User equipment (UE), and method of ue

Abstract

In one implementation, a user equipment (UE) is provided. The UE is configured to receive a set of monitoring resources and a set of parameters from a network node. The set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values. The UE is configured to store artificial intelligence / machine learning (AI / ML) model. Further, the UE is configured to determine performance metric for the set of monitoring resources based on the set of received parameters.

Description

USER EQUIPMENT (UE), AND METHOD OF UE

[0001] The present disclosure generally relates to a user equipment (UE) and a method of the UE.

[0002] In 3GPP RAN#102, Rel-19, there is a work item (WI) on "New WID on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface". The WI objective includes Beam management - DL Tx beam prediction for both UE-sided model and NW-sided model, encompassing [RAN1 / RAN2]. The beam management includes spatial-domain DL Tx beam prediction for Set A of beams based on measurement results of Set B of beams ("BM-Case1"), temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams ("BM-Case2"). The beam management further includes specifying necessary signalling / mechanism(s) to facilitate LCM operations specific to the Beam Management use cases, if any and enabling method(s) to ensure consistency between training and inference regarding NW-side additional conditions (if identified) for inference at user equipment (UE).

[0003] The following presents a simplified summary of the subject matter / disclosure in order to provide a basic understanding of some of the aspects of subject matter embodiments. This summary is not an extensive overview of the subject matter. It is not intended to identify key / critical elements of the embodiments or to delineate the scope of the subject matter. Its sole purpose to present some concepts of the subject matter in a simplified form as a prelude to the more detailed description that is presented later.

[0004] In one implementation, a user equipment (UE) is provided. The UE is configured to receive a set of monitoring resources and a set of parameters from a network node. The set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values. The confidence information of RSRP value is the probability that the predicted RSRP value is correct. The UE is configured to store artificial intelligence / machine learning (AI / ML) model. Further, the UE is configured to determine performance metric for the set of monitoring resources based on the set of received parameters.

[0005] In another implementation, a method of a user equipment is provided. The method comprises receiving a set of monitoring resources from a network node, receiving a set of parameters from the network node, wherein the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values, storing artificial intelligence / machine learning (AI / ML) model, and determining performance metric for the set of monitoring resources based on the set of received parameters.

[0006] 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.

[0007] The foregoing and further objects, features, and advantages of the present subject matter 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.

[0008] It is to be noted, however, that the appended drawings illustrate only typical embodiments of the present subject matter, and are, therefore, not to be considered for limiting of its scope, for the subject matter may admit to other equally effective embodiments.

[0009] 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. 9A illustrates different types of performance metric reporting, in accordance with an embodiment of the present disclosure.Fig. 9B illustrates different types of performance metric reporting, in accordance with an embodiment of the present disclosure.Fig. 9C illustrates different types of performance metric reporting, in accordance with an embodiment of the present disclosure.Fig. 9D illustrates different types of performance metric reporting, in accordance with an embodiment of the present disclosure.Fig. 10 illustrates a flowchart of a method for determining performance metric, in accordance with an embodiment of the present disclosure.Fig. 11 illustrates a flowchart of a method for assisting in determination of performance metric, in accordance with an embodiment of the present disclosure.Fig. 12 illustrates a block diagram of the user equipment (UE) in accordance with another embodiment of the present disclosure.

[0010] 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.

[0011] The present disclosure generally relates to wireless communication systems and more particularly relates to techniques for effective AI / ML based beam management and performance monitoring utilizing UE-sided models.

[0012] The embodiments of the present subject matter are described in detail with reference to the accompanying drawings. However, the present subject matter is not limited to these embodiments which are only provided to explain more clearly the present subject matter to the ordinarily skilled in the art of the present disclosure. In the accompanying drawings, like reference numerals are used to indicate like components.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] In modern wireless communication systems, beam management is a technique used to establish a communication between a user equipment (UE) and base station (for example, gNB). The User Equipment (UE) selects the best beam to establish and maintain a robust connection with the base station (gNB) by evaluating signal quality metrics. To establish or maintain connection (e.g., to maintain optimal connection), the gNB transmits synchronization signals (e.g., synchronization signal blocks (SSBs) , SS / PBCH block etc.) and response signals (or reference signals). The UE measures the signal quality of these beams, typically using metrics such as Reference Signal Received Power (RSRP) and Signal to Interference and noise ratio (SINR), and identifies the beam with the highest quality. After measurement, the UE sends a beam quality report to the gNB, which uses this information to confirm or refine the selected beam. In scenarios of dynamic mobility or fluctuating signal conditions, the process of beam selection and switching becomes critical to ensure uninterrupted connectivity and optimal performance between the UE and the gNB. This particularly is helpful in scenarios where the UE is constantly moving, and an optimal connection is required to be established between the UE and the gNB. To establish initial connection, the UE may select an initial beam to camp on.

[0017] To further enhance the efficiency of beam management, artificial intelligence / Machine learning AI / ML models are being integrated into the UE. These models enable the UE to predict and select the best beams out of the plurality of beams by leveraging historical data, mobility patterns, and environmental context. Such AI / ML-driven predictions reduce the need for exhaustive beam measurements and speed up the decision-making process, particularly in dense urban environments or high-mobility scenarios. The integration of AI / ML models significantly improves beam selection accuracy, reduces beam switching delays, and ensures a more stable connection, enhancing the overall user experience.

[0018] To ensure beam management and overall network performance, performance monitoring is used. Performance monitoring involves the continuous assessment of key metrics related to signal quality and network (or network performance) such as RSRP, SINR, Reference Signal Received Quality (RSRQ), latency, and throughput. This real-time monitoring of the network and signal quality parameters allows the UE and gNB to evaluate the current connection quality, detect issues such as interference or signal degradation, and make timely adjustments to beam configurations (or timely adjustment of beam selection). Performance monitoring is essential not only for maintaining optimal connectivity but also for validating the accuracy of AI / ML predictions and improving network resource utilization. Without effective performance monitoring, the reliability and efficiency of beam management systems would be significantly compromised.

[0019] If the network triggers performance metric report request, the UE needs to measure the performance metric for the AI / ML Model. Further, the UE needs to measure the monitoring resources to determine the performance metric. The network may or may not configure the full set of beams on monitoring resources. If the network triggers performance metric report request, the UE would not be able to compute the result and report right away. The UE needs a certain amount of time (monitoring time window) to perform the measurement and calculate the result. Hence, there is a need to consider different possible definitions for performance metric based on the inference and measurement results. Further, there is a need to consider different mechanisms to configure. the monitoring time window. Additionally, there is a need to consider different mechanisms for model monitoring based on the configured monitoring resource set. For example, considering the WI in RAN#102, Rel-19, TR 38.843, and / or 3GPP RAN1 Agreement (e.g., 3GPP RAN1#118b Agreement), it may need to consider at least one of the performance metric definition, the monitoring resources / resource set, how to configure the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window. For example, considering the WI in RAN#102, Rel-19, TR 38.843, and / or 3GPP RAN1 Agreement (e.g., 3GPP RAN1#118b Agreement), it may need to consider at least one of the performance metric definition, the monitoring resources / resource set, how to configure the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window, as there is no definition of and / or how to configure at least one of the performance metric definition, the monitoring resources / resource set, how to configure the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window. 3GPP RAN 1 may be expressed as RAN 1.

[0020] For example, regarding performance metric definition, if the network triggers performance metric report request, the UE may need to measure the performance metric for the AI / ML Model. Therefore, it may need to consider the possible definitions for performance metric based on the inference and measurement results.

[0021] For example, regarding monitoring resources / resource set, the UE needs to measure the monitoring resources to determine the performance metric. The network may or may not configure the full set of Set A beams on monitoring resources. Therefore, it may need to consider the different mechanisms for model monitoring based on the configured monitoring resource set.

[0022] For example, regarding performance metric reporting framework and monitoring time window, if the network triggers performance metric report request, the UE may not be able to compute the result and report right away. The UE may need a certain amount of time (monitoring time window) to perform the measurement and calculate the result. Therefore, it may need to consider different mechanisms to configure the monitoring time window.

[0023] This disclosure may provide the UE, network node(s), a method of the UE and / or a method of the network node(s) etc. to solve at least one of problems mentioned in this disclosure. For example, this disclosure may provide the UE, network node(s), a method of the UE and / or a method of the network node(s) etc. to solve at least problem that considering the WI in RAN#102, Rel-19, TR 38.843, and / or 3GPP RAN1 Agreement (e.g., RAN1#118b Agreement), it may need to consider at least one of the performance metric definition, the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window. For example, this disclosure may provide the UE, network node(s), a method of the UE and / or a method of the network node(s) etc. to solve the problem that, considering the WI in RAN#102, Rel-19, TR 38.843, and / or 3GPP RAN1 Agreement (e.g., 3GPP RAN1#118b Agreement), it is not clear in current 3GPP specification about at least one of the performance metric definition, the monitoring resources / resource set, how to configure the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window.

[0024] In an overview, the present disclosure relates to determining performance metric and definitions of beam prediction indicator. To ensure beam management and overall network performance, performance monitoring is used. Performance monitoring involves the continuous assessment of key metrics related to signal quality and network such as RSRP, SINR, RSRQ, latency, and throughput. For performance monitoring, performance metric and beam prediction indicator are defined. Definition of the performance metric and the beam prediction indicator depends on various factors, such as RSRP, SINR, RSRQ, Top-K predicted beams, measured beams of the monitoring resource set etc.

[0025] In TR 38.843, for BM-Case1 and BM-Case2 with a UE-side AI / ML model, two types (Type-1 and Type-2) of performance monitoring are defined. Type 1 performance monitoring includes configuration / signalling from gNB to UE for measurement and / or reporting. The UE may have different operations: Option 1 (NW-side performance monitoring) and Option 2 (UE-assisted performance monitoring). Under option 1, UE sends reporting to network (NW) (e.g., for the calculation of performance metric at NW) and under Option 2 (UE-assisted performance monitoring), UE calculates performance metric(s), either reports it to NW or reports an event to NW based on the performance metric(s).

[0026] Type 1 performance monitoring also includes indication from NW for UE to do LCM operations. Here at least the performance and reporting overhead of model monitoring mechanism should be considered.

[0027] Type 2 performance monitoring includes Indication / request / report from UE to gNB for performance monitoring, configuration / Signalling from gNB to UE for performance monitoring measurement and / or reporting. If it is for UE side model monitoring, UE makes decision(s) of model selection / activation / deactivation / switching / fallback operation. In TR 38.843, mechanisms that facilitates the UE to detect whether the functionality / model is suitable or no longer suitable is also provided.

[0028] 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. 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).

[0029] 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).

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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 addition to the parameters that are disclosed by Aspects in this disclosure, following parameters 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

[0036] 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 parameters 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 indication, 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 addition to the parameters that are disclosed by Aspects in this disclosure, following parameters 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 status, 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 number 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 extended DRX parameters, T3447 value, T3448 value, T3324 value, UE radio capability 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 rejected 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 parameters may be included together in the Authentication Request message. 　　- - ngKSI,ABBA, Authentication parameter RAND (5G authentication challenge), Authentication 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 parameters 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 parameters 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 parameters may be populated together in the Authentication Failure message. 　　- - Authentication failure message identity, 5GMM cause and Authentication failure parameter. -　　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 parameters 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, following 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 indication, 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 registration 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.

[0037] 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 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 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.

[0038] 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).

[0039] 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.).

[0040] 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.).

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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).

[0046] 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.

[0047] 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. A set of parameters are received from the network node by the UE 3. The UE 3 also receives a set of monitoring resources from the network node. The performance metric is determined for the set of monitoring resources based on the set of received parameters.. Further, the performance metric is determined based on at least one of a beam prediction indicator and a beam prediction accuracy. In particular, the performance metric is an average of the beam prediction accuracy over the number of monitoring instances. Further, the beam prediction accuracy is defined as the ratio of the beam prediction indicator to the maximum possible count on the number of correct predicted beams. Further, definitions of beam prediction indicator and beam prediction accuracy shall be clear from the below subsequent paragraphs.

[0048] Fig. 3 is a block diagram illustrating the main components of an exemplary (R)AN node 5, also called, network node, 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 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.

[0049] 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.

[0050] 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).

[0051] 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).

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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) signalling 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).

[0059] 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.

[0060] 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).

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] For example, this disclosure can solve the above-mentioned problem. For example, the disclosure can solve a problem that considering the WI in RAN#102, Rel-19, TR 38.843, and / or 3GPP RAN1 Agreement (e.g., 3GPP RAN1#118b Agreement), it may need to consider at least one of the performance metric definition, the monitoring resources / resource set, how to configure the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window. For example, the disclosure can solve a problem that considering the WI in RAN#102, Rel-19, TR 38.843, and / or 3GPP RAN1 Agreement (e.g., 3GPP RAN1#118b Agreement), it may need to consider at least one of the performance metric definition, the monitoring resources / resource set, how to configure the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window, as there is no definition of and / or how to configure at least one of the performance metric definition, the monitoring resources / resource set, how to configure the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window. For example, the disclosure can solve a problem that considering the WI in RAN#102, Rel-19, TR 38.843, and / or 3GPP RAN1 Agreement (e.g., 3GPP RAN1#118b Agreement), it is not clear in current 3GPP specification about at least one of the performance metric definition, the monitoring resources / resource set, how to configure the monitoring resources / resource set, the performance metric reporting framework, and the monitoring time window.

[0068] As explained above, to ensure beam management and overall network performance, performance monitoring is used. Performance monitoring involves the continuous assessment of key metrics related to signal quality and network such as RSRP, SINR, RSRQ, latency, and throughput. For performance monitoring, performance metric and beam prediction indicator are defined. Definition of the performance metric and the beam prediction indicator depends on various factors, such as RSRP, SINR, RSRQ, Top-K beams, etc. Since the UE evaluates the quality of each beam by using parameters such as RSRP, SINR, RSRQ, latency and throughput, each of the beam can be ranked based on the quality. The Top-K beams are defined as a subset of beams out of the total available beams based on the ranks (top k beams having highest quality). In one embodiment, the processing of the Top-K beams is done such that the output received from the processing of the Top-K beams are applicable to all the available beams.

[0069] The performance monitoring can be defined for Network (NW)-side performance monitoring and / or user equipment (UE)- assisted performance monitoring, with UE having AI / ML model stored in. For each of these, certain options and alternatives have been defined in RAN1 Agreements for defining performance metric. The following options and alternatives are considered for providing various definitions of the performance metric and the beam prediction indicators. The definition of the performance metric and the beam prediction indicators are based on different parameters used in the definitions and will be clearer from the subsequent paragraphs.

[0070] For UE-sided model, the following options for inference results are defined: -　　Option 1: Beam information on predicted Top-K beam(s) among a set of beams, -　　Option 2: Beam information on predicted Top-K beam(s) among a set of beams and RSRP of predicted Top-K beam(s) among a set of beams, -　　Option 3: Beam information on predicted Top-K beam(s) among a set of beams and probability information of predicted Top-K beam(s) among a set of beams, -　　Option 4: Beam information on predicted Top-K beam(s) among a set of beams, RSRP of predicted Top-K beam(s) among a set of beams, and confidence information of the RSRP

[0071] With UE-sided model and UE-assisted performance monitoring, following alternatives are defined: -　　Alt 1: Top 1 or Top-K beam prediction accuracy (with or without margin) by comparing the prediction results and the Top 1 or Top-K beam based on the measurements from a resource set / resources for monitoring -　　Alt 2: The L1-RSRP difference information based on actual measurement of the L1-RSRP of one or more of Top-K predicted beam, and L1-RSRP measurements from a resource set / resources for monitoring. -　　Alt 3: The RSRP difference information between the predicted RSRP and measured L1-RSRP of corresponding beam(s) of a resource set / resources for monitoring -　　Alt 4: The probability information of the predicted beam(s) to be the Top 1 or Top-K beam.

[0072] If UE-sided AI / ML model gives inference result beam information on predicted Top-K beam(s) among a set of beams, defining the performance metric and beam prediction indicator involves counting the number of beams from the Top-K predicted beams which are within the Top-K' measured beams from the monitoring resource set / resources based on the beam information.

[0073]

[0074] For performance monitoring, if the UE-sided AI / ML model is supporting option 1 , option 2, option 3 and option 4 agreed in RAN1, to support Alternative 1 agreed in RAN1, the UE can measure the beam prediction accuracy based on the above Definition 1. Since the UE can measure the beam prediction accuracy based on the above Definition 1, beam management and overall network performance can be ensured. For example, it may define that f(t) = 1 if t is true, f(t) = 0otherwise (e.g., if t is false).

[0075]

[0076]

[0077]

[0078] For performance monitoring, if the UE-sided AI / ML model is supporting option 1 , option 2, option 3 and option 4 agreed in RAN1, to support Alternative 1 and Alternative 2 agreed in RAN1, the UE can measure the beam prediction accuracy based on the Definition 2 and Definition 3. Since the UE can measure the beam prediction accuracy based on the above Definition 2 or Definition 3, beam management and overall network performance can be ensured.

[0079]

[0080] For performance monitoring, if the UE-sided AI / ML model is supporting option 2 and option 4 agreed in RAN1, to support Alternative 1 and Alternative 3 agreed in RAN1, the UE can measure the beam prediction accuracy based on the Definition 4. Since the UE can measure the beam prediction accuracy based on the above Definition 4, beam management and overall network performance can be ensured.

[0081] For example, g{a, b} may define logic AND (a,b). For example, g = 1 if a = 1 (e.g., true) and b = 1 (e.g., true), and g = 0 otherwise (e.g., if a = 0 (e.g., false) and b = 0 (e.g., false), if a = 1 (e.g., true) and b = 0 (e.g., false), or if a = 0 (e.g., false) and b = 1 (e.g., true)).

[0082]

[0083] For performance monitoring, if the UE-sided AI / ML model is supporting option 2 and option 4 agreed in RAN1, to support Alternative 1, Alternative 2 and Alternative 3 agreed in RAN1, the UE can measure the beam prediction accuracy based on the Definition 5.

[0084]

[0085] For performance monitoring, if the UE-sided AI / ML model is supporting option 2 and option 4 agreed in RAN1, to support Alternative 1, Alternative 2 and Alternative 3 agreed in RAN1, the UE can measure the beam prediction accuracy based on the Definition 6. Since the UE can measure the beam prediction accuracy based on the above Definition 6, beam management and overall network performance can be ensured.

[0086]

[0087] For performance monitoring, if the UE-sided AI / ML model is supporting option 4 agreed in RAN1, to support Alternative 1 agreed in RAN1, the UE can measure the beam prediction accuracy based on the Definition 7. Since the UE can measure the beam prediction accuracy based on the above Definition 7, beam management and overall network performance can be ensured.

[0088]

[0089] For performance monitoring, if the UE-sided AI / ML model is supporting option 4 agreed in RAN1, to support Alternative 2 agreed in RAN1, the UE can measure the beam prediction accuracy based on the Definition 8. Since the UE can measure the beam prediction accuracy based on the above Definition 8, beam management and overall network performance can be ensured.

[0090]

[0091] For performance monitoring, if the UE-sided AI / ML model is supporting option 4 agreed in RAN1, to support Alternative 2 agreed in RAN1, the UE can measure the beam prediction accuracy based on the Definition 9. Since the UE can measure the beam prediction accuracy based on the above Definition 9, beam management and overall network performance can be ensured.

[0092]

[0093] In one embodiment, the performance metric can be a rank correlation coefficient between the two ranked sets, which are a set of predicted beams from AI / ML model and a set of measured beams of monitoring resource set. Rank correlation coefficient is a statistical index that measure the degree of association between two variables having ordered categories. The rank correlation coefficient can be calculated using any of the statistics that can measure the ordinal association between two measured quantities. For example, but not limited to, techniques such as Goodman and Kruskal, Kendall , Somers and Spearman.

[0094] The ranking of the set of beams can be based on at least one of the beam information, L1-RSRP values and probability information. For example, if the rank of the beams in the set is based on the L1-RSRP values, the beam which has the maximum L1-RSRP can give the highest rank and so on.

[0095]

[0096]

[0097] For example, several mathematical formulas and parameters are used for the explanation for the above-mentioned definition of the performance metric, beam prediction indicator, reporting accuracy, and / or prediction accuracy etc. (the performance metric, beam prediction indicator, reporting accuracy, and / or prediction accuracy etc. may be expressed as the performance metric etc.). However, it is not limited to these examples. For example, the definition of the performance metric etc. may be made using some of the aforementioned mathematical formulas and / or parameters, or by combining some or all of the aforementioned mathematical formulas and / or parameters.

[0098] The subsequent paragraphs explains the operations performed by the network / network node. The network / network node may be a base station, gNB, etc. The network / network node may include a base station, gNB, etc. For UE-assisted performance monitoring, to report either the performance metric or the decisions such as AI / ML model selection, activation, deactivation, switching, and fallback operations, the network can configure the reporting framework within CSI-framework in csi-ReportConfig or within a dedicated resource configuration. The report quantity can be at least one of the performance metric and / or the recommendation for the model management decisions such as AI / ML model selection, activation, deactivation, switching, and fallback operations. The network may configure a performance metric monitoring time window for the UE. UE uses this monitoring time window to calculate the performance metric when the report is triggered.

[0099] The network can configure the gNB to take the model management decisions such as AI / ML model selection, activation, deactivation, switching, and fallback operations based on the report. The network node may configure itself to take the model management decisions such as AI / ML model selection, activation, deactivation, switching, and fallback operations based on the report. The network may configure a waiting time window after the reporting instance for UE to receive at least one of the AI / ML model selection, activation, deactivation, switching, and fallback decision from the gNB. The network or network node may configure a waiting time window after the reporting instance for UE to receive at least one of the AI / ML model selection, activation, deactivation, switching, and fallback decision. If the UE receives the decision within the waiting time window, the UE will follow the decision made by the gNB (or the network node). For all the definitions, the UE may need to perform the L1-RSRP measurements for the monitoring resource set / resources. For Definition 7, Definition 8 and Definition 9, the UE need to determine the preconfigured threshold confidence information of the RSRP values.

[0100] The network may inform the UE regarding the performance monitoring resources through RRC signalling. The performance monitoring time window is in which the UE has to perform all the required measurements for performance metric calculation. The performance monitoring time window may be the time window in which the UE may perform all the required measurements for performance metric calculation. The performance metric monitoring time window at the UE is determined either with the reference to the time instance of the performance metric reporting from UE or with the reference to the time instance of the inference (predicted) results from the model.

[0101] For performance metric reporting, the present disclosure defines a k value as the number of monitoring instances that UE needs to consider calculating and reporting the performance metric of the AI / ML model. In one embodiment, the value of k value can be preconfigured by the network or network node. In another embodiment, the value of k can be preconfigured by the network or network node in the report configuration. In yet another embodiment, the k value can be determined by the UE based on the metric report period.

[0102] The performance metric reporting can be periodic, semi-persistent, aperiodic, and event triggered report. Under periodic reporting, periodicity can be k' multiples of the prediction period. Under period reporting, network can configure the number of prediction instances that UE needs to be considered for calculating the performance metric. Under Aperiodic reporting, network can configure the metric report instance to UE. Further, under event triggered reporting, UE can report by observing a drastic change in performance metric comparing with the desired performance metric, UE can report by observing the L1-RSRP difference of the predicted beams and measured beams lower than a threshold and UE has to report on either MAC-CE or RRC.

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] After the first prediction, the UE may receive first monitoring resource set (e.g., information indicating the first monitoring resource set) from the network / network node. The first monitoring resource set may be included in an RRC signaling or other signaling / message.

[0110] After receiving the first monitoring resource set, the UE may perform first calculation of the first beam prediction accuracy. The UE may perform the first calculation of the first beam prediction accuracy based on one of the above-mentioned definitions in the performance metric definitions. The UE may perform the calculation of the performance metric by considering such k beam accuracies which is shown in Fig.9B. After the first calculation of the first performance metric and / or the first beam prediction accuracy, the UE may perform the second prediction (e.g., the second beam prediction), receive second monitoring resource set (e.g., information indicating the second monitoring resource set) from the network / network node, and perform the second calculation of the second beam prediction accuracy and / or the second beam prediction accuracy. The UE may perform the second processes (e.g., performing the second prediction, receiving the second monitoring resource set, performing the second calculation of the second beam prediction accuracy etc.) in the same manner as the first processes (e.g., performing the first prediction, receiving the first monitoring resource set, performing the first calculation of the first beam prediction accuracy etc.).

[0111]

[0112]

[0113] For example, as shown in Fig.9C, the UE may receive the RRC signaling in the same manner as Figs.9A and 9B, then the UE may receive a lower layer signaling for activating at least one of the prediction, receiving the monitoring resource set, calculating the performance metric and reporting the performance metric. This lower layer signaling may be or may be expressed as a lower layer trigger for activation MAC-CE. In other words, the UE may perform the processes in Fig.9B in a case where the UE receives the lower layer signaling. In addition, in a case where the above-mentioned processes (e.g., at least one of the prediction, receiving the monitoring resource set, calculating the performance metric and reporting the performance metric) are deactivated, terminated or stopped, the UE may receive a lower layer signaling for deactivating at least one of the prediction, receiving the monitoring resource set, calculating the performance metric and reporting the performance metric. This lower layer signaling may be or may be expressed as a lower layer trigger for deactivation MAC-CE. For example, in a case where the UE receives the lower layer trigger for deactivation MAC-CE, the UE may deactivate, terminate or stop at least one of the prediction, receiving the monitoring resource set, calculating the performance metric and reporting the performance metric.

[0114]

[0115] After receiving the RRC signaling, the UE may receive a lower layer signaling (e.g., a lower layer triggering MAC-CE or DCI). For example, the lower layer signaling may indicate one of the metric report instance (e.g., one of the received metric report instance in the RRC signaling) which the UE uses, and indicate one of the resource instance (e.g., one of the received resource instance in the RRC signaling) which the UE uses. After receiving the lower layer signlaing, the UE may perform the prediction, measuring the beam, calculating the performance metric, reporting the performance metric in the same manner as Figs.9B or 9C. For example, the UE may perform the prediction, measuring the beam, calculating the performance metric, reporting the performance metric, using the metric report instance and / or the resource instance indicated by the lower later signaling.

[0116] In one embodiment, the time relation between the time instance of the performance monitoring and performance metric reporting can be as per the following table:

[0117] The table above shows that with periodic performance metric monitoring, the performance metric reporting can be periodic, semi-persistent and aperiodic. When the performance metric monitoring is semi-persistent, the performance metric reporting can be semi-persistent and aperiodic (period reporting not supported). Further, when the performance metric monitoring is Aperiodic, the performance metric reporting will only be aperiodic (periodic and semi-persistent not supported).

[0118] Continuing with the different types of reporting of performance metric, the network can configure the monitoring resources for UE-sided AI / ML model performance monitoring. The network can configure either all the available beams or subset of the available beams in the monitoring resources based on the UE capability. A bit-map can be signaled to UE, which conveys the information of the subset of the available beams configured in the monitoring resources / resource set. Network can configure the CSI-RS beams using the existing CSI-RS framework. In this case, the time relation between the time instance of the performance metric reporting and monitoring resource configuration can be as per the following table:

[0119] As shown in table above, when the monitoring resource configuration is periodic, the performance metric reporting can be periodic, semi-persistent and aperiodic. When the monitoring resource configuration is semi-persistent, the performance metric reporting can be semi-persistent and Aperiodic (period performance metric reporting not supported). Further, when the monitoring resource configuration is Aperiodic, the performance metric reporting is Aperiodic (Periodic and Semi-persistent not supported).

[0120]

[0121] Referring to Fig. 10 now, a flowchart of a method 1300 of a user equipment is illustrated. At step 1302, the method comprises receiving a set of monitoring resources from a network node. At step 1304, the method comprises receiving a set of parameters from the network node, the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), number of monitoring instances (N), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values. At step 1306, the method comprises receiving a set of parameters from the network node, the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), number of monitoring instances (N), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values. At step 1308, the method comprises receiving a set of parameters from the network node, the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), number of monitoring instances (N), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values.

[0122] Referring to Fig. 11 now, a flowchart of a method 1100 for assisting in determination of performance metric performed by a network node is provided. At step 1102, the method comprises sending a set of monitoring resources and a set of parameters from the network node. The set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), number of monitoring instances (N), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values. At step 1104, the method comprises receiving performance metric for the set of monitoring resources from the UE, where the performance metric is determined by the UE based on the set of received parameters.

[0123] Referring to Fig. 12 now, Fig. 12 illustrates a block diagram of the user equipment (UE) 3 in accordance with an embodiment of the present subject matter. The various modules in the UE can be embodied as a hardware that includes, without limitation, the at least one memory 1202, the at least one processor 1204, transmitter / receiver circuitry 1206, and programmable logic or software.

[0124] The at least one memory 1202, which may include both read-only memory (ROM) and random access memory (RAM), can provide instructions and data to the at least one processor 1204. The at least one memory 1202 and the at least one processor 1204 may be operatively coupled. The at least one memory 1202 may store computer readable instructions / computer program code. The at least one processor 1204 in the UE 3 may train the first, second and the third classification models.

[0125] 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).

[0126] The transmitter / receiver (TX / RX) circuitry 1206 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 1204 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.

[0127] The steps of a method (e.g., method 1000, 1100) 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 1204, 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 1202). 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.

[0128] In one example, a user equipment (UE) is provided. The UE comprises a transceiver circuit (31) configured to receive a set of monitoring resources from a network node, a set of parameters from the network node, wherein the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), number of monitoring instances (N), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values. The UE further comprises a memory (36) configured to store artificial intelligence / machine learning (AI / ML) model and a controller (33) coupled to the memory (36) and configured to determine performance metric for the set of monitoring resources based on the set of received parameters.

[0129] In another example, the performance metric is determined based on at least one of a beam prediction indicator and a beam prediction accuracy.

[0130]

[0131] In another example, the beam prediction accuracy is the ratio of the beam prediction indicator to the maximum possible count on the number of correct predicted beams.

[0132] In another example, the beam prediction indicator and beam prediction accuracy can be determined for each prediction of the AI / ML model and can be considered as a single monitoring instance.

[0133] In another example, the performance metric is the average of the beam prediction accuracy over the number of monitoring instances.

[0134] In another example, the determined performance metric is reported to the network node either in periodic, semi-persistent, aperiodic or event triggered way.

[0135] In another example, a method of a user equipment is provided. The method comprises receiving a set of monitoring resources from a network node, receiving a set of parameters from the network node, wherein the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values, storing artificial intelligence / machine learning (AI / ML) model, and determining performance metric for the set of monitoring resources based on the set of received parameters.

[0136] In one example, a method performed by a network node is provided. The network node comprises sending a set of monitoring resources and a set of parameters from the network node. The set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values. The network node further comprises receiving performance metric for the set of monitoring resources from the UE, where the performance metric is determined based on the set of received parameters.

[0137] 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.

[0138] 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".

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] <Supplementary notes> 　　(Supplementary note 1) 　　A user equipment (UE) comprising: 　　a transceiver circuit (31) configured to receive: 　　　　a set of monitoring resources from a network node, 　　　　a set of parameters from the network node, wherein the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), number of monitoring instances (N), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values, 　　a memory (36) configured to store artificial intelligence / machine learning (AI / ML) model; 　　a controller (33) coupled to the memory (36) and configured to determine performance metric for the set of monitoring resources based on the set of received parameters. 　　(Supplementary note 2) 　　The UE according to supplementary note 1, wherein the performance metric is determined based on at least one of a beam prediction indicator and a beam prediction accuracy. 　　(Supplementary note 3) 　　(Supplementary note 4) 　　The UE according to supplementary note 2, wherein the beam prediction accuracy is the ratio of the beam prediction indicator to the maximum possible count on the number of correct predicted beams. 　　(Supplementary note 5) 　　The UE according to supplementary note 2, wherein the beam prediction indicator and beam prediction accuracy can be determined for each prediction of the AI / ML model and can be considered as a single monitoring instance. 　　(Supplementary note 6) 　　The UE according to supplementary note 3, wherein the performance metric is the average of the beam prediction accuracy over the number of monitoring instances. 　　(Supplementary note 7) 　　The UE according to supplementary note 1, wherein the determined performance metric is reported to the network node either in periodic, semi-persistent, aperiodic or event triggered way. 　　(Supplementary note 8) 　　A method of a user equipment (UE), the method comprising: 　　receiving a set of monitoring resources from a network node, 　　receiving a set of parameters from the network node, wherein the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values, 　　storing artificial intelligence / machine learning (AI / ML) model; and 　　determining performance metric for the set of monitoring resources based on the set of received parameters. 　　(Supplementary note 9) 　　The method according to supplementary note 8, further comprising determining performance metric based on at least one of a beam prediction indicator and a beam prediction accuracy. 　　(Supplementary note 10)

[0144] This application is based upon and claims the benefit of priority from Indian Patent Application No. 202511010021, filed on February 6, 2025, the disclosure of which is incorporated herein in its entirety by reference.

[0145] 3　　USER EQUIPMENT(UE) 7　　CORE NETWORK 10　　CORE NETWORK 20　　DATA NETWORK 31　　TRANSCEIVER CIRCUIT 32　　ANTENNA 33　　CONTROLLER 34　　USER INTERFACE 35　　USIM 36　　MEMORY 361　　OPERATING SYSTEM 362　　COMMUNICATIONS CONTROL MODULE 3621　　TRANSCEIVER CONTROL MODULE 5　　RADIO ACCESS NETWORK (RAN) 51　　TRANSCEIVER CIRCUIT 52　　ANTENNA 53　　NETWORK INTERFACE 54　　CONTROLLER 55　　MEMORY 551　　OPERATING SYSTEM 552　　COMMUNICATIONS CONTROL MODULE 5521　　TRANSCEIVER CONTROL MODULE 60　　RU 601　　TRANSCEIVER CIRCUIT 602　　ANTENNA 603　　NETWORK INTERFACE 604　　CONTROLLER 605　　MEMORY 6051　　OPERATING SYSTEM 6052　　COMMUNICATIONS CONTROL MODULE 60521　　TRANSCEIVER CONTROL MODULE 61　　DU 611　　TRANSCEIVER CIRCUIT 612　　NETWORK INTERFACE 613　　CONTROLLER 614　　MEMORY 6141　　OPERATING SYSTEM 6142　　COMMUNICATIONS CONTROL MODULE 61421　　TRANSCEIVER CONTROL MODULE 62　　CU 621　　TRANSCEIVER CIRCUIT 622　　NETWORK INTERFACE 623　　CONTROLLER 624　　MEMORY 6241　　OPERATING SYSTEM 6242　　COMMUNICATIONS CONTROL MODULE 62421　　TRANSCEIVER CONTROL MODULE 70　　AMF 701　　TRANSCEIVER CIRCUIT 702　　NETWORK INTERFACE 703　　CONTROLLER 704　　MEMORY 7041　　OPERATING SYSTEM 7042　　COMMUNICATIONS CONTROL MODULE 70421　　TRANSCEIVER CONTROL MODULE 71　　SMF 72　　UPF 73　　PCF 74　　NEF 75　　UDM 76　　NWDAF 77　　NSSF 78　　NRF 1202　　MEMORY 1204　　PROCESSOR 1206　　TX / RX CIRCUITRY

Claims

1. A user equipment (UE) comprising: 　　a transceiver circuit (31) configured to receive: 　　　　a set of monitoring resources from a network node, 　　　　a set of parameters from the network node, wherein the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), number of monitoring instances (N), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values, 　　a memory (36) configured to store artificial intelligence / machine learning (AI / ML) model; 　　a controller (33) coupled to the memory (36) and configured to determine performance metric for the set of monitoring resources based on the set of received parameters.

2. The UE according to claim 1, wherein the performance metric is determined based on at least one of a beam prediction indicator and a beam prediction accuracy.

3.

4. The UE according to claim 2, wherein the beam prediction accuracy is the ratio of the beam prediction indicator to the maximum possible count on the number of correct predicted beams.

5. The UE according to claim 2, wherein the beam prediction indicator and beam pre-diction accuracy can be determined for each prediction of the AI / ML model and can be considered as a single monitoring instance.

6. The UE according to claim 3, wherein the performance metric is the average of the beam prediction accuracy over the number of monitoring instances.

7. The UE according to claim 1, wherein the determined performance metric is reported to the network node either in periodic, semi-persistent, aperiodic or event triggered way.

8. A method of a user equipment (UE), the method comprising: 　　　　receiving a set of monitoring resources from a network node, 　　　　receiving a set of parameters from the network node, wherein the set of parameters comprises at least one of: number of predicted beams (K), number of measured beams from the monitoring resources (K'), measured / predicted Reference Signal Received Power (L1-RSRP) margins, or preconfigured threshold confidence information of RSRP values, 　　storing artificial intelligence / machine learning (AI / ML) model; and 　　determining performance metric for the set of monitoring resources based on the set of received parameters.

9. The method according to claim 8, further comprising determining performance metric based on at least one of a beam prediction indicator and a beam prediction accuracy.10.

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