AIML applicability reporting in LTM cell switch procedure

Abstract

Systems and methods are disclosed for applicability reporting in relation to lower layer mobility. In one embodiment, a method performed by a User Equipment (UE) comprises receiving, from a network node, an inference configuration for reporting measurement predictions for mobility and reporting, to the network node, applicability information for the inference configuration in an applicability report. In this manner, the UE is enabled to signal to the network information concerning model or functionality applicability, e.g., before or after a potential lower layer mobility.

Description

[0001] AIML APPLICABILITY REPORTING IN LTM CELL SWITCH PROCEDURE

[0002] RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 761,470, filed February 21, 2025, the disclosure of which is hereby incorporated herein by reference in its entirety.

[0004] TECHNICAL FIELD

[0005] The present disclosure relates to a wireless communications system, such as a 3rdGeneration Partnership Project (3 GPP) system, and more particularly to reporting of applicability information for User Equipment (UE)-side Artificial Intelligence (AI) / Machine Learning (ML) models or functionalities for measurement predictions in relation to mobility.

[0006] BACKGROUND

[0007] Artificial Intelligence (Al) / Machine Learning (ML) for Physical layer (PHY) Artificial Intelligence (Al) and Machine Learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. Example use cases include using autoencoders for Channel State Information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying Line-of-Sight (LOS) and Non-LOS (NLOS) conditions to enhance the positioning accuracy; and using reinforcement learning for beam selection at the network side and / or the User Equipment (UE) side to reduce the signaling overhead and beam alignment latency; using deep reinforcement learning to learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.

[0008] In 3rd Generation Partnership Project (3GPP) New Radio (NR) standardization work, a new Release 18 study item on AI / ML for the NR air interface started in May 2022. This study item explored the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), this study item aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques. The analysis carried out during the Release 18 is now considered in the context of Release 19 work item (see RP -234039 - New WID on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface, Source: Qualcomm (Moderator), 3GPP TSG RAN Meeting #102, Edinburgh, Scotland, December 11-15, 2023). Additionally, during the Release 19, a new study itemaddressing AIML for mobility has been approved. In the context of this new study item, 3GPP will investigate methods for cell-level measurement predictions, and mobility event predictions (e.g. Radio Link Failure (RLF), handover failure, mobility -related events predictions such as A3 / A5) (see RP -234055, Study on Artificial Intelligence (AI) / Machine Learning (ML) for mobility in NR, 3GPP TSG RAN Meeting #102, Edinburgh, GB, December 11-15, 2023).

[0009] Applicability Reporting

[0010] Applicability reporting has been discussed during the Release 18 study item to allow the UE to inform the next generation NodeB (gNB) about the applicability of an AI / ML model / functionality while the UE is connected to this gNB. An AI / ML model / functionality may be applicable or not depending on a number of factors, so called applicability conditions, that are only partly under the control of the gNB. For example, whether the UE has an AI / ML model that is applicable given the current location of the UE, or given the current speed of the UE, is not something that the network can control or it can know, because typically it is assumed that the UE-side model is not trained and generated by the gNB (rather, it is typically assumed that the UE-side model is trained and generated by a node outside the Radio Access Network (RAN), such as an Over-The-Top (OTT) server or Core Network (CN) function controlled by the UE-vendor or by the Mobile Network Operator (MNO)).

[0011] Two types of applicability reporting were identified during the Release 18 study item and are currently being discussed in RAN2 for the normative phase, the so-called reactive approach and the proactive approach, further detailed in RP -234039.

[0012] In the proactive approach, the network enquires the UE capabilities and configures the UE to report the applicability of an AI / ML functionality. Based on the reported information, the network configures the UE with an inference configuration. Figure 1 illustrates an example of proactive reporting of applicability. The steps of the procedure of Figure 1 are as follows:

[0013] • Step 1 : Network sends UECapabilityEnquiry message to initiate the procedure to a UE reporting its AI / ML supported functionalities

[0014] • Step 2: UE sends UECapability Information message to network, containing supported functionalities at the UE side

[0015] • Step 3 : Network configures UE that it is allowed to provide its applicable functionalities • Step 4: UE sends applicable functionalities to network upon change of applicable functi onality / condi ti on

[0016] • Step 5: Network sends inference configuration for the applicable functionalities to the UE • Step 6: Start inference / monitoring based on network / UE activation / deactivationIn the reactive approach, the network enquires the UE capabilities and configures the UE with the AI / ML functionality (possibly including the inference configuration) in response to which the UE is able to determine the applicability of the AI / ML functionality and, in case the configured AI / ML functionality is applicable, the functionality could be up and running as soon as possible, without the need to an additional reconfiguration, as shown in the example of reactive reporting of applicability shown in Figure 2. The procedure of Figure 2 is as follows:

[0017] • Step 1 : Network sends UECapabilityEnquiry message to initiate the procedure to a UE reporting its AI / ML supported functionalities.

[0018] • Step 2: UE sends UECapability Information message to network, containing supported functionalities at the UE side.

[0019] • Step 3 : Network provides network configurations and initiates UE to report its applicable functionalities.

[0020] • Step 4: UE sends applicable functionalities to network.

[0021] • Step 5: Network sends updated inference configuration for applicable functionalities reported in Step 4 to the UE.

[0022] • Step 6: Start inference / monitoring based on network / UE activation / deactivation.

[0023] The inference configuration can be a full inference configuration, representative of radio configuration that the UE can use to evaluate whether a certain AI / ML model / functionality is applicable and that it can also use to perform / operate the inference for an AI / ML model / functionality. For example the full inference configuration could be included in a CSI measurement reporting configuration including for example the beam level resources (Synchronization Signal Blocks (SSBs), Channel State Information (CSI) Reference Signal (CSL RS) resources) that the UE should use to perform the said inference, e.g. the beam spatial / temporal prediction, or in a L3 measurement reporting configuration, including for example the cell or frequency in which the UE should perform the L3 inference, such as the cell level measurement prediction or mobility related events (e.g. prediction of A1-A5 events, or RLF or HO failure events). In this case, the applicability report will include information related to whether the AI / ML functionality is applicable given the received full inference configuration(s).

[0024] Alternatively, the inference configuration could be a partial inference configuration representative of one or more of the radio resources (e.g. reference signals, SSBs, cells, frequencies) and related characteristics that the network can configure to the UE for the inference. The partial inference configuration can include for example a list of identifiers of the resources that the gNB can configure in a full inference configuration for the UE to perform / operate the inference. Further, the partial inference configuration can contain additional network (NW)-sideconditions representative of the current gNB configured on / deployment. Hence, the partial inference configuration is sufficient and necessary for the UE to determine whether the AI / ML functionality is applicable given the provided information included in the partial inference configuration. However, that is not sufficient for the UE to operate / perform the AI / ML model / functionality inference. In this case, the applicability report will include information related to whether the AI / ML functionality is applicable given the received partial inference configuration. For example, the UE could include in the applicability report the list of resource identifiers included in the partial inference configuration, according to which the AI / ML functionality is applicable. In order for the UE to operate / perform the inference, the gNB should configure a full inference configuration based on the applicability report that the UE transmit to the gNB related to the received partial inference configuration.

[0025] Given the considerations above, the applicability report could be included in the RRCReconfigurationComplete message transmitted in response of an RRCReconfiguration message including the full / partial inference configuration. Or it can be included in the UEAssistancelnformation message transmitted in response of receiving a configuration to transmit the UEAssistancelnformation (e.g. a partial or full inference configuration received in the Information Element (IE) OtherConfig), or upon the UE determining that there is a change in the applicability of one or more inference configuration, e.g. an inference configuration that was previously reported as appliable becoming non-applicable or vice versa.

[0026] Rel- 19 AI / ML for Mobility Study Item andRel-20 Work Item In the Release 19, a study item for AI / ML for mobility was agreed with the following objectives:

[0027] The study will focus on mobility enhancement in RRC CONNECTED mode over air interface by following existing mobility framework, i.e., handover decision is always made in the network side. Mobility use cases focus on standalone NR PCell change. UE-side and network-side AI / ML model can both be considered, respectively.

[0028] Study and evaluate potential benefits and gains of AI / ML aided mobility for network triggered L3-based handover, considering the following aspects:

[0029] • AI / ML based RRM measurement and event prediction,

[0030] • Cell-level measurement prediction including intra and inter-frequency (UE sided and NW sided model) [RAN2]

[0031] • Inter-cell Beam-level measurement prediction for L3 Mobility (UE sided and NW sided model) [RAN2]• HO failure / RLF prediction (UE sided model) [RAN2]

[0032] • Measurement events prediction (UE sided model) [RAN2]

[0033] • Study the need / benefits of any other UE assistance information for the network side model [RAN2]

[0034] • The evaluation of the AI / ML aided mobility benefits should consider HO performance KPIs (e.g., Ping-pong HO, HOF / RLF, Time of stay, Handover interruption, prediction accuracy, and measurement reduction) etc.) and complexity tradeoffs [RAN2]

[0035] • NOTE: Simulation assumption and methodology can leverage TR 38.901, 38.843 and 36.839. And leave the detail discussion to RAN2

[0036] • Potential Al mobility specific enhancement should be based on the Rell9 AI / ML-air interface WID general framework (e.g. LCM, performance monitoring etc.) [RAN2] • NOTE: This would only be treated after sufficient progress is made in the Rel-19 AI / ML air interface WID

[0037] • Potential specification impacts of AI / ML aided mobility [RAN2]

[0038] • Evaluate testability, interoperability, and impacts on RRM requirements and performance [RAN4]

[0039] The Release 18 study item has focused on performing simulations and showing the gain of AI / ML in the context of mobility. The results shown are promising with a prediction error of less than 1 decibel (dB) for both temporal and frequency domain predictions.

[0040] In Release 20, the work on AI / ML mobility will continue in a work item, and the scope was discussed at the RAN Plenary meeting RAN 106 in December 2024. Most companies propose that the Release 20 work item on AI / ML mobility will include AI / ML for LTM (Lower Layer Triggered Mobility) and it is expected that AI / ML for LTM will be part of the Release 20 scope.

[0041] SUMMARY

[0042] Systems and methods are disclosed for applicability reporting in relation to lower layer mobility. In one embodiment, a method performed by a User Equipment (UE) comprises receiving, from a network node, an inference configuration for reporting measurement predictions for mobility and reporting, to the network node, applicability information for the inference configuration in an applicability report. In this manner, the UE is enabled to signal to the network information concerning model or functionality applicability, e.g., before or after a potential lower layer mobility.In one embodiment, the inference configuration is an inference configuration for reporting measurement predictions for lower layer mobility.

[0043] In one embodiment, the inference configuration is an L1 / L2 Triggered Mobility (LTM) inference configuration for reporting measurement predictions on LTM candidate cells.

[0044] In one embodiment, within the applicability report, the applicability information is associated to an identifier of the inference configuration and / or an identifier of a serving cell configuration of a serving cell on which the UE received the inference configuration.

[0045] In one embodiment, the UE is configured with one or more inference configurations, each associated to an LTM channel state information reporting configuration identifier and all associated with a same serving cell configuration, and that the applicability report comprises one or more instances of applicability information, each instance associated to one or more identifiers of one or more inference configurations and to a serving cell in which the User Equipment received the inference configurations.

[0046] In one embodiment, the applicability information is reported in a message that is either a Radio Resource Control (RRC) reconfiguration complete message or a UE assistance information message. In one embodiment, the method includes that the applicability information is reported in a 5thGeneration (5G) or 6thGeneration (6G) message. In one embodiment, the message comprises a list of applicability reports that includes, for each of one or more mobility configurations, an instance of an applicability report, where each instance comprises one or more identifiers of inference configurations and is associated to a serving cell on which the inference configuration or configurations were received.

[0047] In one embodiment, the inference configuration is a full inference configuration.

[0048] In one embodiment, the method includes that the inference configuration is a partial inference configuration.

[0049] In one embodiment, receiving the inference configuration comprises receiving a first message comprising a mobility-related configuration and a reporting configuration for reporting one or more Artificial Intelligence (Al) or Machine Learning (ML) model or functionality status indications for one or more Al or ML models for functionalities associated to prediction of measurements on radio resources configured for mobility.

[0050] In one embodiment, reporting the applicability information for the inference configuration in the applicability report comprises transmitting, to the network node, a second message comprising one or more AI / ML model or functionality status indications for the one or more AI / ML model functionalities associated to the prediction of measurements on radio resources configured for mobility in accordance with the received reporting configuration.In one embodiment, the second message further comprises measurements and / or measurement predictions.

[0051] In one embodiment, the second message is transmitted via L1 / L2 signaling or uplink control information.

[0052] In one embodiment, the method further comprises receiving, from the network node, a mobility configuration where an Al or ML functionality or model is applicable.

[0053] In one embodiment, the method further comprises transmitting, to the network node, a message comprising additional information related to the applicability information, the additional information comprising an indication of reason(s) for inapplicability and / or an indication of resources and / or functionalities required for Al or ML model or functionality applicability.

[0054] Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE comprises a communication interface including a transmitter and a receiver and processing circuitry associated with the communication interface. The processing circuitry is configured to cause the UE to receive, from a network node, an inference configuration for reporting measurement predictions for mobility and report, to the network node, applicability information for the inference configuration in an applicability report.

[0055] In one embodiment, the processing circuitry is further configured to cause the UE to perform any one or more of the additional operations described in connection with the method performed by a UE.

[0056] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node comprises transmitting, to aUE, an inference configuration for reporting measurement predictions for mobility and receiving, from the UE, applicability information for the inference configuration in an applicability report.

[0057] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node comprises processing circuitry configured to cause the network node to transmit, to a UE, an inference configuration for reporting measurement predictions for mobility and to receive, from the UE, applicability information for the inference configuration in an applicability report.

[0058] Embodiments of a method performed by a Central Unit (CU) of a network node are also disclosed. In one embodiment, a method performed by a CU of a network node comprises transmitting, to a UE, an inference configuration for reporting measurement predictions for mobility and receiving, from either the UE or a Distributed Unit (DU) of the network node, applicability information for the inference configuration.In one embodiment, the method further comprises receiving the applicability information from the DU of the network node.

[0059] In one embodiment, the method further comprises sending, to the UE, a message comprising an LTM configuration where an Al or ML model or functionality is applicable.

[0060] In one embodiment, the method includes receiving, from the User Equipment, a message comprising additional information related to the applicability information.

[0061] In one embodiment, the method includes receiving the applicability information from the UE in an applicability report.

[0062] In one embodiment, the method further comprises sending, to a DU of the network node, a message comprising the applicability information for the inference configuration.

[0063] BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0065] Figure 1 illustrates an example of proactive reporting of applicability.

[0066] Figure 2 illustrates an example of reactive reporting of applicability.

[0067] Figure 3 illustrates the operation of a User Equipment (UE) (i.e., a wireless terminal or wireless device or wireless communication device) and a network node, in accordance with embodiments of the present disclosure.

[0068] Figure 4 illustrates the operation of a UE (i.e., a wireless terminal or wireless communication device) and a network node, in accordance with embodiments of the present disclosure in which the UE reports to the network node using lower layer signaling designed for lower layer mobility.

[0069] Figure 5 illustrates the operation of a UE (i.e., a wireless terminal or wireless communication device) and a network node, in accordance with embodiments of the present disclosure in which the UE reports to the network node using higher layer signaling.

[0070] Figure 6 illustrates the operation of a UE 600 (i.e., a wireless terminal or wireless communication device) in connected state and a network node, in accordance with embodiments of the present disclosure in which the UE reports a first indication to the network node using lower layer signaling designed for lower layer mobility and a second indication or information to the network node, e.g., using higher layer signaling.

[0071] Figure 7 shows an example of a communication system in which embodiments of the present disclosure may be implemented.Figure 8 is another example of a communication system according to some embodiments. Figure 9 shows a wireless device, which may be configured to operate in communication system of Figure 7 or in communication system of Figure 8.

[0072] Figure 10 shows a network node in accordance with some embodiments.

[0073] Figure 11 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0074] DETAILED DESCRIPTION

[0075] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0076] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0077] There currently exist certain challenge(s). The general framework for the applicability report for a User Equipment (UE) connected to the current Primary Cell (PCell) i.e. in case the Artificial Intelligence (AI) / Machine Learning (ML) functionality is associated to that PCell and / or Secondary Cells (SCells) has been addressed in the Work Item on AI / ML for the physical layer (PHY) part of Release 19 and 5thGeneration (5G) evolution. However, for a mobility use case i.e., AI / ML for Mobility study item which has been discussed in Release 19 in Radio Access Network (RAN) Working Group 2 (WG2) in the 3rdGeneration Partnership Project (3GPP), the applicability or inapplicability of the AI / ML model running at the UE is not addressed.

[0078] Unlike the UE capabilities which are fixed over time, applicability of the AI / ML model / functionality can change over time due to environment conditions or UE specific configurations. For example, an AI / ML model designed to predict the Reference Signal Received Power (RSRP) of the neighboring cells / beams might become inapplicable due to the measurement configurations e.g., change in Channel State Information (CSI) Reference Signal (CSLRS) resource configuration (needed for the Layer 1 (LI) beam level predictions) or change in the measurement object configuration (needed for the Layer 3 cell level or beam level predictions) or changes in the measurement gap configurations.One of the issues that lends itself to a careful consideration is changes in the AI / ML models applicability state while the network is expecting the UE to provide the AI / ML predictions to the network during the inference phase. Without addressing this issue, the network relying on UE predictions for mobility decisions may not be able to counteract such situations.

[0079] In addition, without AI / ML model / functionality (in)applicability information, the neighboring next generation NodeBs (gNBs) serving the candidate radio resources (Synchronization Signal (SS) / Physical Broadcast Channel (PBSCH) Block (SSB), CSLRS beams, or neighboring cells) may not be able to optimize their configurations to leverage the AI / ML model / functionality when the UE performs a Ll / Layer 2 (L2) Triggered Mobility (LTM) cell switch toward their radio resources (SSB, CSLRS beams or neighboring cells).

[0080] The above problems exist for all different types of the AI / ML assisted network-controlled mobility procedures such as normal Layer 3 mobility procedures e.g., Handover (HO), Conditional Handover (CHO), Primary Secondary Cell (PSCell) Change or addition as well as lower layer mobility procedure (LTM cell switch) in PCell and PSCell (in dual connectivity).

[0081] The solutions described in the present disclosure are described for the lower layer mobility but can also be utilized for the other types of mobility procedures.

[0082] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Systems and methods are disclosed herein that relate to a UE receiving an inference configuration (e.g., an LTM inference configuration for reporting measurement predictions on LTM candidate cells) and, in response thereto, reporting applicability information (e.g. status on whether that inference configuration is applicable or non-applicable) to a network node in an applicability report (e.g. in an Radio Resource Control (RRC) Reconfiguration Complete or in a UEAssistancelnformation message). The UE also associates the applicability information in the report to an identifier of the LTM inference configuration (e.g. LTM-CSL ReportConfigld, which may be called an LTM applicability report Config Identifier (ID)) and to a serving cell index of a serving cell of the UE in which that LTM inference configuration is included (e.g. ServCelllndex, which may be called an LTM applicability cell ID). Notice that this is not an LTM candidate cell ID, but a serving cell index of a serving cell the UE is currently configured with, in which the inference configuration is included (i.e., in which the UE receives the inference configuration).

[0083] In one embodiment, the UE is configured with multiple inference configurations (i.e., a first set of inference configurations), each of them associated to an LTM CSI reporting configuration ID and all of them within the same serving cell configuration. Then, the UE associates each applicability information in the report to each identifier of the inferenceconfiguration (e.g. LTM-CSI-ReportConfigld, which may be called an LTM applicability report Config ID), and to the same serving cell index in which the first set of inference configurations is included (e.g. ServCelllndex, which may be called an LTM applicability cell ID). In one suboption, this may be nested in the applicability report so that the serving cell index does not have to be repeated for each associated LTM CSI reporting configuration ID.

[0084] In one example, the UE includes in an RRC Reconfiguration Complete or in a UEAssistancelnformation message an Applicability Report list (e.g. Information Element (IE) ApplicabilityReportList) which includes for each inference configuration for LTM an instance of an applicability report (e.g. IE ApplicabilityReport), wherein each instance of the applicability report has the serving cell index (E.g. IE ServCelllndex) and an instance of the CSL ReportConfigld for each inference configuration for which applicability is being reported.

[0085] ApplicabilityReportList

[0086] The IE ApplicabilityReportList comprises information that the UE reports to gNB related to the applicability of the radio measurement predictions at UE.

[0087] ApplicabilityReportList information element

[0088] — ASN1START

[0089] — TAG-APPLICABILITYREPORTLIST-START

[0090] ApplicabilityReportList-rl9 = SEQUENCE ( SIZE ( 1 . . maxNrofApplicabilityReports ) ) OF ApplicabilityReport-r 19

[0091] ApplicabilityReport-rl9 CHOICE {

[0092] predictionConfigApplicabilityReport-rl9 SEQUENCE {

[0093] _ applicabilityCellId-rl9 _ ServCelllndex ,

[0094] _ applicabilityReportConf ig!dList-rl9 _ SEQUENCE (SIZE

[0095] (1. .maxNrofApplicabilityReports) ) OF LTM-CSI-ReportConf igld,

[0096] _ )_

[0097] applicabilitystatus

[0098] }

[0099] — TAG-APPLICABILITYREPORTLIST-STOP

[0100] — ASN1STOP

[0101] In one embodiment, the UE receives a partial inference configuration (for reporting applicability on an LTM candidate cell) and, in response thereto, reports applicability information (e.g. status on whether that partial inference configuration is applicable or non-applicable) to the network in an applicability report (e.g. in a UE Assistance Information message). The UE also associates the applicability information in the report to one or more associated identifier(s) and to an LTM candidate cell. The one or more associated identified s) relates to a network configuration / set of conditions in which the UE has performed a training of an AI / ML model able to provide predictions on an LTM candidate cell.In accordance with embodiments of the present disclosure, a UE receives an inference configuration (e.g. an LTM CSI Reporting Configuration in an RRC Reconfiguration message) and, in response thereto, reports to the network applicability information for an inference configuration related to one or more LTM candidate cells (e.g. reported in an RRC Reconfiguration Complete or in a UEAssistancelnformation message). The UE receives the inference configuration for reporting measurement prediction information (e.g. time-domain, spatial domain, and / or frequency domain measurement prediction information) on resources (e.g. beams and / or Reference Signals) of LTM candidate cells with which the UE is configured, determines applicability information associated to that inference configuration (e.g. ‘applicable’, ‘not applicable’), and transmits the applicability information in an applicability report to the network. The applicability information in the applicability report is associated to an indication of the inference configuration, which in the case of LTM includes an inference configuration identifier (such as an LTM CSI reporting configuration ID, in case the inference configuration is configured as an LTM CSI reporting configuration) and a serving cell index of the serving cell configuration in which the inference configuration is configured. What is called here ‘inference configuration’ may also be called a full inference configuration, in contrast to a partial inference configuration.

[0102] According to embodiments of the present disclosure, the UE may further receive a partial inference configuration (e.g. in an applicability reporting configuration in an RRC Reconfiguration message) and in response report to the network applicability information for that partial inference configuration related to an LTM candidate cell (e.g. reported in a UE Assistance Information message). The UE receives the partial inference configuration for reporting the applicability i.e. it would not be possible to perform measurement prediction information (e.g. time-domain, spatial domain, frequency domain) based on the partial inference configuration. Then, the UE determines the applicability information associated to that partial inference configuration (e.g. ‘applicable’, ‘not applicable’) and transmits the applicability information to the network in an applicability report (e.g. in a UE Assistance Information message). The applicability information in the applicability report is associated to an indication of the partial inference configuration, which in the case of LTM includes an LTM Candidate Identifier and one or more Associated ID(s) e.g. related to the conditions / network setup / configurations in which training for an AI / ML model has been performed.

[0103] Certain embodiments may provide one or more of the following technical advantage(s). An example benefit of embodiments of the solutions disclosed herein is that the UE is enabled to signal to the network information concerning the model applicability before and after a potential lower layer mobility (e.g., LTM) towards a candidate cell (e.g., an LTM candidate cell).The benefit for the network node serving the serving cell is that it gets to know that the UE is able or not able to perform AI / ML based predictions for the configured resources or not. In particular, this is important for the lower layer mobility procedure when the network node (e.g., a gNB -Distributed Unit (DU) in the case of a split architecture) is expecting the UE to send the measurements and prediction associated to the neighboring radio resources (neighboring reference signals or the neighboring cells) configured for the lower layer mobility (LTM purpose). If the UE’s AI / ML model / functionality is inapplicable for the configured neighboring resources the UE indicates it to the network (e.g., along with the UCI message used for LTM mobility), and the network nodes (gNB-DU or gNB-Central Unit (CU)) would be able to resolve the issue e.g., by sending new configuration to the UE or changing / reconfiguring the LTM candidate cells for which the AI / ML model / functionality is not applicable.

[0104] It is noted that the inapplicability indication can be designed for two different purposes:

[0105] • indicating the model / functionality is not applicable for the predictions in the current time instance; and / or

[0106] • indicating that the model / functionality is not applicable for the configured resources such as neighboring beams or neighboring cell if the UE is handed over to the neighboring resources (beam or cells)

[0107] Such information not only allows the serving cell to take the counter actions to fix the inapplicability issue of the AI / ML models / functionality for the predictions of the radio resources configured for lower layer mobility in the serving cell, but also (if forwarded to the network nodes serving the candidate LTM cells) can be used to optimize the configurations within the LTM cell switch command in such a way that the model stays applicable “after” an LTM cell switch to a candidate cell.

[0108] The teachings of certain embodiments may improve the overall performance of the RAN. In more detail, Figure 3 illustrates the operation of a UE 300 (i.e., a wireless terminal or wireless device or wireless communication device) and a network node 302, in accordance with embodiments of the present disclosure. The network node 302 may be a Radio Access Network (RAN) node (i.e., a network node in a RAN of a wireless communication system such as, e.g., a 3GPP 5G or 6G system) such as, e.g., a base station (e.g., a gNB in the case of 5G) or a RAN node that implements part of the functionality of a base station (e.g., a Central Unit (CU) or Distributed Unit (DU) in the case of a base station having a CU-DU split architecture). The steps of the procedure of Figure 3 are as follows:

[0109] • Step 304: The UE 300 receives, from the network node 302, a first message (referred to herein as a Message #1) including a mobility related configuration (e.g., an RRCReconfiguration or RRC Resume message including an LTM configuration, including configurations associated to one or more LTM candidate cell(s)), where the mobility related configuration includes an inference configuration (e.g. in an LTM CSI reporting configuration) for one or more AI / ML model / functionality (in)applicability status indication(s), for one or more AI / ML models / functionalities associated to prediction of radio resources (e.g., measurement prediction on such radio resources) configured for the purpose of lower layer mobility (e.g., for the purpose of an LTM cell switch). An included reporting configuration can include or indicate one or more inference configurations (said full configuration) according to which the AI / ML model / functionality should perform the inference, or one or more network (NW)-side additional conditions (said partial configuration) according to which the UE should determine whether an AI / ML model / functionality is applicable for the inference.

[0110] • Step 306: The UE 300 transmits, to the network node 302, a second message (referred to herein as a Message #2) including the AI / ML model / functionality (in)applicability status indication, wherein the indication can represent one or more AI / ML model / functionality (in)applicability status indication(s) for one or more AI / ML models / functionalities associated to the prediction of radio resources configured for the lower layer mobility purpose. The Message #2 can also include a set of measurements and / or predictions of the neighboring radio resources such as reference signals e.g., SSB or CSLRS beams or cells according to the received configuration included in the Message #1. For example, the Message #2 can include an indication of the inference configurations included in the Message #1 according to which the AI / ML model / functionality is applicable (e.g. IE ApplicabilityReport).

[0111] The Message #2 can further include an implicit or explicit indication that the (in)applicability indication is related to the (in)applicability of the AI / ML model / functionality to report the inference results (e.g. predictions) related to the one or more sets of neighboring radio resources, and / or an implicit or explicit indication that the (in)applicability indication is related to the (in)applicability of the AI / ML model / functionality to report the inference results (e.g. predictions) if configured with one or more set of neighboring resources in the corresponding neighboring cell.

[0112] In one option, the Message#2 is an RRC Reconfiguration Complete message which includes the AI / ML model / functionality (in)applicability status indication, associated to an inference configuration related to LTM. The association may be by the UE including an LTM reporting configuration identifier. The UE may also include one or more indication(s) of LTMcandidate cells (e.g. LTM Candidate ID(s)) for the cells for which the UE is not able to perform the inferences being configured.

[0113] In another option, the Message #2 can be a UEAssistancelnformation or an L3 Measurement report message, which includes the AI / ML model / functionality (in)applicability status indication, associated to an inference configuration related to LTM. In another option, the UE can send the Message #2 by using Layer 1 / 2 signaling e.g., in an Uplink Control Information (UCI) message or Medium Access Control (MAC) Control Element (CE).

[0114] The present disclosure also provides methods for a CU (e.g., a gNB-CU) to provide the UE in a message transmitted in response to receiving (in)applicability indications from a DU (e.g., a gNB-DU) (or from the UE) with an LTM configuration including the radio resources such that the UE can perform and apply the AI / ML functionality / model inference. For example, if the Message #2 indicates the set of radio resources according to which the UE can perform the AI / ML model / functionality inference for LTM purposes, or the inference configuration(s) according to which the UE can perform the AI / ML model / functionality inference for LTM purposes, then the said message can provide an LTM configuration including the set of radio resources, or an indication to the UE to activate / use those applicable inference configurations. In another embodiment, if the UE indicates in the Message #2 that one or more AI / ML functionalities are not applicable if connected to one or more cells / frequencies, then the CU (e.g., gNB-CU) does not configure the concerned cells / frequencies are serving cells or as SpCell.

[0115] Further details regarding the process of Figure 3 and various exemplary extensions thereof are provided below. In particular, the procedure of Figure 3 can be implemented in different solutions (referred to herein as “Solution 1”, “Solution 2”, and “Solution 3”) in particular in a RAN split architecture. These solutions are exemplary and non-limiting.

[0116] According to embodiments of the present disclosure, the UE includes applicability information about at least one inference configuration related to LTM in an applicability report e.g. in an RRCReconfigurationComplete or a UE Assistance information message.

[0117] - In one option, the applicability information is associated to an identifier of the inference configuration related to LTM e.g. an LTM CSI reporting configuration identifier, considering that an inference configuration (configuring the UE to report one or more measurement predict! on(s) for one or more resources / beams / Reference Signal(s) of at least one LTM candidate cell). In one example, the identifier of the inference configuration related to LTM corresponds to the Ttm-CSI-ReportConfigld or IE LTM-CSL ReportConfigId-rl8’. Thus, the UE includes in the applicability report the applicabilityinfo together with the associated identifier of the inference configuration related to LTM for which the applicability info is being reported.

[0118] - In one option, the applicability information is associated to an identifier of a serving cell configuration in which the inference configuration related to LTM is include e.g. a serving cell index of a serving cell the UE is configured which includes the LTM CSI reporting configuration with the inference configuration. Thus, the UE includes in the applicability report the applicability info together with the associated identifier of a serving cell configuration.

[0119] - In one option, the applicability information is associated to both an identifier of the inference configuration related to LTM (e.g. an LTM CSI reporting configuration identifier, considering that an inference configuration (configuring the UE to report one or more measurement prediction(s) for one or more resources / beams / Reference Signal(s) of at least one LTM candidate cell) and an identifier of a serving cell configuration in which the inference configuration is included. Thus, the UE includes both the applicability info together with the associated identifier of a serving cell configuration and the identifier of the inference configuration related to LTM.

[0120] 1 Initial Disclaimers

[0121] In the present disclosure, the predictions refer to any Artificial intelligence (AI) / Machine Learning (ML) based predictions, namely the results of the inference of the AI / ML engine, that is performed based on measurements and / or some additional information or any non-AL / ML based predictions which uses the past / historical information and measurements to predict the feature values.

[0122] The present disclosure describes embodiments of systems and methods in which a UE receives a message (e.g. RRC Reconfiguration, RRC Resume) including at least one AI / ML functionality configuration, including an inference configuration and / or an applicability reporting configuration) for measurement predictions on at least one LTM candidate cell, which may be simply called inference configuration for LTM, received by the UE per cell group and / or per serving cell within the cell group. Furthermore, one inference configuration may be one inference configuration or may be one inference related parameter set which is configured for applicability report only. For example, for Al-based LTM, one inference configuration may include associated ID, CSLRS resource information of Set B for measurements on resources (e.g. beams, Reference Signals, SSBs, CSLRSs) of one or more LTM candidate cells, CSLRS resources related information of Set A for predictions on resources (e.g. beams, Reference Signals, SSBs, CSLRSs)of one or more LTM candidate cells, report content related information; while one inference related parameter set for LTM may include associated ID, Set A related information, Set B related information, report content related information, time instances related information for measurements, time instances related information for prediction and so on.

[0123] In the context of the present disclosure the term “AI / ML functionality” may be called a “supported functionality” the UE can indicate by using UE capability signaling. A supported functionality is one or more functionalities for and / or associated to beam management and / or CSI reporting, or mobility operations, such as the reporting of time domain and / or spatial domain or frequency domain predictions (inference). It could be said as the ability the UE has to produce an output of an inference function. For example, reporting of time-domain prediction(s) of SSB and / or CSLRS measurement information (e.g. predicted RSRP) for one or more LTM candidate cell(s) may be considered as an AI / ML functionality which is a “supported functionality” by the UE when the UE reports a capability associated to it (via RRC or LPP signaling).

[0124] - For example, “spatial domain prediction for LTM” or a related functionality (e.g. reporting and inference of spatial domain info on LTM candidate cells) may be a supported functionality in which the UE may report that is capable of performing and reporting inference / prediction of a set A of beams of LTM candidate cells (e.g. predicted LI RSRP values of one or more beams or one or more SSB indexes of an LTM candidate cell or predicted LI or L3 RSRP values of one or more LTM candidate cells) based on measurements performed on a set B of beams (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell) of an LTM candidate cell (which may be the same or a different cell), in the case of spatial domain predictions.

[0125] - For example, “frequency domain prediction for LTM” may be a supported functionality in which the UE may indicate that is capable of performing and reporting inference (e.g., prediction of the radio link quality of a set A of beams of LTM candidate cell(s) (e.g. predicted LI RSRP values of one or more beams or one or more SSB indexes of a cell or predicted LI or L3 RSRP values of one or more LTM candidate cells) based on measurements performed on a set B of beams of LTM candidate cell(s) (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell or one or more cells), in the case of frequency domain predictions.

[0126] - For example, “time domain prediction for LTM” or a related functionality (e.g. reporting and inference of time domain info for LTM) may be a supported functionality in which the UE may report that is capable of performing and reporting inference of a set of A of beams of one or more LTM candidate cell(s) (e.g. predicted L1 / L3 RSRP values of one or morebeams or one or more SSB indexes of one or more LTM candidate cells in future time instances or the L1 / L3 RSRP value of one or more LTM candidate cells in the future time instances) based on measurements performed on a set B of beams of LTM candidate cells (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell and / or L1 / L3 RSRP value of one or more LTM candidate cells), in the case of time domain predictions.

[0127] According to the present disclosure, the UE receives from the network a message (e.g. RRC Reconfiguration, RRC resume), including at least one AI / ML functionality configuration (e.g. including an inference configuration for LTM in an LTM CSI reporting configuration) based on which the UE is configured to report back to the network applicability information (e.g. including an applicability indication or ‘status’ for an AI / ML functionality) for the at least one AI / ML functionality configuration, wherein the report includes one or more of: i) an identification of the LTM CSI reporting configuration, ii) an identification of a serving cell in which the LTM CSI reporting configuration is included.

[0128] Prior to reporting the applicability information, the UE determines whether an AI / ML functionality is applicable or not applicable. An AI / ML functionality determined to be applicable is an “applicable AI / ML functionality” i.e. is a functionality the UE is ready to apply for model inference, or, in other words, the UE is able to perform the inference and / or report the inference and / or perform further actions based on the inference configuration. So, when the UE is provided with an inference configuration (“at least one inference configuration”) for performing inference(s) using an AI / ML model (e.g. perform predicted LI RSRP for beams and / or SSB indexes and / o CSLRS resource indicator(s) and / or LI RSRP measurements for beams and / or SSB indexes and / o CSLRS resource indicator(s) to be used as input to an AI / ML model) and report inference information derived from the inference(s)), whether the UE can perform inference(s) using an AI / ML model and report inference information derived from the inference(s)) according to the at least one inference related configuration. In this context, the at least one inference related configuration may include one or more parameters for CSI resources (e.g. a CSI resource configuration) to be measured and / or predicted and / or one or more parameters for reporting (e.g. in a CSI reporting configuration); thus, it may be said that an inference related configuration includes a measurement configuration.

[0129] According to embodiments of the present disclosure, the UE may use one or more “applicability condition(s)” which represent a set of conditions for determining whether an AI / ML model / functionality (also denoted a “supported functionality”) is applicable or not. An AI / ML functionality (and / or AI / ML model) is applicable when there is at least an inference relatedconfiguration (or simply inference configuration) received by the UE (provided by the gNodeB) out of multiple inference related configurations received (e.g. in a single RRC Reconfiguration message) for which the AI / ML model / functionality (the supported functionality) is applicable i.e. the UE is able to produce outputs of an AI / ML model, wherein the outputs are called inference(s).

[0130] An AI / ML functionality is not applicable (or non-applicable) when there is no ’’inference related configuration” (which may also be simply called an inference configuration) received by the UE for which the AI / ML model / functionality (the supported functionality) is applicable. As stated earlier an “inference configuration” may include one or more parameters for CSI resources (e.g. a CSI resource configuration) to be measured and / or predicted and / or one or more parameters for reporting (e.g. in a CSI reporting configuration); thus, it could be said that the “inference configuration” includes at least one radio measurement configuration and inference configuration.

[0131] According to one option, the UE determines whether an AI / ML functionality is applicable or not possibly based on one or more UE-side additional condition(s), such as UE speed, scenario, location, cell the UE is connected to, hardware capabilities, etc.

[0132] According to one option, the UE determines whether an AI / ML functionality is applicable or not possibly based on one or more network (NW)-side additional conditions, such as any one or more following:

[0133] Set A (e.g. one or resources of at least one LTM candidate cell) and / or Set B (e.g. one or resources of at least one LTM candidate cell)

[0134] - Mapping relationship of Set A and Set B, including ordering to (a set of ID, or resource ) - Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B.

[0135] - Quasi Co-Located (QCL) assumption

[0136] - The order of model input and model output between RS and transmit (Tx) beams can be pre-defined.

[0137] - Transmission power

[0138] - UE distribution

[0139] - antenna height and / or other antenna properties

[0140] - Deployment scenarios (e.g., Inter-Site Distance (ISD), Umi / Uma)

[0141] - NW-side resource configuration(s) which may be considered as NW implementation-based configurations which may possibly impact the inference performance for a UE sided model. For instance, beam and Tx port mapping relationship in the gNodeB for a given cell, NW antenna shape, Antenna dip angle, height of the tower / gNB, etc.The NW-side additional conditions, configured for the UE to determine the applicability of an AI / ML functionality, may also be characterized as network implementation-based configurations (settings) which can impact the consistency between training and inference for UE sided model. For example, if the UE has performed training for an AI / ML model and / or functionality in the first and / or the second cell for a given set of network configuration(s) (settings), the inference is expected to produce accurate outputs under similar conditions.

[0142] Each NW-side additional condition may be identified by an associated ID.

[0143] It may also be said that for an AI / ML- functionality (or AI / ML-enabled feature), additional conditions refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI / ML-enabled feature / FG. It does not imply that additional conditions are necessarily specified. Additional conditions can be divided into two categories: NW-side additional conditions and UE-side additional conditions. Note: whether specification impact is needed is a separate discussion

[0144] To determine whether an AI / ML functionality is applicable or not the UE may receive one or more AI / ML functionality configuration(s) which may include one or more NW-side additional conditions, such as the ones listed above and / or based on UE-side additional conditions, known at the UE e.g. the cell the UE is connected to, its current location, UE speed, etc.

[0145] AI / ML functionality configuration (e.g. inference configuration)

[0146] In the context of the present disclosure, an AI / ML functionality configuration may in one option include one or more parameters, IE(s), fields and / or configuration(s) necessary and / or sufficient for the UE to operate the AI / ML functionality, such as an inference configuration or an inference related configuration (which may also be considered a full and / or complete inference configuration, sufficient for the operation of the AI / ML functionality in the second cell). In other words, when the UE receives the inference configuration or an inference related configuration for an AI / ML functionality for a given serving cell in a given cell group, the UE can generate inference information (e.g. as output of an AI / ML model associated with the AI / ML functionality) and possibly report to the serving cell.

[0147] In the context of the present disclosure, an inference configuration or an inference related configuration may correspond to an LTM Channel State information (CSI) measurement configuration (e.g. in an IE LTM-CSI-MeasConfig, LTM-CSI-ReportConfig, LTM-CSL ResourceConfig) associated to a set A and or set B of beams of one or more LTM candidate cells for the AI / ML functionality. The inference configuration may further include one or more of:

[0148] Synchronization Signal Block (SSB) identifiers associated to a serving cell and / or an LTM candidate cell;- CSI-RS resource identifiers associated to a serving cell and / or one or more LTM candidate cells;

[0149] - Beam identifiers associated to a serving cell and / or an LTM candidate cell;

[0150] - Mobility Reference Signal(s) identifiers associated to a serving cell and / or one or more LTM candidate cells;

[0151] - Candidate inference configuration(s) Set A and / or B (1); Set A and / or B (2); Set A and / or B (3), etc.

[0152] In the context of the present disclosure, an inference configuration or an inference related configuration may include and / or point to or indicate a first set (set A) of measurement resources (e.g. beams, SSB indexes and / or CSLRS resource identifiers, Mobility Refence Signal identifiers) in which the UE performs radio measurement predictions (inferences, such as predicted RSRP values), and a second set (set B) of radio measurement resources (e.g. beams, SSB indexes and / or CSLRS resource identifiers, Mobility Refence Signal identifiers) in which the UE can perform radio measurement in order to determine the radio measurement predictions on the first set. That may also include one or more configuration(s) associated to network side (NW-side) additional conditions reflecting the NW operational properties, such as any one or more of the following:

[0153] • Mapping relationship of Set A and Set B, including ordering to (a set of IDs, or resources) • Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B.

[0154] • QCL assumption

[0155] • The order of model input and model output.

[0156] • between RS and Tx beams can be pre-defined.

[0157] • Transmission power

[0158] • UE distribution

[0159] • antenna height

[0160] • Deployment scenarios (e.g., ISD, Umi / Uma / rural / indoor / indoor office / indoor factory, specific area(s))

[0161] • UE speed

[0162] The inference configuration or an inference related configuration may include a list of IDs referring to the set A and set B (or to the resources within the set A / B), and referring to one or more NW-side additional conditions.

[0163] The AI / ML functionality configuration may include also an identifier of the AI / ML functionality to which the configuration (e.g. inference configuration) is referred to, wherein theAI / ML functionality could be for example AI / ML for LTM functionality, spatial domain prediction for LTM functionality, temporal LTM prediction functionality, etc.

[0164] According to embodiments of the present disclosure, the AI / ML functionality configuration includes an identifier of that configuration, which is later to be reported by the UE to the network when the UE indicates whether that particular AI / ML functionality configuration is applicable or not. The main examples of identification of the configuration of the AI / ML functionality are: i) an LTM CSI reporting configuration identifier in which the inference configuration is received by the UE, ii) an identification of the serving cell in which the UE is to report the inference.

[0165] In the context of the present disclosure an AI / ML functionality configuration may in another option include one or more parameters, IE(s), fields and / or configuration(s) necessary and / or sufficient for the UE to report the applicability of the AI / ML functionality in the second cell, such as an applicability reporting configuration. In other words, when the UE receives the applicability reporting configuration for an AI / ML functionality the UE can determine whether the AI / ML functionality, supported by the UE, and / or associated configuration(s) of that AI / ML functionality, is applicable or not applicable. The applicability reporting configuration may include one or more of the following:

[0166] - An indication that the UE is allowed to do UE assistance information reporting to the second cell e.g. by configuring it in the IE OtherConfig in the RRCReconfiguration message and / or the HO command.

[0167] An indication of the AI / ML functionality for which the UE transmits the applicability report e.g. indications of the applicability associated with the indicated AI / ML functionality.

[0168] One or more NW-side additional condition(s) (included e.g. in the IE OtherConfig in the RRCReconfiguration message and / or the HO command), e.g. for the UE to determine whether the AI / ML model / functionality has been trained under similar conditions, such as one or more of the following:

[0169] o Configuration(s) related to the Mapping relationship of Set A and Set B, including ordering to (a set of IDs, or resources)

[0170] o Configuration(s) related to the consistency of downlink spatial domain transmission filters corresponding to the beams in Set A of one or more LTM candidate cells and beams of a Set B of one or more LTM candidate cells. In that context, consistency may correspond to one or more of:■ Set size consistency for Set B, Set A: consistency in number of beams and / or associated resources for Set B and Set A, across training and inference

[0171] ■ Set size consistency for Set B, Set A: consistency in number of LTM candidate cells for Set B and Set A, across training and inference

[0172] ■ periodicity consistency for Set B, Set A: consistency in periodicity of beams and / or associated resources for Set B and Set A, across training and inference

[0173] ■ relationship of Set A / Set B (Set B is a subset of Set A or not): consistency in relationship of beams and / or associated resources for Set B and Set A, i.e., whether Set B is a subset of Set A, across training and inference Configuration(s) related to the Quasi-Co-Location (QCL) assumption(s) Beam configuration(s) of the network for one or more LTM candidate cells, such as:

[0174] ■ Beam characteristics, e.g., beam boresight direction (azimuth and elevation), 3dB beamwidth. In one sub-option the beam characteristics may be associated to an identifier indicated to the UE during training and AI / ML configuration, for checking of the consistency between training and inference.

[0175] ■ Set A / Set B related info, e.g., the beam index of set B.

[0176] ■ Information about the beam codebook and / or indexing / mapping of Set A and Set B i.e. info on whether the AI / ML Model / functionality is trained with a data set with a certain beam codebook and index / mapping of Set A / Set B, inference works for the same beam codebook and index / mapping of Set A / Set B.

[0177] Configuration(s) related to the order of model input and model output between RS and Tx beams can be pre-defined.

[0178] Configuration(s) related to the transmission power and / or power levels the gNodeB and / or the serving cells are operating

[0179] Configuration(s) related to the UE distribution

[0180] Configuration(s) related to Antenna height

[0181] Configuration(s) related to the deployment scenarios (e.g., ISD, Umi / Uma / rural / indoor / indoor office / indoor factory, specific area(s)) Configuration(s) related to UE speed- An indication of an identifier (associated ID) associated to one or more network conditions, so that the UE assumes that NW-side additional conditions with the same associated ID are consistent at least within a cell.

[0182] - In one option, NW-side additional condition may be associated to an inference configuration (e.g. resource set A, to be inferred and / or estimated and / or predicted, and / or resource set B, in which the UE should perform the measurement to infer / estimate / predict the radio measurement associated to the set A resources) and / or one training configuration (e.g. resource of CSI resources configured by the gNB at the time of the UE performing UE-side model training) identified by the same associated ID, for the second cell (which is a neighbor cell which may become the target cell in a handover). The UE may perform training of one or AI / ML functionalities / models with different sets of collected data via training configuration identified by its associated ID (i.e. one associated ID->one training configuration->one Al model).

[0183] An inference configuration and / or an indication or pointer to an inference configuration for which the UE is to report the applicability information.

[0184] 2 Overview of Predictions

[0185] In the context of this present disclosure, the predictions may be time-domain predictions: thus, the input of the ML-model comprises at least one or more measurements at (or starting at) a time instance tO (and / or a time interval such as T1 or tO+Tl, which may comprise one or more samples or measurement time occasions, from 1 to K time occasions) of at least one cell, and the output of the ML-model comprises one or more predicted measurements at (or starting at) a future time instance e.g. tO + T, possibly comprising future time instances within a time window of duration T2 and having F predictions.

[0186] In spatial domain predictions, the UE may use cell level or beam level measurements as input and produce cell level or beam level predictions in different cells or beams (at the same time instance).

[0187] In frequency domain predictions, the UE may use cell level or beam level measurements on one or more frequencies as input and produce cell level or beam level predictions in a different frequency (at the same time instance).

[0188] An AI / ML model can be designed to produce the beam-level measurement prediction in frequency domain or spatial domain. Utilizing the predicted beam-level measurement quality(es) and beam IDs generated as output of the AI / ML model inference, a predicted cell-level measurement quality for a cell X can be derived using the approaches described above. An AI / MLmodel can also be designed to directly predicts the cell-level measurement by taking LI and or L3 measurements of a set of beams and or cells as model input. Besides predicted beam-level or / and cell-level measurement quantities and beam / cell IDs, the model may also provide additional information like confidence level of the model output, the validation time of the predicted measurements, etc.

[0189] The designed AI / ML model can be deployed at the UE or at the network side and associated to a beam / cell prediction feature or a RRM prediction feature. When connecting to a network node, a UE can report its support of the AI / ML model for spatial and / or inter-frequency beam and or cell prediction feature or RRM prediction feature to the network node, via UE capability reporting. In addition, UE can report applicability of its AI / ML model for spatial and / or inter-frequency beam and or cell prediction feature or RRM prediction feature to the network node. The applicability indication can be seen as a dynamic UE capability on conducting predictions under certain network configuration and conditions. Based on the received UE capability and applicability indications, together with other conditions, the network node can make decisions on whether to configure / activate the AI / ML model at the UE or not.

[0190] Below we give different examples on how to design an AI / ML model to achieve the beam / cell-level measurement quality prediction in spatial or / and frequency domain i.e., interfrequency prediction.

[0191] For the AI / ML model used for cell prediction, in an example, a neural network-based model is composed of multiple connected neurons. Optionally it contains one or a few of input layer, one or a few of hidden layer, and one output layer. For the input layer, it takes UE measurements results as the model input, where the beam or cell level measurement results are obtained based on measuring some reference signals, e.g., SSBs and / or CSLRSs. Optionally, the measurement results would be normalized before input to the hidden layers. The normalization can change the value of the numeric variable in the dataset to a typical scale which improve model training. For the hidden layer(s), it is located between the input and output, in which the function applies weights to the inputs and directs them through an activation function to the output layer. Optionally, activation function can be one of Softmax function, Sigmoid function, ReLU function, Leaky ReLU, tanh function and Maxout. For the output layer, the output can be predicted RSRP values for each beam or cell in a different frequency. Optionally, the output can be the probability values where each value means the probability of the beam / cell in a target frequency to be the best beam / cell.

[0192] In an example for the AI / ML model, the AI / ML model used for spatial and inter-frequency cell level measurement prediction is based on convolutional neural networks, optionally it containsone or a few of input layers, one of a few of convolution layer, one or a few of pooling layer and output layer. For the input layer, it takes UE cell / beam level measurements results as the model input, where the beam level measurement results are obtained based on measuring some reference signals, e.g., SSBs and / or CSI-RSs and cell level measurement results are derived from the beam level measurements using cell quality derivation procedure. Optionally, the beam / cell level measurement results would be normalized before input to the convention layers. The normalization can change the value of the numeric variable in the dataset to a typical scale which improve model training. For the convention layer(s), it is used to extract the feature from the input. It applies a set of learnable filters (known as the kernels) to the input with smaller size than the whole input. These filters and kernels slide over the input data and computes the dot product between kernel weight and the corresponding input. The output of convention layer is referred as feature maps coming from the input measured RSRP values. For pooling layers, it involves sliding a two-dimensional filter over each channel of feature map and summarizing the features lying within the region covered by the filter. Before the output layer, there can be a fully connected layer to interpret / summarize the features obtained and directs them through activation function to the output layer. For the output layer, the output can be predicted RSRP values for each beam or each cell operating in a target frequency. Optionally, the output can be the probability values where each value means the probability of the beam to be the best beam or best cell in a target frequency.

[0193] In another set of examples, an AI / ML model is designed to predict the beam measurements of one or more beams in the spatial or frequency domain. A predicted cell-level measurement quality for a cell X in spatial or frequency domain (i.e., a cell operating in a frequency different from the cells / beams used as input to the model) can be derived based on the predicted beam-level measurement quality(es) or / and beam IDs generated from the AI / ML model output. In another example an AI / ML model is designed to directly predict the cell level measurements of one or more cells in spatial or frequency domain.

[0194] 3 Details and Examples: Model / Function (In)Applicability Indication While Reporting Measurement and / or Prediction to the Network

[0195] 3.1 Solution 1: UE reporting to the gNB-DU using lower layer signaling designed for lower layer mobility (LTM).

[0196] 3.1.1 Overview of Solution 1

[0197] Solution 1 is an extension of the procedure of Figure 3 to a RAN split architecture, where UE sends the applicability / inapplicability indication to a DU of a base station (e.g., a gNB-DU in the case of a gNB split architecture.In this regard, Figure 4 illustrates the operation of a UE 400 (i.e., a wireless terminal or wireless communication device) and a network node 402, in accordance with embodiments of the present disclosure. The network node 402 is a RAN node such as, e.g., a base station (e.g., a gNB in the case of 5G) having a split architecture such that the network node 402 includes a DU 402 A (e.g., a gNB-DU) and a CU 402B (e.g., a gNB-CU). The steps of the procedure of Figure 4 are as follows:

[0198] • Step 404: This step corresponds to step 304 of Figure 3. In this step, the UE 400 receives, from the network node 402 and in particular the CU 402B, a Message #1 including mobility related configuration (e.g., an RRC Reconfiguration message including the lower layer mobility (LTM) cell switch configuration), including reporting configuration for one or more AI / ML model / functionality (in)applicability status indication(s), for one or more AI / ML model / functionality associated to the prediction of radio resources configured for the lower layer mobility (e.g., LTM) purpose.

[0199] • Step 406: This step corresponds to step 306 of Figure 3. In this step, the UE 400 transmits, to the network node 402 and in particular the DU 402A, a Message #2, wherein the Message #2 includes one or more AI / ML model / functionality (in)applicability status indication(s) for one or more AI / ML model / functionality associated to the prediction of radio resources configured for the lower layer mobility (e.g., LTM) purpose, wherein the status can represent one more AI / ML model(s) are applicable or not. The Message #2 can also include the measurements and / or predictions of the neighboring beams / cells according to the received configuration included in the Message #1. In this embodiment, the UE 406 sends the Message 2 via Layer 1 / 2 signaling e.g., UCI message to the DU 402A. The (in)applicability indication can also be transmitted via the Ll / 2 signaling in the form of an accuracy value related to the AI / ML inference.

[0200] Figure 4 also illustrates a process performed by the DU 402A (e.g., gNB-DU) and CU 402B (e.g., gNB-CU) in RRC Connected state. As illustrated, this process includes:

[0201] • Step 408: Based on the received Message #2, the DU 402 A transmits, to the CU 402B, a Message #3 including the AI / ML model / functionality (in)applicability status indication, wherein the indication represents one or more AI / ML model(s) / functionality(es) at the UE 400 are applicable or not (i.e., inapplicable). The Message #3 can also include the measurements and predictions of the neighboring beams / cells received in the Message #2. In another embodiment, based on a received accuracy indication in the L1 / L2 signaling, the DU 402A determines whether the AI / ML model / functionality is applicable or not, and the DU 402 A transmits the (in)applicability indication to the CU 402B.• Step 410 (Optional): In response, the CU 402B may transmit in a Message #4 (e.g., an RRC Reconfiguration message) a configuration to the UE 400 including an LTM configuration comprising one or more CSI resources for one or more LTM candidate configurations, such that the AI / ML functionality / model is applicable. In one embodiment, the included LTM configuration includes one or more candidate cells such that one or more AI / ML functionalities are applicable if such candidate cells are configured as serving cells or SpCell.

[0202] 3.1.2 Detailed Description of Solution 1

[0203] Further details regarding the steps of the procedure of Figure 4 are provided below.

[0204] Step 404: Message #1. The following details regarding Step 404 and Message #1 are also applicable to the Message #1 and step 304 of Figure 3. In this step, the UE 400 receives the first message, Message #1, from the serving cell (e.g., from the network node 402 and more specifically, in the example illustrated in Figure 4, from the CU 402B of the network node 402). Message #1 is, for example, an RRC Reconfiguration message. Message #1 comprises one or more of the following:

[0205] • Report configuration of one or more (in)applicability indications of one or more AI / ML functionality(es) associated to prediction of one or more neighboring resources configured for lower layer mobility procedure (e.g., a set of neighboring reference signals (CSLRS or SSB beams) or cells or radio frequency).

[0206] o In an embodiment, if a model / functionality is inapplicable, the UE 400 is configured to report the radio measurements for the associated resources instead of the predictions.

[0207] o In an embodiment, if the model / functionality is inapplicable, the UE 400 is expected to report a specific value instead of the predictions, indicating that the model / function is not applicable.

[0208] o In an embodiment, if the model / functionality is inapplicable or applicable, the UE 400 is configured to report the flag indicating (in)applicability of the model / functionality. The prediction value requested by the network can be absent in the report by the UE 400.

[0209] o In an embodiment, if the model / functionality is inapplicable, the UE 400 is configured to report additional information e.g., the inapplicability reason for the model / functionality or the resource required for the model applicability. The prediction value requested by the network can be absent in the report by the UE.o In an embodiment, the (in)applicability indications are transmitted in relationship to one or more inference configurations associated to a certain AI / ML functionality. o In an embodiment, the (in)applicability indications are transmitted in relationship to one or more sets of radio resources (set A / set B where set A is a set of resources on which measurement predictions are to be made based on actual measurements performed on set B) according to which the AI / ML model / functionality inference should operate. For example, the UE could indicate the set of radio resources (via related identifiers) that are applicable or that are not applicable.

[0210] • LTM configurations including a list of LTM candidate cells, e.g. for each candidate LTM cell a list of one or more CSI resources according to which the UE should operate the inference of an AI / ML model / functionality. For example, the said list could contain the set A and B of CSI resources according to which the UE should perform the AI / ML model / functionality inference for the LTM purpose. In one option, the UE indicates the set A and set B for which the inference configuration would be considered applicable.

[0211] • CSI resource configurations associated to the candidate cells including one or more resources e.g., reference signals (e.g., CSLRS reference signals) or synchronization sequence block (SSB) beams for which the UE shall perform the lower layer measurements and report to the network

[0212] • Prediction type e.g., temporal domain prediction, frequency domain prediction or spatial domain prediction functionality types for which the (in)applicability indication report is requested.

[0213] • Reporting type e.g., periodic, aperiodic, semi-persistent or event based.

[0214] Step 406: Message #2. The following details regarding Step 406 and Message #2 are also applicable to the Message #2 and step 306 of Figure 3. In this step, the UE transmits to the serving network node (e.g., the DU 402 A of the network node 402 in the illustrated example of Figure 4) one or more (in)applicability indication(s) of one or more AI / ML functionality(es) associated to one or resources of one or more LTM candidate cells configured for the LTM procedure (e.g., set of neighboring beams or cells or frequencies).

[0215] - In one set of embodiments, the UE transmits one or more (in)applicability indication(s) of one or more AI / ML functionality(es) (e.g. an inference configuration for LTM) associated to prediction of one or more resource(s) of one or more LTM candidate cells configured for the mobility purpose in a lower layer signal to the gNB-DU using uplink control information or a MAC CE.When the UE performs the actions, the UE is in a connected mode e.g. an RRC state optimized for data transmissions / receptions, such as RRC CONNECTED. And the UE is connected to a first cell;

[0216] ■ In one option, the first cell is considered to be a Special Cell (SpCell) e.g. a Primary Cell (PCell) of the Master Cell Group (MCG), a Secondary Cell Group (SCG) Cell (PSCell), etc.

[0217] In one embodiment, Message #2 is triggered at the UE by the fulfillment of an event i.e. is configured as an event triggered lower layer measurement report e.g., CSI-report transmission. For example, the UE receives a configuration indicating an LTM x event (e.g. LTM3 event, LTM5 event); based on that the UE monitors the LTMx (e.g. LTM3) entering condition and the event is considered to be fulfilled when the lower layer measurement (e.g. RSRP, RSRQ, SINR) of a neighbor resource e.g., neighboring reference signals such as SSB or CSI-RS) is an offset better than a measurement of the PCells lower layer measurement for a certain amount of time. The UE includes the (in)applicability indication of the one or more AI / ML model / functionality in the event triggered measurement report. A benefit of including the applicability indication of an AI / ML functionality associated to prediction of LTM candidate cell resources in event triggered lower layer measurement report is that this would not need to be transmitted to the network until at least one LTM candidate cell is becoming a good LTM candidate for a lower layer mobility (or, in more general terms, a candidate for a lower layer mobility procedure in connected mode).

[0218] ■ In one option, the UE includes the applicability indication of an AI / ML functionality associated to prediction of one or more neighboring resources if the neighboring resource (a reference signal such as SSB or CSLRS resource) is a triggered resources or included in the event triggered lower layer measurement report sent to the network.

[0219] ■ In one option, the UE includes the applicability indication of an AI / ML functionality associated to prediction of one or more neighboring resources (a reference signal such as SSB or CSLRS resource) only in the first time the neighboring resource is a triggered cell.

[0220] ■ In one option, the UE includes the applicability indication of an AI / ML functionality associated to prediction of one or more neighboring resources (a reference signal such as SSB or CSLRS resource) only in the first timethe neighboring resource is a triggered resource and such a triggered resource is selected to be included in the lower layer measurement report e.g. based on a sorting function in which the UE selects which of the triggered resources are to be included in the lower layer measurement report.

[0221] • In one sub-option the UE determines the applicability indication of an AI / ML functionality for the top ‘N’ triggered resources according to a sorting quantity (e.g. with highest trigger quantity values), wherein ‘N’ is configured to be the maximum number of triggered resources to include in a lower layer measurement report.

[0222] • In one sub-option the UE determines the applicability indication of an AI / ML functionality for the top ‘N’ triggered resource(s) according to RSRP, RSRQ or SINR wherein ‘N’ is configured to be the maximum number of triggered resources to include in a lower layer measurement report.

[0223] In one embodiment, the measurement report is configured as a periodic lower layer measurement report e.g., periodic CSI-report transmission. For example, the UE receives a configuration indicating a periodicity of the report and the number of reports. One benefit of including the applicability indication of an AI / ML functionality associated to prediction of neighboring resources in a periodic CSI-report is that this would make the information available at the network more often, which is good for faster mobility decisions.

[0224] ■ In one option, the UE includes the applicability indication of an AI / ML functionality associated to prediction of neighboring resources (a reference signal such as SSB or CSLRS resource) if the neighboring resource is included in the periodic CSI-report.

[0225] ■ In one option, the UE includes the applicability indication of an AI / ML functionality associated to prediction of neighboring resources (a reference signal such as SSB or CSLRS resource) only in the first time the neighboring resources is included.

[0226] ■ In one sub-option, the UE includes the applicability indication of an AI / ML functionality associated to prediction of neighboring resources (a reference signal such as SSB or CSLRS resource) whenever there is a change in the applicability state of the AI / ML model / functionality.■ In one sub-option, the UE includes the applicability indication of an AI / ML functionality associated to prediction of neighboring resources (a reference signal such as SSB or CSI-RS resource) with a different periodicity compared to the periodicity in which the measurement reports are to be transmitted. For example, the applicability indication of an AI / ML functionality associated to prediction of neighboring resources (a reference signal such as SSB or CSI-RS resource) is transmitted less often, which may imply lower UE processing in determining the applicability and / or lower overhead in the measurement report, especially when the indication remains the same for some time.

[0227] o In one embodiment, the measurement report is configured as an aperiodic measurement report e.g., CSI report. In that case, it is the network requests the UE to report the AI / ML functionality (in)applicability indication for the prediction of the neighboring resources configured for the lower layer mobility together with the measurements reported for the lower layer mobility to the gNB-DU. o In one embodiment, the measurement report is configured as a semi-persistent measurement report e.g., semi-persistent CSI-report. In that case, it is the network requests the UE to report the AI / ML functionality (in)applicability indication for the prediction of the neighboring resources configured for the lower layer mobility together with the lower layer measurements reported for the lower layer mobility purpose, and once that is requested, the UE transmits the CSI reports including the measurements and the applicability indication(s) periodically.

[0228] - In an embodiment the UE determines whether the AI / ML model / functionality is applicable after a possible lower layer mobility toward a candidate cell i.e., after being connected to the target cell. And report to the serving gNB-CU node that the model / functionality is inapplicable after a potential lower layer mobility toward candidate resources such as neighboring reference signals or neighboring cell or neighboring frequency.

[0229] - In an embodiment the UE determines whether the AI / ML model / functionality is applicable before a possible lower layer mobility toward a candidate cell e.g., right at the time of performing prediction or measurements while being connected to the current serving cell.

[0230] Step 408: Message #3. The network node (in the example of Figure 4 the DU 402A) upon receiving the Message #2 including the measurements and / or the applicability indications transmits to the another network node (the CU 402B in the example of Figure 4) one or more (in)applicability indication(s) of one or more AI / ML functionality(es) associated to one or moreneighboring resources configured for the lower layer mobility procedure (e.g., set of neighboring beams or cells or frequencies). The DU 402A may include the layer 1 measurements such as layer 1 RSRP, Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR) of the neighboring resources in the Message #3.

[0231] The CU 402B can use such information to optimize the LTM candidate cell selections for the UEs based on the AI / ML model / functionality applicability information. For example if the CSI-report transmitted by the UE 400 indicates that the prediction models / functionality(s) are not applicable (in applicable) for a set of neighboring resources (such as neighboring reference signals (SSB or CSI-RS) or neighboring cells or neighboring frequency(es)), the CU 402B may decide to avoid performing the LTM cell switch toward that neighboring resources.

[0232] In another embodiment, the CU 402B may trigger data collection for the model training for the neighboring resources for which the AI / ML model / functionality is reported to be inapplicable. To enable this, the network may configure the UE 400 with a set of neighboring resources such as SSB or CSLRS beams to collect the measurements for the AI / ML model training purpose.

[0233] 3.2 Solution 2: UE reporting to the gNB-CU using RRC signaling.

[0234] 3.2.1 Overvi ew of S oluti on 2

[0235] Solution 2 is an extension of the procedure of Figure 3 to a RAN split architecture where the UE sends the applicability / inapplicability indication to the CU (e.g., gNB-CU).

[0236] In this regard, Figure 5 illustrates the operation of a UE 500 (i.e., a wireless terminal or wireless communication device) and a network node 502, in accordance with embodiments of the present disclosure. The network node 502 may be a RAN node such as, e.g., a base station (e.g., a gNB) having a split architecture such that the network node 502 includes a DU 502A (e.g., a gNB-DU) and a CU 502B (e.g., a gNB-CU). The steps of the procedure of Figure 5 are as follows:

[0237] • Step 504: This step corresponds to step 304 of Figure 3. In this step, the UE 500 receives, from the network node 502 and in particular from the CU 502B of the network node 502, a Message 1 including mobility related configuration (e.g., an RRC Reconfiguration message including the lower layer mobility (LTM) cell switch configuration), including one or more AI / ML model / functionality (in)applicability reporting configuration.

[0238] • Step 506: This step corresponds to step 306 of Figure 3. In this step, the UE 500 transmits, to the network node 502 and in particular the CU 502B of the network node 502, a Message #2 including the AI / ML model (in)applicability status, wherein the status can represent one more AI / ML model(s) / functionality(we) associated to the prediction of the neighboringradio resources configured for the lower layer mobility are applicable or not (i.e., ‘inapplicable’). The Message #2 can also include the measurement and predictions of the neighboring radio resources such as measurements and or prediction of the reference signals such as SSB or CSI-RS or measurements of the neighboring cells, according to the received configuration included in the Message #1. In this solution the UE sends the Message #2 via Layer 3 RRC signaling e.g., UE Assistance Information (UAI) or via Layer 3 Measurement Report.

[0239] Figure 5 also illustrates a process performed by the CU 502B and the DU 502A in RRC Connected state. As illustrated, the process of Figure 5 further includes:

[0240] • Step 508: Based on the received Message #2, the CU 502B transmits, to the DU 502A, a Message #3 including one or more AI / ML model / functionality (in)applicability status indication(s), wherein the status can represent one or more AI / ML model(s) / functionality(es) at the UE are applicable or not (i.e., inapplicable) for predictions associated to the neighboring radio resources. The Message #3 can also include the measurements and predictions of the neighboring beams / cells according to the received Message #2. The message can further include a list of candidate target configuration to apply for LTM such that this list does not include those target configurations according to which the AI / ML model / functionality is not applicable based on the received message#2, or according to which one or more AI / ML model(s) / functionality(ies) are not applicable if the cells associated to such target configurations are configured as serving cells or SpCells.

[0241] 3.2.2 Detailed Description of Solution 2

[0242] Further details regarding the steps of the procedure of Figure 5 are provided below.

[0243] Step 504: Message #1. The UE 500 receives the first message, Message #1, from the serving cell (operated by the network node 502), the first message e.g., RRC Reconfiguration message comprises one or more of the following:

[0244] - Report configuration of one or more (in)applicability indications of one or more AI / ML functionality(es) associated to prediction of one or more neighboring resources configured for lower layer mobility procedure (e.g., a set of neighboring reference signals (CSLRS or SSB beams) or cells or radio frequency (es)).

[0245] o In an embodiment of the UE, if a model / functionality is not applicable (or inapplicable) the UE is configured to report the radio measurements for the associated resources instead of the predictions.o In an embodiment the UE, if the model / functionality is not applicable (or inapplicable) the UE is expected to report a specific value instead of the predictions, indicating that the model / function is not applicable.

[0246] o In an embodiment of the UE, if the model / functionality is inapplicable the UE is configured to report the flag indicating inapplicability of the AI / ML model / functionality. The prediction value requested by the network can be absent in the report by the UE.

[0247] o In an embodiment of the UE, if the model / functionality is inapplicable the UE is configured to report additional information e.g., the inapplicability reason for the model / functionality or set of resources required for the AI / ML model / functionality applicability. The prediction value requested by the network can be absent in the report by the UE.

[0248] - LTM configurations including a list of neighboring resources such as LTM candidate cells - CSI resource configurations associated to the candidate cells including one or more resources e.g., reference signals (e.g., CSLRS reference signals) or synchronization sequence block (SSB) beams for which the UE shall perform the lower layer measurements and report to the network

[0249] - Prediction type e.g., temporal domain prediction, frequency domain prediction or spatial domain prediction functionality types for which the (in)applicability indication report is requested.

[0250] - Reporting type e.g., periodic, aperiodic or event based.

[0251] Step 506 - Message #2. Based on the received first message, Message #1, the UE performs layer 1 measurements and predictions according to the received configurations and reports them to the network node 502 in the Message #2.

[0252] While not explicitly shown in Figure 5, as part of step 506 or prior to step 506, based on the received Message #1, the UE 500 determines whether the AI / ML model / functionality is applicable (or not applicable) to perform the prediction on the configured resources (e.g., cells or reference signals such as CSLRS or SSB beams).

[0253] • In an embodiment, the UE determines whether the AI / ML model / functionality is applicable after a possible lower layer mobility toward a candidate cell i.e., after being connected to the target cell. And report to the serving gNB-CU node that the model / functionality is inapplicable after a potential lower layer mobility toward candidate resources such as neighboring reference signals or neighboring cell or neighboring frequency.• In another embodiment the UE determines whether the AI / ML model / functionality is applicable before a possible lower layer mobility toward a candidate cell e.g., right at the time of performing prediction or measurements while being connected to the current serving cell. And report to the serving gNB-CU node that the model / functionality is inapplicable after a potential lower layer mobility toward candidate resources such as neighboring reference signals or neighboring cell or neighboring frequency.

[0254] Alternative A of Step 506 (Message #2): In one set of embodiments, the UE transmits the applicability indication of an AI / ML functionality associated to prediction of the neighboring resources configured for the lower layer mobility in an RRC message e.g., UE Assistance Information message (e.g. UEAssistancelnformation - UAI) to the serving gNodeB-CU i.e. while the UE is in RRC Connected state. In other words, the Message #2 is or is contained in an RRC message such as, e.g., a UE Assistance Information message sent to the CU 502B while the UE 500 is in RRC Connected state.

[0255] • In one embodiment, the RRC message e.g., UE Assistance Information including the applicability indication of an AI / ML functionality associated to the prediction of the neighboring resources is transmitted when the UE determines the applicability state of an AI / ML functionality associated to prediction of neighboring resources configured for the lower layer mobility purpose.

[0256] • In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the prediction of the neighboring resources configured for the lower layer mobility is transmitted when the UE is configured (e.g. via an RRCReconfiguration) to report the applicability indication of an AI / ML functionality associated to prediction of the radio resources configured for the lower layer mobility.

[0257] • In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the prediction of the neighboring resources configured for the lower layer mobility is transmitted when the UE has previously reported the applicability indication of an AI / ML functionality associated to a configured resources indicating a value, e.g. ‘applicable’, and that value or the model / function applicability status has changed e.g. to ‘not applicable’ or ‘inapplicable’.

[0258] Alternative B of Step 506 (Message #2): In one set of embodiments, the UE transmits the applicability indication of an AI / ML functionality associated to prediction of the neighboring resources configured for the lower layer mobility in an RRC message e.g., Layer 3MeasurementReport to the serving gNodeB-CU i.e. while the UE is in RRC Connected state. In other words, the Message #2 is or is contained in an RRC message such as, e.g., a Layer 3 Measurement Report sent to the CU 502B while the UE 500 is in RRC Connected state.

[0259] • In one embodiment, the Layer 3 MeasurementReport including the applicability indication of an AI / ML functionality associated to prediction of the radio resources configured for the lower layer mobility (LTM) is transmitted by the fulfillment of an event associated to a measurement report i.e. when an event triggered measurement report is triggered for neighboring resources, the UE transmits the measurement report and the including the applicability indication of an AI / ML functionality associated to prediction of the radio resources configured for the lower layer mobility (LTM).

[0260] • In one embodiment, the Layer 3 MeasurementReport including the applicability indication of an AI / ML functionality associated to prediction of the radio resources configured for the lower layer mobility (LTM) is transmitted when a periodic measurement report is transmitted.

[0261] • In one embodiment, the Layer 3 MeasurementReport including the applicability indication of an AI / ML functionality associated to prediction of the radio resources configured for the lower layer mobility (LTM) is transmitted when an aperiodic measurement report is transmitted.

[0262] • In one embodiment, the Layer 3 MeasurementReport including the applicability indication of an AI / ML functionality associated to prediction of the radio resources configured for the lower layer mobility (LTM) is transmitted when a semi-persistent measurement report is transmitted.

[0263] • In one embodiment, the UE receives one or more parameters controlling how to include the applicability indication of an AI / ML functionality associated to prediction of the radio resources configured for the lower layer mobility (LTM) in a MeasurementReport (from a source network node).

[0264] • In one option, these one or more parameters may be included in a report configuration which is received in an RRC Reconfiguration (e.g. reportConfig or otherConfig).

[0265] o In one option the OtherConfig includes a so-called partial inference configuration which indicates an LTM candidate ID (for a configured LTM candidate cell) and an associated ID (associated to that LTM candidate cell). When the UE reports the applicability status (or applicability info for that partial inference configuration) the UE indicates the associated ID and the LTM candidate ID for which that applicability is being reported.• In one option, the UE receives the one or more parameters which point to a measurement configuration identifier (e.g. such as a measurement configuration identifier (measConfigID), reporting configuration identifier (reportConfigID), or measurement object identifier (measObjectID) the UE is configured with). The benefits in creating this link between the neighboring resources and the measurement configuration is that the UE would transit the applicability indication of an AI / ML functionality associated to prediction of the radio resources configured for the lower layer mobility (LTM) in a Layer 3 MeasurementReport only for cells which are candidates in lower layer mobility (LTM).

[0266] • In one option, these one or more parameters comprise one or more of the following: o a field, IE, or parameter indicating that the UE shall report an applicability indication for a neighbor resources e.g. triggered cell(s) or the reference signals associated with the triggered cells, or a neighboring resource indicated in the configuration.

[0267] o a field, IE, or parameter indicating the AI / ML functionality for which the UE shall report an applicability indication for neighboring resources.

[0268] ■ In one option, the UE assumes that the AI / ML functionality for which the UE shall report an applicability indication for neighboring resources are the same functionality the UE is currently configured for operating in a serving cell.

[0269] o a field, IE, or parameter indicating the type of predictions (e.g., temporal domain prediction, spatial domain prediction or frequency domain prediction) for which the UE shall report an applicability indication for neighboring resources.

[0270] ■ In one option, the UE assumes that the prediction type (e.g., temporal domain prediction, spatial domain prediction or frequency domain prediction) for which the UE shall report an applicability indication for neighboring resources are the same functionality the UE is currently configured for operating in a serving cell.

[0271] Step 508: Message #3. The network node 502 (the CU 502B in the example of Figure 5 or a CU-Control Plane (CP) part of the CU 502B in the case of a CP -User Plane (UP) split architecture) upon receiving the Message #2 including the measurements and / or the applicability indications, transmits to the another network node (the DU 502A in the example of Figure 5) one or more (in)applicability indication(s) of one or more AI / ML functionality(es) associated to prediction of one or more neighboring resources configured for the lower layer mobility procedure (e.g., set of neighboring beams or cells or frequencies). The CU 502B may include otherinformation such as Layer 1 and / or Layer 3 measurements e.g., Layer 1 RSRP, RSRQ, SINR or Layer 3 RSRP, RSRQ, SINR of the neighboring resources in the Message #3.

[0272] The DU 502A can use such information to optimize the LTM execution policies as well as the early synchronization toward the neighboring LTM candidate cells or beams served by the neighboring LTM candidate cells. For example if the CSLreport transmitted by the UE 500 indicates that the prediction models / functionality(s) are not applicable (in applicable) for a set of neighboring resources (such as neighboring reference signals (SSB or CSLRS) or neighboring cells or neighboring frequency(es)) after a possible LTM cell switch, the DU 502A, may decide to avoid performing the LTM cell switch toward that neighboring resources (beam or cell).

[0273] In another embodiment, the DU 502A may trigger data collection for the model training for the neighboring resources for which the AI / ML model / functionality is reported to be inapplicable. To enable this, the network may configure the UE with a set of neighboring resources such as SSB or CSLRS beams to collect the measurements for the AI / ML model training purpose.

[0274] In one embodiment, the CU 502B could inform the DU 502A to not transmit LTM cell switch commands in relationship to target LTM configurations for which the UE has transmitted a message#2 indicating inapplicability of the AI / ML model / functionality, or in relationship to candidate target cells in which the UE cannot perform the AI / ML model / functionality inference if such cells are configured as serving cells or SpCells.

[0275] 3.3 Solution 3: UE reporting a first indication to the gNB-DU using lower layer signaling designed for lower layer mobility (LTM) and a second indication / information to the gNB-CU

[0276] 3.3.1 Overvi ew of S oluti on 3

[0277] Solution 3 is a variant in RAN split architecture where the UE sends the applicability / inapplicability indication to the DU (e.g., gNB-DU) and additional information to the CU (e g., gNB-CU).

[0278] In this regard, Figure 6 illustrates the operation of a UE 600 (i.e., a wireless terminal or wireless communication device) in connected state (e.g., RRC connected state) and a network node 602, in accordance with embodiments of the present disclosure. The network node 602 may be a RAN node such as, e.g., a base station (e.g., a gNB) having a split architecture such that the network node 602 includes a DU 602A (e.g., a gNB-DU) and a CU 602B (e.g., a gNB-CU). The steps of the procedure of Figure 6 are as follows:

[0279] • Step 604: This step corresponds to step 304 of Figure 3. In this step, the UE 600 receives, from the network node 602 and in particular the CU 602B, a Message #1 including mobilityrelated configuration (e.g., an RRC Reconfiguration message including the lower layer mobility (LTM) cell switch configuration), including reporting configuration for one or more AI / ML model / functionality (in)applicability status indication(s), for one or more AI / ML model / functionality associated with the prediction of radio resources configured for the lower layer mobility (e.g., LTM) purpose.

[0280] • Step 606: This step corresponds to step 306 of Figure 3. In this step, the UE 600 transmits Message #2 to the network node 602 and in particular the DU 602A (e.g. via lower layer signaling (UCI message) or a MAC CE). The Message #2 includes one or more AI / ML model / functionality (in)applicability status indication(s) for one or more AI / ML model / functionality associated with the prediction of radio resources configured for the lower layer mobility (e.g., LTM) purpose, wherein the status can represent one more AI / ML model(s) are applicable or not.

[0281] • Step 608: The UE 600 transmits a Message #3 to the CU 602B (via a L3 message, e.g. UE Assistance Information or Measurement Report) including additional information related to the inapplicability indication in the Message #2, e.g. reasons for inapplicability and / or measurements and / or required resources / functionalities for the AI / ML model to be applicable.

[0282] Figure 6 also illustrates a process performed by the DU 602A and the CU 602B in RRC Connected state. As illustrated, the process of Figure 6 further includes the following:

[0283] • Step 610: The DU 602 A transmits, to the CU 602B, a Message #4 including the AI / ML model / functionality (in)applicability status indication, wherein the indication represents one or more AI / ML model(s) / functionality(es) at the UE 600 are applicable or not (i.e., inapplicable), based on the received Message #2.

[0284] 3.3.2 Detailed Description of Solution 3

[0285] Further details regarding the steps of the procedure of Figure 6 are provided below.

[0286] Step 604: Message #1. The UE 600 receives the first message, Message #1, from the serving cell, the first message e.g., RRC Reconfiguration message comprises one or more of the following:

[0287] • Report configuration of one or more (in)applicability indications of one or more AI / ML functionality(es) associated to prediction of one or more neighboring resources configured for lower layer mobility procedure (e.g., a set of neighboring reference signals (CSI-RS or SSB beams) or cells or radio frequency).o In an embodiment the UE is configured to report the (in)applicable indication via lower layer signaling (UCI or MAC CE) and additional information in a L3 message, e.g. UE Assistance Information or Measurement Report. The additional information may comprise:

[0288] ■ The inapplicability reason for the model / functionality.

[0289] ■ The resources and / or functionalities required for the model applicability. o In an embodiment of the UE, if the model / functionality is inapplicable the UE is expected to report a specific value instead of the predictions, indicating that the model / function is not applicable.

[0290] o In an embodiment of the UE, if the model / functionality is inapplicable or applicable the UE is configured to report the flag indicating (in)applicability of the model / functionality. The prediction value requested by the network can be absent in the report by the UE.

[0291] o In one option, these one or more parameters may be included in a report configuration which is received in an RRC Reconfiguration (e.g. reportConfig or otherConfig).

[0292] o In one option, the UE receives the one or more parameters which point to a measurement configuration identifier (e.g. such as a measurement configuration identifier (measConfigID), reporting configuration identifier (reportConfigID), or measurement object identifier (measObjectID) the UE is configured with).

[0293] • LTM configurations including a list of neighboring resources such as LTM candidate cells.

[0294] • CSI resource configurations associated with the candidate cells including one or more resources e.g., reference signals (e.g., CSI-RS reference signals) or synchronization sequence block (SSB) beams for which the UE shall perform the lower layer measurements and report to the network.

[0295] • Prediction type e.g., temporal domain prediction, frequency domain prediction or spatial domain prediction functionality types for which the (in)applicability indication report is requested.

[0296] • Reporting type e.g., periodic, aperiodic or event based.

[0297] Step 606: Message #2. Based on the received first message, Message #1, the UE 600 performs layer 1 measurements and predictions according to the received configurations and reports the results (in Message #2) to the network node 602 via lower layer signaling (e.g., UCI message) or a MAC CE.While not explicitly illustrated in Figure 6, when performing the measurements, the UE 600 determines whether the model / functionality is applicable to perform the prediction on the requested resources (e.g., cells or reference signals such as CSI-RS or SSB beams) and includes the corresponding (in)applicability indication in the Message #2.

[0298] In step 606, the UE transmits to the serving network node one or more (in)applicability indication(s) of one or more AI / ML functionality(es) associated with one or more neighboring resources configured for the lower layer mobility procedure (e.g., set of neighboring beams or cells or frequencies) beside the layer 1 measurements such as layer 1 RSRP, RSRQ, SINR of the neighboring resources.

[0299] - In one set of embodiments, the UE transmits one or more (in)applicability indication(s) of one or more AI / ML functionality(es) associated with prediction of one or more neighboring resource(s) configured for the mobility purpose in a lower layer signal to the gNB-DU using uplink control information (UCI) or a MAC CE.

[0300] o When the UE performs the actions, the UE is in a connected mode e.g. an RRC state optimized for data transmissions / receptions, such as RRC CONNECTED. And the UE is connected to a first cell;

[0301] ■ In one option, the first cell is considered to be a Special Cell (SpCell) e.g. a Primary Cell (PCell) of the Master Cell Group (MCG), a Secondary Cell Group (SCG) Cell (PSCell), etc.

[0302] o In one embodiment, the measurement report is configured as a periodic lower layer measurement report e.g., periodic CSI-report transmission. For example, the UE receives a configuration indicating a periodicity of the report and the number of reports. One benefit of including the applicability indication of an AI / ML functionality associated with prediction of neighboring resources in a periodic CSI- report is that this would make the information available at the network more often, which is good for faster mobility decisions.

[0303] ■ In one option, the UE includes the applicability indication of an AI / ML functionality associated with prediction of neighboring resources (a reference signal such as SSB or CSLRS resource) if the neighboring resource is included in the periodic CSI-report.

[0304] ■ In one option, the UE includes the applicability indication of an AI / ML functionality associated with prediction of neighboring resources (a reference signal such as SSB or CSLRS resource) only the first time the neighboring resources is included.■ In one sub-option, the UE includes the applicability indication of an AI / ML functionality associated to prediction of neighboring resources (a reference signal such as SSB or CSI-RS resource) whenever there is a change in the applicability state of the AI / ML model / functionality.

[0305] ■ In one sub-option, the UE includes the applicability indication of an AI / ML functionality associated with prediction of neighboring resources (a reference signal such as SSB or CSLRS resource) with a different periodicity compared to the periodicity in which the measurement reports are to be transmitted. For example, the applicability indication of an AI / ML functionality associated with prediction of neighboring resources (a reference signal such as SSB or CSLRS resource) is transmitted less often, which may imply lower UE processing in determining the applicability and / or lower overhead in the measurement report, especially when the indication remains the same for some time.

[0306] o In one embodiment, the measurement report is configured as an aperiodic measurement report e.g., CSI report. In that case, the network requests the UE to report the AI / ML functionality (in)applicability indication for the prediction of the neighboring resources configured for the lower layer mobility together with the measurements reported for the lower layer mobility to the gNB-DU. o In one embodiment, the measurement report is configured as a semi-persistent measurement report e.g., semi-persistent CSI-report. In that case, it is the network requests the UE to report the AI / ML functionality (in)applicability indication for the prediction of the neighboring resources configured for the lower layer mobility together with the lower layer measurements reported for the lower layer mobility purpose, and once that is requested, the UE transmits the CSI reports including the measurements and the applicability indication(s) periodically.

[0307] Alternative A of Step 608 - Message #3: In one set of embodiments, the UE transmits additional information related to the applicability indication of an AI / ML functionality associated with prediction of the neighboring resources configured for the lower layer mobility in an RRC message e.g., UE Assistance Information message (e.g. UEAssistancelnformation - UAI) to the serving gNodeB-CU i.e. while the UE is in RRC Connected state. The additional information may comprise:

[0308] o The inapplicability reason for the model / functionality.

[0309] o The resources and / or functionalities required for the model applicability.Alternative B of Step 608 -Message #3: In one set of embodiments, the UE transmits additional information related to the applicability indication of an AI / ML functionality associated with prediction of the neighboring resources configured for the lower layer mobility in an RRC message e.g., Layer 3 MeasurementReport to the serving gNodeB-CU i.e. while the UE is in RRC Connected state.

[0310] o The inapplicability reason for the model / functionality.

[0311] o The resources and / or functionalities required for the model applicability. Step 610: Message #4. The network node 602 (the DU 602A in the example of Figure 6) upon receiving the Message #2 including the measurements and / or the applicability indications, transmits to the another network node (the CU 602B in the example of Figure 6) one or more (in)applicability indication(s) of one or more AI / ML functionality(es) associated to one or more neighboring resources configured for the lower layer mobility procedure (e.g., set of neighboring beams or cells or frequencies). The DU 602A may include the layer 1 measurements such as layer 1 RSRP, RSRQ, SINR of the neighboring resources in the message #3.

[0312] The CU 602B can use such information to optimize the LTM candidate cell selections for the UEs based on the AI / ML model / functionality applicability information. For example if the CSLreport transmitted by the UE 600 indicates that the prediction models / functionality(s) are not applicable (in applicable) for a set of neighboring resources (such as neighboring reference signals (SSB or CSLRS) or neighboring cells or neighboring frequency(es)), the CU 602B may decide to avoid performing the LTM cell switch toward that neighboring resources.

[0313] In another embodiment, the CU 602B may trigger data collection for the model training for the neighboring resources for which the AI / ML model / functionality is reported to be inapplicable. To enable this, the network may configure the UE 600 with a set of neighboring resources such as SSB or CSLRS beams to collect the measurements for the AI / ML model training purpose.

[0314] 3.4 Reporting of (in)applicability without prior configuration

[0315] In a simplified variant of all solutions, the UE is not explicitly configured to report (in)applicability. As an implicit solution, the network node having a specific configuration implicitly indicates to the UE that the UE should report (in)applicability. Such a specific configuration could e.g. be the UE being configured to perform predictions, the UE being configured to perform predictions of a certain type, the UE being configured to perform predictions in a certain use case (LTM) etc. That means that the (in)applicability reporting may be triggeredby another type of configuration or trigger, so that no explicit (in)applicability configuration is necessary.

[0316] 3.5 Extension to the neighboring gNBs.

[0317] Upon a gNB-CU or a gNB-CU-CP receiving the applicability information of the AI / ML model / functionality associated to prediction of the neighboring resources configured for the lower layer mobility (LTM) (either via gNB-DU as described in the first or third solution (Solution 1 or Solution 3), or via Layer 3 signaling from the UE as described in the second solution (Solution 2)) the serving gNB-CU can forward the received information to the neighboring gNB as part of LTM cell switch preparation or execution signaling.

[0318] The neighboring gNB serving the candidate LTM cells / resources may optimize its configuration to enable the AEML model / functionality applicability after a possible LTM cell switch toward the target LTM cell / resource.

[0319] The neighboring gNB serving the candidate LTM cells / resources may respond to the serving gNB with additional information or resource configuration that enables the UE’s AI / ML model / functionality to be applicable after a possible LTM cell switch execution. The received respond message from the target / neighbor gNB could be sent to the UE via RRC messages or via LTM cell switch command at the lower layers by the gNB-DU.

[0320] 4 Further Description Applicable to All Embodiments

[0321] Figure 7 shows an example of a communication system 700 in which embodiments of the present disclosure may be implemented. For example, the UE 300, 400, 500 or 600 may be one of the UEs 712 of Figure 7, and the network node 302, 402, 502, or 602 may be one of the access network nodes 710 of Figure 7.

[0322] In the example, the communication system 700 includes a telecommunications network 702 that includes an access network 704, such as a radio access network (RAN), and a core network 706, which includes one or more core network nodes 708. The access network 704 includes one or more access network nodes or base stations of various types, access network nodes 710A and 710B are depicted (which may be collectively referred to as network nodes 710), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 704 may include more than one access network technology. The network nodes 710 of access network 704 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 712A,712B, 712C, and 712D (one or more of which may be generally referred to as UEs 712) to the core network 706 over one or more wireless connections.

[0323] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 702 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 702 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 702, including one or more access network nodes 710 and / or core network nodes 708.

[0324] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.

[0325] The network nodes 710 facilitate direct or indirect connection of one or more UEs 712 to the core network 706 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 700 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wirelessconnections. The communication system 700 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0326] The UEs 712 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 710 and other communication devices. Similarly, the network nodes 708, 710 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 702) with the UEs 712 and / or with other network nodes or equipment in the telecommunications network 702 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 702. More specifically, UEs 712 may send messages, data, and / or other signals to network nodes 708, 710 or other elements of the telecommunications network 702 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 708, 710 may send messages, data, and other signals to UEs 7122, other network nodes 708, 710, and other devices in telecommunications network 702 directly or indirectly. As one specific example, a core network node 108 may transmit a particular message to a UE 712 by transmitting the message to an access network node 710 that will then transmit the message to the intended UE 712. Similarly, a core network node 108 may receive a particular message from a UE 712 by receiving the message from an access network node 710 that itself received the message from the UE 712.

[0327] In the depicted example, the core network 706 connects elements of the access network 704 (e.g., one or more of the network nodes 710) to one or more host computing systems, such as host 716. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 706 includes one or more core network nodes (e.g., core network node 708) of various types, one or more of which may be generally referred to as network nodes 708. Network nodes 708 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 708. Example core network nodes provide functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function(SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0328] The host 716 may be under the ownership or control of a service provider other than an operator or provider of the access network 704 and / or the telecommunications network 702. The host 716 may be operated by the service provider or on behalf of the service provider. The host 716 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0329] As a whole, the communication system 700 of Figure 7 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 700 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (Wi-Fi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li-Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 700 may be configured to support multiple different standards, protocols, or other rule sets, with individual components supporting all of the relevant rule sets or with different components or sub-systems within the communication system 700 supporting different standards, protocols, or rule sets.

[0330] As one example, in certain embodiments, access network 704 may contain some access network nodes 710 that support 3 GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 710 support (or the same access network nodes 710 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 702 may support multiple generations of related communication standards (e.g., 4G and 5G 3GPP communication standards) and, as a result, may include an access network 704 and / or a core network 706 that supports multiple different standard generations ormay include multiple access networks 704 and / or multiple core networks 706 with individual networks 704, 706 supporting different standard generations.

[0331] Telecommunications network 702 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 702. For example, the telecommunications network 702 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0332] In some examples, one or more of the UEs 712 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 704 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 704. Additionally, a UE may be configured for operating in single- or multi -RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0333] In the example, the hub 714 communicates with the access network 704 to facilitate indirect communication between one or more UEs (e.g., UE 712C and / or 712D) and network nodes (e.g., network node 710B). In some examples, the hub 714 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 714 may be a broadband router enabling access to the core network 706 for the UEs. As another example, the hub 714 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 710, or by executable code, script, process, or other instructions in the hub 714.

[0334] As another example, the hub 714 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 714 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 714 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 714 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 714 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0335] The hub 714 may have a constant / persistent or intermittent connection to the network node 710B. The hub 714 may also allow for a different communication scheme and / or schedule betweenthe hub 714 and UEs (e.g., UE 712C and / or 712D), and between the hub 714 and the core network 706. In other examples, the hub 714 is connected to the core network 706 and / or one or more UEs via a wired connection. Moreover, the hub 714 may be configured to connect to an M2M service provider over the access network 704 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 710 while still connected via the hub 714 via a wired or wireless connection. In some embodiments, the hub 714 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 710B. In other embodiments, the hub 714 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 710B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0336] Figure 8 is another example of a communication system 800 according to some embodiments. As used herein, the communication system 800 includes multiple access points (APs) 810 (with four exemplary APs 810A, 810B, 810C, and 810D being depicted) and multiple wireless devices, referred to in the context of communication system 800 as stations (STAs) 812 (referred to individually as STA 812A, STA 812B, STA 812C, STA 812D, and STA 812E). STA 812Ais served by AP 810Ain a first basic service set (BSS) 820A. STA 810B and STA 810C are served by AP 810B in a second BSS, BSS 820B. STA 812D is served by AP 810C in a third BSS, BSS 820C. STA 812E is served by AP 810D in a fourth BSS, BSS 820D. Stations 812 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head-mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 812 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0337] Each of STAs 812 may connect through a radio link to one of APs 810. For example, depending on location or channel conditions experienced by a given STA 812, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0338] Each AP 810 may provide data connectivity to STAs 812 connected to a particular AP 810. As illustrated, APs 810 may be connected to a data network 830. In this way, APs 810 may alsoprovide data connectivity between STAs 812 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like. Accordingly, the radio link established between a given STA 812 and its serving AP 810 may be used for providing various kinds of services to STA 812, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 812 and / or on a device linked to STA 812. By way of example, Figure 8 illustrates an application service platform 832 provided in data network 830. The application(s) executed on STA 812 and / or on one or more other devices linked to STA 812 may use the radio link for data communication with one or more other STA 812 and / or the application service platform 832, thereby enabling utilization of the corresponding service(s) at STA 812.

[0339] Figure 9 shows a wireless device 900, which may be configured to operate in communication system 700 of Figure 7 or in communication system 800 of Figure 8. The wireless device 900 may be alternatively referred to as a UE 900, like a UE 712 within the context of communication system 700, or as a station (STA) 900 or as a non-access-point station (non-AP STA) 900, like a STA 812 within the context of the communication system 800, in accordance with respective embodiments. As used herein, a wireless device refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Examples of a wireless device include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, and wireless terminal. Other examples include any type of UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0340] A wireless device 900 may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, wireless device 900 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 900 may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinklercontroller). Alternatively, wireless device 900 may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0341] In particular embodiments, wireless device 900 includes processing circuitry 902 that is operatively coupled via a bus 904 to an input / output interface 906, a power source 908, a memory 910, a communication interface 912, and / or any other component, or any combination thereof. Certain embodiments of wireless device 900 may include all or a subset of the components shown in Figure 9. The level of integration between the components may vary from one embodiment of wireless device 900 to another. In general, in a particular embodiment of wireless device 900, processing circuitry 902, input / output interface 906, power source 908, memory 910, and communication interface 912 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of wireless device 900. Further, certain embodiments of wireless devices 900 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0342] The processing circuitry 902 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 910. The processing circuitry 902 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 902 may include multiple central processing units (CPUs).

[0343] In the example, the input / output interface 906 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into wireless device 900. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An outputdevice may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0344] In some embodiments, the power source 908 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 908 may further include power circuitry for delivering power from the power source 908 itself, and / or an external power source, to the various parts of wireless device 900 via input circuitry or an interface such as an electrical power cable. Power source 908 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 900 to which power is supplied.

[0345] The memory 910 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 910 includes one or more programs 914, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 916. The memory 910 may store, for use by wireless device 900, any of a variety of various operating systems or combinations of operating systems.

[0346] The memory 910 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 910 may allow wireless device 900 to access instructions, programs, and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 910, which may be or comprise a device-readable storage medium.

[0347] The processing circuitry 902 may be configured to communicate with an access network or other network via or using the communication interface 912. The communication interface 912may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 922. The communication interface 912 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another wireless device or a network node in an access network). Each transceiver may include a transmitter 918 and / or a receiver 920 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 918 and receiver 920 may be coupled to one or more antennas (e.g., antenna 922) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0348] In the illustrated embodiment, communication functions of the communication interface 912 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0349] In particular embodiments, wireless device 900 may provide an output of data captured via a sensor, through its communication interface 912, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 900 can be communicated through a wireless connection to a network node via another wireless device 900. In particular embodiments, such output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected, an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0350] As another example, wireless device 900 comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, wireless device 900 may comprise a motor thatadjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0351] Wireless device 900, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 900 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the example embodiment of wireless device 900 shown in Figure 9.

[0352] As yet another specific example, in an loT scenario, wireless device 900 may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another wireless device and / or a network node. Wireless device 900 may in this case be an M2M device, which may in a 3 GPP context be referred to as an MTC device. As one particular example, wireless device 900 may implement the 3GPP NB-IoT standard. In other scenarios, wireless device 900 may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0353] In practice, any number of wireless devices 900 may be used together with respect to a single use case. For example, a first wireless device 900 might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second wireless device 900 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 900 may adjust the throttle on the drone (e.g., by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second wireless device 900 can also include more than one of the functionalities described above. Forexample, wireless device 900 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0354] Figure 10 shows a network node 1000 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunications network. In accordance with respective embodiments, network node 1000 may be configured to operate in communication system 700 of Figure 7, like network nodes 708 or 710, or in communication system 800 of Figure 8, like an AP 810 or a station 812. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0355] Network nodes 1000 may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. Network node 1000 may be a relay node or a relay donor node controlling a relay. Network nodes 1000 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0356] Other examples of network nodes 1000 include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0357] In particular embodiments, network node 1000 includes a processing circuitry 1002, a memory 1004, a communication interface 1006, and a power source 1008. In general, in a particular embodiment of network node 1000, processing circuitry 1002, memory 1004, communication interface 1006, and power source 1008 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of network node 1000.The network node 1000 may be composed of multiple distinct network entities (e.g., a NodeB entity and an RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 1000 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1000 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 1004 or portions of memory 1004 for different RATs) and some components may be reused (e.g., a same antenna 1010 may be shared by different RATs). The network node 1000 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1000, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1000.

[0358] The processing circuitry 1002 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other components, such as the memory 1004, to provide network node 1000 functionality.

[0359] In some embodiments, the processing circuitry 1002 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1002 includes one or more of radio frequency (RF) transceiver circuitry 1012 and baseband processing circuitry 1014. In some embodiments, the RF transceiver circuitry 1012 and the baseband processing circuitry 1014 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1012 and baseband processing circuitry 1014 may be on the same chip or set of chips, boards, or units.

[0360] The memory 1004 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile ornon-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1002. The memory 1004 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 1002 and utilized by the network node 1000. The memory 1004 may be used to store any calculations made by the processing circuitry 1002 and / or any data received via the communication interface 1006. In some embodiments, the processing circuitry 1002 and memory 1004 is integrated.

[0361] The communication interface 1006 is used in wired or wireless communication of signaling and / or data with UEs, other network nodes, and / or any other network equipment. In the illustrated embodiment, communication interface 1006 comprises port(s) / terminal(s) 1016 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 900 may be capable of wireless communication and communication interface 1006 may also include radio front-end circuitry 1018 that may be coupled to, or in certain embodiments a part of, an antenna 1010. Particular embodiments of radio front-end circuitry 1018 include filter(s) 1020 and amplifier(s) 1022. The radio front-end circuitry 1018 may be connected to an antenna 1010 and processing circuitry 1002. The radio front-end circuitry may be configured to condition signals communicated between antenna 1010 and processing circuitry 1002. The radio front-end circuitry 1018 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1018 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 1020 and / or amplifiers 1022. The radio signal(s) may then be transmitted via the antenna 1010. Similarly, when receiving data, the antenna 1010 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1018. The digital data may be passed to the processing circuitry 1002. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0362] In certain alternative embodiments, network node 1000 may be capable of wireless communication but does not include separate radio front-end circuitry 1018, instead, the processing circuitry 1002 includes radio front-end circuitry and is connected to the antenna 1010. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1012 is part of the communication interface 1006. In still other embodiments, the communication interface 1006 includes one or more ports or terminals 1016, the radio front-end circuitry 1018, and the RF transceiver circuitry 1012, as part of a radio unit (not shown), and the communication interface1006 communicates with the baseband processing circuitry 1014, which is part of a digital unit (not shown).

[0363] The antenna 1010 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1010 may be coupled to the radio front-end circuitry 1018 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1010 is separate from the network node 1000 and connectable to the network node 1000 through one or more interfaces or ports.

[0364] The antenna 1010, communication interface 1006, and / or the processing circuitry 1002 may be configured to perform some or all of the receiving operations and / or obtaining operations described herein as being performed by the network node 1000. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 1010, the communication interface 1006, and / or the processing circuitry 1002 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 1000. Any information, data and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0365] The power source 1008 provides power to the various components of network node 1000 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1008 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1000 with power for performing the functionality described herein. For example, the network node 1000 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1008. As a further example, the power source 1008 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0366] Embodiments of the network node 1000 may include additional components beyond those shown in Figure 10 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 1000 may include user interface equipment to allow input of information into the network node 1000 and to allow output of information from the network node 1000. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1000.Figure 11 is a block diagram illustrating a virtualization environment 1100 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1100 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, in embodiments in which a virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1100 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0367] Applications 1102 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1000 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0368] Hardware 1104 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1106 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 1108 A and VM 1108B (which may be collectively referred to as VMs 1108), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1106 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 1108.

[0369] The VMs 1108 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 1106. Different embodiments of the instance of a virtual appliance 1102 may be implemented on one or more of VMs 1108, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physicalswitches, and physical storage, which can be located in data centers, and customer premise equipment.

[0370] In the context of NFV, each of the VMs 1108 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1108, and that part of hardware 1104 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more of the VMs 1108 on top of the hardware 1104 and corresponds to an application 1102.

[0371] Hardware 1104 may be implemented in a standalone network node with generic or specific components. Hardware 1104 may implement some functions via virtualization. Alternatively, hardware 1104 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1110, which, among others, oversees lifecycle management of applications 1102. In some embodiments, hardware 1104 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1112 which may alternatively be used for communication between hardware nodes and radio units.

[0372] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitionedbetween separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0373] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0374] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0375] Some exemplary embodiments of the present disclosure are as follows:

[0376] Group A Embodiments

[0377] Embodiment 1 : A method performed by a User Equipment, UE, (300; 400; 500; 600), the method comprising: receiving (304; 404; 504; 604), from a network node, an inference configuration for reporting measurement predictions for mobility; and reporting (306; 406; 506; 606), to the network node, applicability information for the inference configuration in an applicability report.

[0378] Embodiment 2: The method of embodiment 1, wherein the inference configuration is an inference configuration for reporting measurement predictions for lower layer mobility.

[0379] Embodiment 3: The method of embodiment 1, wherein the inference configuration is an L1 / L2 Triggered Mobility, LTM, inference configuration for reporting measurement predictions on LTM candidate cells.Embodiment 4: The method of any of embodiments 1 to 3, wherein (e.g., within the applicability report) the applicability information is associated to an identifier of the inference configuration and / or an identifier of a serving cell configuration of a serving cell on which the UE received the inference configuration.

[0380] Embodiment 5: The method of any of embodiments 1 to 3, wherein: the UE is configured with two or more inference configurations, each associated to an LTM CSI reporting configuration ID and all of them associated with a same serving cell configuration; and the applicability report comprises one or more instances of applicability information, each instance of applicability indication associated (e.g., within the applicability report) to one or more identifiers of one or more of the inference configurations and all of the instances of the applicability indication associated to a serving cell identifier (e.g., serving cell index) of a serving cell in which the UE received the inference configurations.

[0381] Embodiment 6: The method of any of embodiments 1 to 3, wherein the applicability information is reported in a message, the message being either an RRC message (e.g., an RRC Reconfiguration Complete) or a UE Assistance Information message.

[0382] Embodiment 7: The method of embodiment 6, wherein: the message comprises a list of applicability reports which includes, for each of one or more mobility configurations (e.g., each of one or more lower layer mobility or LTM configurations), an instance of an applicability report; each instance of the applicability report comprises one or more identifiers (e.g., a list of one or more LTM CSI Report Configuration IDs) of one or more inference configurations to which the applicability report is associated; and each instance of the applicability report is associated to (e.g., comprises) a serving cell index (e.g., of a serving cell on which the UE received the respective inference configuration(s)).

[0383] Embodiment 8: The method of any of embodiments 1 to 7, wherein the inference configuration is a full inference configuration.

[0384] Embodiment 9: The method of any of embodiments 1 to 7, wherein the inference configuration is a partial inference configuration.

[0385] Embodiment 10: The method of any of embodiments 1 to 9, wherein receiving (304; 404; 504; 604) the inference configuration comprises receiving (304; 404; 504; 604) a first message comprising a mobility related configuration and a reporting configuration for reporting one or more AI / ML model or functionality applicability status indications for one or more AI / ML models or functionalities associated to prediction of measurements on radio resources configured for mobility (e.g., lower layer mobility such as, e.g., LTM).Embodiment 11: The method of embodiment 10, wherein reporting (306; 406; 506; 606) the applicability information for the inference configuration in the applicability report comprises transmitting (306; 406; 506; 606), to the network node, a second message comprising one or more AI / ML model or functionality status indications for the one or more AI / ML model functionalities associated to the prediction of measurements on radio resources configured for mobility (e.g., in accordance with the received reporting configuration).

[0386] Embodiment 12: The method of any of embodiments 1 to 9, wherein reporting (306; 406; 506; 606) the applicability information for the inference configuration in the applicability report comprises transmitting (306; 406; 506; 606), to the network node, a second message comprising one or more AI / ML model or functionality status indications for one or more AI / ML model functionalities associated to the prediction of measurements on radio resources configured for mobility.

[0387] Embodiment 13: The method of embodiment 11 or 12, wherein the second message further comprises measurements and / or measurement predictions.

[0388] Embodiment 14: The method of any of embodiments 11 to 13, wherein the second message is transmitted via L1 / L2 signaling or Uplink Control Information (UCI).

[0389] Embodiment 15: The method of any of embodiments 1 to 14, further comprising receiving (410), from the network node (402), a mobility configuration (e.g., an LTM configuration) where AI / ML functionality or model is applicable.

[0390] Embodiment 16: The method of any of embodiments 1 to 14, further comprising transmitting (608), to the network node (602) (e.g., to a CU (602B) of the network node (602), a message comprising additional information related to the applicability information (e.g., indication of reason(s) for inapplicability, indication of resources and / or functionalities required for AI / ML model or functionality applicability).

[0391] Embodiment 17: The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.

[0392] Group B Embodiments

[0393] Embodiment 18: A method performed by a network node (302; 402; 502; 602), the method comprising: transmitting (304; 404; 504; 604), to a User Equipment, UE, (300; 400; 500; 600), an inference configuration for reporting measurement predictions for mobility; and receiving (306; 406; 506; 606), from the UE, applicability information for the inference configuration in an applicability report.Embodiment 19: The method of embodiment 18, wherein the inference configuration is an inference configuration for reporting measurement predictions for lower layer mobility.

[0394] Embodiment 20: The method of embodiment 18, wherein the inference configuration is an L1 / L2 Triggered Mobility, LTM, inference configuration for reporting measurement predictions on LTM candidate cells.

[0395] Embodiment 21: The method of any of embodiments 18 to 20, wherein (e.g., within the applicability report) the applicability information is associated to an identifier of the inference configuration and / or an identifier of a serving cell configuration of a serving cell on which the UE received the inference configuration.

[0396] Embodiment 22: The method of any of embodiments 18 to 20, wherein: the UE is configured with two or more inference configurations, each associated to an LTM CSI reporting configuration ID and all of them associated with a same serving cell configuration; and the applicability report comprises one or more instances of applicability information, each instance of applicability indication associated (e.g., within the applicability report) to one or more identifiers of one or more of the inference configurations and all of the instances of the applicability indication associated to a serving cell identifier (e.g., serving cell index) of a serving cell in which the UE received the inference configurations.

[0397] Embodiment 23: The method of any of embodiments 18 to 20, wherein the applicability information is received in a message, the message being either an RRC message (e.g., an RRC Reconfiguration Complete) or a UE Assistance Information message.

[0398] Embodiment 24: The method of embodiment 23, wherein: the message comprises a list of applicability reports which includes, for each of one or more mobility configurations (e.g., each of one or more lower layer mobility or LTM configurations), an instance of an applicability report; each instance of the applicability report comprises one or more identifiers (e.g., a list of one or more LTM CSI Report Configuration IDs) of one or more inference configurations to which the applicability report is associated; and each instance of the applicability report is associated to (e.g., comprises) a serving cell index (e.g., of a serving cell on which the UE received the respective inference configuration(s)).

[0399] Embodiment 25: The method of any of embodiments 18 to 24, wherein the inference configuration is a full inference configuration.

[0400] Embodiment 26: The method of any of embodiments 18 to 24, wherein the inference configuration is a partial inference configuration.

[0401] Embodiment 27: The method of any of embodiments 18 to 26, wherein transmitting (304; 404; 504; 604) the inference configuration comprises transmitting (304; 404; 504; 604) a firstmessage comprising a mobility related configuration and a reporting configuration for reporting one or more AI / ML model or functionality applicability status indications for one or more AI / ML models or functionalities associated to prediction of measurements on radio resources configured for mobility (e.g., lower layer mobility such as, e.g., LTM).

[0402] Embodiment 28: The method of embodiment 27, wherein receiving (306; 406; 506; 606) the applicability information for the inference configuration in the applicability report comprises receiving (306; 406; 506; 606), from the UE, a second message comprising one or more AI / ML model or functionality status indications for the one or more AI / ML model functionalities associated to the prediction of measurements on radio resources configured for mobility (e.g., in accordance with the received reporting configuration).

[0403] Embodiment 29: The method of any of embodiments 18 to 26, wherein receiving (306; 406; 506; 606) the applicability information for the inference configuration in the applicability report comprises receiving (306; 406; 506; 606), from the UE, a second message comprising one or more AI / ML model or functionality status indications for one or more AI / ML model functionalities associated to the prediction of measurements on radio resources configured for mobility.

[0404] Embodiment 30: The method of embodiment 28 or 29, wherein the second message further comprises measurements and / or measurement predictions.

[0405] Embodiment 31 : The method of any of embodiments 28 to 30, wherein the second message is transmitted via L1 / L2 signaling or Uplink Control Information (UCI).

[0406] Embodiment 32: The method of any of embodiments 18 to 31, further comprising transmitting (410), to the UE (400), a mobility configuration (e.g., an LTM configuration) where AI / ML functionality or model is applicable.

[0407] Embodiment 33: The method of any of embodiments 18 to 31, further comprising receiving (608), from the UE (600), a message comprising additional information related to the applicability information (e.g., indication of reason(s) for inapplicability, indication of resources and / or functionalities required for AI / ML model or functionality applicability).

[0408] Embodiment 34: A method performed by a Central Unit, CU, (402B; 502B; 602B) of a network node (402; 502; 602), the method comprising: transmitting (404; 504; 604), to a User Equipment, UE, (400; 500; 600), an inference configuration for reporting measurement predictions for mobility; and receiving (408; 506; 610), from either the UE or a Distributed Unit, DU, (402A; 502A; 602A) of the network node, applicability information for the inference configuration.Embodiment 35: The method of embodiment 34, wherein receiving (408; 610) the applicability information for the inference configuration comprises receiving (408; 610) the applicability information from the DU of the network node.

[0409] Embodiment 36: The method of embodiment 35, further comprising sending (410), to the UE (400), a message comprising an LTM configuration where an AI / ML model or functionality is applicable.

[0410] Embodiment 37: The method of embodiment 35, further comprising receiving (608), from the UE (600), a message comprising additional information related to the applicability information for the inference configuration.

[0411] Embodiment 38: The method of embodiment 34, wherein receiving (506) the applicability information for the inference configuration comprises receiving (506) the applicability information from the UE in an applicability report.

[0412] Embodiment 39: The method of embodiment 38, further comprising sending (508), to a Distributed Unit, DU, (502A) of the network node (502), a message comprising the applicability information for the inference configuration.

[0413] Embodiment 40: A method performed by a Distributed Unit, DU, (402A; 502A; 602A) of a network node (402; 502; 602), the method comprising:

[0414] receiving (406; 508; 606), from either a User Equipment, UE, (400; 600) or a Central Unit, CU, (502B) of the network node, applicability information for an inference configuration of the UE for reporting measurement predictions for mobility.

[0415] Embodiment 41: The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0416] Group C Embodiments

[0417] Embodiment 42: A wireless device comprising: processing circuitry configured to perform any of the operations of any of the Group A embodiments; and a power source configured to supply power to the processing circuitry.

[0418] Embodiment 43: A network node comprising: processing circuitry configured to perform any of the operations of any of the Group B embodiments; and a power source circuitry configured to supply power to the processing circuitry.

[0419] Embodiment 44: A wireless device comprising: one or more antennas; communication interface connected to the one or more antennas and to processing circuitry; the processing circuitry being configured to perform any of the operations of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input ofinformation into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a power source connected to the processing circuitry and configured to supply power to the UE.REFERENCES

[0420] 1. RP -234039 - New WID on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface, Source: Qualcomm (Moderator), 3GPP TSG RAN Meeting #102, Edinburgh, Scotland, December 11-15, 2023

[0421] 2. R2-2406381 - Title: Report of [POST126]

[0032] [AI / ML PHY] LCM (Intel / Samsung)_Phase 2, Source: Intel Corporation, 3GPP TSG RAN WG2 Meeting #127, Maastricht, Netherlands, Aug 19th- 23rd, 2024

[0422] 3. RP -234055, Study on Artificial Intelligence (AI) / Machine Learning (ML) for mobility in NR, 3GPP TSG RAN Meeting #102, Edinburgh, GB, December 11-15, 2023

Claims

CLAIMS1. A method performed by a User Equipment, UE, (300; 400; 500; 600), the method comprising:receiving (304; 404; 504; 604), from a network node, an inference configuration for reporting measurement predictions for mobility; andreporting (306; 406; 506; 606), to the network node, applicability information for the inference configuration in an applicability report.

2. The method of claim 1, wherein the inference configuration is an inference configuration for reporting measurement predictions for lower layer mobility.

3. The method of claim 1, wherein the inference configuration is an L1 / L2 Triggered Mobility, LTM, inference configuration for reporting measurement predictions on LTM candidate cells.

4. The method of any of claims 1 to 3, wherein within the applicability report the applicability information is associated to an identifier of the inference configuration and / or an identifier of a serving cell configuration of a serving cell on which the UE received the inference configuration.

5. The method of any of claims 1 to 3, wherein:the UE is configured with one or more inference configurations, each associated to an LTM channel state information, CSI, reporting configuration identifier, ID, and all of them associated with a same serving cell configuration; andthe applicability report comprises one or more instances of applicability information, each instance of applicability indication associated within the applicability report to one or more identifiers of one or more of the inference configurations and all of the instances of the applicability indication associated to a serving cell in which the UE received the inference configurations.

6. The method of any of claims 1 to 3, wherein the applicability information is reported in a message, the message being either a radio resource control, RRC, Reconfiguration Complete message or a UE Assistance Information message.

7. The method of any of claims 1 to 3, wherein the applicability information is reported in a 5G or 6G message.

8. The method of claim 6 or 7, wherein:the message comprises a list of applicability reports which includes, for each of one or more mobility configurations, an instance of an applicability report;each instance of the applicability report comprises one or more identifiers of one or more inference configurations to which the applicability report is associated; andeach instance of the applicability report is associated to a serving cell on which the UE received the respective inference configuration(s).

9. The method of any of claims 1 to 8, wherein the inference configuration is a full inference configuration.

10. The method of any of claims 1 to 8, wherein the inference configuration is a partial inference configuration.

11. The method of any of claims 1 to 10, wherein receiving (304; 404; 504; 604) the inference configuration comprises receiving (304; 404; 504; 604) a first message comprising a mobility related configuration and a reporting configuration for reporting one or more Artificial Intelligence, Al, / Machine Learning, ML, model or functionality applicability status indications for one or more AI / ML models or functionalities associated to prediction of measurements on radio resources configured for mobility.

12. The method of claim 11, wherein reporting (306; 406; 506; 606) the applicability information for the inference configuration in the applicability report comprises transmitting (306; 406; 506; 606), to the network node, a second message comprising one or more AI / ML model or functionality status indications for the one or more AI / ML model functionalities associated to the prediction of measurements on radio resources configured for mobility in accordance with the received reporting configuration.

13. The method of claim 12, wherein the second message further comprises measurements and / or measurement predictions.

14. The method of any of claims 12 to 13, wherein the second message is transmitted via L1 / L2 signaling or Uplink Control Information (UCI).

15. The method of any of claims 1 to 14, further comprising receiving (410), from the network node (402), a mobility configuration where AI / ML functionality or model is applicable.

16. The method of any of claims 1 to 14, further comprising transmitting (608), to the network node (602), a message comprising additional information related to the applicability information, the additional information comprising an indication of reason(s) for inapplicability and / or indication of resources and / or functionalities required for AI / ML model or functionality applicability.

17. A User Equipment, UE, (300; 400; 500; 600; 900), comprising:a communication interface (912) comprising a transmitter (918) and a receiver (920); and processing circuitry (902) associated with the communication interface (912), the processing circuitry (902) configured to cause the UE to:receive (304; 404; 504; 604), from a network node, an inference configuration for reporting measurement predictions for mobility; andreport (306; 406; 506; 606), to the network node, applicability information for the inference configuration in an applicability report.

18. The UE of claim 17, wherein the processing circuitry is further configured to cause the UE to perform the method of any of claims 2 to 16.

19. A method performed by a network node (302; 402; 502; 602), the method comprising: transmitting (304; 404; 504; 604), to a User Equipment, UE, (300; 400; 500; 600), an inference configuration for reporting measurement predictions for mobility; andreceiving (306; 406; 506; 606), from the UE, applicability information for the inference configuration in an applicability report.

20. The method of claim 19, wherein the inference configuration is an inference configuration for reporting measurement predictions for lower layer mobility.

21. The method of claim 19, wherein the inference configuration is an L1 / L2 Triggered Mobility, LTM, inference configuration for reporting measurement predictions on LTM candidate cells.

22. The method of any of claims 19 to 21, wherein within the applicability report the applicability information is associated to an identifier of the inference configuration and / or an identifier of a serving cell configuration of a serving cell on which the UE received the inference configuration.

23. The method of any of claims 19 to 21, wherein:the UE is configured with one or more inference configurations, each associated to an LTM CSI reporting configuration ID and all of them associated with a same serving cell configuration; andthe applicability report comprises one or more instances of applicability information, each instance of applicability indication associated within the applicability report to one or more identifiers of one or more of the inference configurations and all of the instances of the applicability indication associated to a serving cell in which the UE received the inference configurations.

24. The method of any of claims 19 to 21, wherein the applicability information is received in a message, the message being either a radio resource control, RRC, Reconfiguration Complete message or a UE Assistance Information message.

25. The method of any of claims 19 to 21, wherein the applicability information is received in a 5G or 6G message.

26. The method of claim 24 or 25, wherein:the message comprises a list of applicability reports which includes, for each of one or more mobility configurations, an instance of an applicability report;each instance of the applicability report comprises one or more identifiers of one or more inference configurations to which the applicability report is associated; andeach instance of the applicability report is associated to a serving cell on which the UE received the respective inference configuration(s).

27. The method of any of claims 19 to 26, wherein the inference configuration is a full inference configuration.

28. The method of any of claims 19 to 26, wherein the inference configuration is a partialinference configuration.

29. The method of any of claims 19 to 28, wherein transmitting (304; 404; 504; 604) the inference configuration comprises transmitting (304; 404; 504; 604) a first message comprising a mobility related configuration and a reporting configuration for reporting one or more AI / ML model or functionality applicability status indications for one or more AI / ML models or functionalities associated to prediction of measurements on radio resources configured for mobility.

30. The method of claim 29, wherein receiving (306; 406; 506; 606) the applicability information for the inference configuration in the applicability report comprises receiving (306; 406; 506; 606), from the UE, a second message comprising one or more AI / ML model or functionality status indications for the one or more AI / ML model functionalities associated to the prediction of measurements on radio resources configured for mobility in accordance with the received reporting configuration.

31. The method of claim 30, wherein the second message further comprises measurements and / or measurement predictions.

32. The method of any of claims 30 to 31, wherein the second message is transmitted via L1 / L2 signaling or Uplink Control Information (UCI).

33. The method of any of claims 19 to 32, further comprising transmitting (410), to the UE (400), a mobility configuration where AI / ML functionality or model is applicable.

34. The method of any of claims 19 to 32, further comprising receiving (608), from the UE (600), a message comprising additional information related to the applicability information (e.g., indication of reason(s) for inapplicability, indication of resources and / or functionalities required for AI / ML model or functionality applicability.

35. A network node (302; 402; 502; 602; 1000), comprising processing circuitry (1002) configured to cause the network node to:transmit (304; 404; 504; 604), to a User Equipment, UE, (300; 400; 500; 600), an inference configuration for reporting measurement predictions for mobility; andreceive (306; 406; 506; 606), from the UE, applicability information for the inference configuration in an applicability report.

36. The network node of claim 35, wherein the processing circuitry is further configured to cause the network node to37. A method performed by a Central Unit, CU, (402B; 502B; 602B) of a network node (402; 502; 602), the method comprising:transmitting (404; 504; 604), to a User Equipment, UE, (400; 500; 600), an inference configuration for reporting measurement predictions for mobility; andreceiving (408; 506; 610), from either the UE or a Distributed Unit, DU, (402A; 502A; 602A) of the network node, applicability information for the inference configuration.

38. The method of claim 37, wherein receiving (408; 610) the applicability information for the inference configuration comprises receiving (408; 610) the applicability information from the DU of the network node.

39. The method of claim 38, further comprising sending (410), to the UE (400), a message comprising an LTM configuration where an AI / ML model or functionality is applicable.

40. The method of claim 38, further comprising receiving (608), from the UE (600), a message comprising additional information related to the applicability information for the inference configuration.

41. The method of claim 37, wherein receiving (506) the applicability information for the inference configuration comprises receiving (506) the applicability information from the UE in an applicability report.

42. The method of claim 41, further comprising sending (508), to a Distributed Unit, DU, (502A) of the network node (502), a message comprising the applicability information for the inference configuration.

43. A method performed by a Distributed Unit, DU, (402A; 502A; 602A) of a network node (402; 502; 602), the method comprising:receiving (406; 508; 606), from either a User Equipment, UE, (400; 600) or a Central Unit, CU, (502B) of the network node, applicability information for an inference configuration of the UE for reporting measurement predictions for mobility.

Images