: This message is sent from the AMF 70 to the UE 3. In addition to the parameters that are disclosed by Aspects in this disclosure, following pa-rameters may be populated together in the Authentication Reject message.　　>> EAP message.-　　Service Request message: This message is sent from the UE 3 to the AMF 70. In addition to the parameters that are disclosed by Aspects in this disclosure, following parameters may be populated together in the Service Request message.　　>> ngKSI,Service type, 5G-S-TMSI, Uplink data status, PDU session status, Allowed PDU session status, NAS message container.-　　Service Accept message: This message is sent from the AMF 70 to the UE 3. In addition to the parameters that are disclosed by Aspects in this disclosure, following parameters may be populated together in the Service Accept message.　　>> PDU session status, PDU session reactivation result, PDU session reactivation result error cause, EAP message and T3448 value.-　　Service Reject message: This message is sent from the AMF 70 to the UE 3. In addition to the parameters that are disclosed by Aspects in this disclosure, following parameters may be populated together in the Service Reject message.　　>> 5GMM cause, PDU session status, T3346 value, EAP message, T3448 value and CAG information list.-　　Configuration Update Command message: This message is sent from the AMF 70 to the UE 3. In addition to the parameters that are disclosed by Aspects in this disclosure, fol-lowing parameters may be populated together in the Configuration Update Command message.　　>> Configuration update indication,5G-GUTI, TAI list, Allowed NSSAI, Service area list, Full name for network, Short name for network, Local time zone, Universal time and local time zone, Network daylight saving time, LADN information, MICO indi-cation, Network slicing indication, configured NSSAI, Rejected NSSAI, Operator-defined access category definitions, SMS indication, T3447 value, CAG information list, UE radio capability ID, UE radio capability ID deletion indication, 5GS registra-tion result, Truncated 5G-S-TMSI configuration, Additional configuration indication and Extended rejected NSSAI.-　　Configuration Update Complete message: This message is sent from the UE 3 to the AMF 70. In addition to the parameters that are disclosed by Aspects in this disclosure, following parameters may be populated together in the Configuration Update Complete message.　　>> Configuration update complete message identity.　　Fig. 2 is a block diagram illustrating the main components of the UE 3 (mobile device 3). As shown, the UE 3 includes a transceiver circuit 31 (also referred as transceiver in subsequent paragraphs) which is operable to transmit signals to and to receive signals from the connected node(s) via one or more antennas 32. Further, the UE 3 may include a user interface 34 for inputting information from outside or outputting information to outside. Although not necessarily shown in the figure, the UE 3 may have all the usual functionality of a conventional mobile device and this may be provided by any one or any combination of hardware, software and firmware, as appropriate. Software may be pre-installed in the memory and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. A controller 33 (also referred as processing circuitry in subsequent paragraphs) controls the operation of the UE 3 in accordance with software stored in a memory 36. The software includes, among other things, an operating system 361 and a communications control module 362 having at least a transceiver control module 3621. The communications control module 362 (using its transceiver control module 3621) is responsible for handling (generating / sending / receiving) signalling and uplink / downlink data packets between the UE 3 and other nodes, such as the (R)AN node 5 and the AMF 10. Such signalling may include, for example, appropriately formatted signalling messages (e.g. a registration request message and associated response messages) relating to access and mobility management procedures (for the UE 3). The controller 33 interworks with one or more Universal Subscriber Identity Module (USIM) 35. If there are multiple USIMs 35 equipped, the controller 33 may activate only one USIM 35 or may activate multiple USIMs 35 at the same time.　　The UE 3 may, for example, support the Non-Public Network (NPN). The NPN may be a Stand-alone Non-Public Network (SNPN) or a Public Network Integrated NPN (PNI-NPN).　　The UE 3 may, for example, be an item of equipment for production or manufacture and / or an item of energy related machinery (for example equipment or machinery such as: boilers; engines; turbines; solar panels; wind turbines; hydroelectric generators; thermal power generators; nuclear electricity generators; batteries; nuclear systems and / or associated equipment; heavy electrical machinery; pumps including vacuum pumps; compressors; fans; blowers; oil hydraulic equipment; pneumatic equipment; metal working machinery; manipulators; robots and / or their application systems; tools; molds or dies; rolls; conveying equipment; elevating equipment; materials handling equipment; textile machinery; sewing machines; printing and / or related machinery; paper converting machinery; chemical machinery; mining and / or construction machinery and / or related equipment; machinery and / or implements for agriculture, forestry and / or fisheries; safety and / or environment preservation equipment; tractors; precision bearings; chains; gears; power transmission equipment; lubricating equipment; valves; pipe fittings; and / or application systems for any of the previously mentioned equipment or machinery etc.).The UE 3 may, for example, be an item of transport equipment (for example transport equipment such as: rolling stocks; motor vehicles; motor cycles; bicycles; trains; buses; carts; rickshaws; ships and other watercraft; aircraft; rockets; satellites; drones; balloons etc.). The UE 3 may, for example, be an item of information and communication equipment (for example information and communication equipment such as: electronic computer and related equipment; communication and related equipment; electronic components etc.). The UE 3 may, for example, be a refrigerating machine, a refrigerating machine applied product, an item of trade and / or service industry equipment, a vending machine, an automatic service machine, an office machine or equipment, a consumer electronic and electronic appliance (for example a consumer electronic appliance such as: audio equipment; video equipment; a loud speaker; a radio; a television; a microwave oven; a rice cooker; a coffee machine; a dishwasher; a washing machine; a dryer; an electronic fan or related appliance; a cleaner etc.).　　The UE 3 may, for example, be an electrical application system or equipment (for example an electrical application system or equipment such as: an x-ray system; a particle accelerator; radio isotope equipment; sonic equipment; electromagnetic application equipment; electronic power application equipment etc.). The UE 3 may, for example, be an electronic lamp, a luminaire, a measuring instrument, an analyzer, a tester, or a surveying or sensing instrument (for example a surveying or sensing instrument such as: a smoke alarm; a human alarm sensor; a motion sensor; a wireless tag etc.), a watch or clock, a laboratory instrument, optical apparatus, medical equipment and / or system, a weapon, an item of cutlery, a hand tool, or the like.　　The UE 3 may, for example, be a wireless-equipped personal digital assistant or related equipment (such as a wireless card or module designed for attachment to or for insertion into another electronic device (for example a personal computer, electrical measuring machine)). The UE 3 may be a device or a part of a system that provides applications, services, and solutions described below, as to "internet of things (IoT)", using a variety of wired and / or wireless communication technologies. Internet of Things devices (or "things") may be equipped with appropriate electronics, software, sensors, network connectivity, and / or the like, which enable these devices to collect and exchange data with each other and with other communication devices. IoT devices may comprise automated equipment that follow software instructions stored in an internal memory. IoT devices may operate without requiring human supervision or interaction. IoT devices might also remain stationary and / or inactive for a long period of time. IoT devices may be implemented as a part of a (generally) stationary apparatus. IoT devices may also be embedded in non-stationary apparatus (e.g. vehicles) or attached to animals or persons to be monitored / tracked.　　It will be appreciated that IoT technology can be implemented on any communication devices that can connect to a communications network for sending / receiving data, regardless of whether such communication devices are controlled by human input or software instructions stored in memory.　　It will be appreciated that IoT devices are sometimes also referred to as Machine-Type Communication (MTC) devices or Machine-to-Machine (M2M) communication devices or Narrow Band-IoT UE (NB-IoT UE). It will be appreciated that a UE 3 may support one or more IoT or MTC applications.　　The UE 3 may be a smart phone or a wearable device (e.g. smart glasses, a smart watch, a smart ring, or a hearable device). The UE 3 may be a car, or a connected car, or an autonomous car, or a vehicle device, or a motorcycle or V2X (Vehicle to Everything) communication module (e.g. Vehicle to Vehicle communication module, Vehicle to Infrastructure communication module, Vehicle to People communication module and Vehicle to Network communication module).　　The memory 36 present in the UE 3 may store AI / ML models for performing performance monitoring. With UE- side AI / ML model, there are two different ways of performing performance monitoring. One is network side-performance monitoring where the UE 3 sends a report to the network (NW) (for the calculation of performance metric at the NW. Another way is of performance monitoring is UE-assisted performance metric where the UE calculates performance metrics and reports it to NW either by self or upon a trigger by the NW.　　For performance monitoring, performance metric is calculated based on at least one parameter such as, but not limited to, Top-K beams, Reference Signal Received Power (RSRP), probability information of the predicted beams, wherein the RSRP is measured and predicted by the AI / ML model. Further, the beam prediction indicator is determined after determining the performance metric. There may be different definitions which may be provided for determining performance metric and beam prediction indicators. Each of these definitions have been defined below in subsequent paragraphs.　　Fig. 3 is a block diagram illustrating the main components of an exemplary (R)AN node 5 (also referred as network node in subsequent paragraphs), for example a base station ('eNB' in LTE, 'gNB' in 5G, a base station for 5G beyond, a base station for 6G). As shown, the (R)AN node 5 includes a transceiver circuit 51 which is operable to transmit signals to and to receive signals from connected UE(s) 3 via one or more antennas 52 and to transmit signals to and to receive signals from other network nodes (either directly or indirectly) via a network interface 53. A controller 54 (also referred as processing circuitry in subsequent paragraphs) controls the operation of the (R)AN node 5 in accordance with software stored in a memory 55. Software may be pre-installed in the memory and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. The software includes, among other things, an operating system 551 and a communications control module 552 having at least a transceiver control module 5521. In one embodiment, the performance metric is calculated by the UE 3 upon receipt of a trigger report sent by the network node 6.　　The communications control module 552 (using its transceiver control sub-module) is responsible for handling (generating / sending / receiving) signalling between the (R)AN node 5 and other nodes, such as the UE 3, another (R)AN node 5, the AMF 70 and the UPF 72 (e.g. directly or indirectly). The signalling may include, for example, appropriately formatted signalling messages relating to a radio connection and a connection with the core network 7 (for a particular UE 3), and in particular, relating to connection establishment and maintenance (e.g. RRC connection establishment and other RRC messages), NG Application Protocol (NGAP) messages (i.e. messages by N2 reference point) and Xn application protocol (XnAP) messages (i.e. messages by Xn reference point), etc. Such signalling may also include, for example, broadcast information (e.g. Master Information and System information) in a sending case.　　The controller 54 is also configured (by software or hardware) to handle related tasks such as, when implemented, UE mobility estimate and / or moving trajectory estimation. The (R)AN node 5 may support the Non-Public Network (NPN). The NPN may be a Stand-alone Non-Public Network (SNPN) or a Public Network Integrated NPN (PNI-NPN).Fig. 4 schematically illustrates a (R)AN node 5 based on O-RAN architecture to which the (R)AN node 5 aspects are applicable. The (R)AN node 5 based on O-RAN architecture represents a system overview in which the (R)AN node is split into a Radio Unit (RU) 60, Distributed Unit (DU) 61 and Centralized Unit (CU) 62. In some aspects, each unit may be combined. For example, the RU 60 can be integrated / combined with the DU 61 as an integrated / combined unit, the DU 61 can be integrated / combined with the CU 62 as another integrated / combined unit. Any functionality in the description for a unit (e.g. one of RU 60, DU 61 and CU 62) can be implemented in the integrated / combined unit above. Further, CU 62 can separate into two functional units such as CU Control plane (CP) and CU User plane (UP). The CU CP has a control plane functionality in the (R)AN node 5. The CU UP has a user plane functionality in the (R)AN node 5. Each CU CP is connected to the CU UP via an appropriate interface (such as the so-called "E1" interface and / or the like).　　The UE 3 and a respective serving RU 60 are connected via an appropriate air interface (for example the so-called "Uu" interface and / or the like). Each RU 60 is connected to the DU 61 via an appropriate interface (such as the so-called "Front haul", "Open Front haul", "F1" interface and / or the like). Each DU 61 is connected to the CU 62 via an appropriate interface (such as the so-called "Mid haul", "Open Mid haul", "E2" interface and / or the like). Each CU 62 is also connected to nodes in the core network 7 (such as the so-called core network nodes) via an appropriate interface (such as the so-called "Back haul", "Open Back haul", "N2" / "N3" interface(s) and / or the like). In addition, a user plane part of the DU 61 can also be connected to the core network nodes 7 via an appropriate interface (such as the so-called "N3" interface(s) and / or the like). Depending on functionality split among the RU 60, DU 61 and CU 62, each unit provides some of the functionality that is provided by the (R)AN node 5. For example, the RU 60 may provide a functionalities to communicate with a UE 3 over air interface, the DU 61 may provide functionalities to support MAC layer and RLC layer, the CU 62 may provide functionalities to support PDCP layer, SDAP layer and RRC layer.　　Fig. 5 is a block diagram illustrating the main components of an exemplary RU 60, for example a RU part of base station ('eNB' in LTE, 'gNB' in 5G, a base station for 5G beyond, a base station for 6G). As shown, the RU 60 includes a transceiver circuit 601 which is operable to transmit signals to and to receive signals from connected UE(s) 3 via one or more antennas 602 and to transmit signals to and to receive signals from other network nodes or network unit (either directly or indirectly) via a network interface 603. A controller 604 controls the operation of the RU 60 in accordance with software stored in a memory 605. Software may be pre-installed in the memory and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. The software includes, among other things, an operating system 6051 and a communications control module 6052 having at least a transceiver control module 60521.　　The communications control module 6052 (using its transceiver control sub-module) is responsible for handling (generating / sending / receiving) signalling between the RU 60 and other nodes or units, such as the UE 3, another RU 60 and DU 61 (e.g. directly or indirectly). The signalling may include, for example, appropriately formatted signalling messages relating to a radio connection and a connection with the RU 60 (for a particular UE 3), and in particular, relating to MAC layer and RLC layer.　　The controller 604 is also configured (by software or hardware) to handle related tasks such as, when implemented, UE mobility estimation and / or moving trajectory estimation. The RU 60 may support the Non-Public Network (NPN). The NPN may be a Stand-alone Non-Public Network (SNPN) or a Public Network Integrated NPN (PNI-NPN). As described above, the RU 60 can be integrated / combined with the DU 61 as an integrated / combined unit. Any functionality in the description for the RU 60 can be implemented in the integrated / combined unit above.Fig. 6 is a block diagram illustrating the main components of an exemplary DU 61, for example a DU part of a base station ('eNB' in LTE, 'gNB' in 5G, a base station for 5G beyond, a base station for 6G). As shown, the apparatus includes a transceiver circuit 611 which is operable to transmit signals to and to receive signals from other nodes or units (including the RU 60) via a network interface 612. A controller 613 controls the operation of the DU 61 in accordance with software stored in a memory 614. Software may be pre-installed in the memory 614 and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. The software includes, among other things, an operating system 6141 and a communications control module 6142 having at least a transceiver control module 61421. The communications control module 6142 (using its transceiver control module 61421 is responsible for handling (generating / sending / receiving) signalling between the DU 61 and other nodes or units, such as the RU 60 and other nodes and units.　　The DU 61 may support the Non-Public Network (NPN). The NPN may be a Stand-alone Non-Public Network (SNPN) or a Public Network Integrated NPN (PNI-NPN). As described above, the RU 60 can be integrated / combined with the DU 61 or CU 62 as an integrated / combined unit. Any functionality in the description for DU 61 can be implemented in one of the integrated / combined unit above.　　Fig. 7 is a block diagram illustrating the main components of an exemplary CU 62, for example a CU part of base station ('eNB' in LTE, 'gNB' in 5G, a base station for 5G beyond, a base station for 6G). As shown, the apparatus includes a transceiver circuit 621 which is operable to transmit signals to and to receive signals from other nodes or units (including the DU 61) via a network interface 622. A controller 623 controls the operation of the CU 62 in accordance with software stored in a memory 624. Software may be pre-installed in the memory 624 and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. The software includes, among other things, an operating system 6241 and a communications control module 6242 having at least a transceiver control module 62421. The communications control module 6242 (using its transceiver control module 62421 is responsible for handling (generating / sending / receiving) signaling between the CU 62 and other nodes or units, such as the DU 61 and other nodes and units. The CU 62 may support the Non-Public Network (NPN). The NPN may be a Stand-alone Non-Public Network (SNPN) or a Public Network Integrated NPN (PNI-NPN).　　As described above, the CU 62 can be integrated / combined with the DU 61 as an integrated / combined unit. Any functionality in the description for the CU 62 can be implemented in the integrated / combined unit above.　　Fig. 8 is a block diagram illustrating the main components of the AMF 70. As shown, the apparatus includes a transceiver circuit 701 which is operable to transmit signals to and to receive signals from other nodes (including the UE 3) via a network interface 702. A controller 703 controls the operation of the AMF 70 in accordance with software stored in a memory 704. Software may be pre-installed in the memory 704 and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. The software includes, among other things, an operating system 7041 and a communications control module 7042 having at least a transceiver control module 70421. The communications control module 7042 (using its transceiver control module 70421 is responsible for handling (generating / sending / receiving) signalling between the AMF 70 and other nodes, such as the UE 3 (e.g. via the (R)AN node 5) and other core network nodes (including core network nodes in the HPLMN of the UE 3 when the UE 3 is roaming-in. Such signalling may include, for example, appropriately formatted signalling messages (e.g. a registration request message and associated response messages) relating to access and mobility management procedures (for the UE 3). The AMF 70 may support the Non-Public Network (NPN). The NPN may be a Stand-alone Non-Public Network (SNPN) or a Public Network Integrated NPN (PNI-NPN).　　Detailed aspects have been described above. As those skilled in the art will appreciate, a number of modifications and alternatives can be made to the above aspects whilst still benefiting from the disclosures embodied therein. By way of illustration only a number of these alternatives and modifications will now be described.　　In the above description, the UE 3 and the network apparatus are described for ease of understanding as having a number of discrete modules (such as the communication control modules). Whilst these modules may be provided in this way for certain applications, for example where an existing system has been modified to implement the disclosure, in other applications, for example in systems designed with the inventive features in mind from the outset, these modules may be built into the overall operating system or code and so these modules may not be discernible as discrete entities. These modules may also be implemented in software, hardware, firmware or a mix of these.　　Each controller may comprise any suitable form of processing circuitry including (but not limited to), for example: one or more hardware implemented computer processors; microprocessors; central processing units (CPUs); arithmetic logic units (ALUs); input / output (IO) circuits; internal memories / caches (program and / or data); processing registers; communication buses (e.g. control, data and / or address buses); direct memory access (DMA) functions; hardware or software implemented counters, pointers and / or timers; and / or the like.　　In the above aspects, a number of software modules were described. As those skilled in the art will appreciate, the software modules may be provided in compiled or un-compiled form and may be supplied to the UE 3 and the network apparatus as a signal over a computer network, or on a recording medium. Further, the functionality performed by part or all of this software may be performed using one or more dedicated hardware circuits.　　In the above aspects, a 3GPP radio communications (radio access) technology is used. However, any other radio communications technology (e.g. WLAN, Wi-Fi, WiMAX, Bluetooth, etc.) and other fix line communications technology (e.g. BBF Access, Cable Access, optical access, etc.) may also be used in accordance with the above aspects. Items of user equipment might include, for example, communication devices such as mobile telephones, smartphones, user equipment, personal digital assistants, laptop / tablet computers, web browsers, e-book readers and / or the like. Such mobile (or even generally stationary) devices are typically operated by a user, although it is also possible to connect so-called 'Internet of Things' (IoT) devices and similar machine-type communication (MTC) devices to the network. For simplicity, the present application refers to mobile devices (or UEs) in the description but it will be appreciated that the technology described can be implemented on any communication devices (mobile and / or generally stationary) that can connect to a communications network for sending / receiving data, regardless of whether such communication devices are controlled by human input or software instructions stored in memory. Various other modifications will be apparent to those skilled in the art and will not be described in further detail here.　　While the disclosure has been particularly shown and described with reference to exemplary aspects thereof, the disclosure is not limited to these aspects. It will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by this document. For example, the aspects above are not limited to 5GS, and the Aspects are also applicable to communication systems other than 5GS.　　Referring to Fig. 9, the present disclosure defines a method 900 for a User Equipment (UE) for determining performance metrics of measurement event prediction in a wireless communication network. The UE refers to UE 3 as discussed and comprises the processing circuitry 33 and the transceiver 31 to determine the performance metrics.　　At step 902, the method 900 provides receiving configuration parameters for the measurement event prediction from a network node. The network node here refers the network node 5 as discussed above. At step 904, the method 900 provides initializing Evaluation Time Window (ETW) timer and setting one or more counters to track measurement event prediction based on the configuration parameters and at step 906, the method 900 provides performing the measurement event prediction over one or more evaluation windows, based on the initialized ETW timer and set one or more counters, using at least one prediction model. At step 908, the method 900 provides comparing a time instance for the predicted measurement event with a time instance received from the network node 5 to determine an accuracy of the predicted measurement event. The time instance received from the network node 5 is within a defined Maximum Event Time Difference (ETDmax) range and at step 910, the method 900 provides updating the one or more counters based on the determined accuracy of the predicted measurement event. The updated one or more counters are used to evaluate the performance metrics of the measurement event prediction.　　Referring to Fig. 10, the present disclosure defines a method 1000 for a network node for performance monitoring of measurement event prediction. The network node refers to the network node 5 as discussed above.　　At step 1002, the method 1000 provides transmitting configuration parameters for measurement event prediction to the at least one UE 3 and at step 1004, the method 1000 provides receiving the report comprising the performance metrics of the measurement event prediction from the at least one UE 3. The performance metrics comprise the one or more counters providing the accuracy of the prediction of the measurement event.　　Referring to Fig. 11, signaling flow 1100 for the UE 3 executing the method 900 is shown. For example, referring to Fig. 11, signaling flow 1100 for the UE 3 executing the method 900 may be shown.　　At step 1101, the network node 5 may request the UE 3 to send UE 3's AI / ML capability including the AI / ML models to support the UE 3's functionality of measurement event prediction.　　At step 1102, the UE 3 may send the model IDs of the applicable AI / ML model and the UE 3's characteristics corresponding to the measurement event prediction through a radio resource control (RRC) message for example, the UE capability information once the network node 5 has requested the UE 3's AI / ML capability.　　At step 1103, the network node 5 may configure the UE 3 with necessary configuration parameters for the F1 score evaluation and the network node 5 may indicate whether the UE 5 needs to perform direct or indirect prediction analysis through network node 5's instruction. For example, the network node 5 may send a RRC message including the configuration parameters to the UE 3. The RRC message me be expressed as a configuration RRC message, a RRC configuration message, a configuration message in this disclosure.The configuration parameters comprise at least one of:-　　Maximum Evaluation Timing Difference (ETD max, maximum ETD) (This may be variable depending on the frequency band of operation)-　　Evaluation Time Window (ETW)-　　Metric evaluation window configuration-　　F1 score threshold-　　Event types for evaluation (e.g. A3, A5)-　　Reporting mode (periodic or trigger-induced)-　　Prediction model (also referred as AI / ML model or model) switching rules (for example, the exact identity of the AI / ML model the UE 5 may need to switch to, if the F1 score is below the configured threshold)　　At step 1104, the UE 3 may start the ETW timer and set the values of each of the counters n1, n2 and n3 to zero. For example, the UE 3 may start the ETW timer which has the value indicated by the ETW received from the network node 5.　　At step 1105, the UE 3 may be configured to monitor the configured measurement event (for example A3, A5), using at least one of the direct prediction or the indirect prediction and the model as instructed in the configuration RRC message. The UE 3 may record or make a note of the time instances of the predicted event. For each prediction, the UE 3 may compare the predicted event time instance with the actual event occurrence time instance received from the network node 5 within the maximum ETD set (or ETD max). For example, in a case where the actual event occurs, the network node 5 may notify, to the UE 3, that the actual event occurs, then the UE 3 may record the time when the UE 3 receives the notification as the actual event time instance. For example, in a case where the actual event occurs, the network node 5 may send a message related to the actual event (e.g. a message related to event (e.g. event A3, A5 etc.), or a message related to handover etc.), to the UE 3, then the UE 3 may record the time when the UE 3 receives the message as the actual event time instance... The UE 3 may initialize the counters n1, n2 and n3 for Indirect prediction. The counters n1, n2 and n3 are updated respectively when the following conditions are met:- Condition 1: Counter n3(true event prediction): The counter n3 increases by 1 when a real event occurs around the predicted event with ETD, whose range is [0, maximum ETD] or vice versa- Condition 2: Counter n1(false event detection): The counter n1 increases by 1 when no real event occurs around the predicted event with ETD, whose range is [0, maximum ETD]- Condition 3: Counter n2(missed event detection): The counter n2 increases by 1 when no event is predicted around the real event with ETD, whose range is [0, maximum ETD]At step 1106, the UE 3 may store the counters n1, n2 and n3 at the end of the evaluation time duration defined in the configuration message (e.g. the RRC message) and may evaluate each of the Precision and Recall values and the F1 score. The UE 3 may also be configured to evaluate the F1 score after multiple such evaluation windows to reduce the overhead. In an example, for the direct prediction, the UE 3 may be configured to maintain and update the counters n1', n2' and n3'. For example, definitions for n1', n2' and n3' may be same to definitions for n1, n2 and n3. For example, conditions for increasing n1', n2' and n3' may be same to the conditions for increasing n1, n2 and n3. For example, in case of applying this disclosure to the direct prediction, n1, n2 and n3 may be replaced with n1', n2' and n3'. For every evaluation window, the UE 3 may repeat step 1106. The F1 score may be calculated as F1 score = 2*Precision*Recall / (Precision + Recall). The Precision and Recall may be calculated as Precision = n3 / (n1+n3) and Recall = n3 / (n2+n3).　　At step 1107, the UE 3 may be configured to compare the calculated F1 score with the F1 score threshold sent in the RRC configuration message for the measurement event prediction from the network node 5. The UE3 may be configured to compare a predetermined threshold.　　At step 1108, the UE 3 may then reset the counters n1, n2 and n3 to zero and the ETW timer to zero for single cell scenario.　　In an example, in case of a multiple cell-based scenario, the UE 3 may keep updating the counter values across multiple cells / gNBs. When the ETW timer reaches the defined limit, the UE 3 may send the metric to a current gNB from the multiple gNBs and the current gNB may report the metric to a previous gNB from the multiple gNBs or may share the information in a common pool of the multiple cells. Then, the UE 3 may restart the counters n1, n2 and n3 from zero.At step 1109, the UE 3 may send the measurement results report (also referred as performance report or metrics or report or measurement reports or performance metrics) to the network node 5 in case of option-a (as discussed below) else the UE 3 may perform the measurement event prediction after switching to the new model and configuration in case of option-b. (as discussed below) The UE3 may send, to the network node 5, the measurement results report in RRC message.-　　Option-a: If the F1 score is above the predefined F1 threshold, then the UE 3 may send the measurement reports to the network node 5. The measurement reports may include at least one of the calculated F1 score, Precision, Recall;-　　Option-b: If the F1 score is below the predefined F1 threshold, then the UE 3 may change the AI / ML model for measurement event prediction.　　In an example for one of the option-b: Multiple prediction models and priority for each of the prediction models is sent by the network node 5 in the RRC message. The UE 3 may select the next prediction model in priority as instructed by the network node 5.　　In an example, for one of the option-b: The UE 3 may randomly select one prediction model from the multiple prediction models.　　In an example, for one of the option-b: The UE 3 may select the prediction model that is sent in the instruction from the network node 5. The prediction model instructed by the network node 5 may be put in step 1103 or may also be sent as an independent RRC message from the network node 5.　　Referring to Fig. 12, signaling flow 1200 for the network node 5 for single cell / node is shown. For example, referring to Fig. 12, signaling flow 1200 for the network node 5 executing the method 1000 for single cell / node may be shown.　　At step 1201, the network node 5 requests the at least one UE 3 (hereafter referred as the UE 3) to send the UE 3's AI / ML capability that comprises information on AI / ML models to support the UE 3's functionality of measurement event prediction.　　At step 1202, the network node 5 receives from the UE 3, the AI / ML model IDs of the applicable AI / ML model and UE 3's characteristics corresponding to the measurement event prediction through an RRC message. The RRC message may comprise, for example, UE 3's capability information once the network node 5 has requested the UE 3's AI / ML capability.　　At step 1203, the network node 5 may configure the UE 3 with required configuration parameters for F1 score evaluation and the network node 5 may indicate through an instruction, whether the UE 3 needs to perform direct prediction analysis or the indirect prediction analysis. For example, the network node 5 may send a RRC message including the configuration parameters to the UE 3. In an example, the configuration parameters comprise at least one of:-　　Maximum Event Timing Difference (ETDmax, maximum ETD) (This may be variable depending on the frequency band of operation)-　　Evaluation Time Window (ETW)-　　Metric evaluation window configuration-　　Single / Multiple ETW based prediction-　　Frequency of operation-　　Event types for evaluation (e.g. A3, A5)-　　Reporting mode (periodic or trigger-induced)　　In an example, the ETDmax ensures consistency between the UE 3 and the network node 5 for true measurement event detection. ETDmax is the maximum time difference around real and predicted event.　　At step 1204, the UE 3 may set the ETW timer and values of each of the counters n1, the counter n2 and the counter n3 to zero. For example, the UE 3 may start the ETW timer which has the value indicated by the ETW received from the network node 5.　　At step 1205, the UE 3 may monitor the configured measurement event (for example A3, A5) using at least one of the direct predictions (through the AI / ML direct prediction model) or the indirect prediction (through the indirect prediction model) as instructed in the configuration RRC message. The UE 3 makes a note of the time instances of the predicted event. For each prediction, the UE 3 may compare the predicted event time instance with the actual event time instance received from the network node 5 within the maximum ETD set. The UE 3 may then initialize each of the counters n1, n2 and n3 for the indirect prediction. The counters n1, n2 and n3 are updated respectively when the following conditions are met.-　　Condition 1: Counter n3 (true event prediction): counter n3 increases by 1 when the real event occurs around the predicted event with ETD, whose range is [0, maximum ETD] or vice versa-　　Condition 2: Counter n1(false event detection): the counter n1 increases by 1 when no real event occurs around a predicted event with ETD, whose range is [0, maximum ETD]-　　Condition 3: Counter n2(missed event detection): the counter n2 increases by 1 when no event is predicted around a real event with ETD, whose range is [0, maximum ETD]For example, at step 1205, the UE 3 may perform same process in step 1105 in Fig. 11.　　At step 1206, the UE 3 may store the values of the counters n1, n2 and n3 at the end of the evaluation time duration defined in the configuration message and the UE 3 may then evaluate the Precision and Recall values and the F1 score. For example, at step 1206, the UE 3 may evaluate same as in step 1106 in Fig. 11.　　In an example, the UE 3 may also evaluate the F1 score after multiple evaluation windows to reduce the overhead. For direct prediction, the UE 3 may maintain and update the counters n1', n2' and n3'. For every evaluation window of F1 score, the UE 3 may repeat step 1206.　　At step 1207, the UE 3 may be configured to communicate or send the measurement report metrics to the network node 5 through at least one of the RRC message or through IE in UAI (e.g. UE Assistance Information). The communication or reporting of the measurement report metrics may comprise at least one of a periodic reporting or a triggered reporting. For example:-　　Option-1: Immediate reporting of metric (e.g. the UE 3 may send the measurement report metrics including at least one of the Precision, Recall and F1 score once the UE 3 evaluates or calculates at least one of the Precision, Recall and F1 score)-　　Option-2: Multiple Calculation based reporting (e.g. the UE 3 may send multiple performance metrics together)The measurement report metrics may be expressed as performance metric. The measurement report metrics may include at least one of the calculated F1 score, Precision, Recall.　　At step 1208, the UE 3 may be configured to reset the counters n1, n2 and n3 to zero and the ETW timer to zero.　　At step 1209, the network node 5 may evaluate the reported F1 score. If the performance of the at least one prediction model based on the F1 score falls below the defined (predefined) F1 threshold, then the network node 5 may trigger the prediction model switch procedure. For example, in a case where the performance of the at least one prediction model based on the F1 score falls below the defined (predefined) F1 threshold, then the network node 5 may proceed to step 1210. If the network 5 doesn't trigger the prediction model switch procedure, the network node 5 may initiate a handover of the UE3.　　At step 1210, the network node 5 may transmit to the UE 3, the prediction model switch command through the RRC message. The RRC message may comprise at least one parameter as listed below:-　　maximum event timing difference-　　evaluation window duration-　　changes in event reporting type-　　Identity or characteristics of the new AI / ML model (after the switching) and it's configuration　　At step 1211, the UE 3 may switch to the new AI / ML model following the identity sent by the network node 5 for measurement event prediction and may apply the updated parameters and configurations. For example, the UE 3 may switch to the AI / ML model indicated by the network node 5.　　Referring to Fig. 13, signaling flow 1300 for the network node 5 for multiple cell / node is shown. For example, referring to Fig. 13, signaling flow 1300 for the network node 5 executing the method 1000 for multiple cell / node may be shown.　　At step 1301, the network node 5 (e.g. source gNB) may request the UE 3 to send it's AI / ML capability including the AI / ML models to support functionality of the UE 3 of measurement event prediction.　　At step 1302, the UE 3 may send the model IDs of the applicable AI / ML model and it's characteristics corresponding to the measurement event prediction through the RRC message for example, the UE capability information once the network node 5 has requested the UE 3's AI / ML capability.　　At step 1303, the network node 5 may configure the UE 3 with necessary parameters for the F1 score evaluation and may indicate whether the UE 3 needs to perform direct / indirect prediction analysis instruction. The parameters comprise at least one of:-　　Maximum Event Timing Difference (ETDmax, maximum ETD) (This may be variable depending on the frequency band of operation, for example, 20ms(FR2) / 40ms(FR1) ).-　　Evaluation Time Window (ETW)-　　Metric evaluation window configuration-　　Single / Multiple ETW based prediction-　　Event types for evaluation (eg A3, A5)-　　Reporting mode (periodic or trigger-induced)-　　Model switching rules-　　Cluster of gNBs / single gNB for the measurement event prediction-　　Number of cells in the cluster　　Each of the steps 1304, 1305, 1306 and 1307 are similar to the steps 1204, 1205, 1206 and 1027 as shown in Fig. 12 and hence are not repeated for the sake of brevity. For example, in a case of handover for the UE 3 is executed after step 1306, the UE 3 may send, to a target gNB (e.g. base station of handover destination), the RRC message including the performance metrics.　　At step 1308, the target gNB may send the metrics reports (e.g. the performance metrics) received from the UE 3 to the source gNB 5 and then the source gNB 5 may consolidate the metric report to determine the performance of the AI / ML model for measurement event prediction.　　At step 1309, the target gNB may evaluate the reported F1 score. If the performance falls below the defined threshold, the network triggers the model switch procedure.　　In an example, with reference to Fig. 13, in case of the multiple cell based scenario, it is assumed that the handover may take place before the ETW timer expires. Thus, in the handover request along with the measurement event prediction report, the source cell 5 (or the source gNB) may communicate or send the UE 3's capability information (for example, the characteristics and configuration of AI / ML model available for measurement event prediction) to the target gNB for future measurement configuration. In multiple cell-based scenario, the counters n1, n2 and n3 keeps updating across multiple gNBs until if the ETW / ETWs timer set at the UE 3 has not expired.　　At step 1310, the network node 5 (e.g. the target gNB) may send the model switch command through the RRC message comprising the at least one parameter as discussed above in step 1210 of Fig. 12.　　At step 1311, the UE 3 may switch to the new AI / ML model following the identity sent by the network node 5 for measurement event prediction and may apply the updated parameters and configurations. The UE 3 may again start the timer for ETW once the UE 3 may receive the RRC message with the same value of ETW else the updated ETW value in case the gNB is operated by different vendor. The gNB may be the source gNB or the target gNB. For example, at step 1311, the UE 3 may perform same process in step 1211 in Fig. 12.　　Referring to Fig. 14, signaling flow 1400 for the UE 3 and the network node 5 for single cell / node is shown. For example, referring to Fig. 14, signaling flow 1400 for the UE 3 and the network node 5 executing the method 900 and the method 1000 for single cell / node may be shown. The signaling flow 1400 shows a hybrid approach for the UE 3 and the network node 5.　　Steps 1401 till steps 1405 are similar to steps 1201 till steps 1205 of Fig. 12 and steps 1301 till steps 1305 of Fig. 13 and hence are not repeated for the sake of brevity.　　At step 1406, the UE 3 may store the counters n1, n2 and n3 at the end of the evaluation time duration defined in the configuration message and store them in the measurement results report. In an example, for the direct prediction, the UE 3 may maintain and updates n1', n2' and n3' and for every evaluation window, the step 1405 is repeated.　　Steps 1407 and 1408 are again similar to steps 1207 and 1208 of Fig. 12 and steps 1307 and 1308 of Fig. 13 and hence are not repeated for the sake of brevity. For example, the UE 3 may send counter values (e.g. values of counter n1, n2 and n3) to the network node 5. The counter values may be included in a RRC message. The UE 3 may send the performance metrics reports including the counter values to the network node 5.　　At step 1409, the network node 5 may evaluate the F1 score based on the reported counters (e.g. counter values). The F1 score is based on evaluation of Precision and Recall values which is dependent on the counter values reported. If the performance based on the calculated F1 score (or the calculated F1 score) falls below the predefined F1 threshold (stored at the network node 5's end), the network node 5 may trigger the model switch procedure. If F1 score is more than the predefined F1 threshold, the network node 5 may initiate the handover.　　Steps 1410 and 1411 are again similar to steps 1210 and 1211 of Fig. 12 and steps 1310 and 1311 of Fig. 13 and hence are not repeated for the sake of brevity.　　In Fig. 15, in an example, when the UE 3 selects the indirect prediction model for the measurement event prediction, the calculation of the F1 score as discussed above may focus on the second block (Measurement event prediction block 1504) in the case of indirect prediction. Radio Resource Management (RRM) prediction model accuracy parameter that comprises an average Reference Signal received Power (RSRP) difference between the predicted L3 RSRP and the L3 actual RSRP or last predicted point L3 cell RSRP difference of measurement results within prediction window (PW) may also be reported along with the measurement event prediction metrics report (e.g. the F1 score, Precision, Recall, counter values etc.) sent to the network node (e.g. gNB) 5 from the UE 3.　　The gNB 5 may consider the metrics of both the blocks, i.e., the RRM prediction model 1504 (based on the RRM measurements 1502) and measurement event prediction model 1508 (based on the RRM prediction results 1506) to decide the AI / ML model selection and configuration for measurement event prediction and RRM prediction module as well during model switch procedure at the expected time of occurrence 1510. For example, the network node 5 may select at least one of the AI / ML model for the UE 3 and configuration for measurement event prediction for the UE 3 and RRM prediction module for the UE 3 based on metrics of the RRM prediction model (e.g. RRM prediction model accuracy parameter) and / or the measurement event prediction metrics (e.g. the F1 score, Precision, Recall, counter values etc.).　　Referring to Fig. 16, an example of the reporting framework of the measurement event prediction performance metric is illustrated. Steps in the reporting of the measurement report will now be discussed. In step 1, the network 5 may send the configuration message specifying the ETW and max ETD and other measurement event prediction parameters to the UE 3. The configuration message may be the RRC message including the ETW and max ETD and other measurement event prediction parameters. In step 2, the UE 3 may initialize each of the counters n1, n2 and n3 and ETW timer is set to zero. In step 3, at the end of each max ETD, the UE 3 may increase the relevant counter based on the prediction results (as discussed above). In step 4, the UE 3 may calculate each of the Precision, Recall and the F1 score based on the counter values and the UE 3 may then report to the network node 5 via the RRC message. In step 5, the UE 3 may reset each of the counters n1, n2 and n3 for the next ETW and again sets the ETW timer to zero.　　In an example, as also discussed above, the UE 3 may be configured to report the measurement event prediction metrics or results after multiple ETWs if configured by the network node 5 and then the UE 3 may reset each of the counters n1, n2 and n3.　　In an example, in case of multiple cell-based scenarios, the ETW timer is restarted after the value reaches the limit set by the source gNB 5 in the initial configuration message shared with the UE 3. The ETW timer may continue to be updated across gNBs and then the performance report is sent to the current gNB after the timer expires. The ETW timer gets an instruction for restart through the RRC message along with the AI / ML model and configuration for measurement event prediction from the source node 5.　　As shown on Fig. 16, the ETW may be divided into multiple ETD (max) at different time intervals such as t=0, t=t1, t=t2, t=t3 and t=ETW. Each ETD(max) time duration / window comprises observation window (OW) and prediction window (PW). At t = 0, the UE 5 may receive the measurement configuration comprising the ETD(max) and the ETW timer value from the network node. Each of the counters n1, n2 and n3 are set to 0. For first OW and PW and first ETD(max), the UE 3 may set the counter n1 to n1+1 (e.g. 1) and the counter n2 and n3 may be set to 0. Between t = t1 and t = t2, the counter n2 may be incremented by 1 and n3 may be set to 0. Between t = t2 to t = t3, the counter n3 may be incremented by 1, now n1 is n1+1 (e.g. 1), n2 is n2+1 (e.g. 1) and n3 is n3+1 (e.g. 1). The UE 3 may monitor the updated counters n1, n2 and n3 and evaluate the F1 score. Based on the F1 score as discussed above, the UE 3 may send the metrics report of the measurement event prediction to the network node 5. At the expiration of the ETW, when t = ETW, the ETW is reset to 0 and each of the counter n1, n2 and n3 is reset to 0.　　In an example, the UE 3 may be configured to transmit / send the measurement event prediction metric comprising the F1 score to the network node 5 at regular intervals. The UE 3 may be configured with a periodic timer by the network node as the ETW and at the end of every ETW, the UE 3 may aggregate the predicted events within the window and may send the aggregated report to the network node 5. The aggregated report sharing is preferred when the network node 5 requires frequent updates from the UE 3.　　In an example, in case of multiple ETWs, the UE 3 may skip the report or send a null report if no significant changes in the F1 score are observed across the ETWs, either instructed by network node 5 or decided by the UE 3.　　In an example, in case of event triggered reporting of the measurement report, the UE 3 may send the reports only when specific conditions are met or some triggering condition are decided at the UE 3's side. When the prediction model detects the certain condition of F1 score threshold, then the UE 3 may report or send the metric report to the network node 5. The network node may explicitly request the UE 3 to send specific prediction data and / or performance metrics and the network node 5 may preconfigure the UE 3 with conditions and thresholds for sending the performance metrics. The threshold may be decided at the UE 3 or may be configured by the network node 5.　　In an example, the UE 3 may consider at least one of the single observation window or multiple observation windows (as discussed in Fig. 16). In case of the single observation window, the UE 3 may consider the predictions made within the single observation window (ETW) and may send the report immediately to the network node 5. The prediction metrics (e.g. F1 score) are processed at the end of the ETW and are sent to the network node 5 through the RRC message. The time framework may be 500 ms.　　As shown in Fig. 16, in case of the multiple time windows, the UE 3 may aggregate predictions from multiple ETWs and may send the aggregated / consolidated report. The network node 5 may configure the UE 3 with multiple observation windows. The UE 3 may aggregate the prediction metrics across the multiple observation windows and may send the aggregated report to the network node 5 and thus reduces signaling overhead. A total time frame may be for example, 5 times of single observation window (ETW).　　In an example, event triggered reporting (ETW reporting) across multiple nodes / gNBs / cells for inter-frequency measurement event prediction will now be discussed.　　At step 1, the source gNB 5 may configure the UE 3 with the ETW by setting the time duration. The counters n1, n2 and n3 may be set for a list of target gNBs and frequency bands. For example, the list for the target gNB is defined below:-　　{gNB_ID_1, Frequency_Band_X, Counter_List}-　　{gNB_ID_2, Frequency_Band_Y, Counter_List}For example, the UE 3 may count counters n1, n2, and n3 per each target gNB (or per each network node). For example, the UE 3 may count counters n1, n2, and n3 per each frequency band. For example, the UE 3 may count counters n1, n2, and n3 per each frequency band of each target gNB (or each network node).　　The steps for trigger of the measurement report are discussed in Fig. 14 and hence are not repeated for the sake of brevity and steps for the AI / ML model and characteristics for measurement event prediction are discussed in Fig. 13 and hence are not repeated for the sake of brevity.　　As discussed above in Figs. 11 to 14, in step 2, the UE 3 may perform the measurement event prediction using the configured counters n1, n2 and n3 and may keep updating values of the counters n1, n2 and n3.　　In step 3, the UE 3 may undergo the handover and the target gNB may check if there is a need to reconfigure the ETW timer for the UE. For example, the ETW time may be needed to reset, when the vendor for the target gNB is different from the source gNB 5. If there is no need to change the ETW timer configuration, the value of the ETW timer and each of the counters n1, n2 and n3 remains unchanged, else the value of the ETW timer and / or the counters n1, n2 and n3 may be reconfigured by the target gNB.　　In step 4, when the ETW timer reaches the maximum limit, the UE 3 may report the counter-based performance metrics that comprises the Precision, the Recall and the F1 score to the current serving gNB. In step 5, the target serving gNB may receive the measurement report (e.g. the performance metrics) and may perform the following steps:-　　The target serving gNB may aggregate the measurement reports received from multiple UEs or multiple reports from one UE 3, to decide network side optimization for improved prediction. In an example, the multiple UEs may run the same prediction model and may send the measurement report to the same target gNB.-　　The target serving gNB may forward the measurement reports received from the UE 3 to the source serving gNB 5 which may be running network side optimization for improved prediction by the UE 3.　　In step 6, the serving target gNB may send the RRC reconfiguration message to the UE 3 to restart the ETW for future or subsequent measurement tracking of the UE 3. If there is no need to change the ETW timer configuration, then the ETW timer and the counters n1, n2 and n3 may continue to run or else the ETW timer value may be reset with a new value and the counters n1, n2 and n3 may restart with a new configuration. In step 7, the UE 3 may run the measurement event prediction based on the updated configuration received from the target gNB. The target gNB may determine, based on the received performance metrics, the appropriate AI / ML model and / or configuration for the model, and send, to the UE 3, the appropriate AI / ML model and / or configuration for the model. Then, the UE 3 may use the AI / ML model and / or the configuration indicated by the target gNB.　　Referring to Fig. 17 illustrates a block diagram of the user equipment (UE) 3 in accordance with an embodiment of the present disclosure. The various modules in the UE can be embodied as a hardware that includes, without limitation, the at least one memory 1702, the at least one processor 1704, transmitter / receiver circuitry 1706, and programmable logic or software.The at least one memory 1702, which may include both read-only memory (ROM) and random access memory (RAM), can provide instructions and data to the at least one processor 1704 / processing circuitry. The at least one memory 1702 and the at least one processor 1704 may be operatively coupled. The at least one memory 1702 may store computer readable instructions / computer program code. The at least one processor 1704 in the UE 3 may train the first, second and the third classification models.　　In the context of this document, the "memory" (also referred to as "computer-readable media" or "computer-readable medium") may be any non-transitory media or medium or means that can contain, store, communicate, propagate or transport the instructions for use by or in connection with an instruction execution system, apparatus, or device, such as a computer. The term "non-transitory," as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM).　　The transmitter / receiver (TX / RX) circuitry 1706 may comprise a transmitter and a receiver that can enable the UE to transmit data to or receive data (e.g., the input image of the crop) from the network or plurality of databases.　　The at least one processor 1704 can be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. The processor can include the logic circuitry with hardware, firmware, and software architecture frameworks for facilitating image processing.　　The steps of a method (e.g., method 900, 1000) described in connection with the embodiments disclosed herein may be embodied directly in hardware (e.g., UE), in a software module executed by the at least one processor 1704, or in a combination of the two. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a tangible, non-transitory computer-readable medium (e.g., the at least one memory 1702). A software module may reside in Random Access Memory (RAM), flash memory, Read Only Memory (ROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD ROM, or any other form of storage medium known in the art. A storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium.　　In the several embodiments provided in this application, the disclosed system, device, and method may be implemented in another manner. For example, some features of the method embodiments described above may be ignored or not performed. The described device embodiments are merely examples.　　The term based on is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of "a", "an" and "the" include plural references. The meaning of "in" includes "in" and "on".　　As used herein the terms "and" and "or" may be used interchangeably to refer to a set of items in both the conjunctive and disjunctive in order to encompass the full description of combinations and alternatives of the items. In either case, the set is to be interpreted as meaning each of the items singularly as alternatives, as well as any combination of the listed items.　　The description above merely illustrating the technical spirit of the present disclosure, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present disclosure. Therefore, the embodiments of the present disclosure described above may be implemented separately or in combination with each other.　　The embodiments disclosed in the present disclosure are intended to illustrate rather than limit the scope of the present disclosure, and the scope of the technical spirit of the present disclosure is not limited by these embodiments. The scope of the present disclosure should be construed by claims below, and all technical spirits within a range equivalent to claims should be construed as being included in the right scope of the present disclosure.　　While only certain features have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the disclosure.<Supplementary notes>　　(Supplementary note 1)　　A method (900) performed by a User Equipment (UE) (3) for determining perfor-mance metric of measurement event prediction, the method (900) comprising:　　receiving (902), from a network node (5), configuration parameters for the measurement event prediction;　　initializing (904) Evaluation Time Window (ETW) timer and setting one or more counters to track measurement event prediction based on the configuration parameters;　　performing (906) the measurement event prediction over one or more evaluation windows, based on the initialized ETW timer and set one or more counters, using at least one prediction model;　　comparing (908) a time instance for the predicted measurement event with a time instance received from the network node (5) to determine an accuracy of the predicted measurement event, wherein the time instance received from the network node (5) is within a defined Maximum Event Time Difference (ETDmax) range; and　　updating the one or more counters based on the determined accuracy of the predicted measurement event, wherein the updated one or more counters are used to evaluate the performance metrics of the measurement event prediction.　　(Supplementary note 2)　　The method (900) according to supplementary note 1, wherein the configuration parameters com-prise at least one of:　　Maximum Event Time Difference (ETDmax);　　Evaluation Time Window (ETW);　　metric evaluation window configuration;　　F1 score threshold;　　single or multiple ETW based prediction;　　frequency of operation;　　event types for evaluation, wherein the event type comprises A3 or A5 event;　　reporting mode comprising periodic reporting or trigger-based reporting;　　prediction model switching rules; or　　Cluster of cells or single cell for the measurement event prediction　　number of cells in a cluster.　　(Supplementary note 3)　　The method (900) according to supplementary note 1, wherein the determining the accuracy com-prises:　　identifying one of:　　a correctly predicted measurement event;　　a missed measurement event; or　　an incorrectly predicted measurement event.　　(Supplementary note 4)　　The method (900) according to supplementary note 1, wherein setting the one or more counter com-prises initializing each counter to zero at start of an evaluation window.　　(Supplementary note 5)　　The method (900) according to supplementary note 1, determining the performance metrics of the measurement event prediction comprising:　　computing a precision and recall value based on the one or more counters;　　calculating an F1 score using the precision and the recall value for the predicted measurement event after completion of an Evaluation Time Window (ETW);　　comparing the determined F1 score with predefined F1 threshold set by the network;; and　　triggering at least one of:　　transmission of a measurement report to the network node (5) if the determined F1 score is less than the predefined F1 threshold,　　wherein measurement report is transmitted as one of an immediate report, multiple evaluation windows-based report, or an event based report; or　　switching of the at least one prediction model if the determined F1 score is less than the predefined F1 threshold based on network defined rules, wherein the switching is performed based on a model switch command received from the network node (5).　　(Supplementary note 6)　　The method (900) according to any one of the preceding supplementary notes, comprising:updating the configuration parameters when the at least one model is switched to an updated prediction model; andperforming a next measurement event prediction based on the updated configuration parameters.　　(Supplementary note 7)　　The method (900) according to any one of the preceding supplementary notes, comprising:　　resetting the ETW timer and the one or more counters before initiating a new measurement event prediction, wherein the resetting is performed when the UE is operating in a single-cell scenario.　　(Supplementary note 8)　　The method (900) according to supplementary note 1, comprising:　　updating the one or more counters across multiple cells when the UE (3) is communicating in a multi-cell environment, wherein the one or more counters are updated for a predefined threshold of the ETW timer;　　aggregating the updated one or more counter values across the multiple cells to determine an aggregated performance metrics of the measurement event prediction; andtransmitting the aggregated performance metrics to a target cell of the multiple cells before resetting the one or more counters,　　wherein the aggregated metrics is transmitted as one of a Radio Resource Control (RRC) message or in UE assistance Information (UAI), wherein the aggregated performance metrics is transmitted to a current target cell of multiple cells;　　wherein the aggregated performance metrics is used to determine a performance of the at least one prediction model;　　receiving, from a source cell (5), an updated configuration parameters and a model switch command to switch the at least one prediction model to an updated prediction model, if the aggregated performance metrics is less than a threshold value; and　　performing a next measurement event prediction based on the updated configuration parameters and the updated prediction model.　　(Supplementary note 9)　　The method (900) according to supplementary note 1, wherein prediction model comprises an Arti-ficial Intelligence / Machine Learning (AI / ML) based direct prediction model or an AI / ML based indirect prediction model, wherein the AI / ML based indirect prediction model comprises an RRM prediction model and a measurement event prediction mod-el.　　(Supplementary note 10)　　A method (1000) performed by a network node (5) for performance monitoring of measurement event prediction, the method (1000) comprising:　　transmitting (1002), to at least one UE (3), configuration parameters for measurement event prediction; andr　　eceiving, from the at least one UE (3), a report comprising a performance metrics of the measurement event prediction, wherein the performance metrics comprises one or more counters providing an accuracy of the prediction of the measurement event.　　(Supplementary note 11)　　The method (1000) according to supplementary note 10, comprising:　　transmitting, to the at least one UE (3), a switching command to switch the at least one prediction model if determined F1 score by the at least one UE (3) is less than a threshold F1 score defined by the network node (5), wherein the F1 score is calculated by at least one of the network node (5) or the at least one UE (3);　　wherein the switching is performed based on network defined rules.　　(Supplementary note 12)　　The method (1000) according to supplementary note 10, comprising:　　receiving from the at least one UE (3), a measurement report if determined F1 score by the at least one UE (3) is less than a predefined F1 threshold defined by the network node (5).　　(Supplementary note 13)　　The method (1000) according to supplementary note 10, comprising:　　triggering, an update in the configuration parameters based on the performance metrics of the measurement event prediction　　(Supplementary note 14)　　The method (1000) according to supplementary note 10, comprising:　　receiving, from the at least one UE (3), an aggregated performance metrics, wherein the at least one UE (3) is operating in a multi-cell environment, wherein the aggregated performance metrics is used by the at least UE (3) to calculate an aggregated F1 score; andtriggering at least one of:　　a model switch command to switch the at least one prediction model if determined aggregated F1 score by the at least one UE (3) is less than a predefined F1 threshold defined by the network node (5), wherein the switching is performed based on network defined rules; or　　triggering, an update in the configuration parameters based on the aggregated performance metrics of the measurement event prediction.　　(Supplementary note 15)　　A User Equipment (UE) (3) for determining performance metric of measurement event prediction, the UE (3) comprising:　　at least one processor (1704); and　　at least one memory (1702) storing instructions that, when executed by the at least one processor (1704), cause the UE (3) at least to:　　receive, from a network node (3), configuration parameters for the measurement event prediction;　　initialize Evaluation Time Window (ETW) timer and setting one or more counters to track measurement event prediction based on the configuration parameters;　　perform the measurement event prediction over one or more evaluation windows, based on the initialized ETW timer and set one or more counters, using at least one prediction model;compare a time instance for the predicted measurement event with a time instance received from 　　the network node (5) to determine an accuracy of the predicted measurement event, wherein the time instance received from the network node (5) is within a defined Maximum Event Time Difference (ETDmax) range; and　　update the one or more counters based on the determined accuracy of the predicted measurement event, wherein the updated one or more counters are used to evaluate the performance metrics of the measurement event prediction.　　(Supplementary note 16)　　A network node (5) for performance monitoring of measurement event prediction, the network node (5) comprising:　　at least one processor; and　　at least one memory storing instructions that, when executed by the at least one processor, cause the network node (5) at least to:　　transmit, to at least one UE (3), configuration parameters for measurement event prediction; and　　receive, from the at least one UE (3), a report comprising a performance metrics of the measurement event prediction, wherein the performance metrics comprises one or more counters providing an accuracy of the prediction of the measurement event.　　　　This application is based upon and claims the benefit of priority from Indian Patent Application No. 202511026165, filed on March 21, 2025, the disclosure of which is incorporated herein in its entirety by reference.10　　CORE NETWORK20　　DATA NETWORK3　　USER EQUIPMENT(UE)31　　TRANSCEIVER CIRCUIT32　　ANTENNA33　　CONTROLLER34　　USER INTERFACE35　　USIM36　　MEMORY361　　OPERATING SYSTEM362　　COMMUNICATIONS CONTROL MODULE3621　　TRANSCEIVER CONTROL MODULE5　　RADIO ACCESS NETWORK (RAN)51　　TRANSCEIVER CIRCUIT52　　ANTENNA53　　NETWORK INTERFACE54　　CONTROLLER55　　MEMORY551　　OPERATING SYSTEM552　　COMMUNICATIONS CONTROL MODULE5521　　TRANSCEIVER CONTROL MODULE60　　RU601　　TRANSCEIVER CIRCUIT602　　ANNTENA603　　NETWORK INTERFACE604　　CONTROLLER605　　MEMORY6051　　OPERATING SYSTEM6052　　COMMUNICATIONS CONTROL MODULE60521　　TRANSCEIVER CONTROL MODULE61　　DU611　　TRANSCEIVER CIRCUIT612　　NETWORK INTERFACE613　　CONTROLLER614　　MEMORY6141　　OPERATING SYSTEM6142　　COMMUNICATIONS CONTROL MODULE61421　　TRANSCEIVER CONTROL MODULE62　　CU621　　TRANSCEIVER CIRCUIT622　　NETWORK INTERFACE623　　CONTROLLER624　　MEMORY6241　　OPERATING SYSTEM6242　　COMMUNICATIONS CONTROL MODULE62421　　TRANSCEIVER CONTROL MODULE7　　CORE NETWORK70　　AMF701　　TRANSCEIVER CIRCUIT702　　NETWORK INTERFACE703　　CONTROLLER704　　MEMORY7041　　OPERATING SYSTEM7042　　COMMUNICATIONS CONTROL MODULE70421　　TRANSCEIVER CONTROL MODULE71　　SMF72　　UPF73　　PCF74　　NEF75　　UDM76　　NWDAF77　　NSSF78　　NRF1702　　MEMORY1704　　PROCESSOR1706　　TX / RX CIRCUITRY

Claims

1. A method performed by a User Equipment (UE) for determining perfor-mance metric of measurement event prediction, the method comprising: 　　receiving, from a network node, configuration parameters for the measurement event prediction; 　　initializing Evaluation Time Window (ETW) timer and setting one or more counters to track measurement event prediction based on the configuration parameters; 　　performing the measurement event prediction over one or more evaluation windows, based on the initialized ETW timer and set one or more counters, using at least one prediction model; 　　comparing a time instance for the predicted measurement event with a time instance received from the network node to determine an accuracy of the predicted measurement event, wherein the time instance received from the network node is within a defined Maximum Event Time Difference (ETDmax) range; and 　　updating the one or more counters based on the determined accuracy of the predicted measurement event, wherein the updated one or more counters are used to evaluate the performance metrics of the measurement event prediction.

2. The method according to claim 1, wherein the configuration parameters com-prise at least one of: 　　Maximum Event Time Difference (ETDmax); 　　Evaluation Time Window (ETW); 　　metric evaluation window configuration; 　　F1 score threshold; 　　single or multiple ETW based prediction; 　　frequency of operation; 　　event types for evaluation, wherein the event type comprises A3 or A5 event; 　　reporting mode comprising periodic reporting or trigger-based reporting; 　　prediction model switching rules; or 　　Cluster of cells or single cell for the measurement event prediction 　　number of cells in a cluster.

3. The method according to claim 1, wherein the determining the accuracy com-prises: 　　identifying one of: 　　a correctly predicted measurement event; 　　a missed measurement event; or 　　an incorrectly predicted measurement event.

4. The method according to claim 1, wherein setting the one or more counter com-prises initializing each counter to zero at start of an evaluation window.

5. The method according to claim 1, determining the performance metrics of the measurement event prediction comprising: 　　computing a precision and recall value based on the one or more counters; 　　calculating an F1 score using the precision and the recall value for the predicted measurement event after completion of an Evaluation Time Window (ETW); 　　comparing the determined F1 score with predefined F1 threshold set by the network;; and 　　triggering at least one of: 　　transmission of a measurement report to the network node if the determined F1 score is less than the predefined F1 threshold, 　　wherein measurement report is transmitted as one of an immediate report, multiple evaluation windows-based report, or an event based report; or 　　switching of the at least one prediction model if the determined F1 score is less than the predefined F1 threshold based on network defined rules, wherein the switching is performed based on a model switch command received from the network node.

6. The method according to any one of claims 1 to 5, comprising: updating the configuration parameters when the at least one model is switched to an updated prediction model; and performing a next measurement event prediction based on the updated configuration parameters.

7. The method according to any one of claims 1 to 6, comprising: 　　resetting the ETW timer and the one or more counters before initiating a new measurement event prediction, wherein the resetting is performed when the UE is operating in a single-cell scenario.

8. The method according to claim 1, comprising: 　　updating the one or more counters across multiple cells when the UE is communicating in a multi-cell environment, wherein the one or more counters are updated for a predefined threshold of the ETW timer; 　　aggregating the updated one or more counter values across the multiple cells to determine an aggregated performance metrics of the measurement event prediction; and transmitting the aggregated performance metrics to a target cell of the multiple cells before resetting the one or more counters, 　　wherein the aggregated metrics is transmitted as one of a Radio Resource Control (RRC) message or in UE assistance Information (UAI), wherein the aggregated performance metrics is transmitted to a current target cell of multiple cells; 　　wherein the aggregated performance metrics is used to determine a performance of the at least one prediction model; 　　receiving, from a source cell, an updated configuration parameters and a model switch command to switch the at least one prediction model to an updated prediction model, if the aggregated performance metrics is less than a threshold value; and 　　performing a next measurement event prediction based on the updated configuration parameters and the updated prediction model.

9. The method according to claim 1, wherein prediction model comprises an Arti-ficial Intelligence / Machine Learning (AI / ML) based direct prediction model or an AI / ML based indirect prediction model, wherein the AI / ML based indirect prediction model comprises an RRM prediction model and a measurement event prediction mod-el.

10. A method performed by a network node for performance monitoring of measurement event prediction, the method comprising: 　　transmitting, to at least one UE, configuration parameters for measurement event prediction; and r　　eceiving, from the at least one UE, a report comprising a performance metrics of the measurement event prediction, wherein the performance metrics comprises one or more counters providing an accuracy of the prediction of the measurement event.

11. The method according to claim 10, comprising: 　　transmitting, to the at least one UE, a switching command to switch the at least one prediction model if determined F1 score by the at least one UE is less than a threshold F1 score defined by the network node, wherein the F1 score is calculated by at least one of the network node or the at least one UE; 　　wherein the switching is performed based on network defined rules.

12. The method according to claim 10, comprising: 　　receiving from the at least one UE, a measurement report if determined F1 score by the at least one UE is less than a predefined F1 threshold defined by the network node.

13. The method according to claim 10, comprising: 　　triggering, an update in the configuration parameters based on the performance metrics of the measurement event prediction14. 　　The method according to claim 10, comprising: 　　receiving, from the at least one UE, an aggregated performance metrics, wherein the at least one UE is operating in a multi-cell environment, wherein the aggregated performance metrics is used by the at least UE to calculate an aggregated F1 score; and triggering at least one of: 　　a model switch command to switch the at least one prediction model if determined aggregated F1 score by the at least one UE is less than a predefined F1 threshold defined by the network node, wherein the switching is performed based on network defined rules; or 　　triggering, an update in the configuration parameters based on the aggregated performance metrics of the measurement event prediction.

15. A User Equipment (UE) for determining performance metric of measurement event prediction, the UE comprising: 　　at least one processor; and 　　at least one memory storing instructions that, when executed by the at least one processor, cause the UE at least to: 　　receive, from a network node, configuration parameters for the measurement event prediction; 　　initialize Evaluation Time Window (ETW) timer and setting one or more counters to track measurement event prediction based on the configuration parameters; 　　perform the measurement event prediction over one or more evaluation windows, based on the initialized ETW timer and set one or more counters, using at least one prediction model; compare a time instance for the predicted measurement event with a time instance received from the network node to determine an accuracy of the predicted measurement event, wherein the time instance received from the network node is within a defined Maximum Event Time Difference (ETDmax) range; and 　　update the one or more counters based on the determined accuracy of the predicted measurement event, wherein the updated one or more counters are used to evaluate the performance metrics of the measurement event prediction.

16. A network node for performance monitoring of measurement event prediction, the network node comprising: 　　at least one processor; and 　　at least one memory storing instructions that, when executed by the at least one processor, cause the network node at least to: 　　transmit, to at least one UE, configuration parameters for measurement event prediction; and 　　receive, from the at least one UE, a report comprising a performance metrics of the measurement event prediction, wherein the performance metrics comprises one or more counters providing an accuracy of the prediction of the measurement event.

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