Sending and receiving information

Abstract

In an example, a method performed by a User Equipment (UE) for sending information to a first network node is provided The UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The method comprises sending information to the first network node. The information identifies one or more of: whether the functionality is currently applicable or currently not applicable; whether applicability of the functionality is dynamic or static; and / or one or more conditions under which the functionality is applicable.

Description

[0001] SENDING AND RECEIVING INFORMATION Background Artificial intelligence (AI) or machine learning (ML) technique comprises of one or more algorithms, which use a set of data as input for training one or more AI / ML models. The output of the AI / ML model is used by the device (e.g. user equipment (UE), base station (BS) or another node) for performing certain operations or taking certain decisions (e.g. handover etc.) fully or partially based on the prediction, which in turn depends on the trained model. The AI / ML model can be trained in the device online (or on-the-fly while processing the data) or offline in the background. More specifically: • Online training is an AI / ML training process where the model being used for inference is (typically continuously) trained in (near) real-time with the arrival of new training samples or data. • Offline training is an AI / ML training process where the model is trained based on collected samples or data, and where the trained model is later used or delivered for inference. AI / ML model inference refers to a process of using a trained AI / ML model to produce a set of outputs based on a set of inputs. AI / ML models can be trained in a device, which can be a UE, a network node, or another node. In this respect, AI / ML models can be broadly classified as: • Case I: UE-side (AI / ML) model. It is an AI / ML model whose inference is performed entirely at the UE. • Case II: Network-side (AI / ML) model. It is an AI / ML model whose inference is performed entirely at the network. • Case III: One-sided (AI / ML) model. It is a UE-side (AI / ML) model or a Network-side (AI / ML) model. • Case IV: Two-sided (AI / ML) model. It is a paired AI / ML model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e., the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa. An AI / ML model can be transferred or delivered over the air interface either in terms of one or more parameters of a model structure known at the receiving end or a new model with parameters. The model delivery may contain a full model or a partial model. The term lifecycle management (LCM) of an AI / ML model refers to the process of developing, deploying and maintaining the AI / ML model. An AI / ML model training pipeline includes several processing stages as gathering unprocessed input data from data repositories (data ingestion), finding high-quality input features (data pre-processing), finding the optimal mapping of the model input features to a desired model output target in a sense determined by a loss function (model training), evaluate model performance on unseen data from a functional level as well as from a system level when relevant (model evaluation). The training pipeline typically ends with a model registration stage, which may comprise of operations to make the ML model runnable via compilation to a specific HW and of steps like versioning and packaging of the model so that it can be executed. An example of the AI / ML model training pipeline illustrating different stages is shown in Figure 1. AI / ML models can be used for UE positioning. A UE or a gNB, depending on capability, can have a trained model stored inside the device, or have an untrained AI / ML that can be trained on-the-fly to either produce measurements that are required to localize a UE within a radio access network (RAN) coverage area or directly predict / determine the UE location by exploiting the measurements performed by the UE or gNodeB (gNB) on reference signals such as positioning reference signal (PRS), sounding reference signal (SRS) etc. within a RAN coverage area. Measurements predicted / determined by the UE by exploiting an AI / ML model can be defined as, but not limited to: • RSTD: It is reference signal time difference (RSTD) between the positioning node j and the reference positioning node i. It is measured on the DL PRS signals and always involves two cells (a cell is interchangeably called a Transmission Reception Point, TRP, or a cell may have multiple TRPs). • UE Rx-Tx time difference: It is defined as TUE-RX-TUE-TX. Where: o TUE-RXis the UE received timing of downlink subframe #i from a positioning node, defined by the first detected path in time. It is measured on PRS signals received from the gNB. o TUE-TXis the UE transmit timing of uplink subframe #j that is closest in time to the subframe #i received from the positioning node. Before 3GPP Release 16 (Rel.16), Long Term Evolution (LTE)-based positioning was the prevalent RAT based positioning solution. Starting from the Rel.16 specification, positioning is also supported in New Radio (NR). Positioning in NR is supported by the architecture shown in Figure 2. Interactions between the gNodeB and the device are supported via the Radio Resource Control (RRC) protocol, while the location node interfaces with the UE via the LTE positioning protocol (LPP). LPP is a common protocol to both NR and LTE. Location Management Function (LMF) is the location node in NR. There are also interactions between the location node and the gNodeB via the NRPPa protocol. The positioning architecture in Figure 2 will also be used to support AI / ML based positioning. Rel.19 work on introducing AI / ML based positioning will not only exploit the legacy protocol but will also rely on already defined / existing reference signals that are used for positioning. 3GPP Technical Specification (TS) 24.501 v 18.1.0, clause 5.4.4.1, includes details on UE radio capability information storage in the Access and Mobility Management Function (AMF). 3GPP TS 23.273 v 18.4.0, clause 4.3.7, provides details on the Access and Mobility Management Function (AMF). Related to the discussion above, applicability reporting has been discussed during the Rel.18 study item. Applicability reporting allows the UE to inform a network node (e.g. gNB, LMF) about the applicability of an AI / ML model / functionality while the UE is connected to this gNB / LMF. An AI / ML model / functionality may be applicable or not depending on a number of factors, so-called applicability conditions, that are only partly under the control of the gNB / LMF. For example, whether the UE has an AI / ML model that is applicable given the current location of the UE, or given the current speed of the UE, is not something that the network can control or can know, because typically it is assumed that the UE-side model is not trained and generated by the gNB / LMF. Rather, it is typically assumed that the UE-side model is trained and generated by a node outside the RAN, such as an OTT server or core network (CN) function controlled by the UE-vendor or by the mobile network operator (MNO). Such UE conditions may also be referred to as UE-side conditions. Additionally, whether an AI / ML model / functionality is applicable depends on network (NW)- side conditions that the gNB / LMF was operating during the UE training (e.g. UE radio configuration configured by the gNB / LMF, network operating conditions such as transmitting power, radio capacity, antenna layout, network load, etc), and the NW-side conditions operated during the inference at the UE (inference configuration). Two types of applicability reporting were identified during the Rel.18 study item, proactive reporting and reactive reporting. Reactive reporting implies the gNB inquiring the UE about the applicability of AI / ML model / functionality, and the UE responding with the AI / ML models / functionalities that are applicable, whereas with the proactive reporting the UE signals to the network autonomously, i.e., without any inquiry, about the AI / ML model / functionalities that are applicable. Reactive reporting can be used for example in response to a network configuration, e.g. inference related configuration, including for example beam resource configuration of Set A and / or Set B. The UE will then respond indicating if the AI / ML model / functionality is applicable based on this inference configuration. Proactive reporting can be configured to the UE to allow the UE to report at any point in time a change in the applicability of an AI / ML model / functionality, e.g. an AI / ML model / functionality that was not applicable becomes applicable or vice versa. There currently exist certain challenges. For example, Rel-19 discusses AI / ML for physical layer. The UE / gNB / LMF may possess multiple AI / ML models / functionalities each associated to different settings: • AI / ML Model / functionality associated to certain area • AI / ML Model / functionality associated to certain NW configurations • AI / ML Model / functionality associated to certain UE conditions Some of the models may be considered static (e.g. because they are applicable over a large area spanning the area coverage of multiple gNBs or LMFs, or because they can be applicable for a longer period of time), whereas some of the models may still need retuning (e.g., if a model trained in one area, to make it applicable in other area some more samples may be needed to retune the model from area 1 to be applicable to area 2). In such cases, the model and its applicability may also be dynamic, and the UE’s capability to perform AI / ML positioning under such cases may also be dynamic. It is unclear on how UE will signal such capability and applicability to the NW node. Further, the NW actions that should be taken based upon such dynamic signaling is unclear. Another problem is how AI / ML positioning would inter-work with existing on-demand PRS configurations. Rel-17 introduced on-demand PRS whereby the NW may save energy by transmitting PRS only when required (UE using PRS available) or by transmitting sporadically if QoS for positioning is not stringent. Similarly, the UE may request preferred DL-PRS configurations. In such cases when on-demand PRS is operational, the NW may maintain a list of DL-PRS configurations and apply one of them. It is possible that the NW may vary the DL-PRS configuration depending upon balance between energy saving and to maintain certain level of QoS. Rel-19 has a work item (WI) for AI / ML for positioning which allows a UE to use an AI / ML model for obtaining measurements or location. The UE may perform training (collect data sets) for the AI / ML model. There should be consistency between the training data set and inference data set, otherwise the output may be erroneous. It is challenging to ensure consistency, especially when there are features such as on-demand PRS. Summary Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. One aspect of the present disclosure provides a method performed by a User Equipment (UE) for sending information to a first network node, wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The method comprises sending information to the first network node, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable. Another aspect of the present disclosure provides a method performed by a first network node for receiving information from a User Equipment (UE), wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The method comprises receiving information from the UE, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable for the UE; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable. A further aspect of the present disclosure provides a tangible, non-transient computer- readable medium comprising instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations in a User Equipment (UE) for sending information to a first network node, wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The operations comprise sending information to the first network node, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable. A still further aspect of the present disclosure provides a tangible, non-transient computer- readable medium comprising instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations a first network node for receiving information from a User Equipment (UE), wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The operations comprise receiving information from the UE, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable. Another aspect of the present disclosure provides apparatus in a User Equipment (UE) for sending information to a first network node, wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The apparatus comprises processing circuitry and a memory. The apparatus is configured to send information to the first network node, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable. An additional aspect of the present disclosure provides apparatus in a first network node for receiving information from a User Equipment (UE), wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The apparatus comprises processing circuitry and a memory. The apparatus is configured to receive information from the UE, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable. Brief Description of the Drawings For a better understanding of the embodiments of the present disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which: Figure 1 illustrates an example of an AI / ML model training pipeline; Figure 2 illustrates an example of architecture supporting positioning in NR; Figure 3 is a flow chart illustrating a method performed by a UE for sending information to a first network node; Figure 4 is a flow chart illustrating a method performed by a first network node for receiving information from a UE; Figure 5 is a flow chart of an example of a method according to embodiments of this disclosure, from a network node perspective; Figure 6 is a flow chart of an example of a method according to embodiments of this disclosure, from a UE perspective; Figure 7 illustrates an example of communications in a network according to examples of this disclosure; Figure 8 illustrates an example of communications in a network according to examples of this disclosure; Figure 9 shows an example of a communication system in accordance with some embodiments; Figure 10 shows a UE in accordance with some embodiments; Figure 11 shows a network node in accordance with some embodiments; and Figure 12 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized. Detailed Description 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. Examples of terminology used in this disclosure is provided as follows: ML model: a manageable representation of an ML model algorithm. An ML model algorithm is a mathematical algorithm through which running a set of input data can generate a set of inference output. A ML model algorithm is proprietary and not in scope for standardization. A ML model may include metadata. Metadata may include e.g. information related to the trained model, and applicable runtime context. ML model training: a process performed by an ML training function to take training data, run it through an ML model algorithm, derive the associated loss and adjust the parameterization of that ML model iteratively based on the computed loss and generate the trained ML model. ML model initial training: a process of training an initial version of an ML model. ML model re-training: a process of training a previous version of an ML model and generate a new version. A new version of a trained ML model may for example support the same type of inference as the previous version of the ML model, i.e., the data type of inference input and data type of inference output remain unchanged between the two versions of the ML model, but parameter values might be different for the re-trained model. ML model joint training: a process of training a group of ML models. ML training function: a logical function with ML model training capabilities. ML model testing: a process of testing an ML model using testing data. ML testing function: a logical function with ML model testing capabilities. AI / ML inference: a process of running a set of input data through a trained ML model to produce set of output data, such as predictions. The inference may for example represent the process to realize AI capabilities by utilizing a trained ML model and other AI enablers if needed, hence the AI / ML prefix is used when referring to inference as compared to training and testing. AI / ML inference function: a logical function that employs trained ML model(s) to conduct inference. AI / ML inference emulation: running the inference process to evaluate the performance of an ML model in an emulation environment before deploying it into the target environment. ML model deployment: a process of making a trained ML model available for use in the target environment. The term AI / ML model / functionality applicability is used to identify whether the AI / ML model / functionality is available at the device, e.g., UE, or network node, and whether that can be applied by such device or network node. For example, an AI / ML model / functionality is determined to be applicable if it was trained to operate under certain conditions (NW- and / or UE-side conditions) Figure 3 depicts a method 300 in accordance with particular embodiments, for example a method performed by a User Equipment (UE) for sending information to a first network node, wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The functionality that uses the AI / ML model may in some examples comprise a UE positioning functionality. The method 300 may be performed by a UE or wireless device (e.g. the UE QQ112 or UE QQ200 as described later with reference to Figures 9 and 10 respectively). The method 300 begins at step 302 with sending information to the first network node, wherein the information identifies one or more of: whether the functionality is currently applicable or currently not applicable; whether applicability of the functionality is dynamic or static; and / or one or more conditions under which the functionality is applicable. The first network node may be, for example, a base station, Radio Access Network (RAN) node, gNodeB (gNB) or location management function (LMF). In some examples, the method 300 may comprise determining whether the functionality is currently applicable or currently not applicable based on the one or more conditions. Additionally or alternatively, in some examples, the method 300 may comprise determining whether the applicability of the functionality is dynamic or static based on the one or more conditions. The one or more conditions may comprise one or more of the following non- limiting examples: • one or more first areas in which the functionality is applicable; • one or more first time durations (or time periods) in which the functionality is applicable; and / or • one or more first configurations of the UE. The first configuration(s) of the UE may comprise for example first reference signal configuration(s) of the UE and / or first Position Reference Signal (PRS) configuration(s) of the UE. The method 300 may also in some examples comprising determining that the applicability of the functionality is dynamic if the one or more conditions include one or more of the first area(s), the first time duration(s) and / or the first configuration(s); and / or determining that the applicability of the functionality is static if the one or more conditions do not include one or more of the first area(s), the first time duration(s) and / or the first configuration(s). In some examples, the method 300 may comprise determining that the applicability of the functionality is static if there are no conditions under which the functionality is applicable and / or if the functionality is applicable under all conditions. The method 300 may also in come examples comprise sending the information to the first network node in response to receiving a request from the first network node. Before receiving the request from the first network node, the method 300 may for example include sending capability information of the UE to the first network node, wherein the capability information identifies the functionality. The request from the first network node may for example identify one or more hypothetical conditions, and the information identifies whether the functionality is applicable or not applicable under the one or more hypothetical conditions. In some examples, if the functionality is not applicable under the one or more hypothetical conditions, the information may identify the one or more conditions under which the functionality is applicable. The one or more hypothetical conditions may comprise one or more of the following non-limiting examples: • one or more hypothetical areas in which the UE may be located; • one or more hypothetical time durations; and / or • one or more hypothetical configurations of the UE. The one or more hypothetical configurations of the UE may comprise for example one or more hypothetical reference signal configurations of the UE and / or one or more hypothetical Position Reference Signal (PRS) configurations of the UE. The method 300 may in some examples comprise determining a change in the applicability of the functionality, and sending further information to the first network node, wherein the further information identifies whether the functionality is currently applicable or currently not applicable. That is, for example, the UE may update the network when the applicability changes. In some examples, the method 300 may comprise determining whether the functionality is currently applicable or currently not applicable based on one or more current conditions. This may in some examples comprise comparing the one or more current conditions to the one or more conditions under which the functionality is applicable. The one or more current conditions comprise one or more of the following non-limiting examples: • an area in which the UE is currently located; • a current time; and / or • a current configuration of the UE. The current configuration of the UE may comprise for example a current reference signal configuration of the UE and / or a current Position Reference Signal (PRS) configuration of the UE. Figure 4 depicts a method in accordance with particular embodiments, such as for example a method performed by a first network node for receiving information from a User Equipment (UE), wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model. The functionality that uses the AI / ML model may in some examples comprise a UE positioning functionality. The method 400 may be performed by a network node (e.g. the network node QQ110 or network node QQ300 as described later with reference to Figures 9 and 11 respectively). For example, the first network node may comprise a base station, gNodeB (gNB) or location management function (LMF). The method 400 begins at step 402 with receiving information from the UE, wherein the information identifies one or more of: whether the functionality is currently applicable or currently not applicable for the UE; whether applicability of the functionality is dynamic or static; and / or one or more conditions under which the functionality is applicable. In some examples, the information identifies the one or more conditions under which the functionality is applicable. The method 300 may also in come examples comprise determining whether the applicability of the functionality is dynamic or static based on the one or more conditions. The one or more conditions may comprise one or more of the following non-limiting examples: • a first area in which the functionality is applicable; • a first time duration in which the functionality is applicable; and / or • a first configuration of the UE. The first configuration of the UE comprises for example a first reference signal configuration of the UE and / or a first Position Reference Signal (PRS) configuration of the UE. The method 300 may also in some examples comprise determining that the applicability of the functionality is dynamic if the one or more conditions include one or more of the first area, the first time duration and / or the first configuration; and / or determining that the applicability of the functionality is static if the one or more conditions do not include one or more of the first area, the first time duration and / or the first configuration. In some examples, the method 300 comprises determining that the applicability of the functionality is static if there are no conditions under which the functionality is applicable and / or if the functionality is applicable under all conditions. In some examples, the method 300 may comprise sending a request for the information to the UE before receiving the information from the UE. Before sending the request to the UE, capability information of the UE may be received from the UE, wherein the capability information identifies the functionality. In some examples, the request may identify one or more hypothetical conditions, and the information identifies whether the functionality is applicable or not applicable under the one or more hypothetical conditions. The one or more hypothetical conditions may comprise one or more of the following non-limiting examples: • a one or more hypothetical areas in which the UE may be located; • a one or more hypothetical time durations; and / or • a one or more hypothetical configurations of the UE. The one or more hypothetical configurations of the UE may comprise for example a one or more hypothetical reference signal configurations of the UE and / or a one or more hypothetical Position Reference Signal (PRS) configurations of the UE. In some examples, if the functionality is not applicable under the one or more hypothetical conditions, the information identifies the one or more conditions under which the functionality is applicable. In some examples, the method 300 comprises receiving further information from the UE, wherein the further information identifies whether the functionality is currently applicable or currently not applicable for the UE. In some examples, the method 300 may comprise determining whether the functionality is currently applicable or currently not applicable for the UE based on one or more current conditions. This may comprise for example comparing the one or more current conditions for the UE to the one or more conditions under which the functionality is applicable. The one or more current conditions for the UE may comprise one or more of the following non-limiting examples: • an area in which the UE is currently located; • a current time; and / or • a current configuration of the UE. The current configuration of the UE may comprise for example a current reference signal configuration of the UE and / or a current Position Reference Signal (PRS) configuration of the UE. In some examples, the method 300 may comprise, if the applicability of the functionality is static or if the functionality is currently applicable for the UE, sending, to a second network node, a request to store information identifying the functionality for the UE. The method 300 may also in some examples comprise, if the applicability of the functionality is dynamic or if the functionality is currently not applicable for the UE, sending, to a second network node, a request to not store information identifying the functionality for the UE and / or a request to delete information identifying the functionality for the UE. The second network node may comprise for example an Access and Mobility Management Function (AMF). Further example embodiments are provided below. Based on this determination, in one example method, the UE indicates in a first indication to the gNB / LMF whether a certain AI / ML model / functionality corresponds to a static or dynamic capability and based on this information the gNB / LMF determines whether to transmit / transfer the capability associated to such AI / ML model / functionality to the AMF or not. In another method, the UE indicates in the first indication to the gNB / LMF, the validity area or time validity associated to a certain gNB / LMF, and based on this information the gNB / LMF determines whether to transmit / transfer the capability associated to such AI / ML model / functionality to the AMF or not. The UE indicating that the AI / ML model / functionality is dynamic comprises the UE indicating the applicability conditions of the AI / ML model / functionality such as: • When the AI / ML model / functionality is applicable, e.g., the validity area or time validity under which the AI / ML model / functionality is applicable • The PRS configuration under which the AI / ML model / functionality is applicable Figure 5 is a flow chart of an example of a method 500 according to embodiments of this disclosure, from a network node perspective. The network node may be for example a gNB, LMF, base station, or other network node. Step 510 of the method 500 comprises sending a request UE to provide capability, optionally flagging if the capability is dynamic. Step 520 comprises receive capability information from UE with dynamic flag indication. Step 530 comprises taking one or more NW actions. Examples of NW actions include filter capabilities for forwarding to AMF for storage depending upon the indicated flag, and / or configure fallback options if the capability is dynamic. Figure 6 is a flow chart of an example of a method 600 according to embodiments of this disclosure, from a UE perspective. Step 610 of the method 600 comprises classifying the UE capability (e.g. the AI / ML functionality applicability) as dynamic or static. Step 620 of the method 600 comprises providing the capability to a gNB (communication capabilities) and LMF (positioning capabilities) with dynamic flag indication. The flag indicates whether the capability (e.g. the AI / ML functionality applicability) is dynamic or static. Figure 7 illustrates an example of communications in a network according to examples of this disclosure. As shown in Figure 7, a gNB or LMF 702 sends a request 704 for AI / ML capabilities to a UE 706, optionally with a dynamic indication request. The UE 706 replies with a provision 708 of AI / ML capabilities with static and dynamic indication to the gNB / LMF 702. The gNB / LMF 702 filters the capabilities at stage 710 and then sends 712 only static capabilities for storage to AMF 714. It is also possible in some examples that the network (NW) quantifies / verifies if the capability can be considered static using its own logic (predictions) depending upon historical info. It is possible in some examples that signaling is split into multiple granular steps. For example, a LMF retrieves static capability first and enquires if there is any dynamic part associated with the provided capability and may decide which action to perform accordingly including the storage of the information for subsequent usage. Figure 8 illustrates another example of communications in a network according to examples of this disclosure. As illustrated in Figure 8, a UE 802 provides UE capability information 804 to LMF 806. The UE capability information 804 may indicate that the UE supports AI / ML for positioning. The LMF 806 then sends an enquiry 808 to the UE 802 as to whether the applicability conditions are met for AI / ML inference for the AI / ML positioning capability. The UE 802 send a reply 810 to indicate either that the applicability conditions are met, or alternatively that the applicability conditions are not met, in which case additional conditions may be provided by the UE 802 under which the applicability conditions would be met. At stage 812, if the applicability conditions are met then the LMF 806 select the AI / ML positioning procedure for inference. If the applicability conditions are not met, then the LMF 806 may decide to use classical (i.e. non-AI / ML) positioning procedure, or to provide configurations to the UE that meet the additional conditions for AI / ML inference. The LMF 806 may then send the AI / ML capability information 814 and the additional conditions to AMF 816 for storage. In some examples, a UE indicates the AI / ML for Positioning capabilities as static or dynamic using one of the following: • Explicit flag indicating that the capability is dynamic. • By providing applicability conditions (implying only if the conditions are met the capability can be applicable; i.e the capability is dynamic) o A mechanism where applicability conditions are expressed using on-demand PRS request indicating which PRS configuration is applicable for inference purpose. o A mechanism where applicability conditions are expressed using area ID indication. • A method performed by UE to request to NW to perform association for positioning configuration between training and inference, i.e., to request same positioning configuration that was used during training for the purpose inference (applicability conditions). In some examples, a signaling mechanism for specifying the applicability conditions of the model uses at least one of the following: • Area ID • On-Demand PRS configurations ID • Explicit PRS configuration parameters • Duration until which the model is deemed valid (expirationTime, UTC time stamp) An example ASN.1 implementation is provided below: NR-AI / ML-ProvideCapabilities ::= SEQUENCE { dynamicCapability ENUMERATED { true } OPTIONAL, areaID-r19 INTEGER (1..65535) OPTIONAL, requiredOD-DL-PRS-ModelInferenceList-r19 SEQUENCE (SIZE (1..maxOD-DL-PRS- Configs- r17)) OF DL-PRS-Configuration-ID-r17 OPTIONAL, predictedModelValidityDuration-r19 ENUMERATED {h1, h10, h24, d2, d4, d7, infinite} AI / MLModelAvailability-r19 BIT STRING { ueBasedStaticModel (0), ueAssistedStaticModel (1), ueBasedDynamicModel (2), ueBasedDynamicModel (3) } OPTIONAL } In some examples, a network node e.g. LMF takes appropriate action based upon the UE capability and applicability condition signaling: • Determine if capability and applicability conditions are static to be stored in AMF for future use. • Determine if any fallback configuration that would be required as part of positioning procedure. The network node e.g. LMF configures an Area ID specific to list of cells (as shown below in ASN.1) which the UE can use to denote the applicability condition of its model. An example ASN.1 implementation is provided below: 6.5.10.1 NR DL-TDOA Assistance Data – NR-DL-TDOA-ProvideAssistanceData The IE NR-DL-TDOA-ProvideAssistanceData is used by the location server to provide assistance data to enable UE-assisted and UE-based NR DL-TDOA. It may also be used to provide NR DL-TDOA positioning specific error reason. -- ASN1START NR-DL-TDOA-ProvideAssistanceData-r16 ::= SEQUENCE { nr-DL-PRS-AssistanceData-r16 NR-DL-PRS-AssistanceData-r16 OPTIONAL, -- Need ON nr-SelectedDL-PRS-IndexList-r16 NR-SelectedDL-PRS-IndexList-r16 OPTIONAL, -- Need ON nr-PositionCalculationAssistance-r16 NR- PositionCalculationAssistance-r16 OPTIONAL, -- Cond UEB nr-DL-TDOA-Error-r16 NR-DL-TDOA-Error-r16 OPTIONAL, -- Need ON ..., [[ nr-On-Demand-DL-PRS-Configurations-r17 NR-On-Demand-DL-PRS- Configurations-r17 OPTIONAL, -- Need ON nr-On-Demand-DL-PRS-Configurations-Selected-IndexList-r17 NR-On-Demand-DL-PRS- Configurations-Selected-IndexList-r17 OPTIONAL, -- Need ON assistanceDataValidityArea-r17 AreaID-CellList-r17 OPTIONAL -- Need ON ]], [[ nr-PeriodicAssistData-r18 NR-PeriodicAssistData-r18 OPTIONAL -- Cond CtrTrans ]], [[ areaID-r19 INTEGER (1.. 65535) OPTIONAL -- Need ON ]] } -- ASN1STOP areaID This field indicates the network area ID for the area indicated by the field assistanceDataValidityArea. Certain embodiments may provide one or more of the following technical advantage(s). For example, Handling of dynamic capability of UEs: There may be some UE chipsets which are capable of producing stable AI / ML models whose capabilities can be stored in longer duration. Enabling AI / ML capabilities stored at AMF is beneficial to reduce signaling and latency to exchange AI / ML capabilities between UE and LMF. Signaling mechanism for specifying the applicability conditions of the model using • Area ID • On-Demand PRS configurations ID • Explicit PRS configuration parameters • Duration until which the model is deemed valid (expirationTime, UTC time stamp) Certain embodiments of this disclosure may improve network performance. For example, according to examples of this disclosure, the UE determines whether an AI / ML model / functionality corresponds to a static or dynamic capability, wherein the UE determines that it is static if the said AI / ML model / functionality is applicable in at least a determined AI / ML model / functionality validity area or for at least a certain AI / ML model / functionality validity time, and wherein the UE determines that it is dynamic if the said AI / ML model / functionality is applicable or not applicable (dynamic applicability) in a determined AI / ML model / functionality validity area or within a certain AI / ML model / functionality validity time. In some examples, the UE may determine that an AI / ML model / functionality correspond to a static capability if such AI / ML model / functionality is determined to be applicable in a determined area, such as the area coverage of one or more gNBs, or one or more areas served by LMF. An AI / ML model / functionality may be determined to correspond to a dynamic capability if such AI / ML model / functionality is determined to be applicable or not applicable in a determined area, such as the area coverage of one or more gNBs, or one or more areas served by LMF. For example, an AI / ML model / functionality may be determined to correspond to a dynamic capability if within a certain area coverage of more than on gNB / LMF, the AI / ML model / functionality is applicable when the UE is operating under a first gNB / LMF, but it is not applicable when it is operating under a second gNB / LMF. Or when for example, the UE has performed partial training within a certain gNB / LMF, e.g., the UE determines that when training under such gNB / LMF the UE has not acquired a sufficient amount of samples, or it has trained only under certain specific gNB / LMF configurations (so called NW-side additional condition), so that if a different configuration (so called NW-side additional condition) is operated or configured to the UE during the inference, the AI / ML model / functionality is not applicable. Similarly, it may depend upon the type of PRS configurations that are being transmitted. If the NW varies the PRS configurations, then the model may not work for all the PRS configurations; e.g: if NW changes the PRS bandwidth from 20MHz to 5MHz or changes the periodicity from 20ms to 200ms etc. The AI / ML model(s) / functionality(ies) for which the UE should perform the above determination may be indicated by the gNB / LMF in a message, in response to which the UE transmits the first indication for the concerned AI / ML model(s) / functionality(ies). In one first method, the AI / ML model / functionality validity time or the AI / ML model / functionality validity area can be configured by the NW and may also be provided via broadcast or dedicated manner. Upon receiving such indications or based upon its own judgement, the UE classifies the AI / ML models as corresponding to a static or dynamic capability and reports it (capability, static or dynamic (valid time), and optional associated setting) to the NW node (gNB / LMF). The UE may for example associate to the capability indication of one AI / ML model / functionality, a first indication indicating whether the said capability is static or dynamic. Alternatively, the UE may indicate in the first indication, the area(s) or the validity time, in which the UE determines that the AI / ML model / functionality is applicable. For example, the UE may indicate the identity of the gNB(s) or cell(s) or frequency(ies) or LMF(s) under which the AI / ML model / functionality is applicable. The areas indicated by the UE may be a subset of the validity area indicated by the NW as per the above methods, e.g., one or more of the gNBs / cells / frequencies indicated by the NW as part of the validity area. In a second method, in which the gNB / LMF may or may not transmit the validity time / area to the UE, the UE does not perform any determination of whether an AI / ML model / functionality corresponds to a dynamic or static capability. It is the NW that quantifies / verifies if the capability associated to a certain AI / ML model / functionality can be considered static or dynamic. For example, the UE may indicate the area(s) or the validity time, in which the UE determines that the AI / ML model / functionality is applicable, and based on this information the gNB determines whether the AI / ML model functionality corresponds to a static or dynamic capability. And based on such determination, the gNB decides whether to transfer / transmit the capability associated to such AI / ML model / functionality to the AMF or not. The same logic can also be applied over NRPPa interface when gNB provides what are the supported configurations for AI / ML that it can apply to LMF. The gNB can indicate whether the models are stable and for how long can it be used for. Hence, LMF does not need to query to each gNB and can store the information for at least the duration of validity time. In some examples, a UE may keep track of usage of its AI / ML model (historical data) and decide how stable it is when it is in certain area. If the training and inference is consistent the UE may assume that the model is stable. The historical data would also provide / reflect for how long the consistency holds, e.g.: 100 Depending upon the outcome of level of consistency, the UE can decide whether the capability is static or dynamic. The UE may receive certain guidance (configuration of thresholds, timers) so that the UE can compare the percentage obtained for level of consistency with NW configured parameters to decide whether the capability is stable or dynamic. The UE may also decide on its own when there is no threshold configured or received from the NW. In some examples, the UE classifies its capability and signals whether its capability is static or dynamic to the NW. The UE may receive further configurations from the NW depending upon whether the capability is static or dynamic. The UE may receive fallback options (what UE should do) if the applicability condition of AI / ML usage / criteria may not be met / satisfied. For example, for the case of positioning, the NW may as part of initial configuration also provide the fallback option or procedure if AI / ML capability would not be available. The LMF may configure the UE to switch to classical method. Above it has been described how the UE in some examples indicates applicability conditions to the network that indicate when a model / function is applicable. Another approach that could be applied in some examples is that the UE indicates explicitly when a model / functionality is applicable and when it is not applicable, i.e., it would not be expressed as a condition which the network would need to evaluate, instead it would be an indication sent from the UE to the network which says that the model / functionality is applicable, or indicates that the model / functionality is not applicable. The UE may trigger sending that indication in response to a change in applicability. For example, if a model is only applicable in a certain area, the UE would indicate that the model / functionality is applicable when the UE enters (or shortly before or shortly after) the area. And the UE would indicate that the model / functionality is not applicable when the UE exits the area. The indication can be comprising a binary indication which is associated with a certain model / functionality and is set to a first value to indicate applicability, and a second value to indicate inapplicability. Alternatively, the UE may indicate an identifier of a model / functionality to indicate that it is applicable, and the UE may provide a set of identifiers to indicate which models / functionalities are applicable. Absence of an identifier for a model / functionality could be interpreted as inapplicability. These indications may be carried in a message carrying UE capability indications, or a separate message. In some examples, the network (NW) node, e.g. gNB / LMF, based on the received indication from the UE as per one or more of the previous example embodiments may determine whether to upload / transfer / transmit the capabilities to AMF or not. For example, in one method, if the UE indicates that the AI / ML model / functionality corresponds to a dynamic capability, then the gNB / LMF does not transfer this capability to the AMF. On the other hand, if the UE indicates that the AI / ML model / functionality corresponds to a static capability, then the LMF / gNB transfers this capability to the AMF. In another method, depending on the area(s) or the validity time indicated by the UE, the gNB / LMF may determine whether to upload / transfer / transmit the capabilities to AMF or not. For example, this determination can be based on how large is / are the validity area(s) or long is the validity time indicated by the UE. If the gNB / LMF transfer this capability to the AMF, the gNB / LMF may also indicate the validity area / time associated to such capability. If the UE indicates that the model is trained on on-demand PRS with on-demand PRS ID, LMF and gNB will determine the on duration / validity time of the on-demand PRS, and decide whether the capability is static or dynamic. The NW based on the received first indication can take different actions such as: • Filter the indicated capabilities for storage. Hence, gNB or LMF would not provide to AMF for storing such capability. In this case, the gNB / LMF may temporarily store this capability information in a local memory and discard it when the UE moves away (e.g. handover) from the coverage area of the gNB / LMF. • Providing fallback options (what UE should do) when the capability indicated is dynamic; that is there is risk that the applicability condition of AI / ML usage / criteria may not be met / satisfied. For example, for the case of positioning, the NW may as part of initial configuration also provide the fallback option or procedure if AI / ML capability would not be available. The LMF may configure the UE to switch to classical measurements. In some examples, a LMF receives capability indication with dynamic or static classification from UE / gNB and decides whether the capability should be stored and whether a switch (fall back) configuration is needed. The LMF may then configure area ID for allowing the UE to report applicability condition using area ID information. The LMF may then allow the UE to indicate preferred PRS configuration either explicitly or using on-demand PRS for applicability condition (ensure consistency between training and inference). An example implementation (for example as part of a 3GPP standard) is provided below. Changes to existing standards are underlined. 6.4.2 Common Positioning – CommonIEsRequestCapabilities The CommonIEsRequestCapabilities carries common IEs for a Request Capabilities LPP message Type. -- ASN1START CommonIEsRequestCapabilities ::= SEQUENCE { ..., [[lpp-message-segmentation-req-r14 BIT STRING { serverToTarget (0), targetToServer (1) } OPTIONAL -- Need ON ]], [[ remoteUE-IndicationReq-r18 ENUMERATED { true } OPTIONAL -- Cond NR ]], [[ nr-AI / ML-RequestCapabilities-r19 NR-AI / ML-RequestCapabilities-r19 OPTIONAL -- Need ON ]] } -- ASN1STOP Conditional presence Explanation NR This field is optionally present, need ON, for NR access. Otherwise it is not present. CommonIEsRequestCapabilities field descriptions lpp-message-segmentation-req This field, if present, indicates that the target device is requested to provide segmentation capabilities. If bit 0 is set to value 1, it indicates that the server is able to send segmented the target device; if bit 0 is set to value 0 it indicates that the server is not able to send segmented LPP messages to the target device. If bit 1 is set to value 1, it indicates that the server is able to receive segmented LPP messages from the target device; if bit 1 is set to value 0 it indicates that the server is not able to receive segmented LPP messages from the target device. remoteUE-IndicationReq This field, if present, indicates that the target device is requested to indicate if it operates as a L2 U2N Remote UE. nr-AI / ML-RequestCapabilities This field, if present, indicates that the target device is requested to indicate if it has AI / ML capability available. – CommonIEsProvideCapabilities The CommonIEsProvideCapabilities carries common IEs for a Provide Capabilities LPP message Type. -- ASN1START CommonIEsProvideCapabilities ::= SEQUENCE { ..., [[ segmentationInfo-r14 SegmentationInfo-r14 OPTIONAL, -- Cond Segmentation lpp-message-segmentation-r14 BIT STRING { serverToTarget (0), targetToServer (1) } OPTIONAL ]], [[remoteUE-Indication-r18 BOOLEAN OPTIONAL, -- Cond NR locationEstimateAndMeasurementReporting-r18 ENUMERATED { supported } OPTIONAL presence Segmentation This field is optionally present, need OP, if lpp-message-segmentation-req has been received from the location server with bit 1 (targetToServer) set to value 1. The field shall be omitted if lpp-message-segmentation-req has not been received in this location session, or has been received with bit 1 (targetToServer) set to value 0. NR This field is optionally present for NR access if remoteUE-IndicationReq has been received from the location server in this location session. Otherwise it is not present. CommonIEsProvideCapabilities field descriptions segmentationInfo This field indicates whether this ProvideCapabilities message is one of many segments, as specified in clause 4.3.5. lpp-message-segmentation This field, if present, indicates the target device's LPP message segmentation capabilities. If bit 0 is set to value 1, it indicates that the target device supports receiving segmented LPP messages; if bit 0 is set to value 0 it indicates that the target device does not support receiving segmented LPP messages. If bit 1 is set to value 1, it indicates that the target device supports sending segmented LPP messages; if bit 1 is set to value 0 it indicates that the target device does not support sending segmented LPP messages. remoteUE-Indication This field indicates whether the target device in NR access is configured as a L2 U2N Remote UE. The target device in NR access may transmit a ProvideCapabilities message with an appropriate value of this field when it starts or stops operation as a U2N Remote UE. locationEstimateAndMeasurementReporting This field, if present, indicates that the PRU supports locationEstimateAndMeasurementsRequired in LocationInformationType. NOTE: In this version of the specification, this capability is only applicable to PRUs. -- ASN1START NR-AI / ML-ProvideCapabilities ::= SEQUENCE { AI / ML-ProvideCapabiltiesList-r19 SEQUENCE (SIZE OF (INTEGER (1..32)) OF AI / ML-ProvideCapabilties } NR-AI / ML-ProvideCapabilities ::= SEQUENCE { dynamicCapability ENUMERATED { true } OPTIONAL, areaID-r19 INTEGER (1..65535) OPTIONAL, requiredOD-DL-PRS-ForModelInferenceList-r19 SEQUENCE (SIZE (1..maxOD-DL-PRS-Configs-r17))OF DL-PRS-Configuration-ID-r17 OPTIONAL,

[0002] NR-AI / ML-ProvideCapabilities field descriptions AI / MLModelAvailability This field, if present, indicates that the UE has AI / ML model available where ueBasedStaticModel indicates that the UE holds a stable AI / ML model for UE based and so on. The capability may also be associated to an ID which maps to a specific DL-PRS configuration. Note: LMF may only forward the capability information to AMF for storage if static AI / ML Model is available. areaID Indicates the area where the model is valid. This can also be indicated using list of cells or list of TRPs. requiredOD-DL-PRS-ForModelInferenceList Indicates the On demand DL-PRS Configuration ID where the UE performed the training on and expects the NW to configure applicablePRS-Configurations Indicates the valid PRS configurations for AI / ML inference. This is indicated using existing IE On- Demand-DL-PRS-Configuration-r17 which contains suitable PRS configuration request parameters such as PRS BW, Periodicity, repetitions 6.5.8.2 NR UL Capability Information Request – NR-UL-RequestCapabilities The IE NR-UL-RequestCapabilities is used by the location server to request the capability of the target device to support UL SRS for positioning and to request UL SRS for positioning capabilities from a target device. -- ASN1START NR-UL-RequestCapabilities-r16 ::= SEQUENCE { ... } -- ASN1STOP 6.5.8.3 NR AI / ML Capability Information Request – NR-AI / ML-RequestCapabilities The IE NR-AI / ML-RequestCapabilities is used by the location server to request the capability of the target device to support AI / ML for positioning and to request AI / ML for positioning capabilities from a target device. -- ASN1START NR-AI / ML-RequestCapabilities-r16 ::= SEQUENCE { nr-dynamicFlag-r19 ENUMERATED {required} ... } -- ASN1STOP NR-AI / ML-RequestCapabilities field descriptions nr-dynamicFlag This field indicates if UE is required to tag the capability if the capability is dynamic. 6.5.10.1 NR DL-TDOA Assistance Data – NR-DL-TDOA-ProvideAssistanceData The IE NR-DL-TDOA-ProvideAssistanceData is used by the location server to provide assistance data to enable UE-assisted and UE-based NR DL-TDOA. It may also be used to provide NR DL-TDOA positioning specific error reason. -- ASN1START -- ASN1STOP Conditional presence Explanation UEB The field is optionally present, need ON, for UE based NR DL-TDOA; otherwise it is not present. CtrTrans The field is mandatory present in the control transaction of a periodic assistance data delivery session as described in clauses 5.2.1a and 5.2.2a, for UE based NR DL-TDOA. Otherwise it is not present. NR-DL-TDOA-ProvideAssistanceData field descriptions nr-DL-PRS-AssistanceData This field specifies the assistance data reference and neighbour TRPs and provides the DL-PRS configuration for the TRPs. Note, if this field is absent but the nr-SelectedDL-PRS-IndexList field is present, the nr-DL-PRS-AssistanceData may be provided in IE NR-Multi-RTT-ProvideAssistanceData or NR-DL-AoD-ProvideAssistanceData. nr-SelectedDL-PRS-IndexList This field specifies the DL-PRS Resources which are applicable for this NR-DL-TDOA-ProvideAssistanceData message. nr-PositionCalculationAssistance This field provides position calculation assistance data for UE-based mode. nr-DL-TDOA-Error This field provides DL-TDOA error reasons. nr-On-Demand-DL-PRS-Configurations This field provides a set of available DL-PRS configurations which can be requested by the target device on- demand. NOTE 1: Void NOTE 2: If this field is absent but the nr-On-Demand-DL-PRS-Configurations-Selected-IndexList is present, the nr- On-Demand-DL-PRS-Configurations may be provided in IE NR-Multi-RTT-ProvideAssistanceData or NR- DL-AoD-ProvideAssistanceData. nr-On-Demand-DL-PRS-Configurations-Selected-IndexList This field specifies the selected available on-demand DL-PRS configurations which are applicable for this NR-DL- TDOA-ProvideAssistanceData message. assistanceDataValidityArea This field specifies the network area for which this NR-DL-TDOA-ProvideAssistanceData is valid. assistanceDataValidityArea This field indicates the network area ID for the area indicated by the field assistanceDataValidityArea. nr-PeriodicAssistData This field specifies the control parameters for a periodic assistance data delivery session (e.g., interval and duration) for UE-based carrier phase positioning. – NR-On-Demand-DL-PRS-Request The IE NR-On-Demand-DL-PRS-Request is used by the target device to request on-demand DL-PRS from a location server. -- ASN1START NR-On-Demand-DL-PRS-Request-r17 ::= SEQUENCE { dl-prs-StartTime-and-Duration-r17 DL-PRS-StartTime-and-Duration-r17 OPTIONAL, nr-on-demand-DL-PRS-Information-r17 NR-On-Demand-DL-PRS-Information-r17 OPTIONAL, dl-prs-configuration-id-PrefList-r17 SEQUENCE (SIZE (1..maxOD-DL-PRS-Configs-r17)) OF DL-PRS-Configuration-ID-r17 OPTIONAL, ..., [[ dl-PRS-AggregationID-PrefList-r18 SEQUENCE (SIZE (1.. maxOD-DL-PRS-Configs- r17)) OF INTEGER (1.. maxOD-DL-PRS-Configs-r17) OPTIONAL, nr-OnDemandDL-PRS-AggregationReqList-r18 SEQUENCE (SIZE (1.. maxOD-DL-PRS-Configs-r17)) OF NR-OnDemandDL-PRS- AggregationReqElement-r18 OPTIONAL ]],[[ AI / ML-ModelTrainUsingOD-DL-PRS-ConfigIDList-r19 SEQUENCE (SIZE (1..maxOD-DL-PRS-Configs- r17)) OF DL-PRS-Configuration-ID-r17 OPTIONAL, requiredOD-DL-PRS-ForModelInferenceList-r19 SEQUENCE (SIZE (1..maxOD-DL-PRS-Configs-r17)) OF DL-PRS-Configuration-ID-r17 OPTIONAL, DL- DL-PRS-StartTime-and-Duration-r17 ::= SEQUENCE { dl-prs-start-time-r17 INTEGER (1..1024) OPTIONAL, dl-prs-duration-r17 SEQUENCE { seconds-r17 INTEGER (0..59) OPTIONAL, minutes-r17 INTEGER (0..59) OPTIONAL, hours-r17 INTEGER (0..23) OPTIONAL, ... } OPTIONAL, ... } NR-OnDemandDL-PRS-AggregationReqElement-r18 ::= SEQUENCE (SIZE (2..3)) OF INTEGER (1..nrMaxFreqLayers-r16) -- ASN1STOP NR-On-Demand-DL-PRS-Request field descriptions This field specifies the on-demand DL-PRS configuration associated with DL-PRS-Configuration-ID in IE NR- On-Demand-DL-PRS-Configurations which the target device has performed AI / ML model training and requires that configuration to perform model inference in the order of preference. The first DL-PRS-Configuration-ID in the list is the most preferred configuration, the second DL-PRS-Configuration-ID the second most preferred, etc for the purpose of model inference. This field specifies the requested start time and duration for the on-demand DL-PRS and comprises the following subfields: - dl-prs-start-time specifies the desired start time for the requested DL-PRS. It indicates the time in seconds from the time the IE NR-On-Demand-DL-PRS-Request was received. - dl-prs-duration specifies the desired duration of the requested DL-PRS. The desired duration is the sum of the seconds, minutes, hours fields. If this field is included, at least one of the seconds, minutes, hours fields shall be present. nr-on-demand-DL-PRS-Information This field specifies the on-demand DL-PRS configuration information requested by the target device. NOTE: If the network provided predefined on-demand DL-PRS configurations (NR-On-Demand-DL-PRS- Configurations), the target device can only request explicit parameters (nr-on-demand-DL-PRS- This field specifies the on-demand DL-PRS configuration associated with DL-PRS-Configuration-ID in IE NR- On-Demand-DL-PRS-Configurations the target device wishes to obtain in the order of preference. The first DL- PRS-Configuration-ID in the list is the most preferred configuration, the second DL-PRS-Configuration-ID the second most preferred, etc. When the field preferredOD-DL-PRS-RequiredForInference is set to true then the field dl-prs-configuration-id-PrefList specifies the on-demand DL-PRS configuration associated with DL-PRS- Configuration-ID in IE NR-On-Demand-DL-PRS-Configurations which the target device has performed AI / ML model training and requires that configuration to perform model inference in the order of preference. dl-PRS-AggregationID-PrefList This field specifies the on-demand DL-PRS aggregated configuration associated with onDemandDL-PRS- AggregationList in IE NR-On-Demand-DL-PRS-Configurations the target device wishes to obtain in the order of preference. The first integer value in the list is the most preferred aggregated configuration; the second integer value in the list is the second most preferred, etc. The integer value corresponds to the entry in the field onDemandDL-PRS-AggregationList in IE NR-On-Demand-DL-PRS-Configurations. nr-OnDemandDL-PRS-AggregationReqList This field specifies the aggregated on-demand DL-PRS configuration information requested by the target device in the order of preference. The first NR-OnDemandDL-PRS-AggregationReqElement in the list is the most preferred aggregated configuration; the second element in the list is the second most preferred, etc. The integer value in NR-OnDemandDL-PRS-AggregationReqElement corresponds to the entry in the IE NR-On-Demand- DL-PRS-Information. TS 38.305 7.6.2 On-Demand PRS transmission procedures Figure 7.6.2-1 shows the general positioning procedure for On-Demand PRS transmission. 0. The LMF may receive information on the possible On-Demand PRS configurations that the gNB can support during the TRP Information Exchange procedure. 1. In case of UE-initiated On-Demand PRS, the LMF may configure the UE with pre- defined PRS configurations via LPP Provide Assistance Data message or via posSI. 2a. In case of UE-initiated On-Demand PRS, the UE sends an On-Demand PRS request to the LMF via LPP Request Assistance Data message. The On-Demand PRS request can be a request for a pre-defined PRS configuration indicated with pre- defined PRS configuration ID or explicit parameter for PRS configuration and may be a request for PRS transmission or change to the PRS transmission characteristics for positioning measurements or to ensure consistency between AI / ML model inference and training (indicate AI / ML model applicability conditions). NOTE 1: The LPP Request Assistance Data message for On-Demand PRS may also be sent in an MO-LR location service request message. NOTE 2: If the NW has provided the pre-defined On-Demand PRS configurations to the UE, the UE is allowed to request On-Demand PRS parameters based on pre-defined PRS configuration ID (index-based request) or explicit parameter requests that is within the scope of the received pre-defined On-Demand PRS configurations. Otherwise, the UE may blindly request On-Demand PRS parameters via an explicit request within the scope of the allowed parameter list, as specified in TS37.355

[0042] . 2b. In case of LMF-initiated On-Demand PRS, the LMF and the UE may exchange LPP messages e.g., to obtain UE measurements or the DL-PRS positioning capabilities of the UE, etc. 3. The LMF determines the need for PRS transmission or change to the transmission characteristics of an ongoing PRS transmission. 4. The LMF requests the serving and non-serving gNBs / TRPs for new PRS transmission or PRS transmission with changes to the PRS configuration via NRPPa PRS CONFIGURATION REQUEST message. 5. The gNBs / TRPs provide the successfully configured or updated PRS transmission in the NRPPa PRS CONFIGURATION RESPONSE message accordingly. 6. LMF may provide the PRS configuration used for PRS transmission or error cause via LPP Provide Assistance Data message to the UE. NOTE 3: If the LPP Request Assistance Data for On-Demand DL-PRS at Step 2a was sent in an MO-LR location service request message, the LMF provides a MO-LR response as described in clause 7.3.3. NOTE 4: It is up to Network (LMF) implementation on the steps to follow (accept / reject / ignore) on receiving UE-initiated On-Demand PRS request. NOTE 5: It is up to Network (TRP) implementation on the steps to follow (accept / reject / ignore) on receiving LMF-initiated On-Demand PRS requests. NOTE 6: The UE may utilize the UE-initiated on-demand DL-PRS procedure to request a DL-PRS configuration which aligns with any configured (e)DRX, e.g., by setting the explicit parameter for the DL-PRS configuration accordingly. NOTE 7: The on-demand DL-PRS procedure may also be used to request a DL- PRS configuration which supports bandwidth aggregation across DL-PRS positioning frequency layers. Figure 9 shows an example of a communication system QQ100 in accordance with some embodiments. In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110), or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points. 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 QQ102 includes one or more Open- RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network QQ102 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 QQ102, including one or more network nodes QQ110 and / or core network nodes QQ108. 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 A1, F1, W1, E1, 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 O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system QQ100 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 QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system. The UEs QQ112 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 QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 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 QQ102. In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more host computing systems, such as host QQ116. 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 QQ106 includes one more core network nodes (e.g., core network node QQ108) 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 QQ108. 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). The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102. The host QQ116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server. As a whole, the communication system QQ100 of Figure 9 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (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. In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 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 IoT services to yet further UEs. In some examples, the UEs QQ112 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 QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved- UMTS Terrestrial Radio Access Network) New Radio – Dual Connectivity (EN-DC). In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 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 QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 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 QQ114 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy IoT devices. The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 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 QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub – that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels. Figure 10 shows a UE QQ200 in accordance with some embodiments. The UE QQ200 presents additional details of some embodiments of the UE QQ112 of Figure 9. 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 IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB- IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE. 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). The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 10. 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. The processing circuitry QQ202 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 QQ210. The processing circuitry QQ202 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 QQ202 may include multiple central processing units (CPUs). The processing circuitry QQ202 may be configured to cause the UE QQ202 to perform the methods as described with reference to Figure VV1. In the example, the input / output interface QQ206 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 QQ200. 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. In some embodiments, the power source QQ208 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 QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied. The memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems. The memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory QQ210 may allow the UE QQ200 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 QQ210, which may be or comprise a device-readable storage medium. The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 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 QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately. In the illustrated embodiment, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth. Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, 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). 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. A UE, when in the form of an Internet of Things (IoT) 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 IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE QQ200 shown in Figure 10. As yet another specific example, in an IoT 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 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation. 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. Figure 11 shows a network node QQ300 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, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU). 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 O- RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS). Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs). The network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a 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 QQ300 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 QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300. The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide network node QQ300 functionality. For example, the processing circuitry QQ302 may be configured to cause the network node to perform the methods as described with reference to Figure VV2. In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units. The memory QQ304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile 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 QQ302. The memory QQ304 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 QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated. The communication interface QQ306 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 QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 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 QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components. In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front- end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown). The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front- end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port. The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment. The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 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. Embodiments of the network node QQ300 may include additional components beyond those shown in Figure 11 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 QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300. In some embodiments providing a core network node, such as core network node 108 of FIGURE 9, some components, such as the radio front-end circuitry QQ318 and the RF transceiver circuitry QQ312 may be omitted. Figure 12 is a block diagram illustrating a virtualization environment QQ400 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments QQ400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or 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 QQ400 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, UE, core network node, or host. Applications QQ402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein. Hardware QQ404 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ408a and QQ408b (one or more of which may be generally referred to as VMs QQ408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ406 may present a virtual operating platform that appears like networking hardware to the VMs QQ408. The VMs QQ408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ406. Different embodiments of the instance of a virtual appliance QQ402 may be implemented on one or more of VMs QQ408, 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. In the context of NFV, a VM QQ408 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 QQ408, and that part of hardware QQ404 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 QQ408 on top of the hardware QQ404 and corresponds to the application QQ402. Hardware QQ404 may be implemented in a standalone network node with generic or specific components. Hardware QQ404 may implement some functions via virtualization. Alternatively, hardware QQ404 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 QQ410, which, among others, oversees lifecycle management of applications QQ402. In some embodiments, hardware QQ404 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 QQ412 which may alternatively be used for communication between hardware nodes and radio units. 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. 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.

Claims

Claims 1. A method (300) performed by a User Equipment (UE) for sending information to a first network node, wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model, the method comprising: sending (302) information to the first network node, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable.

2. The method of claim 1, comprising determining whether the functionality is currently applicable or currently not applicable based on the one or more conditions.

3. The method of claim 1 or 2, wherein the one or more conditions comprise one or more of: a first area in which the functionality is applicable; a first time duration in which the functionality is applicable; and / or a first configuration of the UE.

4. The method of claim 3, wherein the first configuration of the UE comprises a first reference signal configuration of the UE and / or a first Position Reference Signal (PRS) configuration of the UE.

5. The method of any of claims 1 to 4, comprising determining that the applicability of the functionality is dynamic if the one or more conditions include one or more of the first area, the first time duration and / or the first configuration.

6. The method of any of claims 1 to 5, comprising determining that the applicability of the functionality is static if the one or more conditions do not include one or more of the first area, the first time duration and / or the first configuration.

7. The method of any of claims 1 to 6, comprising determining that the applicability of the functionality is static if there are no conditions under which the functionality is applicable and / or if the functionality is applicable under all conditions.

8. The method of any of claims 1 to 7, comprising sending the information to the first network node in response to receiving a request from the first network node.

9. The method of claim 8, comprising, before receiving the request from the first network node, sending capability information of the UE to the first network node, wherein the capability information identifies the functionality.

10. The method of any of claims 1 to 9, comprising: determining a change in the applicability of the functionality; and sending further information to the first network node, wherein the further information identifies whether the functionality is currently applicable or currently not applicable.

11. The method of any of claims 1 to 10, comprising determining whether the functionality is currently applicable or currently not applicable based on one or more current conditions.

12. The method of claim 11, wherein determining whether the functionality is currently applicable or currently not applicable based on the one or more current conditions comprises comparing the one or more current conditions to the one or more conditions under which the functionality is applicable.

13. The method of claim 11 or 12, wherein the one or more current conditions comprise one or more of: an area in which the UE is currently located; a current time; and / or a current configuration of the UE.

14. The method of claim 13, wherein the current configuration of the UE comprises a current reference signal configuration of the UE and / or a current Position Reference Signal (PRS) configuration of the UE.

15. The method of any of claims 1 to 14, wherein the functionality that uses the AI / ML model comprises a UE positioning functionality.

16. The method of any of claims 1 to 15, wherein the first network node comprises a base station, Radio Access Network (RAN) node, gNodeB (gNB) or location management function (LMF).

17. A method (400) performed by a first network node for receiving information from a User Equipment (UE), wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model, the method comprising: receiving (402) information from the UE, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable for the UE; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable.

18. The method of claim 17, wherein the information identifies the one or more conditions under which the functionality is applicable.

19. The method of claim 17 or 18, wherein the one or more conditions comprise one or more of: a first area in which the functionality is applicable; a first time duration in which the functionality is applicable; and / or a first configuration of the UE.

20. The method of claim 19, wherein the first configuration of the UE comprises a first reference signal configuration of the UE and / or a first Position Reference Signal (PRS) configuration of the UE.

21. The method of any of claims 17 to 20, comprising determining that the applicability of the functionality is dynamic if the one or more conditions include one or more of the first area, the first time duration and / or the first configuration.

22. The method of any of claims 17 to 21 when dependent on claim 24, comprising determining that the applicability of the functionality is static if the one or more conditions do not include one or more of the first area, the first time duration and / or the first configuration.

23. The method of any of claims 17 to 22, comprising determining that the applicability of the functionality is static if there are no conditions under which the functionality is applicable and / or if the functionality is applicable under all conditions.

24. The method of any of claims 17 to 23, comprising sending a request for the information to the UE before receiving the information from the UE.

25. The method of claim 24, comprising, before sending the request to the UE, receiving capability information of the UE from the UE, wherein the capability information identifies the functionality.

26. The method of any of claims 17 to 25, comprising receiving further information from the UE, wherein the further information identifies whether the functionality is currently applicable or currently not applicable for the UE.

27. The method of any of claims 17 to 26, comprising determining whether the functionality is currently applicable or currently not applicable for the UE based on one or more current conditions.

28. The method of claim 27, wherein determining whether the functionality is currently applicable or currently not applicable for the UE based on the one or more current conditions comprises comparing the one or more current conditions for the UE to the one or more conditions under which the functionality is applicable.

29. The method of claim 27 or 28, wherein the one or more current conditions for the UE comprise one or more of: an area in which the UE is currently located; a current time; and / or a current configuration of the UE.

30. The method of claim 29, wherein the current configuration of the UE comprises a current reference signal configuration of the UE and / or a current Position Reference Signal (PRS) configuration of the UE.

31. The method of any of claims 17 to 30, comprising, if the applicability of the functionality is static or if the functionality is currently applicable for the UE, sending, to a second network node, a request to store information identifying the functionality for the UE.

32. The method of any of claims 17 to 31, comprising, if the applicability of the functionality is dynamic or if the functionality is currently not applicable for the UE, sending, to a secondnetwork node, a request to not store information identifying the functionality for the UE and / or a request to delete information identifying the functionality for the UE.

33. The method of claim 31 or 32, wherein the second network node comprises an Access and Mobility Management Function, AMF.

34. The method of any of claims 17 to 33, wherein the functionality that uses the AI / ML model comprises a UE positioning functionality.

35. The method of any of claims 17 to 34, wherein the first network node comprises a base station, gNodeB (gNB) or location management function (LMF).

36. A tangible, non-transient computer-readable medium comprising instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations in a User Equipment (UE) for sending information to a first network node, wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model, the operations comprising: sending (302) information to the first network node, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable.

37. The computer-readable medium of claim 36, comprising instructions that, when executed by processing circuitry, cause the processing circuitry to perform the method (300) of any of claims 2 to 16.

38. A tangible, non-transient computer-readable medium comprising instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations a first network node for receiving information from a User Equipment (UE), wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model, the operations comprising: receiving (402) information from the UE, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or• one or more conditions under which the functionality is applicable.

39. The computer-readable medium of claim 38, comprising instructions that, when executed by processing circuitry, cause the processing circuitry to perform the method (400) of any of claims 18 to 35.

40. A computer-readable medium comprising instructions that, when executed by processing circuitry, cause the processing circuitry to carry out the method according to any of claims 1 to 35.

41. A computer program, comprising instructions that, when executed by processing circuitry, cause the processing circuitry to carry out the method according to any of claims 1 to 35.

42. A carrier containing the computer program of claim 41, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer-readable medium.

43. Apparatus in a User Equipment (UE) for sending information to a first network node, wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model, the apparatus comprising processing circuitry and a memory, the apparatus configured to: send (302) information to the first network node, wherein the information identifies one or more of: • whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable.

44. The apparatus of claim 43, wherein the apparatus is configured to perform the method (300) of any of claims 2 to 16.

45. Apparatus in a first network node for receiving information from a User Equipment (UE), wherein the UE has a functionality that uses an artificial intelligence or machine learning (AI / ML) model, the apparatus comprising processing circuitry and a memory, the apparatus configured to: receive (402) information from the UE, wherein the information identifies one or more of:• whether the functionality is currently applicable or currently not applicable; • whether applicability of the functionality is dynamic or static; and / or • one or more conditions under which the functionality is applicable.

46. The apparatus of claim 45, wherein the apparatus is configured to perform the method (400) of any of claims 18 to 35.

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