A method and apparatus for performing applicable functionality for ai / ML in a wireless communication system

Abstract

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments herein provide a method and system for performing applicable functionality for AI / ML in a wireless communication system. The method includes receiving, from a base station, information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML); initiating a radio resource control (RRC) re-establishment procedure in case that a condition for re-establishment is met; identifying whether an attempting conditional reconfiguration for radio link failure (RLF) recovery or an attempting layer 1 / layer 2 triggered mobility (LTM) for RLF recovery is configured, upon initiation of the RRC re-establishment procedure; and releasing the information for reporting applicability configuration associated with AI / ML in case that the attempting conditional reconfiguration for RLF recovery or the attempting LTM for RLF recovery is configured.

Description

A METHOD AND APPARATUS FOR PERFORMING APPLICABLE FUNCTIONALITY FOR AI / ML IN A WIRELESS COMMUNICATION SYSTEM

[0001] The present disclosure is related to the field of wireless communication. More particularly, the present disclosure is related to a method and system for performing handling of applicable functionality for artificial intelligence / machine learning (AI / ML) in a wireless communication system.

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

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

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

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

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

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

[0008] Usage of AI / ML is used in a variety of fields. 3GPP is studying the AI / ML on air interface. AI / ML based techniques can be used for enhanced performance and / or reduced complexity / overhead. Enhanced performance here depends on the use cases under consideration and can be, for example, improved throughput, robustness, accuracy or reliability, and the like. AI / ML models can reside on the User Equipment (UE) or on the network entities like base station or Operations, Administration and Management (OAM). It is also possible that AI / Models are 2 sided models that reside on both UE and network entities.

[0009] Data collection may be performed for different purposes in life cycle management (LCM), for example, model training, model inference, model monitoring, model selection, model update, and the like. For all types of offline model training (for example, UE- / NW- / two-sided model training), there is no latency requirement for data collection. For model inference, when required data comes from other entities, there is a latency requirement for data collection. For real-time performance monitoring, when required monitoring data (for example, performance metric) comes from other entities, there is a latency requirement for data collection.

[0010] A typical usage of AI / ML on air interface is AI / ML for beam management that includes spatial-domain downlink beam prediction for Set A of beams based on measurement results of Set B of beams. Temporal Downlink beam prediction for Set A of beams is based on the historic measurement results of Set B of beams.

[0011] AI / ML on air interface also may be used for positioning, such as

[0012] Direct AI / ML positioning:

[0013] - UE-based positioning with UE-side model, direct AI / ML positioning

[0014] - UE-assisted / (Location Management Function) LMF-based positioning with LMF-side model, direct AI / ML positioning

[0015] - NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning

[0016] AI / ML assisted positioning:

[0017] - UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning

[0018] - NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning

[0019] Another use case of AI / ML on air interface is for Channel State Information (CSI) feedback enhancements. This may be used for CSI prediction, CSI compression etc. AI / ML may be used for other cases such as AI / ML for mobility. The training can be online training or offline training. CN (Core Network) / OAM (Operation and Maintenance) / OTT (Over the Top Server) collection of UE-sided model training (and even Network sided model training) data may be supported. The UE may be configured by the network for reporting layer 1 (L1) measurements for AI / ML. These measurements may be logged and reported to the network. A variation of immediate MDT may be used for the reporting. The measurements are typically layer 1 measurement for beam prediction or CSI feedback enhancements. These measurements would be typically used for training the AI / ML models, but can be used for other use cases also. Version 18.2.0 of TS 38.331, 38.321, 38.306, 37.340,38.300 etc. are considered as background for the present disclosure.

[0020] In conventional methods, there are a number of issues like as follows:

[0021] 1. How does the UE set up and release configuration for sending applicable functionality for AI / ML.

[0022] 2. Methods for controlling the reporting of applicable functionality for AI / ML.

[0023] 3. Handling of configuration for reporting of applicable functionality for AI / ML during RRC Reestablishment.

[0024] 4. Handling of configuration for reporting of L1 measurements for AI / ML during RRC Reestablishment.

[0025] 5. Handling of configuration for reporting of applicable functionality for AI / ML in dual connectivity.

[0026] Hence, is desirable to address the above mentioned problems and disadvantages or at least provide a useful alternative.

[0027] The principal object of the embodiments herein is to provide a method and system for performing the handling of applicable functionality for AI / ML in a wireless communication system.

[0028] Another object of the embodiments herein is to configure the UE with a configuration for sending applicable functionality for AI / ML using a Setup Release structure.

[0029] Yet another object of the embodiments herein is to enable the network to request the UE to release the configuration for sending applicable functionality for AI / ML, and the UE considers itself not to be configured for providing the applicable functionality for AI / ML upon reception of a network request.

[0030] Yet another object of the embodiments herein is to enable the UE to release the configuration for sending applicable functionality for AI / ML upon initiation of a radio resource control (RRC) reestablishment procedure.

[0031] Yet another object of the embodiments herein is to enable the UE to release the configuration for sending applicable functionality for AI / ML upon transitioning from RRC_CONNECTED to RRC_IDLE.

[0032] Yet another object of the embodiments herein is to enable the UE to inform the network about applicability and non-applicability of functionalities in a UE assistance information (UAI) message.

[0033] In an aspect, the objectives are achieved by providing a method for performing handling of applicable functionality for AI / ML in a wireless communication system. The method includes receiving, from a base station, information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML); initiating a radio resource control (RRC) re-establishment procedure in case that a condition for re-establishment is met; identifying whether an attempting conditional reconfiguration for radio link failure (RLF) recovery or an attempting layer 1 / layer 2 triggered mobility (LTM) for RLF recovery is configured, upon initiation of the RRC re-establishment procedure; and releasing the information for reporting applicability configuration associated with AI / ML in case that the attempting conditional reconfiguration for RLF recovery or the attempting LTM for RLF recovery is configured.

[0034] In another aspect, the objectives are achieved by providing a method for performing handling of applicable functionality for AI / ML in a wireless communication system. The method includes transmitting, to a user equipment (UE), information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML); and receiving, from the UE, a UE assistance information message including the applicability configuration associated with AI / ML in case that the applicability configuration is not applicable.

[0035] In another aspect, the objectives are achieved by providing a UE for performing applicable functionality for AI / ML in a wireless communication system. The UE includes at least one processor; and at least one memory, communicatively coupled to the at least one processor, storing instructions executable by the at least one processor individually or in any combination to cause the UE to receive, from a base station, information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML), initiate a radio resource control (RRC) re-establishment procedure in case that a condition for re-establishment is met, identify whether an attempting conditional reconfiguration for radio link failure (RLF) recovery or an attempting layer 1 / layer 2 triggered mobility (LTM) for RLF recovery is configured, upon initiation of the RRC re-establishment procedure, and release the information for reporting applicability configuration associated with AI / ML in case that the attempting conditional reconfiguration for RLF recovery or the attempting LTM for RLF recovery is configured.

[0036] In another aspect, the objectives are achieved by providing a network apparatus for performing applicable functionality for AI / ML in a wireless communication system. The base station includes at least one processor; and at least one memory, communicatively coupled to the at least one processor, storing instructions executable by the at least one processor individually or in any combination to cause the base station to transmit, to a user equipment (UE), information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML), and receive, from the UE, a UE assistance information message including the applicability configuration associated with AI / ML in case that the applicability configuration is not applicable.

[0037] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications be made within the scope of the embodiments herein.

[0038] According to embodiments of the present disclosure, efficient communication can be achieved.

[0039] These and other features, aspects, and advantages of the present embodiments are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:

[0040] Fig. 1 illustrates a schematic diagram that illustrates a high-level overview of a system for the usage of AI / ML in beam prediction according to the prior art.

[0041] Fig. 2 illustrates a schematic diagram that illustrates a schematic of a UE implemented to carry out the disclosed subject matter according to an embodiment as disclosed herein.

[0042] Fig. 3 illustrates a schematic diagram that illustrates a schematic of a network apparatus implemented to carry out the disclosed subject matter according to an embodiment as disclosed herein.

[0043] Fig. 4 illustrates a flow diagram that illustrates a scenario of handling applicable functionalities for AI / ML with attempt conditional reconfiguration / attempt LTM according to an embodiment as disclosed herein.

[0044] Fig. 5 illustrates a flow diagram that illustrates a scenario of cell selection while a T311 timer is running with a configuration for reporting applicable functionalities available at the UE according to an embodiment as disclosed herein.

[0045] Fig. 6 illustrates a sequence diagram that illustrates a method of UE capability reporting for applicable functionalities for AI / ML according to an embodiment as disclosed herein.

[0046] Fig. 7 illustrates a sequence diagram that illustrates a method of releasing the configuration for reporting applicable functionalities for AI / ML according to an embodiment as disclosed herein.

[0047] Fig. 8 illustrates a flow diagram that illustrates a method for performing applicable functionality handling for AI / ML in a wireless communication system by the UE according to an embodiment as disclosed herein.

[0048] Fig. 9 illustrates a flow diagram that illustrates a method for performing applicable functionality handling for AI / ML in a wireless communication system by the network apparatus according to an embodiment as disclosed herein.

[0049] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with a plurality of other embodiments to form new embodiments. The term "or" as used herein, refers to a non-exclusive or, unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples are not be construed as limiting the scope of the embodiments herein.

[0050] As is existing in the field, embodiments are described and illustrated in terms of blocks that carry out a described function or functions.　These blocks, which referred to herein as managers, units, modules, hardware components or the like, are physically implemented by analog and / or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, and the like, and optionally be driven by firmware and software. The circuits, for example, be embodied in a plurality of semiconductor chips, or on substrate supports such as printed circuit boards, and the like.　The circuits constituting a block be implemented by dedicated hardware, or by a processor (e.g., a plurality of programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments be physically separated into two or more interacting and discrete blocks without departing from the scope of the proposed method.　Likewise, the blocks of the embodiments be physically combined into more complex blocks without departing from the scope of the proposed method.

[0051] The accompanying drawings are used to help easily understand various technical features and it is understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the proposed method is construed to extend to any alterations, equivalents and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. used herein to describe various elements, these elements are not be limited by these terms. These terms are generally used to distinguish one element from another.

[0052] Fig. 1 is a schematic diagram that illustrates a high-level overview of a system for the usage of AI / ML in beam prediction according to the prior art. As shown, the schematic diagram includes a UE (100) and a network apparatus (200). The AI / ML operations include multiple steps such as: training, inference and performance monitoring. The UE (100) can indicate its capabilities for AI / ML related functionalities to the network apparatus (200). This can be sent in RRC messages such as UE Capability Information (RRC message UECapabilityInformation) in NR or similar messages in other technologies. In certain scenarios and situations, the UE (100) may not be able to perform the AI / ML related functionality even though it is reported as supported. For example, the UE (100) may not be able to perform the inference for the AI / ML related functionality even though it is supported. This may be due to the absence of a relevant model for the functionality or other reasons such as hardware conditions cannot support the available model or the available model is not applicable for current UE side additional conditions or network side additional conditions. The network apparatus (200) needs to know the applicable functionality. The network apparatus (200) may configure the UE (100) for reporting the applicable functionality.

[0053] Applicable functionality (also referred as applicable functionalities) may be reported using UE Assistance Information or such other signaling. Applicable functionality can be the functionality for which the UE (100) may perform inference. The UE (100) may report the applicable functionality in one or more of the following scenarios (non-exhaustive list):

[0054] 1) Upon being configured to provide applicable functionality

[0055] 2) Upon change of applicable functionality

[0056] 3) As response to network-side additional condition (if available),

[0057] 4) Upon configuration for inference configuration from the network apparatus (200). Inference configuration from the network apparatus may be provided in the CSI configuration or other configuration.

[0058] The above scenarios may be referred to as triggers for reporting the applicable functionality for AI / ML.

[0059] The proposed solution discloses a method and system for performing applicable functionality for AI / ML in a wireless communication system. The UE (100) is configured with a configuration for sending applicable functionality for AI / ML using a Setup Release structure. The network apparatus (200) requests the UE (100) to release the configuration for sending applicable functionality for AI / ML, and the UE (100) considers itself not to be configured for providing the applicable functionality for AI / ML upon reception of a network request. The UE (100) releases the configuration for sending applicable functionality for AI / ML upon initiation of the RRC reestablishment procedure. The UE (100) releases the configuration for sending applicable functionality for AI / ML upon transitioning from RRC_CONNECTED to RRC_IDLE. Further, the UE (100) informs the network apparatus (200) about applicability and non-applicability of functionalities in a UAI message.

[0060] Fig. 2 is a schematic diagram that illustrates a schematic of a UE (100) implemented to carry out the disclosed subject matter according to an embodiment as disclosed herein. Examples of the UE (100) can include, but are not limited to, Consumer Electronics (such as Mobile Phones and Smartphones), Tablets, Wearable Devices, Computing Devices (such as Laptops, Notebooks, Desktops, Workstations, etc.), IoT Devices, Automotive Systems (such as connected cars, Autonomous Vehicles, Vehicle-to-Everything (V2X) communication devices, etc.), Enterprise Devices such as robotics, Specialized Equipment (such as Medical Devices, Public Safety Devices, etc.), Media Devices (such as Gaming Consoles, Streaming Devices, etc.).

[0061] In an embodiment, in Fig. 2, the UE (100) includes a first processor (102), a first memory (104), a first I / O interface (106), and a first applicable functionality controller (108) coupled to the first processor (102) and the first memory (104). The components are explained in further detail below.

[0062] The first processor (102) communicates with the first memory (104), the first I / O interface (106), and the first applicable functionality controller (108). The first processor (102) is configured to execute instructions stored in the first memory (104) and to perform various processes. The first processor (102) includes one or a plurality of processors, is a general-purpose processor such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an Artificial Intelligence (AI) dedicated processor such as a neural processing unit (NPU).

[0063] The first memory (104) includes storage locations to be addressable through the first processor (102). The first memory (104) stores configuration for sending applicable functionality for AI / ML. The first memory (104) is not limited to a volatile memory and / or a non-volatile memory. Further, the first memory (104) includes a plurality of computer-readable storage media. The first memory (104) includes non-volatile storage elements. For example, non-volatile storage elements include magnetic hard disks, optical disks, floppy disks, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.

[0064] The first I / O interface (106) transmits the information between the first memory (104) and external peripheral devices. The peripheral devices are the input-output devices associated with the UE (100). Further, the first applicable functionality controller (108) communicates with the first I / O interface (106) and the first memory (104). The first applicable functionality controller (108) is coupled to the first memory (104) and the first processor (102). This coupling allows for efficient data transfer and communication between the components, ensuring that the first applicable functionality controller (108) can perform applicable functionality for AI / ML in a wireless communication system.

[0065] The first applicable functionality controller (108) is an innovative integrated circuit that is implemented in the UE (300). In an embodiment, the structure of such innovative integrated circuit includes a multi-core architecture that enables performing applicable functionality for AI / ML in a wireless communication system. Each core is optimized for specific tasks, such as initiating a RRC re-establishment procedure, determining whether the UE (100) is configured for attempting conditional reconfiguration for RLF recovery or attempting LTM for RLF recovery, retaining / releasing the configuration for sending applicable functionality for AI / ML, and the like. The innovative integrated circuit for the above-mentioned points is made of a combination of analog and digital components designed to enable performing applicable functionality for AI / ML in a wireless communication system. The analog components include a low-noise amplifier and a high-precision analog-to-digital converter to ensure accurate signal processing. The digital components consist of a microcontroller unit (MCU) and a digital signal processor (DSP) that work in tandem to enables performing applicable functionality for AI / ML in a wireless communication system.

[0066] In an embodiment, the first applicable functionality controller (108) receives a configuration for sending applicable functionality for AI / ML from the network apparatus (200). Upon receiving the configuration, the first applicable functionality controller (108) interprets the configuration instructions and prepares to enable or manage the corresponding AI / ML functionalities within the system.

[0067] In an embodiment, the first applicable functionality controller (108) initiates a RRC re-establishment procedure when the UE (100) is configured for sending applicable functionality for AI / ML (i.e. when the UE (100) already has a configuration for sending the applicable functionality for AI / ML). The RRC re-establishment procedure allows the UE (100) to recover radio connectivity when certain radio link failures occur. It ensures that the user doesn't have to go through the entire connection setup process again, which helps maintain continuity of services such as calls or data sessions. The RRC re-establishment procedure reduces service interruptions and improves user experience by duly maintaining ongoing sessions.

[0068] According to 3gpp specifications, The UE (100) declares Radio Link Failure (RLF) when one of the following criteria are met:

[0069] - Expiry of a radio problem timer started after indication of radio problems from the physical layer (if radio problems are recovered before the timer is expired, the UE (100) stops the timer); or

[0070] - Expiry of a timer started upon triggering a measurement report for a measurement identity for which the timer has been configured while another radio problem timer is running; or

[0071] - Random access procedure failure; or

[0072] - RLC failure; or

[0073] - Detection of consistent uplink LBT failures for operation with shared spectrum channel access as described in 5.6.1;

[0074] After RLF is declared, the UE (100):

[0075] - stays in RRC_CONNECTED;

[0076] - in case of DAPS handover, for RLF in the source cell:

[0077] - stops any data transmission or reception via the source link and releases the source link, but maintains the source RRC configuration;

[0078] - if handover failure is then declared at the target cell, the UE (100):

[0079] - selects a suitable cell and then initiates RRC re-establishment;

[0080] - enters RRC IDLE if a suitable cell was not found within a certain time after handover failure was declared.

[0081] - in case of CHO, for RLF in the source cell:

[0082] - selects a suitable cell and if the selected cell is a CHO candidate and if network configured the UE to try CHO after RLF then the UE (100) attempts CHO execution once, otherwise re-establishment is performed;

[0083] - enters RRC_IDLE if a suitable cell was not found within a certain time after RLF was declared.

[0084] - in case of MCG LTM, for RLF in the source cell:

[0085] - selects a suitable cell and if the selected cell is an LTM candidate cell and if network configured the UE (100) to try LTM after RLF then the UE (100) attempts RACH-based LTM execution once, otherwise re-establishment is performed;

[0086] - enters RRC_IDLE if a suitable cell was not found within a certain time after RLF was declared.

[0087] In an embodiment, the first applicable functionality controller (108) determines whether UE (100) is configured for attempting conditional reconfiguration for RLF recovery or whether the UE (100) is configured for attempting LTM for RLF recovery. This is determined upon initiation of the RRC re-establishment procedure. RLF recovery refers to a process used to re-establish a stable radio connection between the UE (100) and the network apparatus (200) after the radio link is lost or becomes unreliable. For instance, the RLF can occur due to factors such as signal quality drop, interference, high mobility, poor handover execution, and the like.

[0088] In an embodiment, the first applicable functionality controller (108) retains the configuration for sending applicable functionality for AI / ML when the UE (100) is configured for attempting conditional reconfiguration for RLF recovery or for attempting LTM for RLF recovery. If the UE (100) has the LTM configuration or CHO configuration for a cell, it also means that the corresponding configuration is present in the network apparatus which has the cell. So if the UE (100) can select such a cell, there is an option for reusing this configuration thereby saving the effort and the time required for a new configuration. But this is usually under the network control as it depends on the network's ability to store and support such a recovery. Else, the first applicable functionality controller (108) releases the configuration for sending applicable functionality for AI / ML when the UE (100) is not configured for attempting conditional reconfiguration for RLF recovery or for attempting LTM for RLF recovery.

[0089] In an embodiment, the first applicable functionality controller (108) determines whether the UE (100) is unable to perform a CHO based recovery or an LTM based recovery while a timer for cell selection is running. This is determined upon initiation of the RRC re-establishment procedure. Conditional handover is a mechanism where the network apparatus (200) provides the UE (100) with a list of target cells and conditions under which it can perform a handover without waiting for another command from the network apparatus (200). If the UE (100) fails to perform the conditional handover (for example, due to poor signal or radio link failure), it can attempt a recovery using the other candidate target cells already provided in the CHO configuration. LTM refers to a L1 / L2 triggered mobility where the UE is configured with LTM candidate cells and the network instructs UE to move to a target cell by applying the LTM candidate cell configuration.. The first applicable functionality controller (108) accordingly releases the configuration for sending applicable functionality for AI / ML when the UE (100) is unable to perform the CHO based recovery or the LTM based recovery while the timer is running during the cell selection.

[0090] Fig. 3 is a schematic diagram that illustrates a schematic of a network apparatus (200) implemented to carry out the disclosed subject matter according to an embodiment as disclosed herein. The network apparatus (200) includes various hardware and software components that facilitate communication between user equipment and network infrastructure. Examples of the network apparatus (200) can include, but is not limited to Base Stations (such as macro cells, small cells, femtocells, Pico cells) for wireless communication, Antennas and RF Units (e.g., MIMO, beamforming) to enhance signal coverage and data throughput, Core Network Equipment (e.g., MMEs, S-GWs, P-GWs in 4G; AMFs, UPFs in 5G) for data routing, mobility, and session control, Network Function Virtualization (NFV) and Software-Defined Networking (SDN) for dynamic resource allocation and scalability, Edge Computing Nodes (e.g., MEC servers) for low-latency processing,　 Backhaul and Transport Equipment (e.g., fibre-optic links, microwave relays, Ethernet switches) to connect base stations to the core network, Network Management Systems (NMS) and Operation Support Systems (OSS) for network configuration, fault management, and optimization, Radio Network Controllers (RNCs) in 3G, Distributed Units (DUs), and Centralized Units (CUs) in 5G or 6G, Network Slicing Components for virtualized resource allocation, Security elements (e.g., Firewalls, IDS, AAA Servers) for secure communication.

[0091] In an embodiment, in Fig. 3, the network apparatus (200) includes a second processor (202), a second memory (204), a second I / O interface (206), and a second applicable functionality controller (208) coupled to the second processor (202) and the second memory (204). The components are explained in further detail below.

[0092] The second processor (202) communicates with the second memory (204), the second I / O interface (206), and the second applicable functionality controller (208). The second processor (202) is configured to execute instructions stored in the second memory (204) and to perform various processes. The second processor (202) includes one or a plurality of processors, is a general-purpose processor such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an Artificial Intelligence (AI) dedicated processor such as a neural processing unit (NPU).

[0093] The second memory (204) includes storage locations to be addressable through the second processor (202). The second memory (204) stores the configuration for sending applicable functionality for AI / ML for the UE (100). The second memory (204) is not limited to a volatile memory and / or a non-volatile memory. Further, the second memory (204) includes a plurality of computer-readable storage media. The second memory (204) includes non-volatile storage elements. For example, non-volatile storage elements include magnetic hard disks, optical disks, floppy disks, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.

[0094] The second I / O interface (206) transmits the information between the second memory (204) and external peripheral devices. The peripheral devices are the input-output devices associated with the network apparatus (200). Further, the second applicable functionality controller (208) communicates with the second I / O interface (206) and the second memory (204). The second applicable functionality controller (208) is coupled to the second memory (204) and the second processor (202). This coupling allows for efficient data transfer and communication between the components, ensuring that the second applicable functionality controller (208) can perform applicable functionality for AI / ML in a wireless communication system.

[0095] The second applicable functionality controller (208) is an innovative integrated circuit that is implemented in the network apparatus (200). In an embodiment, the structure of such innovative integrated circuit includes a multi-core architecture that performing applicable functionality for AI / ML in a wireless communication system. Each core is optimized for specific tasks, such configuring the UE (100) with a configuration for applicable functionality of AI / ML, sharing the configuration with the UE (100), and the like. The innovative integrated circuit for the above-mentioned points is made of a combination of analog and digital components designed to enable performing applicable functionality for AI / ML in a wireless communication system. The analog components include a low-noise amplifier and a high-precision analog-to-digital converter to ensure accurate signal processing. The digital components consist of a microcontroller unit (MCU) and a digital signal processor (DSP) that work in tandem to enable performing applicable functionality for AI / ML in a wireless communication system.

[0096] In an embodiment, the second applicable functionality controller (208) configures the UE (100) with a configuration for sending applicable functionality for AI / ML. The configuration can include instructions, operational parameters, or data structures that define how the UE (100) should collect, process, or share information relevant to AI / ML tasks. By provisioning the UE (100) with this configuration, the second applicable functionality controller (208) ensures that the UE (100) is appropriately set up to participate in AI / ML-driven operations. The second applicable functionality controller (208) then transmits the configuration for sending applicable functionality for AI / ML to the UE (100).

[0097] In an embodiment, the second applicable functionality controller (208) receives a UAI message from the UE (100) when the UE (100) no longer has any applicable functionality for AI / ML supported. The UAI message may be communicated to the second applicable functionality controller (208) via RRC signaling. Upon receiving the UAI message, the second applicable functionality controller (208) may generate a request message that requests the UE (100) to release the configuration for sending applicable functionality for AI / ML upon receiving the UAI message. The request message is then transmitted to the UE (100). The UE (100) considers itself not to be configured for providing the applicable functionality for AI / ML upon receiving the request message.

[0098] Fig. 4 is a flow diagram that illustrates a scenario of applicable functionalities for AI / ML with attempt conditional reconfiguration / attempt LTM according to an embodiment as disclosed herein. The method includes steps (402-408). At step (402), the UE (100) detects a radio link failure. At step (402), the UE (100) determines whether an attemptCondReconfig or attemptLTM-Switch is configured. If yes, then at step (406), the UE (100) keeps the configuration for applicable functionality for AI / ML reporting. If no, then at step (408), the UE (100) releases the configuration for applicable functionality for AI / ML reporting.

[0099] Fig. 5 is a flow diagram that illustrates a scenario of cell selection while a T311 timer (or an equivalent timer) is running with a configuration for reporting applicable functionalities available at the UE (100) according to an embodiment as disclosed herein. The method includes steps (502-506). At step (502), a cell selection is performed while a T311 timer is running. Either the selected cell is not a candidate for CHO or MCG LTM, or the CHO based recovery or LTM based recovery is not attempted / successful. CHO based recovery or LTM based recovery may not be attempted in multiple cases, for e.g. the selected cell may not be a CHO candidate cell or not a LTM candidate cell. Even when the CHO based recovery or LTM based recovery is attempted, it may fail, for e.g. due to random access problem. At step (504), the UE (100) determines whether an attemptCondReconfig or an attemptLTM-Switch is configured. At step (506), the UE (100) releases the configuration for applicable functionality for AI / ML reporting.

[0100] Fig. 6 is a sequence diagram that illustrates a method of UE capability reporting for applicable functionalities for AI / ML according to an embodiment as disclosed herein. As shown in the sequence diagram, the UE (100) and the network apparatus (200) are in communication with each other. Each step is explained in further detail below.

[0101] At step S1, the UE (100) receives a UE capability enquiry from the network apparatus (200). At step S2, the UE (100) sets the UE capability for interruption for the reporting of applicable functionalities for AI / ML. The UE capability is set as a per-UE capability without FDD-TDD differentiation. At step S3, the UE (100) transmits a UE capability information message to the network apparatus (200). At step S4, the network apparatus (200) configures the UE (100) for reporting applicable functionalities for AI / ML.

[0102] Fig. 7 is a sequence diagram that illustrates a method of releasing the configuration for reporting applicable functionalities for AI / ML according to an embodiment as disclosed herein. As shown in the sequence diagram, the UE (100) and the network apparatus (200) are in communication with each other. Each step is explained in further detail below.

[0103] At step S1, the UE (100) receives a RRC Reconfiguration from the network apparatus (200). The RRC Reconfiguration includes a configuration for reporting of applicable functionalities for AI / ML set as release. At step S2, the UE (100) considers itself as not configured for reporting the applicable functionalities for AI / ML. At step S3, the UE (100) transmits a RRC reconfiguration complete message to the network apparatus (200).

[0104] Fig. 8 is a flow diagram that illustrates a method for performing applicable functionality for AI / ML in a wireless communication system by the UE (100) according to an embodiment as disclosed herein. The method includes steps (802-814). Each step is explained in further detail below.

[0105] At step (802), the UE (100) receives a configuration for sending applicable functionality for AI / ML from the network apparatus (200). Upon receiving the configuration, the first applicable functionality controller (108) interprets the configuration instructions and prepares to enable or manage the corresponding AI / ML functionalities within the system.

[0106] At step (804), the UE (100) initiates the RRC re-establishment procedure when the UE (100) is configured for sending applicable functionality for AI / ML. The RRC re-establishment procedure allows the UE (100) to recover radio connectivity when certain radio link failures occur. It ensures that the user doesn't have to go through the entire connection setup process again, which helps maintain continuity of services such as calls or data sessions. The RRC re-establishment procedure reduces service interruptions and improves user experience by duly maintaining ongoing sessions.

[0107] At step (806), the UE (100) determines whether it is configured for attempting conditional reconfiguration for RLF recovery or whether it is configured for attempting LTM for RLF recovery. This is determined upon initiation of the RRC re-establishment procedure. It may be also possible to perform this check at a different point in time after the RLF. RLF recovery refers to a process used to re-establish a stable radio connection between the UE (100) and the network apparatus (200) after the radio link is lost or becomes unreliable. For instance, the RLF can occur due to factors such as signal quality drop, interference, high mobility, poor handover execution, and the like. The UE (100) declares Radio Link Failure (RLF) when one of the following criteria are met:

[0108] - Expiry of a radio problem timer started after indication of radio problems from the physical layer (if radio problems are recovered before the timer is expired, the UE (100) stops the timer); or

[0109] - Expiry of a timer started upon triggering a measurement report for a measurement identity for which the timer has been configured while another radio problem timer is running; or

[0110] - Random access procedure failure; or

[0111] - RLC failure; or

[0112] - Detection of consistent uplink LBT failures for operation with shared spectrum channel access as described in 5.6.1;

[0113] At step (808), the UE (100) retains the configuration for sending applicable functionality for AI / ML when the UE (100) is configured for attempting conditional reconfiguration for RLF recovery or for attempting LTM for RLF recovery. Else, at step (810), the UE (100) releases the configuration for sending applicable functionality for AI / ML when the UE (100) is not configured for attempting conditional reconfiguration for RLF recovery or for attempting LTM for RLF recovery.

[0114] At step (812), the UE (100) determines whether the UE (100) is unable to perform a CHO based recovery or an LTM based recovery while a timer for cell selection is running. This is determined upon initiation of the RRC re-establishment procedure. Conditional handover is a mechanism where the network apparatus (200) provides the UE (100) with a list of target cells and conditions under which it can perform a handover without waiting for another command from the network apparatus (200). If the UE (100) fails to perform the conditional handover (for example, due to poor signal or radio link failure), it can attempt a recovery using the other candidate target cells already provided in the CHO configuration. Similarly, if the UE (100) faces a radio link failure while it has CHO configuration, it can attempt a recovery using the candidate target cells from the CHO configuration. LTM refers to a Layer1 / Layer2 (lower layers) triggered mobility where the UE may be configured by a LTM candidate cell. If the UE (100) faces a radio link failure while it has LTM candidate configuration, it can attempt a recovery using the candidate target cells from the LTM candidate configuration.

[0115] At step (814), the UE (100) releases the configuration for sending applicable functionality for AI / ML when the UE (100) is unable to perform the CHO based recovery or the LTM based recovery while the timer is running during the cell selection. The timer such as timer T311 is used to ensure that cell selection process for RRC Reestablishment does not take too long. If it takes more time, the UE (100) and the network apparatus (200) can clear the context and move to RRC_IDLE state.

[0116] Fig. 9 is a flow diagram that illustrates a method for performing applicable functionality for AI / ML in a wireless communication system by the network apparatus (200) according to an embodiment as disclosed herein. The method includes steps (902-910). Each step is explained in further detail below.

[0117] At step (902), the network apparatus (200) configures the UE (100) with a configuration for sending applicable functionality for AI / ML. The configuration can include instructions, operational parameters, or data structures that define how the UE (100) should collect, process, or share information relevant to AI / ML tasks. By provisioning the UE (100) with this configuration, this ensures that the UE (100) is appropriately set up to participate in AI / ML-driven operations. At step (904), the network apparatus (200) transmits the configuration for sending applicable functionality for AI / ML to the UE (100).

[0118] At step (906), the network apparatus (200) receives a UAI message from the UE (100) when the UE (100) no longer has any applicable functionality for AI / ML supported. The UAI message may be communicated to the second applicable functionality controller (208) via RRC signaling. Upon receiving the UAI message, at step (908), the network apparatus (200) generates a request message that requests the UE (100) to release the configuration for sending applicable functionality for AI / ML upon receiving the UAI message. At step (910), the request message is transmitted to the UE (100). The UE (100) considers itself not to be configured for providing the applicable functionality for AI / ML upon receiving the request message.

[0119] In an embodiment, the network apparatus (200) configures the UE (100) with configuration for sending applicable functionality for AI / ML (or for sending applicable functionalities for AI / ML, both are used in this invention with the same meaning) using a Setup Release structure. If the UE (100) receives the configuration for sending applicable functionality for AI / ML, and it is set to setup, the UE (100) considers itself as configured for providing the applicable functionality for AI / ML. If the UE (100) receives the configuration for sending applicable functionality for AI / ML, and it is set to release, the UE (100) considers itself as not configured for providing the applicable functionality for AI / ML.

[0120] In an embodiment, the network apparatus (200) explicitly requests the UE (100) to release the configuration for sending applicable functionality for AI / ML, and the UE (100) considers itself not to be configured for providing the applicable functionality for AI / ML upon reception of the network request.

[0121] In an embodiment the applicable functionality for AI / ML includes but not limited to whether and how the temporal downlink beam prediction can be applied, Spatial-domain Downlink beam prediction can be applied, RLF prediction can be applied, measurement prediction can be applied, layer 1 measurement prediction can be applied, layer 3 measurement prediction can be applied, location measurement prediction can be applied, location prediction can be applied, channel state information prediction can be applied, channel state information compression prediction can be applied and so on.

[0122] In an embodiment, the network apparatus (200) explicitly requests the UE (100) to release the configuration for sending applicable functionality for AI / ML for a functionality (for example, for temporal downlink beam prediction), and the UE (100) considers itself not to be configured for providing the applicable functionality for AI / ML for that functionality upon reception of the network request.

[0123] In an embodiment, the network apparatus (200) explicitly requests the UE (100) to release the configuration for sending applicable functionality for AI / ML for a set of functionalities (for example, for temporal downlink beam prediction and spatial downlink beam prediction), and the UE (100) considers itself not to be configured for providing the applicable functionality for AI / ML for that set of functionalities upon reception of the network request.

[0124] In an embodiment, network apparatus (200) configures the UE (100) with a timer related to reporting applicable functionality for AI / ML. In an embodiment, this timer is a prohibit timer for reporting applicable functionality for AI / ML. Once the UE (100) sends the applicable functionality for AI / ML, the UE (100) starts this timer. The UE (100) doesn't send the applicability functionality for AI / ML even if there is another trigger for sending the applicable functionality for AI / ML while this timer is running. If there is a trigger for reporting the applicable functionality for AI / ML while the prohibit timer is running, the UE (100) avoids sending the applicable functionality for AI / ML. Upon expiry of the timer, the UE (100) may send the applicable functionality for AI / ML to the network apparatus (200). If the UE (100) receives a configuration which releases the configuration for reporting applicable functionality for AI / ML, the UE (100) stops this timer. In an embodiment, during RRC Reestablishment, the UE (100) stops this timer. In another embodiment, upon initiation of RRC connection resume procedure, the UE (100) stops this timer. In another embodiment, upon performing MR-DC release, the UE (100) stops this timer where the timer is related to SCG.

[0125] In an embodiment, one or more instances of prohibit timers is configured for applicable functionality for AI / ML. For example, one instance of prohibit timer is configured for applicable functionality for AI / ML per cell group.

[0126] In an embodiment, during RRC reestablishment, the UE (100) releases the configuration for sending applicable functionality for AI / ML. In an embodiment, upon initiating RRC reestablishment procedure, the UE (100) releases the configuration for sending applicable functionality for AI / ML.

[0127] In an embodiment, during RRC connection resume, the UE (100) releases the configuration for sending applicable functionality for AI / ML. In an embodiment, upon initiating RRC connection resume procedure, the UE (100) releases the configuration for sending applicable functionality for AI / ML. In an embodiment, upon initiating RRC connection resume procedure, the UE (100) releases the configuration for sending applicable functionality for AI / ML from the UE Inactive Access stratum (AS) context, if stored.

[0128] In an embodiment, upon initiation of RRC re-establishment procedure, the UE (100) checks if it is configured for attempting conditional reconfiguration for RLF recovery (for example, NR RRC IE attemptCondReconfig is configured) or if it is configured for attempting LTM for RLF recovery (for example, NR RRC IE attemptLTM-Switch is configured) and if any of these configurations are available (i.e. if any of these flags are configured), the UE (100) keeps the configuration for sending applicable functionality for AI / ML. If none of these configurations for the recovery are available (i.e. none of these flags such as attemptCondReconfig or attemptLTM-Switch are configured), the UE (100) releases the configuration for sending applicable functionality for AI / ML. If the UE (100) is not able to perform CHO based recovery or LTM based recovery while the timer for cell selection in the RRC Reestablishment procedure (such as T311 timer in NR is running), the UE (100) releases the configuration for sending applicable functionality for AI / ML following cell selection while T311 is running

[0129] In an embodiment, if both attemptCondReconfig and attemptLTM-Switch are not configured, the UE (100) releases the configuration for sending applicable functionality for AI / ML while initiating RRC connection reestablishment. If any of attemptCondReconfig and attemptLTM-Switch are configured, the UE (100) keeps the configuration for sending applicable functionality for AI / ML. This ensures that the UE (100) can use the applicability configuration if there is a possibility for recovery from the RLF based on CHO or LTM, and the UE applicability configuration is not stored when the possibility is NIL.

[0130] In an embodiment, if the UE (100) is not able to apply LTM candidate configuration or conditional reconfiguration upon cell selection while T311 is running (for example, the selected cell is not a CHO candidate or MCG LTM candidate or the LTM / CHO recovery is not successful and so on) and if at least one of attemptCondReconfig and attemptLTM-Switch are configured, the UE (100) releases the configuration for sending applicable functionality for AI / ML following cell selection while T311 is running. This ensures that applicability configuration is not maintained when the attempt to recover is failed and the UE (100) has no other way than trying to reestablish the connection.

[0131] In an embodiment, upon initiation of RRC re-establishment procedure, the UE (100) checks if it is configured for attempting conditional reconfiguration for RLF recovery (for example, NR RRC IE attemptCondReconfig is configured) or if it is configured for attempting LTM for RLF recovery (for example, NR RRC IE attemptLTM-Switch is configured) and if any of these configurations are available (i.e. if any of these flags are configured), the UE (100) keeps the configuration for performing L1 measurements for AI / ML (for example, for the beam prediction scenarios). If none of these configurations for the recovery are available (i.e. none of these flags such as attemptCondReconfig or attemptLTM-Switch are configured), the UE (100) releases the configuration for performing L1 measurements for AI / ML (for example, for the beam prediction scenarios). If the UE (100) is not able to perform CHO based recovery or LTM based recovery while the timer for cell selection in the RRC Reestablishment procedure (such as T311 timer in NR is running), the UE (100) releases the configuration for performing L1 measurements for AI / ML (for example, for the beam prediction scenarios) following cell selection while T311 is running. This ensures that the UE (100) can use the configuration for performing L1 measurements for AI / ML if there is a possibility for recovery from the RLF based on CHO or LTM, and the UE configuration for performing L1 measurements for AI / ML is not stored when the possibility for recovery is not there.

[0132] In an embodiment, if both attemptCondReconfig and attemptLTM-Switch are not configured, the UE (100) releases the configuration for sending applicable functionality for AI / ML while initiating RRC connection reestablishment. If any of attemptCondReconfig and attemptLTM-Switch are configured, the UE (100) keeps the configuration performing L1 measurements for AI / ML (for example, for the beam prediction scenarios).

[0133] In an embodiment, if the UE (100) is not able to apply LTM candidate configuration or conditional reconfiguration upon cell selection while T311 is running and if at least one of attemptCondReconfig and attemptLTM-Switch are configured, the UE (100) releases the configuration for performing L1 measurements for AI / ML (for example, for the beam prediction scenarios) following cell selection while T311 is running. This ensures that L1 measurement configuration is not maintained when the attempt to recover is failed and the UE (100) has no other way than trying to reestablish the connection.

[0134] In an embodiment, the UE (100) releases the configuration for sending applicable functionality for AI / ML upon transitioning from RRC_CONNECTED to RRC_IDLE. The UE (100) also stops the prohibit timer related to sending the applicable functionality for AI / ML upon transitioning from RRC_CONNECTED to RRC_IDLE. In an embodiment, the UE (100) releases the configuration for sending applicable functionality for AI / ML upon reception of RRC Release including RRC Release with SuspendConfig.

[0135] In an embodiment, the UE (100) keeps the configuration for sending applicable functionality for AI / ML upon transitioning from RRC_CONNECTED to RRC_INACTIVE. The UE (100) further releases the configuration for sending applicable functionality for AI / ML upon initiating RRC Resume procedure (such as in section 5.3.13.2 of TS 38.331). The UE (100) stops the prohibit timer related to sending the applicable functionality for AI / ML upon transitioning from RRC_CONNECTED to RRC_INACTIVE.

[0136] In an embodiment, the UE (100) informs its capability for sending applicable functionalities for AI / ML to the network apparatus (200) in UECapabilityInformation message or any similar message. In an embodiment, this is a per-UE capability without any FDD-TDD difference.

[0137] In an embodiment, in dual connectivity, the secondary node, SN may configure the UE (100) to report the applicable functionalities. In an embodiment this configuration may be send over SRB3 or may be send over SRB1 through the MN. In an embodiment, the UE reports the applicable functionalities directly (either in SRB3 or included in SN RRCReconfigurationComplete embedded in MN RRCReconfigurationComplete in SRB1) to SN, in RRCReconfigurationComplete or UAI. In an embodiment, if the UE (100) is configured to report the applicable functionalities by SN, the UE (100) releases the configuration upon MR-DC release. In an embodiment, if the UE (100) is configured to report the applicable functionalities by SN, the UE (100) releases the configuration upon SCG failure (such as while initiating SCGFailureInformation procedure in NR).

[0138] In an embodiment, the UE (100) transmits the UAI message to provide applicable functionality for AI / ML upon configuration and / or activation of the configuration of sending applicable functionality for AI / ML and / or when applicable functionality or functionalities have changed from the last transmission of applicable functionality for AI / ML-for example, upon configuration and / or when the UE (100) is explicitly asked by the network apparatus (200) to report applicable functionality for AI / ML.

[0139] In an embodiment, the UE (100) transmits the UAI message to provide an indication for no applicable functionality for AI / ML when the UE (100) no longer has any applicable functionality for AI / ML supported. The embodiments for the transmission of UAI message to provide an indication for the applicable functionality for AI / ML is also applicable for the transmission of UAI message to provide an indication for no applicable functionality.

[0140] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein.

Claims

1.A method performed by a user equipment (UE) in a wireless communication system, the method comprising:receiving, from a base station, information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML);initiating a radio resource control (RRC) re-establishment procedure in case that a condition for re-establishment is met;identifying whether an attempting conditional reconfiguration for radio link failure (RLF) recovery or an attempting layer 1 / layer 2 triggered mobility (LTM) for RLF recovery is configured, upon initiation of the RRC re-establishment procedure; andreleasing the information for reporting applicability configuration associated with AI / ML in case that the attempting conditional reconfiguration for RLF recovery or the attempting LTM for RLF recovery is configured.2.The method of claim 1, wherein initiating the RRC re-establishment procedure further comprises:performing cell selection while a timer for cell selection is running;identifying whether a selected cell is one of conditional handover (CHO) candidate cells or LTM candidate cells; andreleasing the information for reporting applicability configuration associated with AI / ML in case that the selected cell is not one of the CHO candidate cells or the LTM candidate cells.3.The method of claim 1, wherein the information is set to setup or set to release.4.The method of claim 1, wherein receiving the information for reporting applicability configuration associated with AI / ML comprises:determining whether the UE has transitioned from an RRC_CONNECTED state to an RRC_IDLE state upon receiving the information; andreleasing the information for reporting applicability configuration associated with AI / ML in case that the UE has transitioned from the RRC_CONNECTED state to the RRC_IDLE state.5.A method performed by a base station in a wireless communication system, the method comprising:transmitting, to a user equipment (UE), information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML); andreceiving, from the UE, a UE assistance information message including the applicability configuration associated with AI / ML in case that the applicability configuration is not applicable.6.The method of claim 5, further comprising:generating a request message that requests the UE to release the information for reporting applicability configuration associated with AI / ML upon receiving the UE assistance information message; andtransmitting the request message to the UE.7.A UE comprising:at least one processor; andat least one memory, communicatively coupled to the at least one processor, storing instructions executable by the at least one processor individually or in any combination to cause the UE to:receive, from a base station, information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML),initiate a radio resource control (RRC) re-establishment procedure in case that a condition for re-establishment is met,identify whether an attempting conditional reconfiguration for radio link failure (RLF) recovery or an attempting layer 1 / layer 2 triggered mobility (LTM) for RLF recovery is configured, upon initiation of the RRC re-establishment procedure, andrelease the information for reporting applicability configuration associated with AI / ML in case that the attempting conditional reconfiguration for RLF recovery or the attempting LTM for RLF recovery is configured.8.The UE of claim 7, wherein the instructions executable by the at least one processor individually or in any combination further cause the UE to:perform cell selection while a timer for cell selection is running,identify whether a selected cell is one of conditional handover (CHO) candidate cells or LTM candidate cells, andrelease the information for reporting applicability configuration associated with AI / ML in case that the selected cell is not one of the CHO candidate cells or the LTM candidate cells.9.The UE of claim 7, wherein the information is set to setup or set to release.10.The UE of claim 7, wherein the instructions executable by the at least one processor individually or in any combination further cause the UE to:determine whether the UE has transitioned from an RRC_CONNECTED state to an RRC_IDLE state upon receiving the information, andrelease the information for reporting applicability configuration associated with AI / ML in case that the UE has transitioned from the RRC_CONNECTED state to the RRC_IDLE state.11.A base station in a wireless communication system, the base station comprising:at least one processor; andat least one memory, communicatively coupled to the at least one processor, storing instructions executable by the at least one processor individually or in any combination to cause the base station to:transmit, to a user equipment (UE), information for reporting applicability configuration associated with artificial intelligence / machine learning (AI / ML), andreceive, from the UE, a UE assistance information message including the applicability configuration associated with AI / ML in case that the applicability configuration is not applicable.12.The base station of claim 11, wherein the instructions executable by the at least one processor individually or in any combination further cause the base station to:generate a request message that requests the UE to release the information for reporting applicability configuration associated with AI / ML upon receiving the UE assistance information message, andtransmit the request message to the UE.

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