User equipment(UE), method of UE, network node and method of network node

Abstract

In one implementation, a User Equipment (UE), network node and method facilitating handover of the UE in a wireless communication network is disclosed. The method comprises receiving measurement configuration message from network node comprising one or more prediction parameters to trigger one or more measurement event predictions by the UE for target node from multiple target nodes. One or more measurement event predictions are performed by UE using one or more prediction models based on the one or more prediction parameters to generate measurement report transmitted to the network node and comprises one or more measurement event prediction results. UE receives prediction-based handover command from the network node based on the measurement report to select the target node from multiple target nodes for the handover of the UE to the selected target node. Prediction-based handover command is executed by the UE to access the target node.

Description

USER EQUIPMENT(UE), METHOD OF UE, NETWORK NODE AND METHOD OF NETWORK NODE

[0001] The present disclosure generally relates to wireless communication systems and more particularly relates to techniques facilitating handover of User Equipment (UE) in a wireless communication network.

[0002] 3GPP RP-240082 focuses on mobility enhancement in RRC_CONNECTED mode over air interface by following existing mobility framework. RP-240082 describes study and evaluate potential benefits and gains of AI / ML aided mobility for network triggered L3-based handover, considering the following aspects: AI / ML based RRM measurement and measurement event prediction, Cell-level measurement prediction including intra and inter-frequency (UE sided and NW sided model) [RAN2], Inter-cell Beam-level measurement prediction for L3 Mobility (UE sided and NW sided model) [RAN2], HO failure / RLF prediction (UE sided model) [RAN2], Measurement events prediction (UE sided model) [RAN2], Study the need / benefits of any other UE assistance information for the network side model [RAN2].

[0003] In addition, 3GPP TR38.744 V0.0.4 (2024-10) describes that the use cases in this study focus on RRC_CONNECTED mode and cover RRM measurement prediction, measurement event prediction and RLF / HOF prediction for PCell change procedure in standalone NR scenario. TR38.744 also describes that the study of the use cases is driven mainly by two study goals, the 1st study goal is to reduce measurement efforts in temporal, spatial or frequency domain by using predicted measurements, and the 2nd study goal is to improve the handover performance (e.g., Ping-pong HO, HOF / RLF, short time of stay, Handover interruption).

[0004] Hence, there is a need to consider the measurement event prediction and improved handover performance.

[0005] The following presents a simplified summary of the disclosure in order to provide a basic understanding of some of the aspects of disclosure embodiments. This summary is not an extensive overview of the disclosure. It is not intended to identify key / critical elements of the embodiments or to delineate the scope of the disclosure. Its sole purpose to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0006] In one aspect, the implementations of the present disclosure provide a method for a User Equipment (UE) facilitating handover of the UE in a wireless communication network. The method comprises receiving, from a network node, a measurement configuration message comprising one or more prediction parameters to trigger one or more measurement event predictions, by the UE, for a target node from multiple target nodes and performing, by the UE, the one or more measurement event predictions using one or more prediction models based on the one or more prediction parameters to generate a measurement report. The method further comprises transmitting, to the network node, the measurement report comprising one or more measurement event prediction results and receiving, from the network node, a prediction-based handover command based on the measurement report. The UE handover command is of use in selecting the target node from multiple target nodes for the handover of the UE to the selected target node. The method further comprises executing the handover command by the UE to access the target node.

[0007] In another aspect, the implementations of the present disclosure provide a method for a network node for performing handover of a user equipment (UE) in a wireless communication network. The method comprises transmitting, to the UE, a measurement configuration message comprising one or more prediction parameters, to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes and receiving, from the UE, a measurement report comprising one or more measurement event prediction results predicted by the UE based on the one or more prediction parameters. The method further comprises sending a handover request to multiple target nodes comprising of predicted time of occurrence for the handover from the measurement report received from the UE and transmitting, to the UE, a prediction-based handover command based on the measurement report. The UE handover command is of use in selecting a target node from the multiple target nodes for handover of the UE to the selected target node.

[0008] In another aspect, the implementations of the present disclosure provide a User Equipment (UE) facilitating a handover of the UE in a wireless communication network. The UE comprises a processing circuitry configured to receive, from a network node, a measurement configuration message comprising one or more prediction parameters to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes and perform the one or more measurement event predictions using one or more prediction models based on the one or more prediction parameters to generate a measurement report. The UE further comprises a transceiver configured to: transmit, to the network node, the measurement report comprising one or more measurement event prediction results, receive, from the network node, a prediction-based handover command based on the measurement report, wherein the prediction-based handover command is of use in selecting the target node from multiple target nodes for handover of the UE to the selected target node; and execute the prediction-based handover command by the UE to access the target node.

[0009] In another aspect, the implementations of the present disclosure provide a network node for performing handover of a user equipment (UE) in a wireless communication network. The network node comprises a transceiver configured to transmit, to the UE, a measurement configuration message comprising one or more prediction parameters, to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes, receive, from the UE, a measurement report comprising one or more measurement event prediction results predicted by the UE based on the one or more prediction parameters, send a handover request to multiple target nodes comprising of predicted time of occurrence for the handover from the measurement report received from the UE and transmit, to the UE, a prediction-based handover command based on the measurement report. The prediction-based handover command is of use in selecting a target node from the multiple target nodes for handover of the UE to the selected target node.

[0010] In one or more implementations, the present disclosure provides at least one of the following exemplary advantages: -　　To improve the accuracy of event predictions, for example, radio link failure (RLF) prediction, or measurement event prediction (e.g., A3, A5) and thus reduce issues like handover failures, ping-pong handovers and short stays in cells. -　　To reduce measurement efforts in temporal, spatial or frequency domain by using predicted measurements. -　　To dynamically adapt measurement configurations based on predictive insights using AI / ML based prediction models. -　　To select the target node with a highest probability of a successful handover, ensuring optimal resource allocation and reducing handover failures. -　　To reduce signaling overhead by predicting measurement events and minimize a need for frequent real-time measurement reporting.

[0011] 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.

[0012] The foregoing and further objects, features, and advantages of the present disclosure will become apparent from the following description of exemplary embodiments with reference to the accompanying drawings, wherein like numerals are used to represent like elements.

[0013] It is to be noted, however, that the appended drawings illustrate only typical embodiments of the present disclosure, and are, therefore, not to be considered for limiting of its scope, for the disclosure may admit to other equally effective embodiments.

[0014] For a better understanding of the present disclosure, reference is made to the following description of an exemplary embodiment thereof, considered in conjunction with the accompanying drawings, in which:Fig. 1 schematically illustrates a telecommunication system, in accordance with an embodiment of the present disclosure.Fig. 2 illustrates a block diagram illustrating main components of a user equipment (UE), in accordance with an embodiment of the present disclosure.Fig. 3 illustrates a block diagram illustrating main components of an exemplary (R)AN node, in accordance with an embodiment of the present disclosure.Fig. 4 illustrates a (R)AN node based on O-RAN architecture in accordance with an embodiment of the present disclosure.Fig. 5 illustrates a block diagram of a radio unit, in accordance with an embodiment of the present disclosure.Fig. 6 illustrates a block diagram of a distributed unit, in accordance with one embodiment of the present disclosure.Fig. 7 illustrates a block diagram of a centralized unit, in accordance with an embodiment of the present disclosure.Fig. 8 illustrates a block diagram illustrating main components of Access Management Function (AMF), in accordance with an embodiment of the present disclosure.Fig. 9 illustrates flow diagram for the UE facilitating handover of the UE in a wireless communication network, in accordance with an embodiment of the present disclosure.Fig. 10 illustrates a flowchart of a method for a network node for performing handover of the UE in the wireless communication network, in accordance with an embodiment of the present disclosure.Fig. 11 illustrates a signaling flow for the handover of the UE, in accordance with an embodiment of the present disclosure.Fig. 12 illustrates a block diagram for the UE, in accordance with an embodiment of the present disclosure.

[0015] 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.

[0016] Wireless communication systems, such as LTE and 5G networks, have revolutionised connectivity by enabling high-speed reliable data transfer. These wireless communication systems are designed to support diverse user scenarios, from stationary users to highly mobile users. One critical aspect of maintaining seamless connectivity is ability to manage handovers, where a User Equipment (UE) transitions from one cell or node to another cell or node as the UE moves through the network. To facilitate this, the UE may perform radio measurements and report to the network, enabling intelligent handover decisions.

[0017] Traditional handover procedures are typically based on measurement events triggered by signal quality thresholds, such as events like A1, A2 or A3. While effective in many scenarios, these handover methods may lack adaptability to rapidly changing environments or dynamic UE behaviour.

[0018] Traditional systems use fixed signal thresholds to trigger measurement events, which do not adapt dynamically to the UE's movement patterns or environmental conditions (such as urban areas, or high-speed trains). Such lack of adaptability may lead to inefficiencies, such as unnecessary handovers (ping-pong effect), resource wastage, or degraded user experience due to inaccurate or outdated measurements. Additionally, inefficient target node selection may result in unnecessary resource allocation, reducing overall network efficiency.

[0019] Furthermore, predicting handover events and preparing resources at the target node may be challenging without an efficient prediction mechanism. Current handover mechanisms often lack predictive intelligence and rely heavily on reactive responses to signal degradation. These methods limit the network's ability to anticipate and optimize handover scenarios.

[0020] Hence, there is a need to consider effective handover management for improving handover reliability and resource utilization.

[0021] The embodiments of the present disclosure are described in detail with reference to the accompanying drawings. However, the present disclosure is not limited to these embodiments which are only provided to explain more clearly the present disclosure to the ordinarily skilled in the art of the present disclosure. In the accompanying drawings, like reference numerals are used to indicate like components.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] In an overview, the present disclosure relates to mobility management in 5G / beyond 5G / 6G or future networks using AI / ML aided measurement event prediction for network-triggered handovers.

[0026] Traditional handover procedures are typically based on measurement events triggered by signal quality thresholds, such as events A1, A2 or A3. While effective in many scenarios, these handover methods may lack adaptability to rapidly changing environments or dynamic UE behaviour.

[0027] Traditional systems use fixed signal thresholds to trigger measurement events, which do not adapt dynamically to the UE's movement patterns or environmental conditions (such as urban areas, or high-speed trains). Such lack of adaptability may lead to inefficiencies, such as unnecessary handovers (ping-pong effect), resource wastage, or degraded user experience due to inaccurate or outdated measurements. Additionally, inefficient target node selection may result in unnecessary resource allocation, reducing overall network efficiency.

[0028] Furthermore, predicting handover events and preparing resources at the target node may be challenging without an efficient prediction mechanism. Current handover mechanisms often lack predictive intelligence and rely heavily on reactive responses to signal degradation. These methods limit the network's ability to anticipate and optimize handover scenarios.

[0029] The present disclosure relates to method(s) where the UE and the network coordinate to predict measurement events, such as signal quality thresholds and radio link failure (RLF) to optimize handover decisions. The present disclosure reduces UE measurement efforts, signaling overhead, and handover interruptions while improving reliability and efficiency using prediction models. The present disclosure discloses method(s) for configuring observation prediction windows, selecting target nodes based on predicted probabilities, and enabling proactive handover preparation, resulting in smoother transitions and enhanced user experience.

[0030] Fig. 1 schematically illustrates a telecommunication system 1 for a mobile (cellular or wireless) to which example embodiments disclosed herein and / or the above aspects are applicable. The telecommunication system 1 represents a system overview in which an end to end communication is possible. For example, UE (or user equipment, 'mobile device') communicates with other UEs or service servers in the data network 20 via respective (R)AN (Radio Access Network) nodes 5 and a core network 7 (also referred as network node in subsequent paragraphs). The (R)AN node 5 supports any radio accesses including a 5G radio access technology (RAT), an E-UTRA (Evolved Universal Terrestrial Radio Access ) Radio Access Technology (RAT), a beyond 5G RAT, a 6G RAT and non-3GPP RAT including wireless local area network (WLAN) technology as defined by the Institute of Electrical and Electronics Engineers (IEEE).

[0031] 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).

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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

[0038] 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 parameters 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.

[0039] Fig. 2 is a block diagram illustrating the main components of the UE 3 (mobile device 3). As shown, the UE 3 includes a transceiver circuit 31 (also referred as transceiver in subsequent paragraphs) which is operable to transmit signals to and to receive signals from the connected node(s) via one or more antennas 32. Further, the UE 3 may include a user interface 34 for inputting information from outside or outputting information to outside. Although not necessarily shown in the figure, the UE 3 may have all the usual functionality of a conventional mobile device and this may be provided by any one or any combination of hardware, software and firmware, as appropriate. Software may be pre-installed in the memory and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. A controller 33 (also referred as processing circuitry in subsequent paragraphs) controls the operation of the UE 3 in accordance with software stored in a memory 36. The software includes, among other things, an operating system 361 and a communications control module 362 having at least a transceiver control module 3621. The communications control module 362 (using its transceiver control module 3621) is responsible for handling (generating / sending / receiving) signalling and uplink / downlink data packets between the UE 3 and other nodes, such as the (R)AN node 5 and the AMF 10. Such signalling may include, for example, appropriately formatted signalling messages (e.g. a registration request message and associated response messages) relating to access and mobility management procedures (for the UE 3). The controller 33 interworks with one or more Universal Subscriber Identity Module (USIM) 35. If there are multiple USIMs 35 equipped, the controller 33 may activate only one USIM 35 or may activate multiple USIMs 35 at the same time.

[0040] 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).

[0041] 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.).

[0042] 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.).

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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).

[0048] 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.

[0049] For performance monitoring, performance metric is calculated based on at least one parameter such as, but not limited to, Top-K beams, Reference Signal Received Power (RSRP), probability information of the predicted beams, wherein the RSRP is measured and predicted by the AI / ML model. Further, the beam prediction indicator is determined after determining the performance metric. There may be different definitions which may be provided for determining performance metric and beam prediction indicators. Each of these definitions have been defined below in subsequent paragraphs.

[0050] Fig. 3 is a block diagram illustrating the main components of an exemplary (R)AN node 5 (also referred as network node in subsequent paragraphs), for example a base station ('eNB' in LTE, 'gNB' in 5G, a base station for 5G beyond, a base station for 6G). As shown, the (R)AN node 5 includes a transceiver circuit 51 which is operable to transmit signals to and to receive signals from connected UE(s) 3 via one or more antennas 52 and to transmit signals to and to receive signals from other network nodes (either directly or indirectly) via a network interface 53. A controller 54 (also referred as processing circuitry in subsequent paragraphs) controls the operation of the (R)AN node 5 in accordance with software stored in a memory 55. Software may be pre-installed in the memory and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. The software includes, among other things, an operating system 551 and a communications control module 552 having at least a transceiver control module 5521. In one embodiment, the performance metric is calculated by the UE 3 upon receipt of a trigger report sent by the network node 6.

[0051] 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.

[0052] 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).

[0053] 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).

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] Fig. 7 is a block diagram illustrating the main components of an exemplary CU 62, for example a CU part of base station ('eNB' in LTE, 'gNB' in 5G, a base station for 5G beyond, a base station for 6G). As shown, the apparatus includes a transceiver circuit 621 which is operable to transmit signals to and to receive signals from other nodes or units (including the DU 61) via a network interface 622. A controller 623 controls the operation of the CU 62 in accordance with software stored in a memory 624. Software may be pre-installed in the memory 624 and / or may be downloaded via the telecommunication network or from a removable data storage device (RMD), for example. The software includes, among other things, an operating system 6241 and a communications control module 6242 having at least a transceiver control module 62421. The communications control module 6242 (using its transceiver control module 62421 is responsible for handling (generating / sending / receiving) signaling between the CU 62 and other nodes or units, such as the DU 61 and other nodes and units. The CU 62 may support the Non-Public Network (NPN). The NPN may be a Stand-alone Non-Public Network (SNPN) or a Public Network Integrated NPN (PNI-NPN).

[0061] 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.

[0062] 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).

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] Referring to Fig. 9, the present disclosure defines a method 900 for a User Equipment (UE) facilitating handover of the UE in a wireless communication network. The UE refers to UE 3 as discussed and comprises the processing circuitry 33 and the transceiver 31 to facilitate the handover.

[0070] The method 900 at step 902 comprises receiving by the UE 3, a measurement configuration message that comprises one or more prediction parameters to trigger one or more measurement event predictions by the UE 3 for a target node from multiple target nodes. The configuration message is received from the network node 5. The measurement configuration message may be received through the processing circuitry 33.

[0071] In an example, the prediction model parameters comprise at least one of prediction model identity, threshold values for triggering one or more events from a plurality of events, hysteresis, time-to-trigger parameter for triggering the one or more events, measurement event interval, observation window, prediction window length, maximum event timing difference (ETD), event measurement objects, signal strength value, mobility patterns of the UE 3, environment related parameters, carrier frequency, cell configuration, observation window parameters; or a prediction window for the measurement event prediction.

[0072] At step 904, the method 900 comprises performing the one or more measurement event predictions by the UE 3 using one or more prediction models based on the one or more prediction model parameters to generate a measurement report. The one or more measurement event predictions may be performed through the processing circuitry 33. At step 906, the method 900 comprises transmitting the measurement report to the network node 5. The measurement report comprises one or more measurement event prediction results. The measurement report to the network node 5 may be transmitted through the transceiver 31.

[0073] In an example, the one or more prediction models comprise a direct prediction model or an indirect prediction model. The one or more prediction models are trained on at least one of input parameters comprising radio quality metrics, configuration metrics, or measurement event contextual data.

[0074] In an example, each measurement event prediction result of the one or more measurement event prediction results is associated with an occurrence probability within one or more prediction windows. The one or more prediction windows comprise a short-term prediction window, a medium-term prediction window or a long-term prediction window (discussed later in reference to Fig. 11). The prediction window is defined based on at least one of a speed of the UE 3, UE 3's location, or Signal strengths of a serving cell serving to the UE 3.

[0075] At step 908, the method 900 comprises receiving a prediction-based UE handover command (also referred as handover command) based on the measurement report. The handover command may be received from the network node 5 through the transceiver 31. The UE handover command is of use in selecting the target node from multiple target nodes for the handover of the UE 3 to the selected target node. At step 910, the method 900 comprises executing the handover command by the UE 3 to access the target node.

[0076] In an example, the UE 3 then establishes a connection with the selected target node in response to the handover command. The selected target node comprises a node with a highest probability of successful handover with respect to probabilities of successful handover for other target nodes from the multiple target nodes. Alternatively, the UE 3 may select the target node based on the RRC message from the source node.

[0077] Referring to Fig. 10, the present disclosure defines a method 1000 performing handover of the UE 3 in the wireless communication network. The method 1000 may be performed by the network node 5. The UE 3, the network node 5 and the wireless network communication are similar to the UE 3, the network node 5 and the wireless communication network as discussed in Fig. 9 above. The network node 5 comprises the processing circuitry 54 and the transceiver 51 for performing the handover of the UE 3.

[0078] At step 1002, the method 1002 comprises transmitting the measurement configuration message comprising one or more prediction parameters to the UE 3 through the transceiver 51. The measurement configuration message is transmitted to trigger one or more measurement event predictions by the UE 3 for the target node from the multiple target nodes.

[0079] At step 1004, the method 1000 comprises receiving the measurement report comprising one or more measurement event prediction results predicted by the UE 3 based on the one or more prediction parameters. The measurement report may be received by the transceiver 51.

[0080] At step 1006, the method 1000 comprises sending a handover request to multiple target nodes comprising of predicted time of occurrence for the handover along with the part of the measurement report or full of the measurement report received from the UE. At step 1008, the method 1000 comprises transmitting the prediction-based handover command based on the measurement report to the UE 3 through the transceiver 51. The prediction-based handover command is of use in selecting the target node from the multiple target nodes for handover of the UE 3 to the selected target node.

[0081] In an example, the method 1000 further comprises prioritizing the one or more measurement event prediction results based on preconfigured signal thresholds defined for each measurement event prediction result. The prioritization of the one or more measurement event prediction results may be performed by the processing circuitry of the network node 5.

[0082] The method 1000 further comprises identifying the node with the highest probability of successful handover with respect to probabilities of successful handover for other target nodes from the multiple target nodes or based on predicted earliest time of handover occurrence (HO) with respect to time of HO for other target nodes (or multiple target nodes) as reported by the UE 3 in the measurement report. The node with the highest probability of successful handover is selected as the target node from the multiple target nodes. The node with the highest probability may be identified through the processing circuitry 54. The identification is performed based on the prioritization of the one or more measurement event prediction results.

[0083] In an example, the methods 900 and 1000 are performed when the UE 3 is in RRC_CONNECTED mode. In an example, the RRC_CONNECTED mode is a state in which the UE 3 (mobile device) has established a dedicated connection with a cellular network (e.g., the network node 5), for allowing active data transmission and reception. The network node 5 may allocate specific radio resources for the UE 3, enabling continuous communication.

[0084] The method 900 and 1000 further comprises Radio resource management (RRM) measurement prediction, measurement event prediction and Radio link failure / handover failure (RLF / HOF) prediction for changing or shifting the target cell (target node), PCell, in a standalone network case. The methods 900 and 1000 reduce measurement efforts in temporal, spatial or frequency domain by using measurement event prediction and also improve the UE 3's handover performance (e.g., Ping-pong HO, HOF / RLF, short time of stay, Handover interruption).

[0085] Referring to Fig. 11, signaling flow 1100 for each of the UE 3 and the network node 5 executing the method 900 and the method 1000 is shown. The UE 3 may be configured / preconfigured with already downloaded different AI / ML models associated with the measurement event prediction. The AI / ML model may be downloaded from a server or network. In step 1101 of signaling flow, the network node 5 may request the UE 3 to share its AI / ML capabilities. The network node 5 may request the UE 3 to send AI / ML capabilities of the UE 3. The AI / ML capabilities may comprise the AI / ML models (alternatively referred as model) to support UE's functionality of measurement event prediction. The UE 3 may receive, from the network node 5, a request message. The request message may include information that indicates a request for the UE 3 to send the AI / ML capability of the UE3. For example, the AI / ML capabilities of the UE 3 may indicate the AI / ML model(s) that the UE 3 supports. For example, the AI / ML capabilities of the UE 3 may comprise / include information indicating the AI / ML model(s) that the UE 3 supports. For example, the AI / ML model(s) may be used by the UE 3 to support UE's functionality of measurement event prediction.

[0086] In an example, the event comprises any event from A1 to A5. For example, events considered here in the present disclosure comprises event A3 when neighbouring cell's offset becomes better than the serving cell (SpCell) or event A5 when the SpCell becomes worse than threshold 1 and the neighbouring cell becomes better than threshold 2. The events A1 to A5 may be the events A1 to A5 which are defined in the 3GPP standard(s). The event may comprise event(s) other than the events A1 to A5.

[0087] In step 1102, the UE 3 may be configured to send AL / ML model identities of an applicable AI / ML model from the at least one of: one or more AI / ML model and / or model's characteristics corresponding to the measurement event prediction through an RRC message. The RRC message may help the network node 5 to understand the models and functionality supported by the UE 3 to select the appropriate (or most appropriate) AI / ML model from the one or more AI / ML models. Other signal(s) / message(s) other than the RRC message may be used, instead of the RRC message. The expression "A and / or B" may mean "at least one of A and B" or "at least one of A or B". For example, the UE 3 may send the AI / ML capabilities of the UE 3 in a case where the UE 3 receives the request message in step 1101. For example, the UE 3 may send the AI / ML capabilities of the UE 3 without receiving the request message in step 1101. The AI / ML capabilities of the UE 3 may include the AL / ML model identity of an applicable AI / ML model and / or model's characteristics corresponding to the measurement event prediction.

[0088] For example, the RRC message may comprise UE capability information once the network node 5 has requested the UE AI / ML capability. In an example, each measurement event type may have some AI / ML model information that includes at least one of AI / ML model identity and model characteristics and the associated event that the UE 3 may be configured to predict.

[0089] In an example, the AI / ML model comprises at least one of direct model (also referred as the direct prediction model) or the indirect model (also referred as the indirect prediction model).

[0090] In Step 1103, the UE 3 may be configured by the network node 5 through the measurement configuration via the RRC message where the appropriate model identity and type of prediction model (e.g., the direct prediction model or the indirect prediction model) is shared by the network node 5 with the UE 3. The network node 5 may send the RRC message. The RRC message may include the measurement configuration. The measurement configuration may be for event prediction. The type of prediction model is sent from the network node 5. The UE 3 may receive, from the network node 5, the RRC message sent. The UE 3 may receive, from the network node 5, the measurement configuration. For example, the UE 3 may send the AI / ML capabilities of the UE 3 in a case where the UE 3 receives the request message in step 1101. For example, the UE 3 may send the AI / ML capabilities of the UE 3 without receiving the request message in step 1101. The AI / ML capabilities of the UE 3 may include the AL / ML model identity of an applicable AI / ML model and / or model's characteristics corresponding to the measurement event prediction.

[0091] In an example, the measurement configuration message or the measurement configuration comprises information about at least one of: observation window or prediction window for measurement event prediction for the UE. The length of the observation window and / or prediction window may be configured by the network node 5 to the UE 3. The network node 5 may configure a starting point for the observation window and / or the prediction time window for the UE 3 for measurement event prediction. The measurement configuration message may be the RRC message. The RRC message may include observation window and / or prediction window for measurement event prediction for the UE.

[0092] In another example, the measurement configuration message or the measurement configuration may comprise multiple prediction windows. Each prediction window may be associated with different conditions that may comprise UE location, UE speed, or signal strengths of the serving cell. The method 1000 may apply the short prediction window when the UE speed is higher than a threshold and may apply the long prediction window may apply when the UE speed is lower than a threshold. For example, a lower UE mobility speed will be 30 km / h and higher UE mobility speed may be considered 120 km / h. For each measurement configuration message or each measurement configuration, a specific or predefined UE speed threshold may be configured to the UE and when the UE speed is above the threshold, the measurement event prediction may be disabled, since the prediction accuracy at high UE speed decrease significantly.

[0093] In an example, the measurement configuration message or the measurement configuration further comprises measurement configuration options for measurement resolution that may comprise cell-level or beam-level, based on a use case that comprises intra-frequency handovers or inter-frequency handovers.

[0094] In an example, the network node 5 may select the AI / ML model for the UE 3 and input parameters for the selected AI / ML model. In case 1, the network node 5 may inform the UE 3 which model to use (e.g., based on the model identity and model characteristics) and input parameters to use for the selected AI / ML model. In alternate case 2, the UE 3 may follow the default configuration set for the AI / ML model (for example, defined by UE specifications or UE configuration). The RRC message (e.g., the measurement configuration message) may include the parameters for the selected AI / ML model.

[0095] The network node 5 may also send to the UE 3, the configuration parameters associated with the AI / ML model for the event. The RRC message (e.g., the measurement configuration message) may include the configuration parameters associated with the AI / ML model for the event. For example, in case 1, the model may reuse the legacy measurement configuration for prediction of measurement event that comprises legacy parameters such as hysteresis, threshold, time to trigger, or offsets. If the legacy measurement event is configured, these configuration parameters may can be used and the model is referred as direct prediction model. In case 2, measurement configuration is performed with new additional parameters that comprises hysteresis, threshold, time to trigger, or offsets for measurement event prediction. The prediction model may then be referred to as the indirect prediction model.

[0096] In an example, the input and output parameters for the AI / ML model configuration comprises at least one of: 　　Input parameters: 　　Radio Quality Signal Parameters- Measured RSRP, RSRQ: Measured for both serving and neighboring cells reported from SSB (Synchronization Signal Block) or CSI-RS (channel state information-reference signal); SINR (signal-to-interference-plus-noise ratio): provides quality measures based on signal and interference or CQI: Channel Quality Indicator;

[0097] Measurement configuration parameters (for example for events A3 / A5)- Threshold for A3 or A5: these are configured signal strength values that need to be met or exceeded to trigger an event; Hysteresis: The value to avoid frequent changing in events or Time to Trigger (TTT): duration for which the criteria must be met for an event to be triggered; observation Window and Prediction window length, maximum ETD;

[0098] Historical event data: Past data of signal strengths of serving and neighboring cells (target cells / nodes);

[0099] UE mobility: UE location and trajectory that comprise predicted locations where signal quality may degrade and where the handovers may be frequent; or

[0100] Carrier frequency, cell configuration that comprises gNB height, ISD, power, or beam pattern.

[0101] Output parameters for the AI / ML model configuration comprises at least one of:

[0102] Event type: The type of event predicted to trigger one of the events that comprises A3 or A5. In an example, the event may comprise anyone from A1 to A5.

[0103] Triggering cell: The predicted event triggering cell identities.

[0104] Triggering time: Predicted time instance when the event may be triggered.

[0105] Time Window: Probability of event trigger within a time window.

[0106] Model Accuracy or accuracy of the prediction mode: (confidence score) Certainty of prediction for the AI / ML model that may be defined in terms of a prediction probability, and variance of the event along with some historical data.

[0107] The RRC message may include the input and / or output parameters for the AI / ML model configuration.

[0108] Referring again to step 1103 as shown in Fig. 11, the network node 5 may instruct the UE 3 to run the selected prediction model for multiple events and network node 5 may configure a priority of prediction for each event type of the multiple events.

[0109] In an example, when the indirect prediction model is used, the network node 5 may configure the UE 3 to use the historical measurements for prediction. In case the direct prediction model is used, the network node 5 may configure information about the occurrence of the historical measurement event to the UE 3. For example, the network node 5 may configure the historical occurrence(s) of A3 event on a particular measurement object for the measurement event prediction.

[0110] In an example, when the direct prediction model is used, the network node 5 may configure the occurrence of the measurement event within a time window [t, t+T], where t is the time point of configuration of measurement event prediction and T is the window length.

[0111] As part of step 1103, the network node 5 may further provide measurement report configuration for measurement event prediction by the UE 3. The measurement report configuration comprises configuration of how the UE reports the measurement prediction to the network and an outcome of the measurement event prediction (measurement event prediction results) and what is to be reported in the measurement report.

[0112] In an example, the network node 5 may configure a periodic based reporting of the measurement event prediction by specifying a reporting interval of the measurement event predictions for the UE 3. The network node 5 may also configure an event-based reporting where the measurement report condition may comprise the occurrence of the measurement event based on the prediction results (for example, above the threshold of the configured probability as discussed above).

[0113] In step 1104, the UE 3 may be configured to execute or run the AI / ML model for measurement event prediction based on one of the default configuration of the UE 3 (i.e., based on the auto selection of the AI / ML model by the UE 3) or based on getting the instruction from the network node 5 through the measurement configuration message. In case of configuration of multiple events, the UE 3 may be configured to execute multiple events depending on the resource constraints following the priority of events as set by the network node 5. The UE 3 may do measurement event prediction with AI / ML model for multiple prediction windows. For example, the UE 3 may perform the measurement event prediction using information received from the network node 5 in step 1103. For example, the UE 3 may determine whether the event (e.g., one of the events A1 to A5) will occur using information received from the network node 5 in step 1103. For example, the UE 3 may predict whether the events (e.g., one of the events A1 to A5) will occur using information received from the network node 5 in step 1103.

[0114] As part of step 1104, the UE 3 may also execute a long-term measurement event prediction that may span multiple prediction windows. For example, the event prediction may span 800 ms and the UE 3 may predict multiple measurement events for the same event type sequentially.

[0115] In step 1105, the measurement event prediction results are reported back to the network node 5 through the measurement report associated with a MeasID for the event. The MeasID is used to identify a measurement configuration, i.e., linking of a measurement object (event) and the reporting configuration. The measurement report may comprise periodic reporting or the event-based reporting following the configuration as discussed above in step 3. The UE 3 may send the measurement report to the network node 5. The measurement report may be sent from the UE 3 to the network node 5 by an RRC message. The RRC message may include the measurement report. The measurement report may include the measurement event prediction report. The MeasID may not be included in the measurement report.

[0116] In step 1105, the UE 3 may share at least one of the following information through the measurement report: -　　Measurement identities -　　An indication that may be used to indicate this is a predicted measurement event -　　Prediction accuracy information -　　Timing information of the predicted occurrence of the measurement event that may comprise: time instance, or a time window -　　Prediction method i.e., measurement event prediction through the indirect predication model or the direct prediction model; or -　　Model identity of the AI / ML model used The measurement event prediction report may include at least one of the information above. For example, the measurement event prediction report may be expressed as a measurement report. For example, the measurement event prediction report may comprise information related to the prediction which the UE 3 does or has done.

[0117] In step 1106, the network node 5 may initiate a proactive handover decision by issuing the handover command based on the measurement event prediction results (e.g., based on the measurement prediction report). The handover decision by the network node 5 may help in optimizing the timing and selection of the target cell or optimize the timing and selection of multiple possible target cells for the handover of the UE 3. The network node 5 may make the handover decision based on the measurement event prediction. The network node 5 may make the handover decision based on the measurement report from the UE 3. For example, the network node 5 which is configured to make the handover decision based on the event (e.g., one of the events A1 to A5) may make the handover decision based on the measurement report which indicates the prediction result of the event. The measurement report may indicate that the event occurs or will occur.

[0118] In step 1107, the network node 5 may send a handover request to the single target node (e.g., target gNB) or the multiple target nodes (e.g., target gNBs) along with relevant context information for the UE 3 and necessary resource requirements for the handover. In the handover request message, the source gNB may send to the target node at least one of: the measurement event prediction results, the measurement report, and the predicted time of occurrence of measurement event for pre-assigning resources in those target gNBs. In an example, the source gNB may also sort the predicted time of occurrence according to the ascending order and may send the handover request only to the target node with the earliest predicted time of occurrence of the event to save resources in multiple target gNBs. The network node 5 may be a source gNB. The network node 5 may send, to another network node, the handover request based on the handover decision. Another network node may be a target gNB. The network node 5 may send the handover request if the network node 5 determines or decides that the UE 3 needs to take the handover to another cell. The network node 5 (e.g. source gNB) may send the handover request in a case where the network node 5 determines or decides that the handover for the UE 3 to another cell (or to the target gNB). The network node 5 (e.g. source gNB) may send the handover request in a case where the network node 5 made the handover decision for the UE 3 to another cell (or to the target gNB) in step 1106. The network node 5 may send, to the target gNB, the handover request based on the measurement report sent from the UE3.

[0119] In step 1108, the target gNB may perform admission control to evaluate if the target node may accommodate the incoming connection for the UE 3 based on assessment of it's resource availability and QoS capability. In step 1109, following successful admission control, the target gNB may send an acknowledgement back to the source gNB, confirming preparedness of the target node to proceed with the handover of the UE 3. For example, in a case where the target gNB accepts the handover for the UE 3, the target gNB may send the acknowledgement (e.g., Handover Acknowledgement) to the source gNB.

[0120] In step 1110, the source gNB may then issue a new RRC message that may comprise the prediction-based handover command containing the handover command for the UE 3, guided by specific conditions, to ensure that the handover to be executed by the UE 3 when the specific condition is met. In an example, the specific conditions may be defined according to the measurement event prediction results of short-term measurement event prediction. In an example, the specific conditions may be defined as the measurement event prediction results of short-term measurement event prediction. Alternatively, the specific conditions may be defined based on a real time measurement about the target cell (for example, the RSRP value of the target node is above a threshold). The prediction-based handover command may also comprise a suggested time window for the execution of the handover for the UE 3 for each target gNB of the multiple target nodes or the single target node gNB. The network node 5 may send a new RRC message. The new RRC message may be the prediction-based handover command. The new RRC message may include information that indicates specific conditions related to the prediction. The UE 3 may receive the new RRC message.

[0121] Further in step 1110, in an alternate scenario, the source gNB may send the RRC message of only one of the target nodes gNB with the earliest predicted time of handover occurrence based on the measurement report received from the UE 3.

[0122] Alternatively, in step 1110, the UE 3 may be configured with a new AI / ML model and corresponding input parameters to execute the measurement event prediction for a failure case that comprises radio Link Failure, as a type of validation of the specific condition as discussed above.

[0123] In step 1111, the UE 3 may perform the short-term measurement prediction (for example 100 ms as the prediction window) for the same event that is used to trigger the handover decision by using the same prediction model as used previously for all the target gNBs. The UE 3 may switch to the target gNB that may have the highest probability of prediction. Alternatively, the UE 3 may just switch to the target node gNB when the real time measurement on the target node gNB meets the configured measurement threshold. The UE 3 may perform the prediction based on the new RRC message sent from the network node 5. In a case where the prediction meets the condition, the UE 3 may switch or perform the handover to the target gNB. Switching to the target gNB may be expressed as performing the handover to the target gNB. Switching or performing the handover to the target gNB may mean deciding to switch or perform the handover to the target gNB. For example, the UE 3 may perform the measurement prediction, and the UE 3 may select the target gNB and switch to the target gNB. The target gNB selected by the UE may be a target gNB that the UE has determined to be the most suitable based on the prediction results. The UE 3 perform the prediction in step 1111 which is same to the prediction in step 1104.

[0124] Furthermore, in step 1111 (in case of alternate step 10) when there is a failure case, if the UE 3 predicts that the radio link failure event may occur within a configured time window (with a probability above the given threshold), the UE 3 may trigger step 1112 (discussed below) and switch to the target node gNB as configured by step 10.

[0125] In step 1112: once the UE 3 switches to the target node gNB, the UE 3 may establish a connection to the new gNB (selected target node gNB) and thus completes the handover process and start communicating with the target gNB.

[0126] In one implementation, the method 900 comprises receiving the request regarding capability information of the UE 3 from the network node 5 and transmitting the capability information of the UE 3 to the network node 3. The capability information comprises supported frequency bands, and prediction model capabilities of the one or more prediction models. The method 900 further comprises receiving the execution command from the network node 5 to execute the one or more prediction models selected by the network node 3 for the one or more measurement event predictions.

[0127] In another implementation, the direct prediction model uses pre-trained models and real-time measurement trends for the one or more measurement event predictions and the indirect prediction model uses historical measurement data for the one or more measurement event predictions.

[0128] In another implementation, the one or more measurement event prediction results comprise an event type, a triggering cell, a trigger time, a time window for each measurement event prediction, a probability of occurrence for each measurement event prediction, or model accuracy for each of the prediction model.

[0129] In one implementation, the method 1000 provides selecting by the network node 5, the one or more prediction models for the UE 3 to be used for the one or more measurement event predictions and configuring the UE 3 by the network node 5 to execute the selected one or more prediction models.

[0130] In addition, a resource reliability time window can be defined which is a time range around the actual measurement event occurrence time where a measurement event prediction is considered acceptable. Predictions falling outside this time window is considered invalid. If the predicted event time is Tpredicted and the actual event time is Tactual, then the prediction is considered resource reliable if (for example, |Tactual- Tpredicted| ? Treliability_time ). Treliability_time is the threshold for how much deviation the prediction from actual event is resource reliable, which can be configured by network node 5 to the UE 3 through measurement configuration message.

[0131] Referring to FIG. 12 illustrates a block diagram of the user equipment (UE) 3 in accordance with an embodiment of the present disclosure. The various modules in the UE can be embodied as a hardware that includes, without limitation, the at least one memory 1202, the at least one processor 1204, transmitter / receiver circuitry 1206, and programmable logic or software.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] The steps of a method (e.g., method 900, 1000) described in connection with the embodiments disclosed herein may be embodied directly in hardware (e.g., UE), in a software module executed by the at least one processor 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.

[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 method for a User Equipment (UE) facilitating handover of the UE in a wireless communication network, the method comprising: 　　receiving, from a network node, a measurement configuration message comprising one or more prediction parameters to trigger one or more measurement event predictions, by the UE, for a target node from multiple target nodes; 　　performing, by the UE, the one or more measurement event predictions using one or more prediction models based on the one or more prediction parameters to generate a measurement report; 　　transmitting, to the network node, the measurement report comprising one or more measurement event prediction results; and 　　 　　receiving, from the network node, a prediction-based handover command based on the measurement report, wherein the UE handover command is of use in selecting the target node from multiple target nodes for the handover of the UE to the selected target node; and 　　executing the handover command by the UE to access the target node. 　　(Supplementary note 2) 　　The method according to supplementary note 1, wherein the one or more prediction parameters comprise at least one of: prediction model identity; threshold values for triggering one or more events from a plurality of events; hysteresis; time-to-trigger parameter for triggering the one or more events; measurement event interval; observation Window; prediction window length; maximum event timing difference (ETD); event measurement objects; signal strength value; mobility patterns of the UE; environment related parameters; carrier frequency; cell configuration; observation window parameters; or a prediction window for the measurement event prediction. 　　(Supplementary note 3) 　　The method according to in supplementary note 1, wherein each measurement event prediction result of the one or more measurement event prediction results is associated with an occurrence probability within one or more prediction windows, wherein the one or more prediction windows comprise a short-term prediction window, a medium-term prediction window or a long-term prediction window, 　　wherein the prediction window is defined based on at least one of a speed of the UE, UE location, or Signal strengths of a serving cell serving to the UE. 　　(Supplementary note 4) 　　The method according to supplementary note 1, comprises: 　　establishing, by the UE, a connection with the selected target node in response to the handover command, wherein the selected target node comprises a node with a highest probability of successful handover with respect to probabilities of successful handover for other target nodes from the multiple target nodes. 　　(Supplementary note 5) 　　The method according to supplementary note 1, comprises at least one of: switching to the target node when real time measurement on the target cell meets a configured measurement threshold; or switching to the target node with an earliest time of Handover occurrence (HO) with respect to earliest time of HO of other target nodes, based on the measurement report shared by the UE through the Radio Resource Control (RRC) message. 　　(Supplementary note 6) 　　A method for a network node for performing handover of a user equipment (UE) in a wireless communication network, the method comprising: transmitting, to the UE, a measurement configuration message comprising one or more prediction parameters, to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes; receiving, from the UE, a measurement report comprising one or more measurement event prediction results predicted by the UE based on the one or more prediction parameters; sending a handover request to multiple target nodes comprising of predicted time of occurrence for the handover from the measurement report received from the UE; and transmitting, to the UE, a prediction-based handover command based on the measurement report, wherein the UE handover command is of use in selecting a target node from the multiple target nodes for handover of the UE to the selected target node. 　　(Supplementary note 7) 　　The method according to supplementary note 6, comprising: prioritizing the one or more measurement event prediction results based on preconfigured signal thresholds defined for each measurement event prediction result; and identifying a node with a highest probability of successful handover with respect to probabilities of successful handover for other target nodes from the multiple target nodes, wherein the node with the highest probability of successful handover is selected as the target node, or based on a earliest time of handover occurrence (HO) with respect to time of HO for other target nodes as reported by the UE in the measurement report, wherein the identification is performed based on the prioritization of the one or more measurement event prediction results. 　　(Supplementary note 8) 　　A User Equipment (UE) facilitating a handover of the UE in a wireless communication network, the UE comprising: 　　processing circuitry configured to: 　　receive, from a network node, a measurement configuration message comprising one or more prediction parameters to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes; and 　　perform the one or more measurement event predictions using one or more prediction models based on the one or more prediction parameters to generate a measurement report; and a transceiver configured to: 　　transmit, to the network node, the measurement report comprising one or more measurement event prediction results; 　　receive, from the network node, a prediction-based handover command based on the measurement report, wherein the prediction-based handover command is of use in selecting the target node from multiple target nodes for handover of the UE to the selected target node; and 　　wherein the processing circuitry is configured to: 　　execute the prediction-based handover command by the UE to access the target node. 　　(Supplementary note 9) 　　A network node for performing handover of a user equipment (UE) in a wireless communication network, the network node comprising: 　　a transceiver configured to: 　　transmit, to the UE, a measurement configuration message comprising one or more prediction parameters, to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes; 　　receive, from the UE, a measurement report comprising one or more measurement event prediction results predicted by the UE based on the one or more prediction parameters; 　　send a handover request to multiple target nodes comprising of predicted time of occurrence for the handover from the measurement report received from the UE; and 　　transmit, to the UE, a prediction-based handover command based on the measurement report, wherein the prediction-based handover command is of use in selecting a target node from the multiple target nodes for handover of the UE to the selected target node. 　　(Supplementary note 10) 　　The network node according to supplementary note 9, wherein the processing circuitry is configured to: 　　prioritize the one or more measurement event prediction results based on preconfigured signal thresholds defined for each measurement event prediction result; and 　　identify a node with a highest probability of successful handover with respect to probabilities of successful handover for other target nodes from the multiple target nodes, wherein node with the highest probability of successful handover is selected as the target node from the multiple target nodes, or based on a earliest time of handover occurrence (HO) with respect to time of HO for other target nodes as reported by the UE in the measurement report, 　　wherein the identification is performed based on the prioritization of the one or more measurement event prediction results.

[0144] This application is based upon and claims the benefit of priority from Indian Patent Application No. 202511009815, filed on February 6, 2025, the disclosure of which is incorporated herein in its entirety by reference.

[0145] 10 CORE NETWORK 20 DATA NETWORK 3 USER EQUIPMENT 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　　ANNTENA 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 7　　CORE NETWORK 70　　AMF 701　　TRANSCEIVER CIRCUIT 702　　NETWORK INTERFACE 703　　CONTROLLER 704　　MEMORY 7041　　OPERATING SYSTEM 7042　　COMMUNICATIONS CONTROL MODULE 70421　　TRANSCEIVER CONTROL MODULE 1202　　MEMORY 1204　　PROCESSOR 1206　　TX / RX CIRCUITRY

Claims

1. A method for a User Equipment (UE) facilitating handover of the UE in a wireless communication network, the method comprising: 　　receiving, from a network node, a measurement configuration message comprising one or more prediction parameters to trigger one or more measurement event predictions, by the UE, for a target node from multiple target nodes; 　　performing, by the UE, the one or more measurement event predictions using one or more prediction models based on the one or more prediction parameters to generate a measurement report; 　　transmitting, to the network node, the measurement report comprising one or more measurement event prediction results; and 　　 　　receiving, from the network node, a prediction-based handover command based on the measurement report, wherein the UE handover command is of use in selecting the target node from multiple target nodes for the handover of the UE to the selected target node; and 　　executing the handover command by the UE to access the target node.

2. The method as claimed in claim 1, wherein the one or more prediction parameters comprise at least one of: prediction model identity; threshold values for triggering one or more events from a plurality of events; hysteresis; time-to-trigger parameter for triggering the one or more events; measurement event interval; observation Window; prediction window length; maximum event timing difference (ETD); event measurement objects; signal strength value; mobility patterns of the UE; environment related parameters; carrier frequency; cell configuration; observation window parameters; or a prediction window for the measurement event prediction.

3. The method as claimed in claim 1, wherein each measurement event prediction result of the one or more measurement event prediction results is associated with an occur-rence probability within one or more prediction windows, wherein the one or more prediction windows comprise a short-term prediction window, a medium-term predic-tion window or a long-term prediction window, 　　wherein the prediction window is defined based on at least one of a speed of the UE, UE location, or Signal strengths of a serving cell serving to the UE.

4. The method as claimed in claim 1, comprises: 　　establishing, by the UE, a connection with the selected target node in response to the handover command, wherein the selected target node comprises a node with a highest probability of successful handover with respect to probabilities of successful handover for other target nodes from the multiple target nodes.

5. The method as claimed in claim 1, comprises at least one of: switching to the target node when real time measurement on the target cell meets a configured measurement threshold; or switching to the target node with an earliest time of Handover occurrence (HO) with respect to earliest time of HO of other target nodes, based on the measurement report shared by the UE through the Radio Resource Control (RRC) message.

6. A method for a network node for performing handover of a user equipment (UE) in a wireless communication network, the method comprising: transmitting, to the UE, a measurement configuration message comprising one or more prediction parameters, to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes; receiving, from the UE, a measurement report comprising one or more measurement event prediction results predicted by the UE based on the one or more prediction parameters; sending a handover request to multiple target nodes comprising of predicted time of occurrence for the handover from the measurement report received from the UE; and transmitting, to the UE, a prediction-based handover command based on the measurement report, wherein the UE handover command is of use in selecting a target node from the multiple target nodes for handover of the UE to the selected target node.

7. The method as claimed in claim 6, comprising: prioritizing the one or more measurement event prediction results based on preconfigured signal thresholds defined for each measurement event prediction result; and identifying a node with a highest probability of successful handover with respect to probabilities of successful handover for other target nodes from the multiple target nodes, wherein the node with the highest probability of successful handover is selected as the target node, or based on a earliest time of handover occurrence (HO) with respect to time of HO for other target nodes as reported by the UE in the measurement report, wherein the identification is performed based on the prioritization of the one or more measurement event prediction results.

8. A User Equipment (UE) facilitating a handover of the UE in a wireless communica-tion network, the UE comprising: 　　processing circuitry configured to: 　　receive, from a network node, a measurement configuration message comprising one or more prediction parameters to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes; and 　　perform the one or more measurement event predictions using one or more prediction models based on the one or more prediction parameters to generate a measurement report; and a transceiver configured to: 　　transmit, to the network node, the measurement report comprising one or more measurement event prediction results; 　　receive, from the network node, a prediction-based handover command based on the measurement report, wherein the prediction-based handover command is of use in selecting the target node from multiple target nodes for handover of the UE to the selected target node; and 　　wherein the processing circuitry is configured to: 　　execute the prediction-based handover command by the UE to access the target node.

9. A network node for performing handover of a user equipment (UE) in a wireless communication network, the network node comprising: 　　a transceiver configured to: 　　transmit, to the UE, a measurement configuration message comprising one or more prediction parameters, to trigger one or more measurement event predictions by the UE for a target node from multiple target nodes; 　　receive, from the UE, a measurement report comprising one or more measurement event prediction results predicted by the UE based on the one or more prediction parameters; 　　send a handover request to multiple target nodes comprising of predicted time of occurrence for the handover from the measurement report received from the UE; and 　　transmit, to the UE, a prediction-based handover command based on the measurement report, wherein the prediction-based handover command is of use in selecting a target node from the multiple target nodes for handover of the UE to the selected target node.

10. The network node as claimed in claim 9, wherein the processing circuitry is configured to: 　　prioritize the one or more measurement event prediction results based on preconfigured signal thresholds defined for each measurement event prediction result; and 　　identify a node with a highest probability of successful handover with respect to probabilities of successful handover for other target nodes from the multiple target nodes, wherein node with the highest probability of successful handover is selected as the target node from the multiple target nodes, or based on a earliest time of handover occurrence (HO) with respect to time of HO for other target nodes as reported by the UE in the measurement report, 　　wherein the identification is performed based on the prioritization of the one or more measurement event prediction results.

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