Applicability reporting for connected mobility

Abstract

Systems and methods are disclosed herein that are related to Artificial Intelligence (AI) / Machine Learning (ML) functionality reporting for connected mode mobility in a wireless network In one embodiment, a method performed by a User Equipment (UE) for configuring an AI / ML functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell to a second cell which becomes a target cell in the connected mode mobility procedure, comprises receiving a connected mode mobility command including an AI / ML functionality configuration for the second cell (target cell), while connected to the first cell. The method further comprises applying the connected mode mobility command comprising the AI / ML functionality configuration for the second cell and accessing the second cell, wherein the second cell is a neighbor cell.

Description

[0001] APPLICABILITY REPORTING FOR CONNECTED MOBILITY

[0002] RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 697,823, filed September 23, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.

[0004] TECHNICAL FIELD

[0005] The present disclosure relates toa a mobile communications system and, more specifically, to reporting of the applicability of an Artificial Intelligence (Al) or Machine Learning (ML) model or functionality in a mobile communications system.

[0006] BACKGROUND

[0007] 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 (i.e., the Physical layer (PHY)) 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 (Rel-) 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 Rel-18 is now considered in the context of Rel-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 Rel-19, a new study item addressing AI / ML 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, 3 GPP TSG RAN Meeting #102, Edinburgh, GB, December 11-15, 2023).

[0009] Applicability reporting has been discussed during the Rel-18 study item to allow the UE to inform the NR base station (i.e., the gNodeB, gNB) about the applicability of an AI / ML model or 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)).

[0010] Two types of applicability reporting were identified during the Rel-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.

[0011] Figure 1 illustrates an example of the proactive approach (i.e., proactive reporting of applicability). As illustrated in Figure 1, in the proactive approach, the network enquires the UE capabilities and configures the UE to report the applicability of an AI / ML functionality and, based on the reported information the network configures the UE with an inference configuration. The steps of the process of Figure 1 are as follows:

[0012] • Step 1: Network sends UECapabilityEnqiry message to initiate the procedure to a UE reporting its AI / ML supported functionalities.

[0013] • Step 2: The UE sends UECapablity Information message to network, containing supported functionalities at the UE side.

[0014] • Step 3: The network configures the UE such that it is allowed to provide its applicable functionalities.

[0015] • Step 4: The UE sends applicable functionalities to the network upon change of applicable functi onality / condi ti on .

[0016] • Step 5: The network sends inference configuration for the applicable functionalities to the UE. • Step 6: Start inference / monitoring based on network / UE activation / deactivation.

[0017] Figure 2 illustrates an example of the reactive approach (i.e., reactive reporting of applicability). As illustrated in Figure 2, in 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. The steps of the procedure are as follows:

[0018] • Step 1: Network sends UECapabilityEnquiry message to initiate the procedure to a UE reporting its AI / ML supported functionalities.

[0019] • Step 2: The UE sends UECapablity Information message to the network, containing supported functionalities at the UE side.

[0020] • Step 3: The network provides network configurations and initiates the UE to report its applicable functionalities.

[0021] • Step 4: The UE sends applicable functionalities to the network.

[0022] • Step 5: The network sends updated inference configuration for applicable functionalities reported in Step 4 to the UE.

[0023] • Step 6: Start inference / monitoring based on network / UE activation / deactivation.

[0024] SUMMARY

[0025] Systems and methods are disclosed herein that are related to Artificial Intelligence (Al) / Machine Learning (ML) functionality reporting for connected mode mobility in a wireless network. In one embodiment, a method performed by a User Equipment (UE) for configuring an AI / ML functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell to a second cell which becomes a target cell in the connected mode mobility procedure, comprises receiving a connected mode mobility command including an AI / ML functionality configuration for the second cell (target cell), while connected to the first cell. The method further comprises applying the connected mode mobility command comprising the AI / ML functionality configuration for the second cell and accessing the second cell, wherein the second cell is a neighbor cell. In this manner, the UE receives the AI / ML functionality configuration for the second cell in the connected mode mobility command to either be able to operate in the second cell without the need of one or two reconfiguration procedures after the mobility procedure (which would be required to configure the report of the applicability of a configured AI / ML functionality) or to configure the inference for the AI / ML functionality. In one embodiment, the method further comprises performing an AI / ML functionality in the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

[0026] In one embodiment, the method further comprises, after the connected mode mobility procedure, transmitting, to the second cell, an applicability indication for an AI / ML functionality of the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command. _In one embodiment, the applicability indication for an AI / ML functionality of the second cell is transmitted within a UE assistance information message to the second cell or within a HO complete message (e.g., RRC Reconfiguration Complete generated in the HO procedure).

[0027] In one embodiment, the method further comprises, prior to receiving the connected mode mobility command comprising the AI / ML functionality configuration for the second cell, transmitting, to the first cell, an applicability indication for an AI / ML functionality of the second cell.

[0028] In one embodiment, the method further comprises transmitting an applicability indication for an AI / ML functionality of the second cell during and / or in preparation for the connected mobility procedure.

[0029] In one embodiment, the AI / ML functionality configuration for the second cell comprises an inference configuration enabling the UE to operate in the second cell after the connected mode mobility procedure.

[0030] In one embodiment, the AI / ML functionality configuration for the second cell comprises an applicability reporting configuration enabling the UE to report, to the second cell, an applicability indication for an AI / ML functionality of the second cell, after the connected mode mobility procedure and / or as part of the connected mode mobility procedure.

[0031] In one embodiment, the method further comprises determining to activate an AI / ML functionality associated to the AI / ML functionality configuration for the second cell when that AI / ML functionality is determined by the UE to be applicable and / or based on one or more parameters within the connected mode mobility command and / or based on a further message received in the second cell after the connected mode mobility procedure.

[0032] In one embodiment, the UE determines the applicability indication for the AI / ML functionality of the second cell based on one or more UE-conditions and / or one or more network conditions. In one embodiment, the one or more UE-conditions and / or one or more network conditions and / one or more inference configuration(s) are included in an applicability reporting configuration transmitted in the connected mode mobility command. In one embodiment, an AI / ML functionality is determined to be applicable or not applicable for the second cell based on the AI / ML inference configuration included in the connected mode mobility command.

[0033] In one embodiment, an AI / ML functionality is determined to be applicable or not applicable for the second cell based on an AI / ML inference configuration received in a further reconfiguration message received while connected to the second cell after the connected mode mobility procedure.

[0034] In one embodiment, an inference configuration is applied if the AIML functionality is applicable given the received inference configuration.

[0035] Corresponding embodiments of a UE are also disclosed.

[0036] Embodiments of a method performed by a source network node are also disclosed. In one embodiment, a method performed by a source network node for configuring an AI / ML functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of the source network node to a second cell of a target network node which becomes a target cell in the connected mode mobility procedure, comprises transmitting, to the target network node, a connected mode mobility request message (e.g. Handover Request over XnAP) and receiving, in response from the target network node, a connected mode mobility response message (e.g. Handover Request Ack over XnAP) including a connected mode mobility command comprising an AI / ML functionality configuration for a second cell (target cell). The method further comprises, in response, transmitting, to a UE, the connected mode mobility command comprising the AI / ML functionality configuration for the second cell (target cell).

[0037] In one embodiment, the method further comprises, prior to transmitting the connected mode mobility request message (e.g., Handover Request over XnAP) to the target network node, receiving, from the UE, an applicability indication for an AI / ML functionality of the second cell and / or one or more UE capabilities associated to the AI / ML functionality configuration. In one embodiment, the method further comprises, prior to receiving from the UE the applicability indication for an AI / ML functionality of the second cell, transmitting, to the UE, an applicability reporting configuration configuring the UE to report the applicability indication for an AI / ML functionality of the second cell. In one embodiment, the method further comprises, prior to transmitting the applicability reporting configuration, obtaining the applicability reporting configuration from the target network node. In one embodiment, the applicability indication for an AI / ML functionality of the second cell is included in the connected mode mobility request message (e.g., Handover Request over XnAP) to the target network node.

[0038] Corresponding embodiments of a source network node are also disclosed. Embodiments of a method performed by a target network node are also disclosed. In one embodiment, a method performed by a target network node for configuring an AI / ML functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of a source network node to a second cell of the target network node which becomes a target cell in the connected mode mobility procedure, comprises receiving, from the source network node, a connected mode mobility request message (e.g. Handover Request over XnAP) and, in response, transmitting, to the source network node, a connected mode mobility response message (e.g. Handover Request Ack over XnAP) including a connected mode mobility command comprising an AI / ML functionality configuration for the second cell (target cell).

[0039] In one embodiment, the method further comprises receiving an applicability indication for an AI / ML functionality of the second cell in the connected mode mobility request message (e.g., Handover Request over XnAP) received from the source network node. In one embodiment, the method further comprises, prior to receiving the applicability indication for an AI / ML functionality of the second cell in the connected mode mobility request message (e.g., Handover Request over XnAP) from the source network node, providing to the source network node an applicability reporting configuration.

[0040] BRIEF DESCRIPTION OF THE DRAWINGS

[0041] 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.

[0042] Figure 1 illustrates an example of a proactive approach for reporting of applicability of AI / ML functionality in a wireless network.

[0043] Figure 2 illustrates an example of a reactive approach for reporting of applicability of AI / ML functionality in a wireless network.

[0044] Figure 3 illustrates a first example of a first solution for AI / ML functionality applicability reporting for connected mode mobility in a wireless network, in accordance with an embodiment of the present disclosure.

[0045] Figure 4 illustrates an example of a second solution for AI / ML functionality applicability reporting for connected mode mobility in a wireless network, in accordance with an embodiment of the present disclosure.

[0046] Figure 5 illustrates an example of a third solution for AI / ML functionality applicability reporting for connected mode mobility in a wireless network, in accordance with an embodiment of the present disclosure. Figure 6 illustrates a second example of the first solution for AI / ML functionality applicability reporting for connected mode mobility in a wireless network, in accordance with an embodiment of the present disclosure.

[0047] Figure 7 illustrates a third example of the first solution for AI / ML functionality applicability reporting for connected mode mobility in a wireless network, in accordance with an embodiment of the present disclosure.

[0048] Figure 8 shows an example of a communication system in accordance with some embodiments of the present disclosure.

[0049] Figure 9 shows a User Equipment device (UE) in accordance with some embodiments of the present disclosure.

[0050] Figure 10 shows a network node in accordance with some embodiments of the present disclosure.

[0051] Figure 11 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized in accordance with some embodiments of the present disclosure.

[0052] DETAILED DESCRIPTION

[0053] 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.

[0054] 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.

[0055] There currently exist certain challenge(s). The framework for applicability reporting has been described for a User Equipment (UE) connected to the current Primary Cell (PCell), i.e. in case the Artificial Intelligence (Al) and / or Machine Learning (ML) (i.e., AI / ML) functionality is associated to that PCell. However, when the UE needs to perform a mobility procedure, such as a handover (i.e. change of PCell), Primary Secondary Cell Group (SCG) Cell (PSCell) change, Layer 2 (L2) Triggered Mobility (LTM) Cell Switch, Conditional Handover (CHO) execution, Conditional PSCell change, or Conditional LTM (CLTM) execution, the UE would have to go through all these procedures again after it connects to the target cell, which would delay the process of activating an AI / ML functionality associated to the target cell. In other words, in the proactive approach, only after the mobility procedure in connected mode (e.g. a handover) when the UE is in the target cell, would the UE be configured to report the applicability of an AI / ML functionality, which would require a first reconfiguration loop (reception of an Radio Resource Control (RRC) Reconfiguration and transmission of an RRC Reconfiguration Complete) and, after the report, a second reconfiguration loop to receive the inference configuration. Even if the reactive approach is used in which the UE receives the inference configuration directly without having to report the applicability, another reconfiguration is required in the target cell after the handover.

[0056] This problem is related to the Work Item on AI / ML for the Physical layer (PHY) part of Rel-19 and 5thGeneration (5G) evolution. However, the same problem exists in the AI / ML for Mobility feature which may be specified in Rel-20 (ongoing Study Item), and possible specifications for 6thGeneration (6G), for Rel-10 and Rel-21 related to AI / ML.

[0057] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. In one general aspect of the present disclosure, embodiments of a method performed by a User Equipment (UE) are disclosed in which the UE receives a connected mode mobility command (e.g. HO command) while connected to a first cell (source cell) including an AI / ML functionality configuration (e.g. inference configuration) for the second cell i.e. for the target cell in the mobility procedure. The method also comprises the UE reporting an applicability indication for an AI / ML functionality during (or in preparation for) a mobility procedure in Connected mode. Different sets of embodiments are proposed.

[0058] A first solution (“Solution 1”) is described herein and is an enabler for a reactive approach in connected mode mobility. As illustrated in Figure 3, in a first example of the first solution, a UE is connected to a first cell (which is a serving cell e.g. a PCell or a PSCell) and is configured to report to a source network node (e.g. a source gNB which is serving the UE) an applicability indication for an AI / ML functionality of a second cell (Step 300), wherein the second cell is a neighbor cell in a target network node e.g. a neighbor cell which becomes a triggered cell in an event triggered measurement report (i.e. a cell fulfilling the entering condition of that event). The UE reports (i.e., sends) to the second network node (via the first network node, i.e., the source network node) an applicability indication for an AI / ML functionality of the second cell, e.g., in accordance with the configuration of Step 300 (Step 302). In one option, the UE determines the applicability indication for the AI / ML functionality of the second cell based on one or more UE- conditions and / or one or more network conditions and / or based on an inference configuration.

[0059] As part of the method, the source network node receives from the UE the applicability indication for an AI / ML functionality of the second cell in Step 302. Then, the source network transmits the applicability indication for an AI / ML functionality for the second cell to the target network node (e.g. in a Handover Request message) (Step 304) and, in response, the source network node receives a handover (HO) command which includes an AI / ML functionality configuration (e.g. inference configuration) for the second cell (e.g. in a Handover Request Ack message) (Step 306), wherein the second cell is the target cell configured in the HO command, enabling the UE to operate the AI / ML functionality in the second cell e.g. enabling the UE to produce inferences as output of an AI / ML model. Then, the source network node transmits the HO command including the AI / ML functionality configuration (e.g., inference configuration) for the second cell to the UE (Step 308).

[0060] The UE receives the HO command (e.g., RRC Reconfiguration with a reconfiguration with sync) including the AI / ML functionality configuration (e.g., inference configuration) for the second cell and applies the HO command with the AI / ML functionality configuration (e.g., inference configuration) for the second cell (Step 308). Then, the UE accesses the second cell (which is the target cell) and, having all necessary configuration to operate the AI / ML functionality (e.g. the inference configuration), the UE determines to activate and / or deactivate (or keep deactivated) the AI / ML functionality e.g. based on a parameter in the HO command, and / or based on the applicability of the AI / ML functionality (determined by the UE) or based on a further signaling received from the second cell (e.g. a MAC CE for activating and / or deactivating the AI / ML functionality) (Steps 310, 312, 314, 316, and 318). In this example, upon accessing or connecting to the second cell e.g. Step 310 (e.g. upon successfully completing the random access to the second cell or a Random Access Channel (RACH)-less procedure), the UE may activate the AI / ML functionality in case for example the AI / ML functionality configuration associated to the second cell included in the HO command of Step 308 makes the AI / ML functionality applicable. In other words, the UE accesses the second cell, e.g., via a random access procedure (Step 310) and sends a complete message (e.g., an RRC Reconfiguration Complete message) to the target RAN node on the second cell (Step 312). The UE is thus connected to the second cell (Step 314). The UE applies the AI / ML functionality configuration (e.g., inference configuration) for the second cell (Step 316) and reports time domain predictions (e.g., beam measurements and / or cell measurements and / or other information associated to the AI / ML functionality) on the second cell (Step 318).

[0061] Solution 1 may be considered as enabler for a reactive approach for connected mode mobility, assisted by UE reports to the source network node of applicability information for neighbor cells, to assist a target network node to configure an AI / ML functionality.

[0062] Further details and examples of Solution 1 are provided below. A second solution (“Solution 2”) is described herein and is an enabler for a proactive approach in connected mode mobility. As illustrated in Figure 4, in a first example of the second solution, a UE is connected to a first cell (which is a serving cell e.g. PCell, PSCell) receives a HO command (e.g. RRC Reconfiguration with a reconfiguration with sync) indicating a second cell as a target cell, the HO command including an AI / ML functionality configuration (Step 406). Based on that configuration, the UE transmits to the second cell (which is the target cell indicated in the HO command), after the HO, an applicability indication for an AI / ML functionality of the second cell (Steps 408, 410, and 412), such as in a UE assistance information message to the second cell, or in a HO complete message (e.g. RRC Reconfiguration Complete generated in the HO procedure). The UE may determine the applicability indication for the AI / ML functionality of the second cell based on one or more UE-conditions and / or one or more network conditions included as part of the AI / ML functionality configuration in Step 406. The AI / ML functionality configuration in Step 406 may further include one or more configurations for the transmission of the applicability indication.

[0063] After the report of the applicability indication to the target network node, after the HO, the UE may receive further AI / ML functionality configuration (e.g., inference configuration). Based on such received AI / ML functionality configuration, the UE may be able, in the second cell, to determine whether to activate the AI / ML functionality (Step 414). For example, after the UE connects to the second cell (target cell) and reports the applicability indication for an AI / ML functionality of the second cell the UE may receive a reconfiguration message from the target network node (e.g. RRC Reconfiguration) including further AI / ML functionality configuration e.g. inference configuration, for the operation of the AI / ML functionality in the second cell (Step 414) and may send a corresponding response (e.g., an RRC Reconfiguration Complete) to the target cell (Step 416).

[0064] After receiving that reconfiguration in Step 414 and hence having all necessary configuration to operate the AI / ML functionality (e.g. the inference configuration), the UE determines to activate and / or deactivate (or keep deactivated) the AI / ML functionality e.g. based on a parameter in the RRC Reconfiguration received from the target network node, and / or based on the applicability of the AI / ML functionality (determined by the UE after reception of the RRC Reconfiguration from the target network node) or based on a further signaling received in the second cell (e.g. a MAC CE for activating and / or deactivating the AI / ML functionality). Then, once the AI / ML functionality is activated, the UEs starts to operate accordingly, e.g. by reporting information associated to the AI / ML functionality such as time / spatial domain predictions of beam measurements and / or cell measurements and / or other info associated to the AI / ML functionality (Step 418).

[0065] Still according to the first example of solution 2 illustrated in Figure 4, the HO command is transmitted by a source network node in Step 406. Prior to that, the source network node transmits a Handover Request to a target network node (Step 402), so the target network node generates the HO command including the AI / ML functionality configuration for the UE to transmit to the second cell the applicability indication for an AI / ML functionality of the second cell. The target network node may determine to include the AI / ML functionality configuration based on the UE capabilities (e.g. obtained from the HO Request message from the source network node or applicability indication for an AI / ML functionality of the second cell obtained by the source network node based on applicability indication for an AI / ML functionality of the second cell transmitted by the UE to the source network node). Then, the target network node sends the HO command to the source network node (Step 404), wherein the HO command includes the AI / ML functionality configuration for the UE to transmit to the second cell the applicability indication for an AI / ML functionality of the second cell.

[0066] Solution 2 may be considered as the proactive approach for a connected mode mobility (handover) to a target cell since the UE requires an additional reconfiguration (Step 414) after it accesses the target cell to be able to start operating the AI / ML functionality in the second cell. One benefit is that the UE receives in the HO command the AI / ML configuration necessary for reporting the applicability to the target network node, i.e. with less signaling, since the HO command is used to configure the report to the target network node after the HO.

[0067] Further details of Solution 2 are provided below.

[0068] A third solution (“Solution 3”) is described herein and is an enabler for a reactive approach in connected mode mobility. As illustrated in Figure 5, in a first example of Solution 3, the UE is in connected mode (Step 500) and receives the HO command (e.g. RRC Reconfiguration with a reconfiguration with sync) including the AI / ML functionality configuration (e.g. inference configuration) for the second cell and applies the HO command with the AI / ML functionality configuration (e.g. inference configuration) for the second cell (Step 506). Then, the UE accesses the second cell (which is the target cell) and, having all necessary configuration to operate the AI / ML functionality (e.g. the inference configuration), the UE determines to activate and / or deactivate (or keep deactivated) the AI / ML functionality e.g. based on a parameter in the HO command, and / or based on the applicability of the AI / ML functionality (determined by the UE) or based on a further signaling received in the second cell (e.g. a Medium Access Control (MAC) Control Element (CE) for activating and / or deactivating the AI / ML functionality) (Steps 508, 510, 512, 514, and 516).

[0069] The difference here compared to Solution 1 is that the target network node, which generates the HO command including the AI / ML functionality configuration (e.g. inference configuration) for the second cell, does not necessarily receive an applicability indication for an AI / ML functionality of the second cell from the source network node (as in solution 1), but a HO request (Step 500) without such information on the applicability. Thus, the target network node either prepares the AI / ML functionality configuration (e.g. inference configuration) for the second cell based on the UE’s current configuration in the first cell (i.e. the source network node), which may include information about AI / ML functionality in the first cell and / or the second cell, or based on the UE capabilities, or based on previous assumption(s) about the same UE in the target network node.

[0070] Certain embodiments may provide one or more of the following technical advantage(s). The overall benefit is that the UE receives AI / ML functionality configuration in the HO command to either be able to operate in the target cell without the need of one or two reconfiguration procedures after the Handover (which would be required to configure the report of the applicability of a configured AI / ML functionality) or to configure the inference for the AI / ML functionality. That reduces the delay to operate an AI / ML functionality in a target cell during a handover, and reduces the signaling involved, which reduces the overhead over the Uplink in the air interface and minimizes the UE energy consumption.

[0071] For the specific solutions and options there are different benefits.

[0072] An advantage of Solution 1 is that the UE reporting to the source network node the applicability indication for an AI / ML functionality of a second cell is a way to enable the target network node to directly configure in the HO the inference configuration, sort of a reactive approach in a connected mode mobility.

[0073] An advantage of Solution 2, compared to Solution 1, is that in Solution 2 less information is required in the source network node about the target network node concerning the AI / ML functionality of the second cell.

[0074] An advantage of Solution 3 is that it works also for UEs not capable of reporting to the source network node a reported applicability indication for an AI / ML functionality of the second cell. The difference here compared to solution 1 is that the target network node, which generates the HO command including the AI / ML functionality configuration (e.g. inference configuration) for the second cell does not receive a reported applicability indication for an AI / ML functionality of the second cell, but a HO request (Step 500). Thus, the target network node either prepares the AI / ML functionality configuration (e.g. inference configuration) for the second cell based on the UE’s current configuration in the first cell (i.e. the source network node), which may include information about AI / ML functionality in the first cell and / or the second cell, or based on the UE capabilities, or based on previous assumption(s) about the same UE in the target network node.

[0075] The teachings of certain embodiments may improve, e.g., the performance of the RAN. Now, further details regarding embodiments of the present disclosure will be provided.

[0076] In one general aspect, embodiments of systems and methods are disclose in which a UE receives a HO command including an AI / ML functionality configuration (e.g. including an inference configuration and / or an applicability reporting configuration) for a second cell, where the second cell is a neighbor cell in a (e.g., operated by) a target network node (e.g., a neighbor cell which becomes a triggered cell in an event triggered measurement report), and applies the HO command with the AI / ML functionality configuration (e.g. inference configuration) for the second cell.

[0077] In the context of the present disclosure a “HO command” corresponds to a command and / or message received and / or applied by the UE to trigger a connected mode mobility, e.g. from a first cell to a second cell, or in more general terms from a first network entity to a second network entity. The HO command may equally be called a “mobility connected mode command”. The “HO command” may correspond, for example, to an RRC Reconfiguration message (e.g., RRCReconfiguration) including an Information Element (IE) reconfigurationWithSync, or an RRCConnectionReconfiguration including the IE MobilityControlInfo. The HO command includes one or more parameters and / or configurations based on which the UE accesses the second cell (which is the target cell).

[0078] In the context of the present disclosure, a “connected mode mobility procedure” is a procedure in which the UE in connected mode (e.g., RRC CONNECTED state) changes from one network entity to another, e.g. from a first cell to a second cell. A connected mode mobility procedure may correspond to a handover, a reconfiguration with sync, a PSCell change execution, a Conditional Handover (HO) execution, a Conditional PSCell Change (CPC) execution, a Conditional Reconfiguration execution, an LTM cell switch execution, a conditional LTM (CLTM) execution, or any steps prior to the actual execution, in preparation to these.

[0079] 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 described as the ability the UE has to produce an output of an inference function. For example, reporting of time-domain predict! on(s) of Synchronization Signal Block (SSB) and / or CSI Reference Signal (CSI-RS) measurement information (e.g. predicted Reference Signal Received Power (RSRP)) 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 Long Term Evolution (LTE) Positioning Protocol (LPP) signaling).

[0080] For example, “spatial domain prediction for beam management or mobility procedure e.g., handover or reconfiguration with sync, or Primary cell (PCell) change, or Primary Secondary Cell Group cell (PSCell) change” or a related functionality (e.g. reporting and inference of spatial domain info) 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 or cells (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 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), in the case of spatial domain predictions.

[0081] For example, “frequency domain prediction for beam management or mobility procedure e.g., handover” or a related functionality (e.g. reporting and inference of frequency domain info) 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 or cells (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 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 or one or more cells), in the case of frequency domain predictions.

[0082] For example, “time domain prediction for beam management or a mobility procedure e.g., handover or reconfiguration with sync, or Primary cell (PCell) change, or Primary Secondary Cell Group cell (PSCell) change” or a related functionality (e.g. reporting and inference of time domain info) 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 (e.g. predicted L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell in future time instances or the L1 / L3 RSRP value of one or more cells in the future time instances) based on measurements performed on a set B of beams or 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 cells), in the case of time domain predictions. For example, beam management - DL Tx beam prediction for both UE-sided model and NW-sided model, including: Spatial-domain DL Transmitted (Tx) beam prediction for Set A of beams based on measurement results of Set B of beams (“BM-Casel”) and / or Temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams (“BM-Case2”)

[0083] For example, positioning accuracy enhancements, including:

[0084] • Direct AI / ML positioning, such as:

[0085] • UE-based positioning with UE-side model, direct AI / ML positioning

[0086] • UE-assisted / Location Management Function (LMF)-based positioning with LMF-side model, direct AI / ML positioning

[0087] • Next Generation Radio Access Network (NG-RAN) node assisted positioning with LMF-side model, direct AI / ML positioning

[0088] • AI / ML assisted positioning, such as:

[0089] • UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning

[0090] • NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning

[0091] For example, CSI compression e.g., considering extending the spatial / frequency compression to spatial / temporal / frequency compression, cell / site specific models, CSI compression plus prediction (compared to Rel-18 non-AI / ML based approach)

[0092] For example, “Radio Link Failure prediction of serving and / or neighbor cells” or a related functionality (e.g. reporting and inference of RLF prediction of serving and / or neighbor cell(s)) may be a supported functionality in which the UE may report that is capable of performing and reporting inference of an RLF in future time instances.

[0093] For example, “Handover Failure (HOF) prediction of a cell” or a related functionality (e.g., reporting and inference of HOF prediction of a cell) may be a supported functionality in which the UE may report that is capable of performing and reporting inference of an HOF in future time instances.

[0094] According to the present disclosure, the UE connected to a first cell (which is a serving cell e.g. PCell, PSCell) is configured to report to a source network node or to a target network node an applicability indication for an AI / ML functionality of a second cell (of that target network node), wherein the second cell is a neighbor cell in a target network node. Prior to that, 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”) from the source network node and / or from the target network node (e.g. a gNodeB) 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.

[0095] According to the present disclosure, the source network node may correspond to a source radio access network (RAN) node or function, such as a gNodeB, a Centralized Unit gNodeB (CU- gNodeB), a Distributed Unit gNodeB (DU-gNodeB), of a 6G RAN node. According to the present disclosure, the target network node may correspond to a target radio access network (RAN) node or function, such as a gNodeB, a Centralized Unit gNodeB (CU-gNodeB), a Distributed Unit gNodeB (DU-gNodeB), of a 6G RAN node.

[0096] According to the present disclosure, the UE may use one or more “applicability condition(s)” which represent a set of conditions for determining whether an AIML 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 related configuration (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 AIML 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).

[0097] An AI / ML functionality is not applicable (or non-applicable) when there is no “inference related configuration” received by the UE (provided by the target network node and / or the source network node) for which the AI / ML model / functionality (the supported functionality) is applicable. As stated earlier an “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 could be said that the “inference related configuration” includes at least one radio measurement configuration and inference configuration.

[0098] According to one embodiment of the method, 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.

[0099] According to one embodiment of the method, 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:

[0100] Set A and / or Set B

[0101] - Mapping relationship of Set A and Set B, including ordering to (a set of ID, or resource)

[0102] - Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B.

[0103] - Quasi Co-Located (QCL) assumption

[0104] - The order of model input and model output between RS and Tx beams can be pre-defined.

[0105] - Transmission power

[0106] - UE distribution

[0107] - antenna height and / or other antenna properties

[0108] - Deployment scenarios (e.g., ISD, Umi / Uma)

[0109] - 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.

[0110] 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.

[0111] Each NW-side additional condition may be identified by an associated ID.

[0112] It may also be said that for an AI / ML- functionality (or AI / ML-enabled feature / FG), 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

[0113] 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.

[0114] In the context of the present disclosure, an AI / ML functionality configuration for a second cell, which is a neighbor cell and / or a target cell in a connected mode mobility procedure, 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 in the second cell, such as, for example, 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 the second cell, 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 target network node, while connected to the second cell (after the connected mode mobility procedure).

[0115] In the context of the present disclosure, an inference configuration or an inference related configuration may correspond to a Channel State information (CSI) measurement configuration (e.g. in an IE CSLMeasConfig, CSLReportConfig, CSLResourceConfig) associated to a set A and or set B of beams for a beam management AI / ML functionality. The inference configuration may further include one or more of

[0116] Synchronization Signal Block (SSB) identifiers associated to a serving cell and / or a neighbor cell;

[0117] - CSLRS resource identifiers associated to a serving cell and / or a neighbor cell;

[0118] - Beam identifiers associated to a serving cell and / or a neighbor cell;

[0119] - Mobility Reference Signal(s) identifiers associated to a serving cell and / or a neighbor cell;

[0120] - Candidate inference configuration(s) Set A and / or B (1); Set A and / or B (2); Set A and / or B (3), etc.

[0121] In the context of the present disclosure, an inference configuration or an inference related configuration may correspond to a mobility prediction configuration or Radio resource management (RRM) measurement configuration associated to a mobility procedure to run the inference and report the predictions. That may include one or more of

[0122] - Measurement configurations (e.g., measConfig) including one or more of o a list of one or more measurement objects to add / modify / remove by the UE which may further includes

[0123] ■ SSB frequency

[0124] ■ CSI-RS frequency

[0125] ■ SSB subcarrier spacing

[0126] ■ measurement timing configuration

[0127] ■ SSB and or CSI-RS beam consolidation configuration / threshold

[0128] ■ Number of SSB or CSI-RS measurements to average o a list of one or more report configuration to add / modify / remove by the UE o SpCell RSRP measurement controlling when the UE is required to perform measurements on non-serving cells o Measurement gap configuration e.g., a measurement gap ID, identifying the measurement gap ID per FR. o Measurement quantity configuration

[0129] In the context of the present disclosure, an inference configuration or an inference related configuration may include a first set (set A) of measurement resources (e.g. beams, SSB indexes and / or CSI-RS 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 CSI-RS 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:

[0130] • Mapping relationship of Set A and Set B, including ordering to (a set of IDs, or resources)

[0131] • Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B.

[0132] • QCL assumption

[0133] • The order of model input and model output.

[0134] • between RS and Tx beams can be pre-defined.

[0135] • Transmission power

[0136] • UE distribution

[0137] • antenna height

[0138] • Deployment scenarios (e.g., ISD, Umi / Uma / rural / indoor / indoor office / indoor factory, specific area(s)) • UE speed

[0139] 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.

[0140] In the context of the present disclosure, the AI / ML functionality configuration may include one or more of the following:

[0141] - An inference configuration for a beam management functionality (e.g., time-domain prediction of beam information) o In one example, the inference configuration includes the reporting configuration including parameters indicating how the UE is to report time-domain predictions of beam information (e.g., beam indexes and / or SSB indexes and / or time-domain prediction of beam measurements) and / or spatial-domain predictions of beam information. o In one example, the inference configuration includes the resource configuration for resources (e.g. SSB indexes and / or CSI-RS resources) which the UE measures and provides as input to an AI / ML model (or inference function) to produce inference outputs e.g. the actual time-domain predictions of beam information (e.g. beam indexes and / or SSB indexes and / or time-domain prediction of beam measurements) and / or spatial-domain predictions of beam information to be included in a report.

[0142] An inference configuration for a L3 Mobility functionality o In one example, the inference configuration includes the reporting configuration including parameters indicating how the UE is to report time-domain predictions for neighbor cell(s) which are candidates for a connected mode inter-cell mobility procedure, or for serving cells. The time-domain predictions may be cell identifiers, time-domain predictions of measurements, such as predicted RSRP, predicted RSRQ, as predicted SINR and / or spatial-domain predictions of cell(s). o In one example, the inference configuration includes the configuration for the UE to predict the occurrence of a Radio Link failure (RLF) in the second cell. o In one example, the inference configuration includes the configuration for the UE to predict the future occurrence of a Handover failure (HOF) when the UE is in second cell and later would move to yet another cell. o In one example, the inference configuration includes the configuration for the UE to predict the future occurrence of the fulfillment of a measurement reporting event such as an event Al, A2, A3, A4, A5, A6, Bl, B2, etc. An inference configuration for positioning functionality

[0143] - An inference configuration for CSI reporting functionality

[0144] - A configurating enabling the UE to determine whether the AI / ML functionality is applicable or not. o In one option the UE has reported whether the AI / ML functionality of the second cell (which is the target cell) was applicable or not. However, as that may have changed from the time the UE has transmitted the report (until the time in which the UE is to access the second cell in the handover), the UE. . .

[0145] - Network conditions such as Set A / set B configuration(s).

[0146] - A state indication for the AI / ML functionality, e.g., ‘activated’, ‘inactivated’, ‘deactivated’.

[0147] - An indication on whether the UE is allowed to consider the AI / ML functionality as ‘activated’ when the functionality is determined by the UE to be applicable.

[0148] The AI / ML functionality configuration may include also an identifier of the AIML functionality to which the configuration (e.g. inference configuration) is referred to, wherein the AIML functionality could be for example, beam management functionality, spatial beam management functionality, temporal beam management functionality, L3 mobility functionality, positioning functionality, CSI compression functionality, CSI prediction functionality, etc.

[0149] In the context of the present disclosure, an AI / ML functionality configuration for a second cell, which is a neighbor cell and / or a target cell in a connected mode mobility procedure, 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 for the second cell 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:

[0150] - 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.

[0151] - An indication of the AIML functionality for which the UE should transmit the applicability reporting to the second cell, e.g. indications of the applicability associated with the indicated AIML functionality. 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: o Configuration(s) related to the Mapping relationship of Set A and Set B, including ordering to (a set of IDs, or resources) o Configuration(s) related to the consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B. In that context, consistency may correspond to one or more of:

[0152] ■ 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

[0153] ■ 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

[0154] ■ 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 o Configuration(s) related to the Quasi-Co-Location (QCL) assumption(s) o Beam configuration(s) of the network such as:

[0155] ■ 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.

[0156] ■ Set A / Set B related info, e.g., the beam index of set B.

[0157] ■ 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. o Configuration(s) related to the order of model input and model output between RS and Tx beams can be pre-defined. o Configuration(s) related to the transmission power and / or power levels the gNodeB and / or the serving cells are operating o Configuration(s) related to the UE distribution o Configuration(s) related to Antenna height o Configuration(s) related to the deployment scenarios (e.g., ISD, Umi / Uma / rural / indoor / indoor office / indoor factory, specific area(s)) o Configuration(s) related to UE speed

[0158] - 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.

[0159] - 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).

[0160] An inference configuration.

[0161] Further details and examples for Solution 1 will now be described.

[0162] As discussed above, Solution 1 is an enabler for a reactive approach in connected mode mobility. In this regard, Figures 6 and 7 illustrate further example embodiments of a procedure in accordance with Solution 1. Figure 6 illustrates an example of Solution 1 in which an RRC Measurement Report the UE transmits includes an applicability indication of an AI / ML functionality associated to a second cell. Figure 7 illustrates an example of Solution 1 in which a UE Assistance Information the UE transmits includes an applicability indication of an AI / ML functionality associated to a second cell. The steps of the procedures of Figures 6 and 7 are as follows.

[0163] Step 600 (Figure 6) and Step 700 (Figure 7): A UE is connected to a first cell, which is a serving cell of the UE (e.g., a PCell or PSCell). The first cell is in (e.g., operated by) a source network node (e.g., source base station such as, e.g., a source gNB). Step 602a (Figure 6) and 702a (Figure 7) (Optional): The UE receives, from the source network node (e.g., on the first cell), a configuration including an indication for the UE to include an applicability indication for an AI / ML functionality of a second cell, where the second cell is a neighbor cell in (e.g., operated by) a target network node. In the illustrated example, this indication is included in a measurement configuration contained in an RRC reconfiguration message. The neighbor cell may be, for example, a neighbor cell which becomes a triggered cell in an event triggered measurement report (i.e., a cell fulfilling an entering condition of that event).

[0164] Step 602b (Figure 6) and 702b (Figure 7): The UE transmits, to the source network node (e.g., to a source gNodeB), an applicability indication of an AI / ML functionality associated to the second cell (e.g., in accordance with the configuration of Step 602a or 702a). Figures 6 and 7 show two different variations, i.e., Step 602b and Step 702b.

[0165] Looking at the variation of Step 602b shown in Figure 6, in one set of embodiments, the UE transmits the applicability indication of an AI / ML functionality associated to the second cell (which is a neighbor cell), in a measurement report (e.g. an RRC Measurement Report, MAC CE measurement report) i.e. a message including measurements of the second cell such as, for example, Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), or Signal to Interference plus Noise Ratio (SINR) measurements.

[0166] When the UE performs the actions of Step 602b, 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 the first cell as described above with respect to Step 600, 700. 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 Primary Secondary Cell Group (SCG) Cell (PSCell), or the like.

[0167] In one embodiment, the measurement report containing the applicability indication is triggered at the UE by the fulfillment of an event, i.e. is configured as an event triggered measurement report. For example, the UE receives an Information Element (IE) ReportConfigNR indicating an Ax event (e.g. A3 event, A5 event, or the like) and, based on that, the UE monitors the Ax (e.g. A3) entering condition and the event is considered to be fulfilled when a measurement (e.g. RSRP, RSRQ, SINR) of a neighbor cell (in an SSB frequency indicated in the Measurement Object associated to the ReportConfigNR) is an offset better than a measurement of the PCell (e.g. RSRP, RSRQ, SINR), which in this case is the second cell. In this case, the second cell is also called a triggered cell, i.e. a neighboring cell fulfilling the entering condition and a cell to be included in the measurement report. One benefit of including the applicability indication of an AI / ML functionality associated to the second cell in an event triggered measurement report is that this would not need to be transmitted to the network until at least one neighbor cell is becoming a good candidate for a handover (or, in more general terms, a candidate for a mobility procedure in connected mode).

[0168] In one option, the UE includes the applicability indication of an AI / ML functionality associated to a second cell as long as the second cell is a triggered cell. In one option, the UE includes the applicability indication of an AI / ML functionality associated to a second cell in the report only for the first time the second cell is a triggered cell. In one option, the UE includes the applicability indication of an AI / ML functionality associated to the second cell in the report the first time the second cell is a triggered cell and whenever there is a change in the indication. For example, at time instance tO the second cell is a triggered cell, and the UE includes the applicability indication of an AI / ML functionality associated to a second cell indicating that the AI / ML functionality is ‘applicable’. Then, for the next KI measurement reports including the second cell as triggered cell, the UE does not include the applicability indication of an AI / ML functionality associated to a second cell as long as the AI / ML functionality is ‘applicable’. However, when the Kl+1 measurement report is transmitted, when the applicability of the AI / ML functionality associated to the second cell becomes ‘non-applicable’, the UE includes the applicability indication of an AI / ML functionality associated to a second cell in the Kl+1 measurement report.

[0169] In one option, the UE includes the applicability indication of an AI / ML functionality associated to a second cell in the report only for the first time the second cell is a triggered cell and such a triggered cell is selected to be included in the measurement report e.g. based on a sorting function in which the UE selects which of the triggered cells are to be included in the measurement report. In one sub-option the UE determines the applicability indication of an AI / ML functionality for the top ‘N’ triggered cell(s) according to a sorting quantity (e.g., with highest trigger quantity values), wherein ‘N’ is configured to be the maximum number of triggered cells to include in a measurement report. In one sub-option the UE determines the applicability indication of an AI / ML functionality for the top ‘N’ triggered cell(s) according to RSRP, RSRQ or SINR wherein ‘N’ is configured to be the maximum number of triggered cells to include in a measurement report.

[0170] In one embodiment, the measurement report (including the applicability indication for an AI / ML functionality of the second cell) is configured as a periodic measurement report. For example, the UE receives an Information Element (IE) ReportConfigNR indicating a periodicity. One benefit of including the applicability indication of an AI / ML functionality associated to a second cell in a periodic measurement report is that this would make the information available at the network more often, which is good for faster mobility decisions. In one option, the UE includes the applicability indication of an AI / ML functionality associated to a second cell in the report as long as the second cell is included in the report. In one option, the UE includes the applicability indication of an AI / ML functionality associated to a second cell in the report only for the first time the second cell is included. In one sub-option, the UE includes the applicability indication of an AI / ML functionality associated to a second cell in the report the first time the second cell is a triggered cell and whenever there is a change in the indication. In one sub-option, the UE includes the applicability indication of an AI / ML functionality associated to a second cell 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 a second cell 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. In one sub-option the UE determines the applicability indication of an AI / ML functionality for the top ‘N’ triggered cell(s) according to a sorting quantity (e.g., with highest sorting quantity values), wherein ‘N’ is configured to be the maximum number of triggered cells to include in a measurement report. In one sub-option the UE determines the applicability indication of an AI / ML functionality for the top ‘N’ triggered cell(s) according to RSRP, RSRQ or SINR wherein ‘N’ is configured to be the maximum number of triggered cells to include in a measurement report.

[0171] In one embodiment, the measurement report is configured as an aperiodic measurement report. In that case, it is the network which requests the UE to report the applicability indication together with the measurements.

[0172] In one embodiment, the measurement report is configured as a semi -persistent measurement report. In that case, it is the network which requests the UE to report the applicability indication together with the measurements, and once that is requested, the UE transmits the measurement reports periodically. Then, similar sub-options for the periodic measurement reports are also applicable for the semi-persistent measurement reports. In one sub-option the UE determines the applicability indication of an AI / ML functionality for one or more cell(s) indicated in the request for reporting the applicability indication together with the measurements.

[0173] In one embodiment, the UE receives one or more parameters controlling how the UE is to include the applicability indication of an AI / ML functionality associated to a second cell in a measurement report, e.g., in the configuration of Step 602a. The one or more parameters may correspond to 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, after or during the mobility procedure. These one or more parameters may be considered an AI / ML functionality configuration the UE receives and may be called an applicability reporting configuration. Above, there are further details and examples of an “AI / ML functionality configuration”, in particular the applicability reporting configuration. Here, further examples / options are presented.

[0174] - In one option, these one or more parameters may be included in a measurement configuration (e.g., IE MeasConfig) which the UE receives: o In one example, the one or more parameters are part of the reporting configuration. o In one example, the one or more parameters are part of the measurement object associated to the reporting configuration in which the measurement report is configured. o In one example, the one or more parameters are part of the measurement identifier associated to the reporting configuration and the measurement object in which the measurement report is configured.

[0175] - In one option, these one or more parameters are not included in a measurement configuration (e.g. IE MeasConfig), but a configuration identifier associated to the configuration of these one or more parameters is included in a measurement configuration e.g. in the reporting configuration, the measurement object, or in the measurement identifier associated to the reporting configuration and the measurement object in which the measurement report is configured.

[0176] - In one option, these one or more parameters comprise one or more NW-conditions, for the UE to check the consistency between the inference and the training data set.

[0177] - In one option, these one or more parameters comprise an identifier associated to a network configuration which is currently operating in the second cell, so that the UE is able to compare that with the configuration identifier associated to the training of the AI / ML model.

[0178] In one option, these one or more parameters comprise one or more of the following: o i) a field, IE, or parameter indicating that the UE shall report an applicability indication for the triggered cell(s), or a second cell indicated in the configuration. o ii) a field, IE, or parameter indicating the AI / ML functionality for which the UE shall report an applicability indication for a second cell.

[0179] ■ In one option, the UE assumes that the AI / ML functionality for which the UE shall report an applicability indication for a second cell is the same functionality the UE is currently configured for operating in a serving cell. o iii) one or more fields, IES, or parameters associated to the AI / ML functionality (e.g., inference configuration, set A / set B, network conditions, etc.) for which the UE shall report an applicability indication for a second cell. o iv) Any other configuration required for the UE to determine the applicability of an AI / ML functionality of a second cell. This may have been previously obtained by the source gNodeB, before a handover is triggered (and stored in a neighbor relation table); or, this may have been obtained by the source gNodeB during the HO procedure; or, this may be in memory in case the second cell is a cell of the source gNodeB i.e. source gNodeB and target gNodeB are the same gNodeB. o v) One or more indications of the second cell, or neighbor cells for which the UE shall report the applicability indication of an AI / ML functionality associated to a second cell, e.g. a list of neighbor cells. o vi) (reactive approach during mobility) one or more fields, IES, or parameters associated to the AI / ML functionality enabling the UE to report an applicability indication for a second cell, but also to start operating accordingly. o vii) (proactive approach during mobility) one or more fields, IEs, or parameters associated to the AI / ML functionality enabling the UE to report an applicability indication for a second cell, but not sufficient to operate in the second cell i.e. to operate in the second cell the UE would require further inference configuration in the HO command.

[0180] Looking at the variation of Step 702b shown in Figure 7, in another set of embodiments, the UE transmits the applicability indication of an AI / ML functionality associated to the second cell, which is a candidate cell for a handover, in a UE Assistance Information message (e.g., UEAssistancelnformation - UAI) to the source network node (e.g., source gNodeB), i.e. while the UE is connected to the first cell.. In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the second cell is transmitted when the UE determines the applicability indication of an AI / ML functionality associated to the second cell. In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the second cell is transmitted when the UE is configured (e.g., reception of an RRCReconfiguration) to report the applicability indication of an AI / ML functionality associated to a second cell. In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the second cell is transmitted when the UE has previously reported the applicability indication of an AI / ML functionality associated to the second indicating a value, e.g. ‘applicable’, and that value has changed e.g. to ‘not applicable’. In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the second cell is transmitted by the fulfillment of an event associated to a measurement report i.e. when an event triggered measurement report is triggered for the second cell, the UE transmits the measurement report and the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the second cell. In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the second cell is transmitted when a periodic measurement report is transmitted. In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to a second cell is transmitted when an aperiodic measurement report is transmitted. In one embodiment, the UE Assistance Information including the applicability indication of an AI / ML functionality associated to the second cell is transmitted when a semi-persistent measurement report is transmitted.

[0181] In one embodiment, the UE receives one or more parameters controlling how to include the applicability indication of an AI / ML functionality associated to a second cell in a UE Assistance Information message (from a source network node in Step 702a).

[0182] In one option, these one or more parameters may be included in a configuration which is received in an RRC Reconfiguration (e.g., otherConfig).

[0183] In one option, the UE receives the one or more parameters which point to a measurement configuration identifier (e.g. such as a measurement identifier, reporting configuration identifier, or measurement object identifier the UE is configured with). The benefits in creating this link between the second cell which is a neighbor cell, and the measurement configuration is that the UE would transit the applicability indication of an AI / ML functionality associated to a second cell in a UE Assistance Information only for cells which are candidates in a handover. In one example, the measurement object identifier indicates that the second cell is in the same Synchronization Signal Block (SSB) frequency as that measurement object. In one example, the measurement object identifier indicates that the second cell is a cell configured in a list in the indicated measurement object. In one example, the reporting configuration identifier or the measurement identifier indicates that the second cell is a triggered cell associated to an event which is fulfilled.

[0184] In one option, the one or more parameters controlling how to include the applicability indication of an AI / ML functionality associated to a second cell in a UE Assistance Information message are not included in a measurement configuration (e.g. IE MeasConfig), but a configuration identifier associated to the configuration of these one or more parameters is included in a measurement configuration e.g. in the reporting configuration, the measurement object, or in the measurement identifier associated to the reporting configuration and the measurement object in which the measurement report is configured. In one option, the one or more parameters controlling how to include the applicability indication of an AI / ML functionality associated to a second cell in a UE Assistance Information message comprise one or more of the following:

[0185] - i) a field, IE, or parameter indicating that the UE shall report an applicability indication for a neighbor cell, e.g. triggered cell(s), or a second cell indicated in the configuration.

[0186] - ii) a field, IE, or parameter indicating the AI / ML functionality for which the UE shall report an applicability indication for a second cell. o In one option, the UE assumes that the AI / ML functionality for which the UE shall report an applicability indication for a second cell is the same functionality the UE is currently configured for operating in a serving cell.

[0187] - iii) one or more fields, IES, or parameters associated to the AI / ML functionality (e.g., inference configuration, set A / set B, network conditions, etc.) for which the UE shall report an applicability indication for a second cell.

[0188] - iv) Any other configuration required for the UE to determine the applicability of an AI / ML functionality of a second cell. This may have been previously obtained by the source gNodeB, before a handover is triggered (and stored in a neighbor relation table); or, this may have been obtained by the source gNodeB during the HO procedure; or, this may be in memory in case the second cell is a cell of the source gNodeB i.e. source gNodeB and target gNodeB are the same gNodeB. v) One or more indications of the second cell, or neighbor cells for which the UE shall report the applicability indication of an AI / ML functionality associated to a second cell, e.g. a list of neighbor cells. vi) in another option, the UE determines which neighbor cells for which to include the applicability indication of an AI / ML functionality based on its understanding of which neighbor cells are in the same gNodeB.

[0189] - vi) (reactive approach during mobility) one or more fields, IEs, or parameters associated to the AI / ML functionality enabling the UE to report an applicability indication for a second cell, but also to start operating accordingly.

[0190] - vii) (proactive approach during mobility) one or more fields, IEs, or parameters associated to the AI / ML functionality enabling the UE to report an applicability indication for a second cell, but not sufficient to operate in the second cell i.e. to operate in the second cell the UE would require further inference configuration in the HO command.

[0191] - An indication indicating that the AI / ML functionality is ‘applicable’ o In one option, this indication corresponds to a field and / or IE and / or parameter; - An indication indicating that the AI / ML functionality is ‘not applicable’ (or ‘non- applicable’) o In one option, this indication corresponds to a field and / or IE and / or parameter; o In one option, this indication corresponds to the absence of a field and / or the absence of an IE and / or the absence of a parameter;

[0192] - An indication of an applicability status, which may take one or more values, such as ‘applicable’ or ‘not applicable’;

[0193] A recommended (or preferred) AI / ML functionality configuration for which the AI / ML functionality becomes applicable, i.e. the UE indicates that it may not be applicable for a configuration(x), but it may be applicable for a configuration^), wherein the applicability indication corresponds to an indication of configuration^) o In one option that is included only when the UE includes the indication indicating that the AI / ML functionality is ‘not applicable’; o In one option the recommended or preferred AI / ML functionality configuration is provided via an identifier associated to the configuration.

[0194] - An identifier associated to a one or more NW-side additional conditions associated to the AI / ML model, e.g. the identifier of the NW configuration(s) (settings) in which the AI / ML model was trained.

[0195] In a set of embodiments, before the UE transmits the applicability indication of an AI / ML functionality associated to the second cell, the UE determines whether the AI / ML functionality associated to a second cell is ‘applicable’ or ‘not applicable’. In one option, the UE is configured to include the applicability indication of an AI / ML functionality associated to a second cell in a measurement report (e.g. an RRC Measurement Report) or UE Assistance Information, and when the UE is configured, the UE determines whether the AI / ML functionality associated to a second cell is ‘applicable’ or ‘not applicable’.

[0196] - In one example, the UE first waits until a neighbor cell is a triggered cell (i.e., fulfills the entering condition of the event) and only then determines whether the AI / ML functionality associated to a second cell is applicable or not applicable. The advantage is that determining applicability may require UE processing power and / or battery consumption, so here the UE only performs the actions when it knows it needs to report.

[0197] - In one example, the UE does not wait for a neighbor cell to be a triggered cell and determines whether the AI / ML functionality associated to a second cell is applicable or not applicable when it receives the configuration for reporting. The advantage is that determining applicability may take some time, so such an approach would not delay the transmission of a measurement report because the UE needs to determine the applicability of the AI / ML functionality of the second cell.

[0198] In one example, the UE periodically determines whether the AI / ML functionality associated to a second cell is applicable or not applicable after it receives the configuration for reporting. The advantage is that the applicability is always checked, so when it is time to report, no further delay is added to it due to the applicability check.

[0199] In one embodiment, before the UE transmits the applicability indication associated to the AI / ML functionality of the second cell, the UE receives from the source network node (e.g., in Step 702a) a configuration with one or more parameters controlling how to include the applicability indication of an AI / ML functionality associated to the second cell.

[0200] - In one option, these one or more parameters are known to the source network node, when the second cell is a cell of the source network node.

[0201] - In one option, these one or more parameters are received from the target network node (of the second cell) in an earlier handover (e.g., in a first HO request ack message), so that these one or more parameters are stored in the source network node and known to the source network node when a handover needs to be triggered.

[0202] - In one option, these one or more parameters are received from the target network node of the second cell to the source network node during the setup of an XnAp interface, e.g. together with neighbor relations. These one or more parameters may be considered as a property of a neighbor cell in a neighbor relation stored in the source network node.

[0203] - In one option, these one or more parameters are received from the target network node in response to a first request from the source network node, sort of a pre-HO Request message. These one or more parameters could be limited to be needed only for the UE to report the applicability indication of an AI / ML functionality associated to a second cell, but the overall inference configuration to operate in the second cell (i.e., that would be required to be included in the HO command).

[0204] Step 604 (Figure 6) and Step 704 (Figure 7): The source network node transmits to the target network node the applicability indication of an AI / ML functionality associated to a second cell, reported by the UE.

[0205] In a set of embodiments, the source network node (e.g. source gNodeB, source CU- gNodeB, or source DU gNodeB) associated to the first cell to which the UE is connected (e.g. Special Cell for the UE) receives, from the UE (e.g. in an RRC Measurement Report, UE Assistance Information or in an RRC Reconfiguration Complete message), the applicability indication associated to an AI / ML functionality of the second cell (associated to the target network node), wherein the second cell is a neighbor cell of the first cell and the second cell is associated to a target network node (e.g. target gNodeB) (Step 702b).

[0206] Then, in Step 604 / 704, the source network node transmits, to the target network node (e.g. target gNodeB, target CU-gNodeB, or target DU-gNodeB), the applicability indication associated to the AI / ML functionality of the second cell, for example, in a Handover Request message over an XnAP interface, or in more generic terms, in a message requesting the target network node to configure the second cell as a target cell and / or as a candidate target cell for a connected mode mobility procedure e.g. a handover. The applicability indication associated to the AI / ML functionality of the second cell may be included together with one or more measurements the UE may have also reported to the source network node e.g. associated to the second cell like RSRP and / or RSRQ and / or SINR of the second cell and / or of beams (e.g. SSB indexes and / or CSLRS identifiers) of the second cell. One benefit of transmitting the applicability indication associated to the AI / ML functionality of the second cell is that it enables the target network node to understand the applicability situation for that UE for the AI / ML functionality in the second cell, which is a cell requested by the source network node to the target network node to be a target cell in a handover (reconfiguration with sync). Thanks to that the target network node (e.g., target gNodeB) is informed of the applicability indication associated to the AI / ML functionality of the second cell for an incoming UE and may determine to configure or not the AI / ML functionality in the HO command i.e., in the RRCReconfiguration with reconfiguration with sync to be provided to the UE to access the second cell.

[0207] - In one option, the applicability indication associated to the AI / ML functionality of the second cell may is included as part of a payload of the message from the source network node to the target network node, e.g. payload and / or IE of the HO request message.

[0208] - In one option, the applicability indication associated to the AI / ML functionality of the second cell may is included as an RRC container included in the message from the source network node to the target network node, e.g. inter-node RRC message.

[0209] In addition, together with the applicability information, the source network node may also include one or more UE capabilities associated to the AI / ML functionality of the second cell, so that the target network node becomes aware of the AI / ML functionalities for which it may configure the UE with, in the HO command.

[0210] Step 606 (Figure 6) and Step 706 (Figure 7): The target network node transmits a response to the source network node possibly including an AI / ML functionality configuration (e.g., inference configuration) associated to the second cell, which is accepted as a target cell for the connected mode mobility procedure, e.g. handover. In a set of embodiments, the target network node receives the applicability indication associated to the AI / ML functionality of the second cell (and possibly further information, as discussed above) in Step 606 / 706. In response, in Step 606 / 706, the target network node transmits to the source network node a response (e.g. HO Request Ack, over XnAP interface) which includes a HO command (e.g. an RRCReconfiguration including a reconfiguration with sync to be provided to the UE to access the second cell), wherein the HO command may include an AI / ML functionality configuration (e.g. inference configuration) associated to the second cell, wherein the second cell has been accepted as a target cell for the handover and the AI / ML functionality configuration is to be applied by the UE for the operation of the AI / ML functionality with the second cell after the handover. In one option, the response from the target network node corresponds to a Handover Request Ack message. In one option the HO command corresponds to an RRCReconfiguration including reconfiguration with sync (see other alternatives for the HO command described above).

[0211] In one embodiment, the response sent from the target network node to the source network node includes (e.g., in the HO command to be transmitted to the UE) one or more configurations for the AI / ML functionality of the second cell for the UE, such as an inference configuration for the AI / ML functionality, e.g., when the applicability indication associated to the AI / ML functionality of the second cell indicates to the target gNodeB that the AI / ML functionality is applicable. In this case, thanks to response, the UE receives in the HO command the inference configuration and, in case the AI / ML functionality is determined by the UE to be applicable, the UE has the opportunity to have the AI / ML functionality activated as soon as it is applying the HO command (i.e., the RRCReconfiguration including the reconfiguration with sync for the second cell) and activates the AI / ML functionality.

[0212] In another embodiment, the response does not include one or more configurations for the AI / ML functionality of the second cell for the UE, such as an inference configuration for the AI / ML functionality, e.g., when the applicability indication associated to the AI / ML functionality of the second cell indicates to the target gNodeB that the AI / ML functionality is NOT applicable, or when the target gNodeB does not want to configure the AI / ML functionality.

[0213] In step 606 / 706, the source network node receives the response which includes a HO command (e.g., an RRCReconfiguration including a reconfiguration with sync to be provided to the UE to access the second cell), wherein the HO command may include an AI / ML functionality configuration (e.g., inference configuration) associated to the second cell, which is accepted as a target cell for a handover, and to be applied by the UE for the operation of the AI / ML functionality with the second cell after the handover. Steps 608, 610, 612, and 614 (Figure 6) and Steps 708, 710, 712, and 714 (Figure 7): The UE receives, from the source network node, the HO command, possibly including an AI / ML functionality configuration (e.g. inference configuration) associated to the second cell, which is the target cell for the handover (Step 608 or 708).

[0214] In a set of embodiments, the UE receives the HO command (e.g., an RRC Reconfiguration including a reconfiguration with sync) including an AI / ML functionality configuration (e.g., inference configuration) associated to the second cell, which is the target cell for the handover. That AI / ML functionality configuration (e.g., inference configuration) associated to the second cell enables the UE to operate according to the AI / ML functionality configuration (e.g., inference configuration) and not just report the applicability for the AI / ML functionality, e.g. it includes an inference configuration.

[0215] The UE then applies the HO command, i.e. the RRC Reconfiguration message including the reconfiguration with sync, and in response, the UE accesses the second cell (which is the target cell) (Step 610 or 710). Then, the UE performs one or more actions in the second cell according to the AI / ML functionality configuration (e.g., inference configuration), such as the reporting or one or more output(s) of an inference function and / or AI / ML model (Steps 616 / 716 and 618 / 718).

[0216] In one embodiment, in response to applying the HO command, the UE accesses the second cell (which is the target cell indicated in the HO command) in Step 610 or 710 by either: (i) performing a random access procedure, including the steps of: transmitting a preamble to the second cell, receiving a random access response, and transmitting a msg3 including the HO command complete (e.g. RRC Reconfiguration Complete); or (ii) performing a RACH-less procedure in which the UE transmits a scheduling request to receive an uplink (UL) grant to transmit the HO complete message (e.g. RRC Reconfiguration Complete message) and / or the UE transmits the HO complete message using a pre-configured UL grant (e.g. also configured in the HO command).

[0217] In one embodiment, in response to applying the HO command including the AI / ML functionality configuration (e.g., including the inference configuration) for the second cell, which is the target cell in the HO, the UE determines whether the configured AI / ML functionality is applicable or not.

[0218] - In one option, when the UE is in the second cell, which is the target for the HO, the UE reports the applicability of the configured AI / ML functionality, e.g. applicable, nonapplication. In one option that is reported in a UE assistance information.

[0219] - In one option, when the UE is in the second cell, which is the target for the HO, the UE reports the applicability of the configured AI / ML functionality e.g. applicable, non- application, but only when the applicability has changed since the last time the UE has reported the applicability indication of the AI / ML functionality for the second cell while the UE was connected to the first cell.

[0220] In one option, in response to determining that the configured AI / ML functionality is applicable, the UE applies the inference configuration when that is included in the HO command (Step 616 or 716), i.e. the UE applies the HO command for the mobility execution, and the inference configuration included in the HO command e.g. the inference configuration may be part of the AI / ML functionality configuration. When the HO command includes more than one possible inference configurations, the UE applies one of the one or more inference configuration for which it is determined that the AI / ML functionality is ‘applicable’.

[0221] In one option, in response to determining that the configured AI / ML functionality is ‘not applicable’, the UE does not apply the inference configuration when that is included in the HO command, i.e. the UE applies the HO command for the mobility execution, but it does not apply the inference configuration included in the HO command. When the HO command includes more than one possible inference configurations, this method is applied when none of the inference configurations included therein are applicable.

[0222] The above options may further be such that the inference configuration is applied upon reception of the HO command, or upon successful execution of the concerned mobility operation (e.g., HO completion).

[0223] The above options may be further be such that the determination of whether the configured AI / ML functionality is applicable or not is performed upon reception of the HO command, or upon successful execution of the concerned mobility operation (e.g., HO completion).

[0224] In one embodiment, for the case in which the inference configurations are included as part of a conditional reconfiguration for a candidate target cell, the UE applies the inference configuration of the candidate target cell to which the conditional reconfiguration is executed.

[0225] In some embodiments, for the case in which the AI / ML functionality configuration is included as part of a conditional reconfiguration for a candidate target cell, the UE determines the applicability of the AIML functionality based on the AIML functionality configuration included in the conditional reconfiguration of the candidate target cell to which the conditional mobility is executed.

[0226] In one alternative to Step 608 / 708, instead of receiving the HO command and applying it upon reception, the UE receives and stores the HO command, as part of a Layer 1 / Layer 2 (L1 / L2) Triggered Mobility (LTM) configuration. Then, the UE applies the HO command (possibly including the AI / ML functionality configuration) for the second cell in response to the reception of a lower layer signaling (such as a MAC CE for an LTM cell switch) indicating the second cell as the target cell.

[0227] In one alternative to Step 608 / 708, instead of receiving the HO command and applying it, the UE first receives and stores the HO command, as part of a Conditional Reconfiguration configuration, e.g. Conditional Handover (CHO), which also includes an execution condition. Then, the UE applies the HO command (possibly including the AI / ML functionality configuration) for the second cell when the execution condition is fulfilled for the second cell and the UE selects the second cell as the cell to be the target cell.

[0228] In one alternative to Step 608 / 708, instead of receiving the HO command and applying it, the UE first receives and stores the HO command, as part of a Conditional LTM, which also includes an execution condition. Then, the UE applies the HO command (possibly including the AI / ML functionality configuration) for the second cell when the LTM execution condition is fulfilled for the second cell and the UE selects the second cell as the cell to be the target cell.

[0229] Step 616 (Figure 6) and Step 716 (Figure 7): After the UE has applied the configuration for the AI / ML functionality for the UE to operate in the second cell,

[0230] In a set of embodiments, after the UE has applied the configuration for the AI / ML functionality for the UE to operate in the second cell (e.g., including an inference configuration) after the HO or at reception of the HO command, the UE determines to activate and / or to deactivate the AI / ML functionality in the second cell.

[0231] In one embodiment, the UE receives the HO command from the source network node while in the first cell including the AI / ML functionality configuration (e.g. inference configuration) for the second cell, and the UE determines that the AI / ML functionality configuration is ‘applicable’ or ‘not applicable’ for the second cell and depending on that the UE determines to activate or not the AI / ML functionality in the second cell after the HO. In one option, the UE applies the HO command including the AI / ML functionality configuration (e.g., inference configuration) and determines that the AI / ML functionality configuration is ‘applicable’ and activates the AI / ML functionality for the second cell. As an outcome, the UE may report one or more outputs of an AI / ML inference function / model, e.g. time domain prediction(s) of beam measurement information or cell measurement information. In this example, the UE applies the inference configuration and activates the AI / ML functionality accordingly. In one option, the UE applies the HO command including the AI / ML functionality configuration (e.g., inference configuration) and determines that the AI / ML functionality configuration is ‘not applicable’ and deactivates the AI / ML functionality for the second cell (or does not activate). In one embodiment, the UE receives the HO command including the AI / ML functionality configuration (e.g. inference configuration), and the UE determines that the AI / ML functionality configuration is ‘applicable’ or ‘not applicable’ and, when a parameter is included, the UE determines to activate or not the AI / ML functionality depending on the applicability of the AI / ML functionality. That parameter in the HO command has been set by the target network node, for the second cell. In this example, the UE applies the inference configuration and activates the AI / ML functionality based on such parameter.

[0232] In one embodiment, the UE receives the HO command including the AI / ML functionality configuration (e.g. inference configuration), and the UE determines that the AI / ML functionality configuration is ‘applicable’ or ‘not applicable’ and considers the AI / ML functionality as deactivated, until the UE further receives an indication from the target network node to activate the AI / Mol functionality. In this example, the UE applies the inference configuration, but it does not activate the AI / ML functionality. In one option of that embodiment, after the UE determines whether the AI / ML functionality is applicable or not applicable the UE transmits the applicability indication of an AI / ML functionality associated to a second cell_to the second cell e.g. in a UE assistance information message, after the handover or in an HO complete message such as the RRCReconfigurationComplete transmitted to the gNB upon successfully completing the HO. For example, the applicability indication may comprise information on whether the AIML functionality is applicable or not, and / or whether the inference configuration provided by the gNB makes the AIML functionality applicable (which may imply that the inference configuration is applied by the UE), which of the one or more inference configurations provided by the gNB make the AIML functionality applicable (if more than one inference configurations are provided by the gNB) (which may imply that the inference configuration is applied by the UE). Then, in response to that, the UE may receive the message indicating that the AI / ML functionality is to be activated or deactivated, e.g. via DCI or MAC CE.

[0233] Further details and examples for Solution 2 will now be described.

[0234] In regard to Solution 2, consider Figure 4 as a reference for the different steps.

[0235] Step 400: The UE is connected to a first cell operated by a first network node, i.e., the source network node for the connected mode mobility procedure.

[0236] Step 402: The source network node transmits to the target network node a message to request a connected mode mobility procedure for a second cell of the target network node.

[0237] In a set of embodiments, a source network node transmits a message to a target network node for requesting a connected mode mobility procedure to a second cell of the target network node. For example, the source network node sends a Handover Request message over XnAP to the target network node. That may possibly include one or more UE capabilities for the incoming UE in the mobility procedure, wherein the one or more UE capabilities are associated to an AI / ML functionality.

[0238] The target network node receives the message from the source network node (e.g., the HO Request message) for configuring the second cell as a target cell.

[0239] Step 404: Based on the included one or more UE capabilities for the incoming UE associated to an AI / ML functionality in the request message (e.g. the HO request), the target network node generates a HO command to that UE for the second cell (i.e. indicating the second cell as the target cell) and determines to include an AI / ML functionality configuration (e.g. an applicability reporting configuration) to indicate the UE to report an applicability indication of the AI / ML functionality to the second cell.

[0240] In Step 404, the target network node sends a response including the HO command to the source network node, wherein the HO command includes the AI / ML functionality configuration for the UE to transmit to the second cell the applicability indication for an AI / ML functionality of the second cell. For example, the AI / ML functionality configuration may comprise the NW-side additional conditions and / or the inference configuration(s).

[0241] Step 406: The source network node receives the response from the target network node including the HO command and transmits the HO command to the UE

[0242] Steps 408, 410, and 412: The UE connected to a first cell (which is a serving cell e.g. PCell, PSCell) receives and applies the HO command (e.g. RRC Reconfiguration with a reconfiguration with sync) indicating a second cell as a target cell, the HO command including an AI / ML functionality configuration, wherein based on that configuration the UE transmits to the second cell (which is the target cell indicated in the HO command), after the HO or as part of the HO (.g. in an RRC Reconfiguration Complete message) an applicability indication for an AI / ML functionality of the second cell, such as in a UE assistance information message to the second cell, or in a HO complete message (e.g. RRC Reconfiguration Complete generated in the HO procedure). The applicability indication may indicate the one or more NW-side additional conditions (e.g. among the ones included the AIML functionality configuration) for which the AIML functionality is applicable, or for which the AIML functionality is not applicable, or the one or more inference configurations (e.g. among the ones included the AIML functionality configuration) for which the AIML functionality is applicable, or for which the AIML functionality is not applicable.

[0243] The above methods, wherein for the case in which the AIML functionality configuration is included as part of a conditional reconfiguration for a candidate target cell, the UE transmits / reports the applicability indication based on the AIML functionality configuration included in the conditional reconfiguration of the candidate target cell to which the conditional mobility is executed.

[0244] Step 414: After the report of the applicability indication to the target network node, the UE may receive further AI / ML functionality configuration to be able to in the second cell (e.g., the inference configuration) and to be able to activate the AI / ML functionality. For example, after the UE connects to the second cell (target cell) and reports the applicability indication for an AI / ML functionality of the second cell the UE may receive a reconfiguration message from the target network node (e.g. RRC Reconfiguration) including further AI / ML functionality configuration e.g. inference configuration for the operation of the AI / ML functionality in the second cell.

[0245] Step 416: The UE then applies the RRC Reconfiguration message received from the second cell, after the HO, sends an RRC Reconfiguration Complete to the second cell.

[0246] Step 418: Further, considering that the functionality is applicable, the UE performs one or more actions in the second cell according to the AI / ML functionality configuration (e.g. inference configuration), such as the reporting or one or more output(s) of an inference function and / or AI / ML model.

[0247] In one embodiment, in response to applying the RRC Reconfiguration received from the second cell after the HO, including the AI / ML functionality configuration (e.g., including the inference configuration) for the second cell, the UE determines whether the configured AI / ML functionality is applicable or not.

[0248] - In one option, when the UE is in the second cell the UE reports the applicability of the configured AI / ML functionality, e.g. applicable, non-application. In one option that is reported in a UE assistance information.

[0249] - In one option, when the UE is in the second cell, after the HO, the UE reports to the second cell the applicability of the configured AI / ML functionality e.g. applicable, nonapplication, but only when the applicability has changed since the last time the UE has reported the applicability indication of the AI / ML functionality for the second cell.

[0250] After receiving that reconfiguration in the second cell, and having all necessary configuration to operate the AI / ML functionality (e.g. the inference configuration), the UE determines to apply the inference configuration for the concerned AIML functionality and activate and / or deactivate (or keep deactivated) the AI / ML functionality e.g. based on a parameter in the RRC Reconfiguration received from the target network node, and / or based on the applicability of the AI / ML functionality (determined by the UE after reception of the RRC Reconfiguration from the target network node) or based on a further signaling received in the second cell (e.g. a MAC CE for activating and / or deactivating the AI / ML functionality). Then, once the AI / ML functionality is activated the UE starts to operate according, e.g. by reporting information associated to the AI / ML functionality such as time domain predictions of beam measurements and / or cell measurements and / or other info associated to the AI / ML functionality (Step 418).

[0251] In regard to Solution 3, further details will now be provided. Consider Figure 5 as a reference for the different steps.

[0252] Step 500: The UE is connected to a first cell operated by a first network node, i.e., the source network node for the connected mode mobility procedure.

[0253] Step 502: The source network node transmits to the target network node a message requesting a connected mode mobility procedure for a second cell of the target network node.

[0254] In a set of embodiments, a source network node transmits a message to a target network node for requesting a connected mode mobility procedure to a second cell of the target network node. For example, the source network node sends a Handover Request message over XnAP to the target network node. That may possibly include one or more UE capabilities for the incoming UE in the mobility procedure, wherein the one or more UE capabilities are associated to an AI / ML functionality.

[0255] The message (e.g., HO request) includes the UE’s current configuration which may include the configuration of an AI / ML functionality (e.g., possibly including an inference configuration) and / or one or more UE capabilities indicating that the UE is capable of an AI / ML functionality and / or applicability indication for the first cell.

[0256] The target network node receives the message from the source network node (e.g., the HO Request message) for configuring the second cell as a target cell.

[0257] Step 504: The target network node receives the connected mode mobility request message (e.g. HO request) from the source network node and responds with a response message including a HO command which includes an AI / ML configuration configuring (e.g. inference configuration) the UE to operate according to the AI / ML functionality in the second cell, after the HO execution.

[0258] The target network node, in response to the request message from the source network node, transmits a response to the source network node including a HO command.

[0259] The target network node may opportunistically try to include in the HO command the AI / ML functionality configuration (e.g. including an inference configuration enabling the UE to operate in the second cell according to the AI / ML functionality), assuming that the AI / ML functionality may be applicable; that may be based on the included one or more UE capabilities for the incoming UE associated to an AI / ML functionality in the request message (e.g. the HO request) and based on the UE’s current configuration, indicating an inference configuration (and possibly its applicability for the first cell).

[0260] The reason for including the AI / ML functionality opportunistically is that when the UE determines that to not be applicable, the UE would simply consider the functionality deactivated and indicate that to the network in an applicability indication anyways after the handover, e.g. in a UE assistance information and / or in the HO complete message (RRC Reconfiguration Complete).

[0261] Steps 506-516: In step 506, the UE receives from the source network node the connected mode mobility command (HO command) including the AI / ML configuration configuring (e.g., inference configuration) the UE to operate according to the AI / ML functionality in the second cell, after the HO execution.

[0262] In step 506, the UE receives the HO command (e.g., an RRC Reconfiguration including a reconfiguration with sync) including an AI / ML functionality configuration (e.g., inference configuration) associated to the second cell, which is the target cell for the handover. That AI / ML functionality configuration (e.g., inference configuration) associated to the second cell enables the UE to operate according to the AI / ML functionality configuration (e.g., inference configuration) and not just report the applicability for the AI / ML functionality, e.g. it includes an inference configuration. That was included opportunistically by the target network node, with the hope that it would be applicable.

[0263] The UE then applies the HO command, i.e. the RRC Reconfiguration message including the reconfiguration with sync, and in response, it accesses the second cell (which is the target cell) (see Step 508). Then, the UE performs one or more actions in the second cell according to the AI / ML functionality configuration (e.g., inference configuration), such as the reporting or one or more output(s) of an inference function and / or AI / ML model, e.g. when it determines that the AI / ML functionality is applicable. In other words, once the UE is connected to the second cell (step 512), the UE applies the configuration for the AI / ML functionality for the UE to operate in the second cell, e.g., applies an inference configuration for the AI / ML functionality (step 514), and reports resulting time-domain predictions to the second cell (step 516).

[0264] In one embodiment, in response to applying the HO command, the UE accesses the second cell (which is the target cell indicated in the HO command) in Step 508 by (i) performing a random access procedure, including the steps of transmitting a preamble to the second cell, receiving a random access response, and transmitting a msg3 including the HO command complete (e.g. RRC Reconfiguration Complete); or (ii) by performing a RACH-less procedure in which the UE transmits a scheduling request to receive an uplink (UL) grant to transmit the HO complete message (e.g. RRC Reconfiguration Complete message) and / or the UE transmits the HO complete message using a pre-configured UL grant (e.g. also configured in the HO command).

[0265] In one embodiment, in response to applying the HO command including the AI / ML functionality configuration (e.g., including the inference configuration) for the second cell, which is the target cell in the HO, the UE determines whether the configured AI / ML functionality is applicable or not.

[0266] - In one option, when the UE is in the second cell, which is the target for the HO, the UE reports the applicability of the configured AI / ML functionality, e.g. applicable, nonapplication. In one option that is reported in a UE assistance information.

[0267] - In one option, when the UE is in the second cell, which is the target for the HO, the UE reports the applicability of the configured AI / ML functionality e.g. applicable, nonapplication, but only when the applicability has changed since the last time the UE has reported the applicability indication of the AI / ML functionality for the second cell while the UE was connected to the first cell.

[0268] In one alternative to step 508, instead of receiving the HO command and applying it upon reception, the UE first receives and stores the HO command, as part of a Layer 1 / Layer 2 (L1 / L2) Triggered Mobility (LTM) configuration. Then, the UE applies the HO command (possibly including the AI / ML functionality configuration) for the second cell when the UE receives a lower layer signaling (such as a MAC CE for an LTM cell switch) indicating the second cell as the target cell.

[0269] In one alternative to step 508, instead of receiving the HO command and applying it, the UE first receives and stores the HO command, as part of a Conditional Reconfiguration configuration, e.g. Conditional Handover (CHO), which also includes an execution condition. Then, the UE applies the HO command (possibly including the AI / ML functionality configuration) for the second cell when the execution condition is fulfilled for the second cell and the UE selects the second cell as the cell to be the target cell.

[0270] In one alternative to step 508, instead of receiving the HO command and applying it, the UE first receives and stores the HO command, as part of a Conditional LTM, which also includes an execution condition. Then, the UE applies the HO command (possibly including the AI / ML functionality configuration) for the second cell when the LTM execution condition is fulfilled for the second cell and the UE selects the second cell as the cell to be the target cell.

[0271] The reason for including the AI / ML functionality opportunistically is that when the UE determines that to not be applicable, the UE would simply consider the functionality deactivated and indicate that to the network in an applicability indication anyways after the handover, e.g. in a UE assistance information and / or in the HO complete message (RRC Reconfiguration Complete).

[0272] The difference here compared to solution 1 is that the target network node, which generates the HO command including the AI / ML functionality configuration (e.g. inference configuration) for the second cell does not receive a reported applicability indication for an AI / ML functionality of the second cell, but a HO request. Thus, the target network node either prepares the AI / ML functionality configuration (e.g. inference configuration) for the second cell based on the UE’s current configuration in the first cell (i.e. the source network node), which may include information about AI / ML functionality in the first cell and / or the second cell, or based on the UE capabilities, or based on previous assumption(s) about the same UE in the target network node.

[0273] Figure 8 shows an example of a communication system 800 in accordance with some embodiments.

[0274] In the example, the communication system 800 includes a telecommunication network 802 that includes an access network 804, such as a Radio Access Network (RAN), and a core network 806, which includes one or more core network nodes 808. The access network 804 includes one or more access network nodes, such as network nodes 810A and 810B (one or more of which may be generally referred to as network nodes 810), or any other similar Third Generation Partnership Project (3GPP) access nodes or non-3GPP Access Points (APs). Moreover, as will be appreciated by those of skill in the art, 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 telecommunication network 802 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 802 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 nodes to implement one or more functionalities of any node in the telecommunication network 802, including one or more network nodes 810 and / or core network nodes 808.

[0275] 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). The 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 access 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 0-2 interface defined by the 0-RAN Alliance or comparable technologies. The network nodes 810 facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 812A, 812B, 812C, and 812D (one or more of which may be generally referred to as UEs 812) to the core network 806 over one or more wireless connections.

[0276] 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 800 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 wireless connections. The communication system 800 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0277] The UEs 812 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 810 and other communication devices. Similarly, the network nodes 810 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 812 and / or with other network nodes or equipment in the telecommunication network 802 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 802.

[0278] In the depicted example, the core network 806 connects the network nodes 810 to one or more hosts, such as host 816. 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 806 includes one more core network nodes (e.g., core network node 808) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 808. Example core network nodes include 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).

[0279] The host 816 may be under the ownership or control of a service provider other than an operator or provider of the access network 804 and / or the telecommunication network 802, and may be operated by the service provider or on behalf of the service provider. The host 816 may host a variety of applications to provide one or more services. 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.

[0280] As a whole, the communication system 800 of Figure 8 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 800 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 Second, Third, Fourth, or Fifth Generation (2G, 3G, 4G, or 5G) standards, or any applicable future generation standard (e.g., Sixth Generation (6G)); Wireless Local Area Network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any Low Power Wide Area Network (LPWAN) standards such as LoRa and Sigfox.

[0281] In some examples, the telecommunication network 802 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunication network 802 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 802. For example, the telecommunication network 802 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 Internet of Things (loT) services to yet further UEs. In some examples, the UEs 812 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 804 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 804. Additionally, a UE may be configured for operating in single- or multi -Radio Access Technology (RAT) or multi-standard mode. For example, a UE may operate with any one or combination of WiFi, New Radio (NR), and LTE, i.e. being configured for Multi-Radio Dual Connectivity (MR-DC), such as Evolved UMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).

[0282] In the example, a hub 814 communicates with the access network 804 to facilitate indirect communication between one or more UEs (e.g., UE 812C and / or 812D) and network nodes (e.g., network node 810B). In some examples, the hub 814 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 814 may be a broadband router enabling access to the core network 806 for the UEs. As another example, the hub 814 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 810, or by executable code, script, process, or other instructions in the hub 814. As another example, the hub 814 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 814 may be a content source. For example, for a UE that is a Virtual Reality (VR) headset, display, loudspeaker or other media delivery device, the hub 814 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 814 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 814 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0283] The hub 814 may have a constant / persistent or intermittent connection to the network node 810B. The hub 814 may also allow for a different communication scheme and / or schedule between the hub 814 and UEs (e.g., UE 812C and / or 812D), and between the hub 814 and the core network 806. In other examples, the hub 814 is connected to the core network 806 and / or one or more UEs via a wired connection. Moreover, the hub 814 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 804 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 810 while still connected via the hub 814 via a wired or wireless connection. In some embodiments, the hub 814 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 810B. In other embodiments, the hub 814 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and the network node 81 OB, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0284] Figure 9 shows a UE 900 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged, and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, Voice over Internet Protocol (VoIP) phone, wireless local loop phone, desktop computer, Personal Digital Assistant (PDA), wireless camera, 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, etc. Other examples include any UE identified by the 3GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0285] A UE may support Device-to-Device (D2D) communication, for example by implementing a 3GPP 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, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE 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 sprinkler controller). Alternatively, a UE 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).

[0286] The UE 900 includes processing circuitry 902 that is operatively coupled via a bus 904 to an input / output interface 906, a power source 908, memory 910, a communication interface 912, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 9. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0287] 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).

[0288] 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 the UE 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 output device 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.

[0289] 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. 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 the UE 900 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 908. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 908 to make the power suitable for the respective components of the UE 900 to which power is supplied.

[0290] The memory 910 may be or be configured to include memory such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 910 includes one or more application 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 the UE 900, any of a variety of various operating systems or combinations of operating systems. 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 RAM (SDRAM), external micro-DIMM SDRAM, smartcard memory such as a tamper resistant module in the form of a Universal Integrated Circuit Card (UICC) including one or more Subscriber Identity Modules (SIMs), such as a Universal SIM (USIM) and / or Internet Protocol Multimedia Services Identity Module (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 a ‘SIM card.’ The memory 910 may allow the UE 900 to access instructions, application 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.

[0291] The processing circuitry 902 may be configured to communicate with an access network or other network using the communication interface 912. The communication interface 912 may 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 UE 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., the antenna 922) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0292] In the illustrated embodiment, communication functions of the communication interface 912 may include cellular communication, WiFi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, NFC, 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 CDMA (WCDMA), GSM, LTE, NR, UMTS, WiMax, Ethernet, Transmission Control Protocol / Internet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth.

[0293] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 912, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The 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).

[0294] As another example, a UE 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, the UE may comprise a motor that adjusts 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.

[0295] A UE, when in the form of an loT device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city 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 television, 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 head-mounted display for Augmented Reality (AR) or VR, 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. A UE in the form of an loT device 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 UE 900 shown in Figure 9.

[0296] As yet another specific example, in an loT scenario, a UE 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 UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0297] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE 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 UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator and handle communication of data for both the speed sensor and the actuators.

[0298] 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 telecommunication network. Examples of network nodes include, but are not limited to, APs (e.g., radio APs), Base Stations (BSs) (e.g., radio BSs, Node Bs, evolved Node Bs (eNBs), NR. Node Bs (gNBs)), and 0-RAN nodes or components of an 0-RAN node (e.g., 0-RU, 0-DU, O-CU).

[0299] Base stations 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. A base station may be a relay node or a relay donor node controlling a relay. A network node 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 0-RAN access node), and / or Remote Radio Units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such RRUs 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).

[0300] Other examples of network nodes include multiple Transmission Point (multi-TRP) 5G access nodes, Multi -Standard Radio (MSR) equipment such as MSRBSs, network controllers such as Radio Network Controllers (RNCs) or BS 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).

[0301] The network node 1000 includes processing circuitry 1002, memory 1004, a communication interface 1006, and a power source 1008. The network node 1000 may be composed of multiple physically separate components (e.g., a NodeB component and an RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 1000 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components 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 RATs. In such embodiments, some components may be duplicated (e.g., separate 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, WiFi, Zigbee, Z-wave, Long Range Wide Area Network (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 the network node 1000.

[0302] The processing circuitry 1002 may comprise a combination of one or more of a microprocessor, controller, microcontroller, CPU, DSP, ASIC, FPGA, 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 network node 1000 components, such as the memory 1004, to provide network node 1000 functionality.

[0303] 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 the RF transceiver circuitry 1012 and the baseband processing circuitry 1014 may be on the same chip or set of chips, boards, or units.

[0304] 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, RAM, 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 or non-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 the memory 1004 are integrated.

[0305] The communication interface 1006 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the 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. The communication interface 1006 also includes radio front-end circuitry 1018 that may be coupled to, or in certain embodiments a part of, the antenna 1010. The radio front-end circuitry 1018 comprises filters 1020 and amplifiers 1022. The radio front-end circuitry 1018 may be connected to the antenna 1010 and the processing circuitry 1002. The radio front-end circuitry 1018 may be configured to condition signals communicated between the antenna 1010 and the 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 having the appropriate channel and bandwidth parameters using a combination of the filters 1020 and / or the amplifiers 1022. The radio signal 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 1006 may comprise different components and / or different combinations of components.

[0306] In certain alternative embodiments, the network node 1000 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 the 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 interface 1006 communicates with the baseband processing circuitry 1014, which is part of a digital unit (not shown).

[0307] 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 an interface or port.

[0308] The antenna 1010, the communication interface 1006, and / or the processing circuitry 1002 may be configured to perform any receiving operations and / or certain 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 any transmitting 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.

[0309] The power source 1008 provides power to the various components of the 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 or an electricity outlet) via input circuitry or an 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.

[0310] 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. In some embodiments providing a core network node, such as core network node 108 of FIG. 8, some components, such as the radio front-end circuitry 1018 and the RF transceiver circuitry 1012 may be omitted.

[0311] 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 virtualization environments 1100 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, a UE, a core network node, or a host. Further, in embodiments in which the 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. Virtualization may facilitate distributed implementations of a network node, a UE, a core network node, or a host.

[0312] 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 1100 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0313] 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, an 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 VMs 1108 A and 1108B (one or more of which may be generally 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 the VMs 1108.

[0314] The VMs 1108 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding 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, physical switches, and physical storage, which can be located in data centers and customer premise equipment.

[0315] In the context of NFV, a VM 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 the 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 VMs 1108 on top of the hardware 1104 and corresponds to the application 1102.

[0316] The hardware 1104 may be implemented in a standalone network node with generic or specific components. The hardware 1104 may implement some functions via virtualization. Alternatively, the 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 the applications 1102. In some embodiments, the 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.

[0317] Although the computing devices described herein (e.g., UEs, network nodes) 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 partitioned between 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.

[0318] 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.

[0319] 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.

[0320] Some exemplary embodiments of the present disclosure are as follows:

[0321] Group A Embodiments

[0322] Embodiment 1: A method performed by a User Equipment, UE, for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell to a second cell which becomes a target cell in the connected mode mobility procedure, the method comprising: receiving (308; 406; 506; 608; 708) a connected mode mobility command including an AI / ML functionality configuration for the second cell (target cell), while connected to the first cell; applying (308; 406; 506; 608; 708) the connected mode mobility command comprising the AI / ML functionality configuration for the second cell; and accessing (310; 408; 508; 610; 710) the second cell, wherein the second cell is a neighbor cell. Embodiment 2: The method of embodiment 1, further comprising performing (316; 410; 514; 616; 716) an AI / ML functionality in the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

[0323] Embodiment 3: The method of embodiment 1 or 2, further comprising, after the connected mode mobility procedure, transmitting (412), to the second cell, an applicability indication for an AI / ML functionality of the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

[0324] Embodiment 4: The method of embodiment 3, wherein the applicability indication for an AI / ML functionality of the second cell is transmitted within a UE assistance information message to the second cell or within a HO complete message (e.g. RRC Reconfiguration Complete generated in the HO procedure).

[0325] Embodiment 5: The method of embodiment 1 or 2, further comprising, prior to receiving the connected mode mobility command comprising the AI / ML functionality configuration for the second cell, transmitting (302; 602b; 702b), to the first cell, an applicability indication for an AI / ML functionality of the second cell.

[0326] Embodiment 6: The method of embodiment 1 or 2, further comprising transmitting (302; 602b; 702b) an applicability indication for an AI / ML functionality of the second cell during and / or in preparation for the connected mobility procedure.

[0327] Embodiment 7: The method of any of embodiments 1 to 6, wherein the AI / ML functionality configuration for the second cell comprises an inference configuration enabling the UE to operate in the second cell after the connected mode mobility procedure.

[0328] Embodiment 8: The method of any of embodiments 1 to 7, wherein the AI / ML functionality configuration for the second cell comprises an applicability reporting configuration enabling the UE to report, to the second cell, an applicability indication for an AI / ML functionality of the second cell, after the connected mode mobility procedure and / or as part of the connected mode mobility procedure.

[0329] Embodiment 9: The method of any of embodiments 1 to 8, further comprising determining to activate an AI / ML functionality associated to the AI / ML functionality configuration for the second cell when that AI / ML functionality is determined by the UE to be applicable and / or based on one or more parameters within the connected mode mobility command and / or based on a further message received in the second cell after the connected mode mobility procedure.

[0330] Embodiment 10: The method of any of embodiments 3 to 6 or 8, wherein the UE determines the applicability indication for the AI / ML functionality of the second cell based on one or more UE-conditions and / or one or more network conditions. Embodiment 11 : The method of embodiment 10, wherein the one or more UE-conditions and / or one or more network conditions and / one or more inference configuration s) are included in an applicability reporting configuration transmitted in the connected mode mobility command.

[0331] Embodiment 12: The method of any of embodiments 1 to 11, wherein an AI / ML functionality is determined to be applicable or not applicable for the second cell based on the AI / ML inference configuration included in the connected mode mobility command.

[0332] Embodiment 13: The method of any of embodiments 1 to 11, wherein an AI / ML functionality is determined to be applicable or not applicable for the second cell based on an AI / ML inference configuration received in a further reconfiguration message received while connected to the second cell after the connected mode mobility procedure.

[0333] Embodiment 14: The method of embodiment 12 or 13, wherein an inference configuration is applied if the AIML functionality is applicable given the received inference configuration.

[0334] Group B Embodiments

[0335] Embodiment 15: A method performed by a source network node for configuring an AI / ML functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of the source network node to a second cell of a target network node which becomes a target cell in the connected mode mobility procedure, the method comprising: transmitting (304; 402; 502; 604; 704), to the target network node, a connected mode mobility request message (e.g., Handover Request over XnAP); receiving (306; 404; 504; 606; 706), in response from the target network node, a connected mode mobility response message (e.g. Handover Request Ack over XnAP) including a connected mode mobility command comprising an AI / ML functionality configuration for a second cell (target cell); and, in response, transmitting (308; 406; 506; 608; 708), to a UE, the connected mode mobility command comprising the AI / ML functionality configuration for the second cell (target cell).

[0336] Embodiment 16: The method of embodiment 15, further comprising, prior to transmitting the connected mode mobility request message (e.g. Handover Request over XnAP) to the target network node, receiving (302; 602b; 702b), from the UE, an applicability indication for an AI / ML functionality of the second cell and / or one or more UE capabilities associated to the AI / ML functionality configuration.

[0337] Embodiment 17: The method of embodiment 16, further comprising, prior to receiving from the UE the applicability indication for an AI / ML functionality of the second cell, transmitting (602a; 702a), to the UE, an applicability reporting configuration configuring the UE to report the applicability indication for an AI / ML functionality of the second cell. Embodiment 18: The method embodiment 17, further comprising, prior to transmitting the applicability reporting configuration, obtaining the applicability reporting configuration from the target network node.

[0338] Embodiment 19: The method of any of embodiments 16 to 18, the applicability indication for an AI / ML functionality of the second cell is included in the connected mode mobility request message (e.g. Handover Request over XnAP) to the target network node.

[0339] Embodiment 20: A method performed by a target network node for configuring an AI / ML functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of a source network node to a second cell of the target network node which becomes a target cell in the connected mode mobility procedure, the method comprising: receiving (304; 402; 502; 604; 704), from the source network node, a connected mode mobility request message (e.g., Handover Request over XnAP); and, in response, transmitting (306; 404; 504; 606; 706), to the source network node, a connected mode mobility response message (e.g. Handover Request Ack over XnAP) including a connected mode mobility command comprising an AI / ML functionality configuration for the second cell (target cell).

[0340] Embodiment 21 : The method of embodiment 20, further comprising receiving (304; 604; 704) an applicability indication for an AI / ML functionality of the second cell in the connected mode mobility request message (e.g. Handover Request over XnAP) received from the source network node.

[0341] Embodiment 22: The method of embodiment 21, further comprising, prior to receiving the applicability indication for an AI / ML functionality of the second cell in the connected mode mobility request message (e.g. Handover Request over XnAP) from the source network node, providing (602a; 702a) to the source network node an applicability reporting configuration.

[0342] Group C Embodiments

[0343] Embodiment 23: A user equipment comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0344] Embodiment 24: A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry.

[0345] Embodiment 25: A user equipment (UE) comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information 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 battery connected to the processing circuitry and configured to supply power to the UE.

Claims

CLAIMS1. A method performed by a User Equipment, UE, for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell to a second cell, the method comprising: receiving (308; 406; 506; 608; 708) a connected mode mobility command including an AI / ML functionality configuration for the second cell, while connected to the first cell; and accessing (310; 408; 508; 610; 710) the second cell, in accordance with the connected mode mobility command.

2. The method of claim 1, further comprising performing (316; 410; 514; 616; 716) an AI / ML functionality in the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

3. The method of claim 1 or 2, further comprising, after the connected mode mobility procedure, transmitting (412), to the second cell, an applicability indication for an AI / ML functionality of the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

4. The method of claim 3, wherein the applicability indication for an AI / ML functionality of the second cell is transmitted to the second cell within a UE assistance information message or within a handover, HO, complete message or within a Radio Resource Control, RRC, reconfiguration complete message generated in the connected mode mobility procedure.

5. The method of claim 1 or 2, further comprising, prior to receiving the connected mode mobility command comprising the AI / ML functionality configuration for the second cell, transmitting (302; 602b; 702b), to the first cell, an applicability indication for an AI / ML functionality of the second cell.

6. The method of claim 1 or 2, further comprising transmitting (302; 602b; 702b) an applicability indication for an AI / ML functionality of the second cell during and / or in preparation for the connected mobility procedure.

7. The method of any of claims 1 to 6, wherein the AI / ML functionality configuration for thesecond cell comprises an inference configuration enabling the UE to operate in the second cell after the connected mode mobility procedure.

8. The method of any of claims 1 to 7, wherein the AI / ML functionality configuration for the second cell comprises an applicability reporting configuration enabling the UE to report, to the second cell, an applicability indication for an AI / ML functionality of the second cell, after the connected mode mobility procedure and / or as part of the connected mode mobility procedure.

9. The method of any of claims 1 to 8, further comprising determining to activate an AI / ML functionality associated to the AI / ML functionality configuration for the second cell when that AI / ML functionality is determined by the UE to be applicable and / or based on one or more parameters within the connected mode mobility command and / or based on a further message received in the second cell after the connected mode mobility procedure.

10. The method of any of claims 3 to 6 or claim 8, wherein the UE determines the applicability indication for the AI / ML functionality of the second cell based on one or more UE-conditions and / or one or more network conditions.

11. The method of claim 10, wherein the one or more UE-conditions and / or one or more network conditions and / or one or more inference configuration(s) are included in an applicability reporting configuration transmitted in the connected mode mobility command.

12. The method of any of claims 1 to 11, wherein an AI / ML functionality is determined to be applicable or not applicable for the second cell based on the AI / ML inference configuration included in the connected mode mobility command.

13. The method of any of claims 1 to 11, wherein an AI / ML functionality is determined to be applicable or not applicable for the second cell based on an AI / ML inference configuration received in a further reconfiguration message received while connected to the second cell after the connected mode mobility procedure.

14. The method of claim 12 or 13, wherein an inference configuration is applied if the AI / ML functionality is applicable given the received inference configuration.

15. A User Equipment, UE, for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell to a second cell, the UE configured to: receive (308; 406; 506; 608; 708) a connected mode mobility command including an AI / ML functionality configuration for the second cell, while connected to the first cell; and access (310; 408; 508; 610; 710) the second cell, in accordance with the connected mode mobility command.

16. The UE of claim 15, wherein the UE is further configured to perform (316; 410; 514; 616; 716) an AI / ML functionality in the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

17. The UE of claim 15 or 16, wherein the UE is further configured to, after the connected mode mobility procedure, transmit (412), to the second cell, an applicability indication for an AI / ML functionality of the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

18. The UE of claim 17, wherein the applicability indication for an AI / ML functionality of the second cell is transmitted to the second cell within a UE assistance information message or within a handover, HO, complete message or within a Radio Resource Control, RRC, reconfiguration complete message generated in the connected mode mobility procedure.

19. A User Equipment, UE, (900) for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell to a second cell, the UE (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 (900) to: receive (308; 406; 506; 608; 708) a connected mode mobility command including an AI / ML functionality configuration for the second cell, while connected to the first cell; and access (310; 408; 508; 610; 710) the second cell, in accordance with the connected mode mobility command.

20. The UE of claim 19, wherein the processing circuitry is further configured to cause the UE to perform (316; 410; 514; 616; 716) an AI / ML functionality in the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

21. The UE of claim 19 or 20, wherein the processing circuitry is further configured to cause the UE to, after the connected mode mobility procedure, transmit (412), to the second cell, an applicability indication for an AI / ML functionality of the second cell, based on the AI / ML functionality configuration for the second cell received in the connected mode mobility command.

22. The UE of claim 21, wherein the applicability indication for an AI / ML functionality of the second cell is transmitted to the second cell within a UE assistance information message or within a handover, HO, complete message or within a Radio Resource Control, RRC, reconfiguration complete message generated in the connected mode mobility procedure.

23. A method performed by a source network node for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of the source network node to a second cell of a target network node, the method comprising: transmitting (304; 402; 502; 604; 704), to the target network node, a connected mode mobility request message for mobility of a User Equipment, UE, from the first cell of the source network node to the second cell of the target network node; receiving (306; 404; 504; 606; 706), in response from the target network node, a connected mode mobility response message including a connected mode mobility command for the UE, the connected mode mobility command comprising an AI / ML functionality configuration for the second cell; in response, transmitting (308; 406; 506; 608; 708), to the UE, the connected mode mobility command comprising the AI / ML functionality configuration for the second cell.

24. The method of claim 23, further comprising, prior to transmitting the connected mode mobility request message to the target network node, receiving (302; 602b; 702b), from the UE, an applicability indication for an AI / ML functionality of the second cell and / or one or more UE capabilities associated to the AI / ML functionality configuration.

25. The method of claim 24, further comprising, prior to receiving from the UE the applicability indication for an AI / ML functionality of the second cell, transmitting (602a; 702a), to the UE, an applicability reporting configuration configuring the UE to report the applicability indication for an AI / ML functionality of the second cell.

26. The method claim 25, further comprising, prior to transmitting the applicability reporting configuration, obtaining the applicability reporting configuration from the target network node.

27. The method of any of claims 24 to 26, the applicability indication for an AI / ML functionality of the second cell is included in the connected mode mobility request message to the target network node.

28. A source network node for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of the source network node to a second cell of a target network node, the source network node configured to: transmit (304; 402; 502; 604; 704), to the target network node, a connected mode mobility request message for mobility of a User Equipment, UE, from the first cell of the source network node to the second cell of the target network node; receive (306; 404; 504; 606; 706), in response from the target network node, a connected mode mobility response message including a connected mode mobility command for the UE, the connected mode mobility command comprising an AI / ML functionality configuration for the second cell; in response, transmit (308; 406; 506; 608; 708), to the UE, the connected mode mobility command comprising the AI / ML functionality configuration for the second cell.

29. A source network node for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of the source network node to a second cell of a target network node, the source network node comprising processing circuitry configured to cause the source network node to: transmit (304; 402; 502; 604; 704), to the target network node, a connected mode mobility request message for mobility of a User Equipment, UE, from the first cell of the source network node to the second cell of the target network node;receive (306; 404; 504; 606; 706), in response from the target network node, a connected mode mobility response message including a connected mode mobility command for the UE, the connected mode mobility command comprising an AI / ML functionality configuration for the second cell; in response, transmit (308; 406; 506; 608; 708), to the UE, the connected mode mobility command comprising the AI / ML functionality configuration for the second cell.

30. A method performed by a target network node for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of a source network node to a second cell of the target network node which becomes a target cell in the connected mode mobility procedure, the method comprising: receiving (304; 402; 502; 604; 704), from the source network node, a connected mode mobility request message for handover of a User Equipment, UE, from the first cell of the source network node to the second cell of the target network node; and in response, transmitting, to the source network node, a connected mode mobility response message including a connected mode mobility command for the UE, the connected mode mobility command comprising an AI / ML functionality configuration for the second cell.

31. The method of claim 30, further comprising, after completion of the connected mobility procedure once the UE is connected to the second cell of the target network node, receiving (412) an applicability indication for an AI / ML functionality of the second cell from the UE.

32. The method of claim 31, wherein the applicability indication for an AI / ML functionality of the second cell is received on the second cell within a UE assistance information message or within a handover, HO, complete message or within a Radio Resource Control, RRC, reconfiguration complete message generated in the connected mode mobility procedure.

33. The method of claim 30, further comprising receiving (304; 604; 704) an applicability indication for an AI / ML functionality of the second cell in the connected mode mobility request message received from the source network node.

34. The method of claim 33, further comprising, prior to receiving the applicability indication for an AI / ML functionality of the second cell in the connected mode mobility request messagefrom the source network node, providing (602a; 702a) to the source network node an applicability reporting configuration.

35. A target network node for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of a source network node to a second cell of the target network node which becomes a target cell in the connected mode mobility procedure, the target network node configured to: receive (304; 402; 502; 604; 704), from the source network node, a connected mode mobility request message for handover of a User Equipment, UE, from the first cell of the source network node to the second cell of the target network node; and in response, transmit, to the source network node, a connected mode mobility response message including a connected mode mobility command for the UE, the connected mode mobility command comprising an AI / ML functionality configuration for the second cell.

36. A target network node for configuring an Artificial Intelligence, Al, or Machine Learning, ML, (AI / ML) functionality for a connected mode mobility procedure, wherein the connected mode mobility procedure is from a first cell of a source network node to a second cell of the target network node which becomes a target cell in the connected mode mobility procedure, the target network node comprising processing circuitry configured to cause the target network node to: receive (304; 402; 502; 604; 704), from the source network node, a connected mode mobility request message for handover of a User Equipment, UE, from the first cell of the source network node to the second cell of the target network node; and in response, transmit, to the source network node, a connected mode mobility response message including a connected mode mobility command for the UE, the connected mode mobility command comprising an AI / ML functionality configuration for the second cell.

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