Model availability indication via applicability report for ai-based solutions

Abstract

Systems and methods are disclosed for indicating Artificial Intelligence, AI, / Machine Learning, ML, (AIML) model or functionality availability via an applicability report In one embodiment, a method performed by a User Equipment (UE) for AIML model or functionality availability reporting comprises sending, to a network node, an applicability report for an AIML model or AIML-based functionality. The applicability report comprises information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE. In this manner, the network is made aware of the reason that the AIML model or functionality is not applicable and acts accordingly.

Description

[0001] MODEL AVAILABILITY INDICATION VIA APPLICABILITY REPORT FOR AL

[0002] BASED SOLUTIONS

[0003] RELATED APPLICATIONS

[0004] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 645,463, filed May 10, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.

[0005] TECHNICAL FIELD

[0006] The present disclosure relates to a wireless communications system and, more specifically, to the use of User Equipment (UE) sided Artificial Intelligence (AI) / Machine Learning (ML) models or AI / ML-based functionality in a wireless communications system.

[0007] BACKGROUND

[0008] The 3rdGeneration Partnership Project (3GPP) has started a new work item (WI) in New Radio (NR) Release (Rel-) 19 for Artificial Intelligence (Al)-based beam management and AI- based positioning as well as another two new study items (SI) in NR Rel- 19 for Al-based Channel State Information (CSI) compression and CSI prediction and Radio Resource Management (RRM) measurement prediction, RRM measurement event prediction, radio link failure prediction and handover failure prediction. The scope of the WI includes the solution design to support functionality-based Life Cycle Management (LCM), applicability report, and so on for Al-based solutions with UE-sided models.

[0009] In RAN2#125bis meeting, the following agreements were made:

[0010] • Which AI / ML-enabled Feature s / Feature Groups (FGs) and functionalities are supported should be standardized. The details wait for RANl’s progress, “supported” means that the User Equipment (UE) is capable of supporting the functionality and doesn ’t mean necessarily that the UE has the model available. For Future Study (FFS) what functionality refers to.

[0011] • Supported AI / ML-enabled Features / FGs and supported functionalities are included in UE capability.

[0012] • Support proactive reporting of UE-sided applicable functionality, e.g., the UE reports its applicable AI / ML functionalities via UE Assistance Information (UAI) message / Long Term Evolution (LTE) Positioning Protocol (LPP) message.

[0013] • Support reactive reporting of UE-sided applicable functionality. The network (NW) configures AI / ML functionalities via Radio Resource Control (RRC) / LPP message. FFS what the configuration contains. FFS how to report applicable functionality and what is applicable functionality.

[0014] • FFS how the two approaches will be specified and whether we can combine them into one procedure. FFS how to report applicable functionality, what is applicable functionality, how the UE determines which function is applicable or not (if it is needed).

[0015] SUMMARY

[0016] Systems and methods are disclosed for indicating Artificial Intelligence, Al, / Machine Learning, ML, (AIML) model or functionality availability via an applicability report. In one embodiment, a method performed by a User Equipment (UE) for AIML model or functionality availability reporting comprises sending, to a network node, an applicability report for an AIML model or AIML-based functionality. The applicability report comprises information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE. In this manner, the network is made aware of the reason that the AIML model or functionality is not applicable and act accordingly.

[0017] In one embodiment, the indicated reason that the AIML model or the one or more AIML models used for the AIML-based functionality is or are not available at the UE comprises any one or more of the following: the AIML model or the one or more AIML models used for the AIML- based functionality is or are not available at the UE, the AIML model or the one or more AIMLs used for the AIML-based functionality is or are in need of training or re-training, the AIML model or the one or more AIML models used for the AIML-based functionality is or are in need of training or re-training for one or more current or given conditions, the AIML model or the one or more AIML models used for the AIML-based functionality needs network assistance for training or re-training to be applicable, the AIML model or the one or more AIML models used for the AIML-based functionality needs network assistance for training or re-training to be applicable under one or more current or given conditions, the AIML model or the one or more AIML models used for the AIML-based functionality is or are currently being downloaded or updated, and the AIML model or the one or more AIML models used for the AIML-based functionality is or are trained but not yet available at the UE.

[0018] In one embodiment, the applicability report further comprises time delay information which indicates when the AIML model or the one or more AIML models used for the AIML-based functionality will be available. In one embodiment, the applicability report further comprises information related to a request for data collection for training or re-training of the AIML model or the one or more AIML models used for the AIML-based functionality.

[0019] In one embodiment, the applicability report further comprises information that indicates one or more recommended configurations for the AIML model or the one or more AIML models used for the AIML-based functionality.

[0020] In one embodiment, the method further comprises receiving an activation message from the network node that enables at least one corresponding functionality after a preconfigured, defined, or indicated amount of time or after a time delay indicated in the applicability report.

[0021] In one embodiment, the information comprised in the applicability report indicates that the reason that the AIML model or AIML-based functionality if not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality is or are unavailable because the AIML model or the one or more AIML models used for the AIML-based functionality need to be trained or re-trained. In one embodiment, the method further comprises receiving, from the network node, information that configures one or more parameters that enable the UE to collect data for training or re-training of the AIML model or the one or more AIML models used for the AIML-based functionality.

[0022] In one embodiment, the information comprised in the availability report indicates that the reason that the AIML model or the AIML-based functionality is not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality need to be downloaded or updated. In one embodiment, the method further comprises receiving, from the network node, information that configures one or more parameters that enable the UE to download or update the AIML model or the one or more AIML models used for the AIML-based functionality.

[0023] In one embodiment, the information explicitly indicates that the AIML model or AIML- based functionality is not applicable at the UE.

[0024] In one embodiment, the information implicitly indicates that the AIML model or AIML- based functionality is not applicable at the UE.

[0025] In one embodiment, the information explicitly indicates the reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality is not available at the UE.

[0026] In one embodiment, the information implicitly indicates the reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality is or are not available at the UE. Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE for AIML model or functionality availability reporting is adapted to send, to a network node, an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE.

[0027] In one embodiment, a UE for AIML model or functionality availability reporting comprises a communication interface comprising a transmitter and a receiver, and processing circuitry associated with the communication interface. The processing circuitry is configured to cause the UE to send, to a network node, an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML- based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE.

[0028] Embodiments of a method performed by a network node for reception and handling of AIML model or functionality availability reporting are also disclosed. In one embodiment, a method performed by a network node for AIML model or functionality availability reporting comprises receiving, from a UE, an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE. The method further comprises performing one or more actions based on the applicability report and the reason that the AIML model or AIML-based functionality is not applicable at the UE.

[0029] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node for reception and handling of AIML model or functionality availability reporting is adapted to receive, from a UE, an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML- based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE. The network node is further adapted to perform one or more actions based on the applicability report and the reason that the AIML model or AIML-based functionality is not applicable at the UE. In one embodiment, a network node for reception and handling of AIML model or functionality availability reporting comprises processing circuitry configured to cause the network node to receive, from a UE, an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML- based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE. The processing circuitry is further configured to cause the network node to perform one or more actions based on the applicability report and the reason that the AIML model or AIML-based functionality is not applicable at the UE.

[0030] BRIEF DESCRIPTION OF THE DRAWINGS

[0031] 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.

[0032] Figure 1 illustrates the operation of a User Equipment (UE) and a network node for Artificial Intelligence (AI) / Machine Learning (ML), i.e., AIML, model or functionality applicability reporting including an explicit or implicit indication of a reason(s) that the AIML model or AIML-based functionality is or is not applicable, in accordance with embodiments of the present disclosure.

[0033] Figure 2 shows an example of a communication system in accordance with some embodiments.

[0034] Figure 3 shows a UE in accordance with some embodiments.

[0035] Figure 4 shows a network node in accordance with some embodiments.

[0036] Figure 5 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0037] DETAILED DESCRIPTION

[0038] 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. 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.

[0039] There currently exist certain challenge(s). Systems and methods have been proposed for enabling a User Equipment (UE) to transmit an indication to the network (e.g. to a Radio Access Network (RAN) node, e.g., a New Radio (NR) base station which is called a gNodeB (gNB)), indicating the availability of an Artificial Intelligence (AI) / Machine Learning (ML) (i.e., AIML) model / functionality trained for the RAN node and / or for a given configuration of a cell in (e.g., operated by) the RAN node e.g. configuration of a serving cell, like the Primary Cell (PCell), to which the UE is connected. The indication may also include one or more radio resources or configurations that need to be configured to the UE in order to make an available AIML model / functionality applicable. In response to the transmission of the indication, the UE may receive from the RAN node (e.g. gNB) a command and / or message to activate / re- configure / deactivate the concerned AIML model / functionality. The command or message received by the UE may include the necessary configuration to make the concerned AIML model / functionality applicable at the UE side e.g. configuration for AIML model training, retraining, etc. However, the aforementioned existing solution does not support model availability indication via functionality applicability report.

[0040] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Embodiments of systems and methods are disclosed herein for reusing an AIML functionality or model (sometimes denoted herein as “functionality / model”) applicability report from a UE to a network node to support model availability indication as well as a cause reason(s) that the functionality / model is applicable or not. The model availability indication may further indicate further details as to help network assisted data collection for UE-sided model training, and / or to help the network to know when the AIML model will be available and / or whether to activate the corresponding functionality.

[0041] Note that embodiments of the solution described herein are described primarily in terms of NR, but the solution is applicable also to other types of wireless networks (e.g., 6thGeneration (6G) networks).

[0042] Note that ‘functionality’ can be replaced by ‘model’ in the description provided herein. In other words, the embodiments described herein are applicable to the AIML model or the corresponding functionality (e.g., Channel State Information (CSI) compression, CSI prediction, beam management, or AIML-based positioning). Note that, in the described embodiments, the UE is one UE terminal unless otherwise indicated. However, in alternative embodiments, the UE in the embodiments described herein can be replaced by one network node, and the network in the embodiments described herein can be replaced by another network node.

[0043] In the following embodiments, the network node can be a mobile network node, such as the gNB, or the Location Management Function (LMF), or the Operations, Administration, and Maintenance (0AM) node.

[0044] As used herein, the term “training entity” refers to a node inside or outside the mobile network (inside or outside of the network operated by the Mobile Network Operator (MNO)) (e.g. an Over-The-Top (OTT) server) or an application function of a node inside or outside the mobile network (e.g. an application function such as the Network Data Analytics Function (NWDAF), or Data Collection Application Function (DCAF), or 0AM).

[0045] As used herein, the term “available” is used to identify an AIML model for a certain AIML functionality that it is available at the UE, e.g. an AIML model that the UE has stored in its memory. At a given point in time, an AIML model stored in the UE memory may be determined by the UE to be available if the said AIML model can be used by the UE, e.g. if the said AIML model has been trained to operate under the network node to which the UE is connected. At a given point in time, an AIML model stored in the UE memory may be determined by the UE to be not available if the said AIML model cannot be used by the UE, e.g. if the said AIML model has not been trained to operate under the network node to which the UE is connected.

[0046] One UE proactively or reactively reports one information to the network, where this information indicates that one or more (requested) functionality(ties) is(are) not applicable due to at least one of the following cause reasons, i.e. Al model for the corresponding functionality is not available at the UE side, lack of training of an Al model for the corresponding functionality under for example current or given conditions, Al model needs the network’s assistance for the training to be applicable for the corresponding functionality under for example current or given conditions, or Al model is in the download process (i.e. in the update process). Note that the reactive report from the UE to the network means that the UE sends this report to the network due to receiving a corresponding request message from the network or due to receiving one message from the network where this message results in this report from the UE to the network; and the proactive report from the UE to the network means that the UE sends this report to the network even the UE does not receive a corresponding request message from the network. For example, the request message from the network may be to request the UE to provide the applicability of one or more Al models for one or more specific Al functionalities. Alternatively, the UE may indicate the availability of the one or more Al models associated to one or more Al functionalities of which the UE is capable.

[0047] The cause reasons from the UE to the network may further include the time delay information which indicates the network when the Al model(s) for the corresponding functionality(ties) will be available.

[0048] The cause reasons from the UE to the network may further include the data collection request information for model training.

[0049] The cause reason from the UE to the network may further include the configuration recommendation for the corresponding functionality(ties).

[0050] The cause reasons from the UE to the network may further include that one or more Al models associated to one or more Al functionalities is trained but it is not yet available at the UE.

[0051] The network, after receiving the information report from the UE, may send an activation message to the UE to enable at least one corresponding functionality after a preconfigured time duration, or after the indicated time delay in the information report from the UE.

[0052] The network, after receiving the information report from the UE, may configure a signal transmission and / or may send one configuration information to enable data collection for the training / retraining of the Al model(s) so that the Al model(s) at the UE side for the expected functionality could be available.

[0053] The network, after receiving the information report from the UE, may configure the UE with a necessary radio configuration to download one or more Al models for one or more Al functionalities.

[0054] Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the present disclosure reuse the functionality applicability report for model availability related indication from UE to the network to also indicate the cause reason(s), which simplifies the solution design and provides complexity reduction at both UE and network sides.

[0055] More specifically, there is a need for a system and methods that enable the network to better respond to an indication from a UE that an AIML model / functionality is not applicable. However, using the existing mechanisms, there is no way for the network node to know when the reason for the non-appliable indication is that the AIML model is unavailable. Embodiments of the present disclosure enable the network to not only know that an AIML model / functionality is not applicable but also know that the cause is that the AIML model is not available (i.e., the reason is not that the AIML model / functionality is not applicable due to, e.g., not being applicable for the current network configurations, but is instead that the AIML model is itself not available). Further, in some embodiments, the availability indication may be even more specific by giving a specific reason that the AIML model / functionality is not available (e.g., because the AIML model needs to be trained or retrained, because the AIML model is in the process of being downloaded, etc.). This way, the network knows better how to handle the non-applicable indication. For example, it may configure the UE with resources needed to train the AIML model or may give the UE info that it needs to download the AIML model. Without the reason being communicated to the network, the network will not know why the AIML model / functionality is non-applicable and therefore not know how best to handle the situation.

[0056] Still further, embodiments of the present disclosure may provide a low complexity solution by signaling the availability indication as a new indication without the existing applicability report. This simplifies the solution by using an existing signaling mechanism.

[0057] In addition, in some embodiments, an indication of a time in the future when the AIML model or the AIML model(s) used by the AIML functionality will be available is also sent from the UE to the network. In this way, the network has further information to enable the network node to determine how best to handle the non-applicable indication received in the report for the AIML model / functionality. For example, the network may determine, based on the indicated time, to wait until the indicated time before configuring the UE with inference related configurations (e.g., in a measurement configuration or report configuration).

[0058] Figure 1 illustrates the operation of a UE 100 and a network node 102 for AIML model / functionality applicability reporting including an explicit or implicit indication of a reason(s) that the AIML model or AIML-based functionality is or is not applicable, in accordance with embodiments of the present disclosure. Optional steps are represented by dashed lines / boxes. The network node 102 is a network node of a mobile (i.e., wireless) communications network and as such is sometimes referred to herein as a “mobile network node”.

[0059] As illustrated in Figure 1, the UE 100 detects a trigger for sending an applicability report message for an AIML model or AIML-based functionality (herein referred to as an “AIML model / functionality”) (step 104). Note that while the report is shown in Figure 1 as being a report for a single AIML model / functionality, the report may be for one or more AIML models / functionalities, each having an associated cause reason. The detection of the trigger for sending the applicability report may be any one or more of the following actions:

[0060] • Receiving from the network node 102 a request to indicate the availability of one or more AIML functionalities / models; o For example, the network node 102 may transmit this request upon determining that the UE 100 is capable of certain AIML models / functionalities. • Receiving, from the network node 102, a configuration configuring the UE 100 to perform radio measurement predictions according to one or more AIML models / functionalities;

[0061] • Receiving, from the network node 102, an activation command for the UE 100 to perform radio measurement predictions according to one or more AIML models / functionalities;

[0062] • Connecting to the network node 102 (e.g., a gNB) or to a cell operated by the network node 102, upon entering RRC CONNECTED mode (from IDLE / IN ACTIVE state), or upon successful execution of mobility (e.g. reconfiguration with synch, or Layer 1 / Layer 2 Triggered Mobility (LTM), or Conditional Handover (CHO)) to the network node 102 or cell, or upon successfully performing a reestablishment to the network node 102 or cell;

[0063] • Connecting to a Location Management Function (LMF), which in some embodiments corresponds to the network node 102 and in other embodiments is another network node;

[0064] • Receiving an AIML model / functionality from a training entity; o For example, upon receiving an AIML model / functionality, for which the network node 102 (e.g., gNB) previously transmitted a request to the UE 100 to indicate its availability, the UE 100 may indicate that now the AIML model / functionality is available. o The AIML model / functionality may be delivered or transferred by the training entity to the UE 100 upon completing the training of the AIML model / functionality, or upon it being determined that the UE 100 has connected to a gNB or LMF for which there is a trained AIML model / functionality available at the training entity.

[0065] • Receiving an indication from the training entity that an AIML model / functionality already available at the UE 100 can be used in the network node 102 (e.g., gNB or LMF) to which the UE 100 is connected. o For example, the training entity may determine that the UE 100 has connected to a gNB or LMF for which the UE 100 already has an available, trained AIML model / functionality.

[0066] The UE sends, to the network node 102, a first applicability report message indicating whether the AIML model / functionality is applicable with a reported cause reason that the AIML model or AIML model(s) for this functionality is or is not available (step 106). In one embodiment, the first applicability report message indicates that the AIML model / functionality is not applicable with the reported cause reason that the AIML model or AIML model(s) for this functionality being unavailable. If the AIML model or the AIML model(s) for the functionality is(are) available, the UE 100 reports to the network node 102 in the first applicability report message an indication that the AIML model / functionality is available due to the AIML model or AIML model(s) associated to the said functionality being available at the UE 100.

[0067] The applicability report message may contain applicability information associated to one or more AIML models / functionalities. Note that, as will be appreciated by one of ordinary skill in the art, the applicability indication for a certain AIML model or AIML-based functionality may take many forms. For example, the applicability indication may be an indication that the certain configuration or set of configurations (e.g., a CSI-ReportConfig for inference configuration or a set of inference related parameters) is or is not applicable.

[0068] Before sending the first applicability report message in step 106, the UE may determine whether for the gNB to which the UE is connected (e.g. and / or cell of that gNB), or for the LMF, there are one or more available UE-side AIML models / functionalities that can operate in the gNB, or in the LMF. For example, this determination may be based on the AIML models / functionalities that the UE has available or based on indication received from the training entity indicating the availability or unavailability of one or more UE-side AIML models / functionalities for operating in the gNB or cell of the gNB, or LMF.

[0069] In one embodiment, the UE 100 transmits the first applicability report message only if doing so is allowed in the cell, e.g. gNB indicating that the UE 100 is allowed to transmit the first applicability report message.

[0070] In one embodiment, the UE may first transmit the first applicability report message in step 106 indicating that the AIML model / functionality is not applicable with the reported cause reason that the AIML model or AIML model(s) for this functionality is not available. The UE 100 may thereafter transmit a successive first applicability report message (see, e.g., step 112) in which the UE 100 indicates that the AIML model / functionality is now available at the UE 100. For example, this may happen in case the UE does not have an AIML model / functionality available at the time sending the report in step 106, and then at a later point in time the UE 100 receives an AIML model / functionality from the training entity. Thus, after sending the applicability report in step 106, the UE 100 may later detect a trigger for sending another applicability report for the same AIML model / functionality (e.g., receive the trained AIML model(s) from the training entity) (step 110), at which time the UE transmits another applicability report indicating that the AIML model / functionality is now available and the cause reason (e.g., trained AIML model(s) received from the training entity) (step 112).

[0071] In another embodiment, the UE 100 may first transmit a first applicability report message in step 106 indicating that the AIML model / functionality is available, and in a successive first applicability report message in step 112 the UE 100 may indicate that the AIML model / functionality is not any longer available at the UE 100 along with the cause reason. This can happen for example, in case the UE 100 determines that an AIML model / functionality that was previously available for use cannot be used anymore due to e.g. the UE 100 receiving a new configuration from the network node or the UE changing some of its properties (e.g. device orientation, speed, etc.).

[0072] In response to transmitting the first applicability report message (e.g., in step 106) indicating that the AIML model / functionality is available, the network node 102 may perform one or more actions (step 108). For example, the network node 102 may transmit a second message to the UE 100, and the UE 100 may receive the second message from the network node 102, where the second message comprises, e.g. a request to the UE to include the applicability conditions associated to the indicated available AIML model(s) / functionalities, the applicability conditions comprising e.g. the set A and / or B of resources (step 108 A). The second message may also comprise the set A and / or B of resources that the network node can configure to the for the indicated available AIML model(s) / functionalities.

[0073] In one embodiment, the UE 100 reports to the network node 102 a first applicability report message (e.g., in step 106) indicating that the AIML-based functionality is not applicable with the reported cause reason that the AIML model(s) for this functionality is(are) not available due to the AIML model(s) are in need of training or re-training. The cause reason may further include a request to initiate data collection for the purpose of UE-side model (re-)training. The request may comprise one or more recommended resources (e.g. CSI Reference Signals (CSLRS) indications, Synchronization Signal Block (SSB) indications) in which the UE 100 needs to perform measurements for the purpose of data collection for UE-side model training. In response to receiving this first applicability report message, the network node 102 may decide (e.g., in step 108) whether to configure the UE 100 to perform the data collection for the training / retraining of UE-sided AIML model(s). If the network node 102 decides to configure the UE 100 to perform UE-sided data collection for the training / retraining, then the network node 102 may transmit a second message (e.g., in step 108A) to the UE 100 to configure the UE 100 with the corresponding signal / data transmission, and the network node 102 may also send at least the necessary configuration information to the UE 100 so that the UE 100 is able to do corresponding data collection. The collected data is used by the UE 100 or by the training entity performing the training (e.g. Over-The-Top (OTT) server, or any other entity in a mobile network node performing the training for the UE-side model), for the training / retraining of the AIML model(s). After the training / retraining AIML model(s) is available at the UE-side, the UE 100 sends another functionality applicability report to the network node 102 (e.g., in step 112) in a successive first applicability report message, where the functionality applicability report may indicate the AIML- based functionality is applicable due to available AIML model(s). In another embodiment, before transmitting the said successive first applicability report message, the UE 100 may need to wait for the training entity to deliver the trained AIML model(s) to the UE 100, or for the training entity to indicate that the concerned AIML model(s) can be used by the UE 100.

[0074] In one embodiment, the UE 100 reports to the network node 102 in the first applicability report message (e.g., in step 106) that the AIML-based functionality is not applicable with the reported cause reason that the AIML model(s) for this functionality is(are) not available due to the AIML model(s) being in the process of being downloaded (e.g., in an update process) or due to the AIML model(s) having to be downloaded from the training entity. The cause reason may further include time delay information for AIML-based functionality activation. In one embodiment, after the UE 100 completes the corresponding AIML model update, the UE 100 reports to the network node 102 in the first applicability report message about that the functionality being applicable due to the available AIML model(s) (e.g., in step 112). In another embodiment, the network node 102 sends a request for the first applicability report message to the UE 10 after a predefined time duration or a given time delay (where the request detected by the UE 100 in step 110 as a trigger), and then the UE 100 reports to the network node 102 that the functionality is applicable due to the available AIML model(s) (e.g., in step 112). For example, the second message of step 108 A may be or include this request. In another embodiment, the network node 102 sends an activation message to the UE 100 to enable the functionality after a predefined time duration or a given time delay, and then the UE 100 reports to the network node 102 that the functionality is applicable due to the available AIML model(s) and / or starts the functionality. For example, the second message of step 108 A may be or include this activation message.

[0075] In one embodiment, the network node 102, upon receiving the first applicability report message from the UE 100 indicating that the AIML models / functionalities is not applicable with the reported cause reason that the AIML model / functionality is not available due to the AIML model or AIML model(s) for functionality having to be downloaded, the network node 102 (e.g., in step 108) may transmit a second message (e.g., in step 108 A) comprising a configuration for the UE 100 to download the concerned AIML model(s), wherein the configuration may comprise for example a radio configuration, including e.g. the radio bearer for downloading the concerned AIML model(s). The network node 102 may also allocate the radio resources for the UE 100 to download the concerned AIML model(s).

[0076] In some of the previous embodiments, the indication that the AIML model / functionality is not available is signaled explicitly within the first applicability report message. In another embodiment, the indication that the AIML model / functionality is not available is signaled implicitly within the first applicability report message. For example:

[0077] • The first applicability report message may indicate for one or more AIML models / functionalities that the one or more AIML models / functionalities are not applicable with no further information conveyed in the message. In this case, the network node determines that the one or more AIML models / functionalities are not appliable due to not being available.

[0078] • The first applicability report message may indicate that one or more AIML models / functionalities are not applicable, and the message may further convey indication that data collection for UE-side model training needs to be performed by the UE for the concerned one or more AIML models / functionalities. In this case, the network node determines that the one or more AIML models / functionalities are not appliable due to not being available, and that a training / retraining / finetuning of the AIML models / functionalities need to be performed. In response to this indication, the network node may configure the UE to perform data collection for UE-side model training.

[0079] In another embodiment, if the first applicability report message includes information and parameters associated to resources that the network needs to configure to the UE 100 for the UE 100 to apply the one or more AIML models / functionalities, then the network node receiving the first applicability report message (i.e., the network node 102 in the example of Figure 1) determines that the said one or more AIML models / functionalities are available at the UE 100. Along with this information, the UE 100 may also include an indication that the one or more AIML models / functionalities are not applicable. In this case, the network node 102 receiving the first applicability report message determines that the said one or more AIML models / functionalities are available, and that by configuring the UE 100 according to the information indicated by the UE 100 in the first applicability report message, the one or more AIML models / functionalities would be applicable at the UE 100.

[0080] The resources included in the first applicability report message being for example any of

[0081] • A set of SSB(s) e.g. list of SSB indexes of a cell of the network node

[0082] • A set of CSLRS resources e.g. list of CSLRS resources of a cell of the network node

[0083] • A set of SSB (i.e., Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) block) resource set

[0084] • A set of frequencies

[0085] • A set of cells • A set of parameters associated to mobility event predictions, such as radio link failure (RLF), handover failure (HOF), beam failure detection (BFD)

[0086] • A set of parameters associated to radio measurement predictions (e.g. radio measurements filtering coefficients, periodicity, subcarrier spacing, Multiple Input Multiple Output (MIMO) configurations, etc. used for radio measurement predictions)

[0087] The UE 100 may transmit a single implicit or explicit indication associated to one or more AIML models / functionalities or separate implicit / explicit indications for each of the one or more AIML models / functionalities of which the UE is capable.

[0088] The first applicability report message (e.g., in step 106 and / or in step 112) may be conveyed within one or more of the following Radio Resource Control (RRC) messages: UEInformationResponse, rrcSetupComplete, rrcReestablishmentComplete, rrcReconfigurationComplete, rrcResumeComplete, UEAssistancelnformation.

[0089] Figure 2 shows an example of a communication system 200 in accordance with some embodiments.

[0090] In the example, the communication system 200 includes a telecommunication network 202 that includes an access network 204, such as a Radio Access Network (RAN), and a core network 206, which includes one or more core network nodes 208. The access network 204 includes one or more access network nodes, such as network nodes 210A and 210B (one or more of which may be generally referred to as network nodes 210), 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 202 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 202 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 202, including one or more network nodes 210 and / or core network nodes 208.

[0091] 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 210 facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 212A, 212B, 212C, and 212D (one or more of which may be generally referred to as UEs 212) to the core network 206 over one or more wireless connections.

[0092] 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 200 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 200 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0093] The UEs 212 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 210 and other communication devices. Similarly, the network nodes 210 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 212 and / or with other network nodes or equipment in the telecommunication network 202 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 202.

[0094] In the depicted example, the core network 206 connects the network nodes 210 to one or more hosts, such as host 216. 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 206 includes one more core network nodes (e.g., core network node 208) 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 208. 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).

[0095] The host 216 may be under the ownership or control of a service provider other than an operator or provider of the access network 204 and / or the telecommunication network 202, and may be operated by the service provider or on behalf of the service provider. The host 216 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.

[0096] As a whole, the communication system 200 of Figure 2 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 200 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.

[0097] In some examples, the telecommunication network 202 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunication network 202 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 202. For example, the telecommunication network 202 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.

[0098] In some examples, the UEs 212 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 204 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 204. 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).

[0099] In the example, a hub 214 communicates with the access network 204 to facilitate indirect communication between one or more UEs (e.g., UE 212C and / or 212D) and network nodes (e.g., network node 210B). In some examples, the hub 214 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 214 may be a broadband router enabling access to the core network 206 for the UEs. As another example, the hub 214 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 210, or by executable code, script, process, or other instructions in the hub 214. As another example, the hub 214 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 214 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 214 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 214 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 214 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0100] The hub 214 may have a constant / persistent or intermittent connection to the network node 210B. The hub 214 may also allow for a different communication scheme and / or schedule between the hub 214 and UEs (e.g., UE 212C and / or 212D), and between the hub 214 and the core network 206. In other examples, the hub 214 is connected to the core network 206 and / or one or more UEs via a wired connection. Moreover, the hub 214 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 204 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 210 while still connected via the hub 214 via a wired or wireless connection. In some embodiments, the hub 214 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 210B. In other embodiments, the hub 214 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 21 OB, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0101] Figure 3 shows a UE 300 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 3 GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0102] 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).

[0103] The UE 300 includes processing circuitry 302 that is operatively coupled via a bus 304 to an input / output interface 306, a power source 308, memory 310, a communication interface 312, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 3. 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.

[0104] The processing circuitry 302 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 310. The processing circuitry 302 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 302 may include multiple Central Processing Units (CPUs).

[0105] In the example, the input / output interface 306 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 300. 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.

[0106] In some embodiments, the power source 308 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 308 may further include power circuitry for delivering power from the power source 308 itself, and / or an external power source, to the various parts of the UE 300 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging the power source 308. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 308 to make the power suitable for the respective components of the UE 300 to which power is supplied.

[0107] The memory 310 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 310 includes one or more application programs 314, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 316. The memory 310 may store, for use by the UE 300, any of a variety of various operating systems or combinations of operating systems.

[0108] The memory 310 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 310 may allow the UE 300 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 310, which may be or comprise a device-readable storage medium.

[0109] The processing circuitry 302 may be configured to communicate with an access network or other network using the communication interface 312. The communication interface 312 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 322. The communication interface 312 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 318 and / or a receiver 320 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 318 and receiver 320 may be coupled to one or more antennas (e.g., the antenna 322) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0110] In the illustrated embodiment, communication functions of the communication interface 312 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.

[0111] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 312, 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).

[0112] 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.

[0113] 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 300 shown in Figure 3. 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.

[0114] 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.

[0115] Figure 4 shows a network node 400 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).

[0116] 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).

[0117] 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).

[0118] The network node 400 includes processing circuitry 402, memory 404, a communication interface 406, and a power source 408. The network node 400 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 400 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 400 may be configured to support multiple RATs. In such embodiments, some components may be duplicated (e.g., separate memory 404 for different RATs) and some components may be reused (e.g., a same antenna 410 may be shared by different RATs). The network node 400 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 400, 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 400.

[0119] The processing circuitry 402 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 400 components, such as the memory 404, to provide network node 400 functionality.

[0120] In some embodiments, the processing circuitry 402 includes a System on a Chip (SOC). In some embodiments, the processing circuitry 402 includes one or more of Radio Frequency (RF) transceiver circuitry 412 and baseband processing circuitry 414. In some embodiments, the RF transceiver circuitry 412 and the baseband processing circuitry 414 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 412 and the baseband processing circuitry 414 may be on the same chip or set of chips, boards, or units. The memory 404 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 402. The memory 404 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 402 and utilized by the network node 400. The memory 404 may be used to store any calculations made by the processing circuitry 402 and / or any data received via the communication interface 406. In some embodiments, the processing circuitry 402 and the memory 404 are integrated.

[0121] The communication interface 406 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 406 comprises port(s) / terminal(s) 416 to send and receive data, for example to and from a network over a wired connection. The communication interface 406 also includes radio front-end circuitry 418 that may be coupled to, or in certain embodiments a part of, the antenna 410. The radio front-end circuitry 418 comprises filters 420 and amplifiers 422. The radio front-end circuitry 418 may be connected to the antenna 410 and the processing circuitry 402. The radio front-end circuitry 418 may be configured to condition signals communicated between the antenna 410 and the processing circuitry 402. The radio front-end circuitry 418 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 418 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of the filters 420 and / or the amplifiers 422. The radio signal may then be transmitted via the antenna 410. Similarly, when receiving data, the antenna 410 may collect radio signals which are then converted into digital data by the radio front-end circuitry 418. The digital data may be passed to the processing circuitry 402. In other embodiments, the communication interface 406 may comprise different components and / or different combinations of components.

[0122] In certain alternative embodiments, the network node 400 does not include separate radio front-end circuitry 418; instead, the processing circuitry 402 includes radio front-end circuitry and is connected to the antenna 410. Similarly, in some embodiments, all or some of the RF transceiver circuitry 412 is part of the communication interface 406. In still other embodiments, the communication interface 406 includes the one or more ports or terminals 416, the radio front-end circuitry 418, and the RF transceiver circuitry 412 as part of a radio unit (not shown), and the communication interface 406 communicates with the baseband processing circuitry 414, which is part of a digital unit (not shown).

[0123] The antenna 410 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 410 may be coupled to the radio front-end circuitry 418 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 410 is separate from the network node 400 and connectable to the network node 400 through an interface or port.

[0124] The antenna 410, the communication interface 406, and / or the processing circuitry 402 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node 400. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 410, the communication interface 406, and / or the processing circuitry 402 may be configured to perform any transmitting operations described herein as being performed by the network node 400. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0125] The power source 408 provides power to the various components of the network node 400 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 408 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 400 with power for performing the functionality described herein. For example, the network node 400 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 408. As a further example, the power source 408 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.

[0126] Embodiments of the network node 400 may include additional components beyond those shown in Figure 4 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 400 may include user interface equipment to allow input of information into the network node 400 and to allow output of information from the network node 400. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 400. In some embodiments providing a core network node, such as core network node 208 of FIG. 2, some components, such as the radio frontend circuitry 418 and the RF transceiver circuitry 412 may be omitted.

[0127] Figure 5 is a block diagram illustrating a virtualization environment 500 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 500 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 500 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.

[0128] Applications 502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 500 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0129] Hardware 504 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 506 (also referred to as hypervisors or Virtual Machine Monitors (VMMs)), provide VMs 508A and 508B (one or more of which may be generally referred to as VMs 508), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0130] The VMs 508 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, 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.

[0131] In the context of NFV, a VM 508 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 508, and that part of the hardware 504 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 508 on top of the hardware 504 and corresponds to the application 502.

[0132] The hardware 504 may be implemented in a standalone network node with generic or specific components. The hardware 504 may implement some functions via virtualization. Alternatively, the hardware 504 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 510, which, among others, oversees lifecycle management of the applications 502. In some embodiments, the hardware 504 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 512 which may alternatively be used for communication between hardware nodes and radio units.

[0133] 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.

[0134] 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.

[0135] 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.

Claims

CLAIMS1. A method performed by a User Equipment, UE, (100) for Artificial Intelligence, Al, / Machine Learning, ML, (AIML) model or functionality availability reporting, the method comprising: sending (106), to a network node (102), an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE (100).

2. The method of claim 1, wherein the indicated reason that the AIML model or the one or more AIML models used for the AIML-based functionality is or are not available at the UE (100) comprises any one or more of the following: the AIML model or the one or more AIML models used for the AIML-based functionality is or are not available at the UE (100); the AIML model or the one or more AIMLs used for the AIML-based functionality is or are in need of training or re-training; the AIML model or the one or more AIML models used for the AIML-based functionality is or are in need of training or re-training for one or more current or given conditions; the AIML model or the one or more AIML models used for the AIML-based functionality needs network assistance for training or re-training to be applicable; the AIML model or the one or more AIML models used for the AIML-based functionality needs network assistance for training or re-training to be applicable under one or more current or given conditions; the AIML model or the one or more AIML models used for the AIML-based functionality is or are currently being downloaded or updated; the AIML model or the one or more AIML models used for the AIML-based functionality is or are trained but not yet available at the UE (100).

3. The method of claim 1 or 2, wherein the applicability report further comprises time delay information which indicates when the AIML model or the one or more AIML models used for the AIML-based functionality will be available.

4. The method of any of claims 1 to 3, wherein the applicability report further comprisesinformation related to a request for data collection for training or re-training of the AIML model or the one or more AIML models used for the AIML-based functionality.

5. The method of any of claims 1 to 4, wherein the applicability report further comprises information that indicates one or more recommended configurations for the AIML model or the one or more AIML models used for the AIML-based functionality.

6. The method of any of claims 1 to 5, further comprising receiving (108A) an activation message from the network node (102) that enables at least one corresponding functionality after a preconfigured, defined, or indicated amount of time or after a time delay indicated in the applicability report.

7. The method of any of claims 1 to 6, wherein the information comprised in the applicability report indicates that the reason that the AIML model or AIML-based functionality if not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality is or are unavailable because the AIML model or the one or more AIML models used for the AIML-based functionality need to be trained or re-trained.

8. The method of claim 7, further comprising receiving (108A), from the network node (102), information that configures one or more parameters that enable the UE (100) to collect data for training or re-training of the AIML model or the one or more AIML models used for the AIML- based functionality.

9. The method of any of claims 1 to 6, wherein the information comprised in the availability report indicates that the reason that the AIML model or the AIML-based functionality is not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality need to be downloaded or updated.

10. The method of claim 9, further comprising receiving (108A), from the network node (102), information that configures one or more parameters that enable the UE (100) to download or update the AIML model or the one or more AIML models used for the AIML-based functionality.

11. The method of any of claims 1 to 10, wherein the information explicitly indicates that the AIML model or AIML-based functionality is not applicable at the UE (100).

12. The method of any of claims 1 to 10, wherein the information implicitly indicates that the AIML model or AIML-based functionality is not applicable at the UE (100).

13. The method of any of claims 1 to 12, wherein the information explicitly indicates the reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality is not available at the UE (100).

14. The method of any of claims 1 to 12, wherein the information implicitly indicates the reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality is or are not available at the UE (100).

15. A User Equipment (100) for Artificial Intelligence, Al, / Machine Learning, ML, (AIML) model or functionality availability reporting, the UE (100) adapted to: send (106), to a network node (102), an applicability report for an AIML model or AIML- based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE (100).

16. The UE (100) of claim 15, further adapted to perform the method of any of claims 2 to 14.

17. A User Equipment (100; 300) for Artificial Intelligence, Al, / Machine Learning, ML, (AIML) model or functionality availability reporting, the UE (100; 300) comprising: a communication interface (312) comprising a transmitter (318) and a receiver (320); and processing circuitry (302) associated with the communication interface (312), the processing circuitry (302) configured to cause the UE (100; 300) to: send (106), to a network node (102), an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE (100).

18. The UE (100) of claim 17, wherein the processing circuitry (302) is further configured to cause the UE (100; 300) to perform the method of any of claims 2 to 14.

19. A method performed by a network node (102) for reception and handling of Artificial Intelligence, Al, / Machine Learning, ML, (AIML) model or functionality availability reporting, the method comprising: receiving (106), from a User Equipment, UE, (100), an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE (100); and performing (108) one or more actions based on the applicability report and the reason that the AIML model or AIML-based functionality is not applicable at the UE (100).

20. The method of claim 19, wherein the indicated reason that the AIML model or the one or more AIML models used for the AIML-based functionality is not available at the UE (100) comprises any one or more of the following: the AIML model or the one or more AIML models used for AIML-based functionality is or are not available at the UE (100); the AIML model or one or more AIML models for the AIML-based functionality is or are in need of training or re-training; the AIML model or the one or more AIML models used for the AIML-based functionality is or are in need of training or re-training for one or more current or given conditions; the AIML model or the one or more AIML models used for the AIML-based functionality needs network assistance for training or re-training to be applicable; the AIML model or the one or more AIML models used for the AIML-based functionality needs network assistance for training or re-training to be applicable under one or more current or given conditions; the AIML model or the one or more AIML models used for the AIML-based functionality is or are currently being downloaded or updated; the AIML model or the one or more AIML models used for the AIML-based functionality is or are trained but not yet available at the UE (100).

21. The method of claim 19 or 20, wherein the applicability report further comprises time delay information which indicates when the AIML model or the one or more AIMLs used for the AIML- based functionality will be available.

22. The method of any of claims 19 to 21, wherein the applicability report further comprises information related to a request for data collection for training or re-training of the AIML model or the one or more AIMLs used for the AIML-based functionality.

23. The method of any of claims 19 to 22, wherein the applicability report further comprises information that indicates one or more recommended configurations for the AIML model or the one or more AIML models used for the AIML-based functionality.

24. The method of any of claims 19 to 23, further comprising sending (108A) an activation message to the UE (100) that enables at least one corresponding functionality after a preconfigured, defined, or indicated amount of time or after a time delay indicated in the applicability report.

25. The method of any of claims 19 to 24, wherein the information comprised in the availability report indicates that the reason is that the AIML model or the one or more AIML models used for the AIML-based functionality needs to be trained or re-trained.

26. The method of claim 25, wherein performing (108) the one or more actions comprises sending (108 A), to the UE (100), information that configures one or more parameters that enable the UE (100) to collect data for training or re-training of the AIML model or the one or more AIML models used for the AIML-based functionality.

27. The method of any of claims 19 to 24, wherein the information comprised in the availability report indicates that the reason is that the AIML model or the one or more AIML models used for the AIML-based functionality needs to be downloaded or updated.

28. The method of claim 27, wherein performing (108) the one or more actions comprises sending (108 A), to the UE (100), information that configures one or more parameters that enable the UE (100) to download or update the AIML model or the one or more AIML models used for the AIML-based functionality.

29. The method of any of claims 19 to 28, wherein the information explicitly indicates that the AIML model or AIML-based functionality is not applicable at the UE (100).

30. The method of any of claims 19 to 28, wherein the information implicitly indicates that the AIML model or AIML-based functionality is not applicable at the UE (100).

31. The method of any of claims 19 to 30, wherein the information explicitly indicates the reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or the one or more AIML models used for the AIML-based functionality is not available at the UE (100).

32. The method of any of claims 19 to 30, wherein the second information implicitly indicates the reason that the AIML model or AIML-based functionality is not applicable at the UE (100) is that the AIML model or the one or more AIML models used for the AIML-based functionality is not available at the UE (100).

33. A network node (102) for reception and handling of Artificial Intelligence, Al, / Machine Learning, ML, (AIML) model or functionality availability reporting, the network node (102) adapted to: receive (106), from a User Equipment, UE, (100), an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is or are not available at the UE (100); and perform (108) one or more actions based on the applicability report and the reason that the AIML model or AIML-based functionality is not applicable at the UE (100).

34. The network node (102) of claim 33, further adapted to perform the method of any of claims 20 to 32.

35. A network node (102; 400) for reception and handling of Artificial Intelligence, Al, / Machine Learning, ML, (AIML) model or functionality availability reporting, the network node(102; 400) comprising processing circuitry (402) configured to cause the network node (102; 400) to: receive (106), from a User Equipment, UE, (100), an applicability report for an AIML model or AIML-based functionality, the applicability report comprising information that indicates that the AIML model or AIML-based functionality is not applicable and that a reason that the AIML model or AIML-based functionality is not applicable is that the AIML model or one or more AIML models used for the AIML-based functionality is are not available at the UE (100); and perform (108) one or more actions based on the applicability report and the reason that the AIML model or AIML-based functionality is not applicable at the UE (100).

36. The network node (102; 400) of claim 35, wherein the processing circuitry (402) is further configured to cause the network node (102; 400) to perform the method of any of claims 20 to 32.

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