Methods to support enhanced reporting of positioning information based on GNB-du ai / ML inference side model

Abstract

Systems and methods for reporting of positioning information based on a Distributed Unit (DU)-side Artificial Intelligence (AI) / Machine Learning (ML) model are disclosed. In one embodiment, a method performed by a DU of a Radio Access Network (RAN) node for generating inferred measurements based on a DU-side AI / ML model for AI / ML assisted positioning comprises receiving, from a Centralized Unit (CU) of the RAN node, a measurement request and transmitting, to the CU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request. In this manner, support for AI / ML based positioning based on a DU-side AI / ML model is provided.

Description

[0001] METHODS TO SUPPORT ENHANCED REPORTING OF POSITIONING INFORMATION BASED ON GNB-DU AI / ML INFERENCE SIDE MODEL RELATED APPLICATIONS This application claims the benefit of provisional patent application serial number 63 / 614,128, filed December 22, 2023, the disclosure of which is hereby incorporated herein by reference in its entirety. TECHNICAL FIELD The present disclosure relates to Artificial Intelligence (AI) / Machine Learning (ML) assisted positioning in a wireless communications system. BACKGROUND Third Generation Partnership Project (3GPP) Technical Report (TR) 38.843 V2.0.0 describes the Radio Access Network (RAN) Working Group (WG) 1 (i.e., RAN1) study on the analysis of potential enhancements for Release 19 necessary to enable Artificial Intelligence (AI) / Machine Learning (ML) for positioning accuracy enhancements with New Radio (NR) Radio Access Technology (RAT)-dependent positioning methods. Evaluation scenarios and Key Performance Indicators (KPIs) were identified for system level analysis of AI / ML enabled RAT- dependent positioning techniques as described in clause 6.4 of 3GPP TR 38.843. Direct AI / ML positioning and AI / ML assisted positioning were identified and selected as the representative sub- use cases. Evaluation results have shown that in considered evaluation scenarios, both direct AI / ML positioning and AI / ML assisted can significantly improve the positioning accuracy compared to existing RAT-dependent positioning methods. Various aspects of AI / ML for positioning accuracy enhancement were investigated and evaluated, as described in clause 6.4 of 3GPP TR 38.843, which provides summary of evaluation results from different sources. The necessity, feasibility, and potential enhancements to facilitate the support of AI / ML for positioning accuracy enhancements with NR RAT-dependent positioning methods were studied and the outcome is outlined in clause 7 of 3GPP TR 38.843. Measurements, signaling and procedures were studied to enable AI / ML for positioning accuracy enhancements with NR RAT- dependent positioning methods and is recommended to be further investigated in normative work, and specified if necessary. A variety of enhancements for measurements (e.g., based on extensions to current positioning measurements or with new measurements) were identified as potentially beneficial (e.g., trade-off positioning accuracy requirement and signalling overhead) and are recommended to be investigated further and if needed, specified during normative work. Based on conducted analysis, 3GPP TR 38.843 recommends that 3GPP proceed with normative work for AI / ML based positioning. It is recommended for 3GPP to specify necessary measurement, signaling, and procedures to facilitate training, inference, monitoring, and / or other Life Cycle Management (LCM) operations for both direct AI / ML positioning and AI / ML assisted positioning, specifically: • specify necessary signalling of data collection; investigate the necessity of other information for supporting data collection, and if needed, specify during normative work • investigate on the necessity and signalling details of measurement enhancements, and if needed, specify during normative work • investigate on the necessity and signalling details of monitoring method(s), and if needed, specify during normative work Clause 5.3 of 3GPP TR 38.843 states the following: The following are selected as representative sub-use cases: - Direct AI / ML positioning: - AI / ML model output: UE location - e.g., fingerprinting based on channel observation as the input of AI / ML model - AI / ML assisted positioning: - AI / ML model output: new measurement and / or enhancement of existing measurement - e.g., LOS / NLOS identification, timing and / or angle of measurement, likelihood of measurement More specifically, the following Cases are considered for the study: - Case 1: UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioning - Case 2a: UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning - Case 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning - Case 3a: NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning - Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning One-sided model whose inference is performed entirely at the UE or at the network is prioritized in Release 18 (Rel-18) System Information (SI). For all five positioning cases (Case 1 / 2a / 2b / 3a / 3b), RAN1 has not considered prioritization. For positioning enhancement use case: - For model training, training data can be generated by UE / PRU / gNB / LMF. - For LMF-side model inference (Case 2b, Case 3b), input data can be generated by UE / gNB and terminated at LMF. - For gNB-side model inference (Case 3a), input data is internally available at gNB. - For UE-side model inference (Case 1, Case 2a), input data is internally available at UE. - For performance monitoring at the LMF side, calculated performance metrics (if needed) or data needed for performance metric calculation (if needed) can be generated by UE / gNB and terminated at LMF. - For performance monitoring at the gNB side, calculated performance metrics (if needed) or data needed for performance metric calculation (if needed) can be generated by at least gNB. The Next Generation (NG) RAN (NG-RAN) consists of a set of gNodeBs (gNBs) connected to the 5thGeneration (5G) Core (5GC) through the NG interface. 3GPP Technical Specification (TS) 38.401 (see, e.g., V17.6.0) describes the overall NG-RAN architecture. A disaggregated gNB may consist of a gNB-Centralized Unit (CU) and one or more gNB-Distributed Unit(s) (DUs). A gNB-CU and a gNB-DU is connected via F1 interface. FIGURE 1 illustrates the F1 interface between the gNB-CU and the gNB-DU. This interface is responsible for possible information and control signaling from the Packet Data Convergence Protocol (PDCP) entity located in CU and Radio Link Control (RLC) entity located in DU. FIGURE 2 illustrates the User Equipment (UE) positioning architecture applicable to NG- RAN. The Access and Mobility Management Function (AMF) receives a request for some location service associated with a particular target UE from another entity (e.g., Gateway Mobile Location Centre (GMLC) or UE), or the AMF itself decides to initiate some location service on behalf of a particular target UE (e.g., for an Internet Protocol (IP) Multimedia Subsystem (IMS) emergency call from the UE). The AMF then sends a location services request to a Location Management Function (LMF). The LMF processes the location services request which may include transferring assistance data to the target UE to assist with UE-based and / or UE-assisted positioning and / or may include positioning of the target UE. The LMF then returns the result of the location service back to the AMF (e.g., a position estimate for the UE. In the case of a location service requested by an entity other than the AMF (e.g., a GMLC or UE), the AMF returns the location service result to this entity. An NG-RAN node may control several Transmission / Reception Points / Transmission Points (TRPs / TPs), such as remote radio heads or downlink (DL)-Positioning Reference Signal (PRS)-only TPs for support of PRS-based Terrestrial Beacon System (TBS). In NG-RAN architecture, gNB-CU terminates the NR Positioning Protocol A (NRPPa) protocol and the gNB-DU hosts the TRPs. SUMMARY Systems and methods for reporting of positioning information based on a Distributed Unit (DU)-side Artificial Intelligence (AI) / Machine Learning (ML) model are disclosed. In one embodiment, a method performed by a DU of a Radio Access Network (RAN) node for generating inferred measurements based on a DU-side AI / ML model for AI / ML assisted positioning comprises receiving, from a Centralized Unit (CU) of the RAN node, a measurement request and transmitting, to the CU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request. In this manner, support for AI / ML based positioning based on a DU-side AI / ML model is provided. In one embodiment, the DU-side AI / ML model is hosted at the DU. In one embodiment, the method further comprises using the DU-side AI / ML model to generate the at least one inferred measurement. In one embodiment, the at least one inferred measurement is at least one inferred measurement related to AI / ML assisted positioning of a User Equipment (UE). In one embodiment, the measurement request comprises a request for at least one inferred measurement. In one embodiment, the at least one value associated with the at least one inferred measurement is transmitted in a report or message comprising an indication that the at least one inferred measurement is an inferred measurement. In one embodiment, the DU is associated to one or more Transmission and Reception Points (TRPs), and transmitting the at least one value associated with the at least one inferred measurement comprises transmitting the at least one value associated with the at least one inferred measurement in a TRP measurement result Information Element (IE). In one embodiment, transmitting the at least one value associated with the at least one inferred measurement comprises transmitting the at least one value associated with the at least one inferred measurement to the CU via an F1 interface between the DU and the CU. In one embodiment, transmitting the at least one value associated with the at least one inferred measurement comprises transmitting the at least one value associated with the at least one inferred measurement to the CU in a prediction report. In one embodiment, the method further comprises transmitting, to the CU, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement, time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement. In one embodiment, the measurement request is associated with a Location Management Function (LMF) and is forwarded to the DU via the CU. In one embodiment, transmitting the at least one value associated with the at least one inferred measurement comprises transmitting the at least one value associated with the at least one inferred measurement in accordance with at least one condition indicated in association with the measurement request, the at least one condition comprising any one or more of: the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold, the at least one value is calculated for a specific future point in time or within a time window, the at least one predicted measurement is to be reported periodically at a given periodicity, the at least one inferred measurement is to be inferred and reported only once, the at least one predicted measurement is to be reported periodically at a given periodicity for a given time duration, the at least one value is associated with a quality level that is equal to or greater than a threshold. In one embodiment, the method further comprises, in response to receiving the measurement request, transmitting a response message comprising an indication that one or more of the at least one inferred measurements can be provided. In one embodiment, the method further comprises, in response to receiving the measurement request, transmitting a response message comprising an indication that one or more of the at least one inferred measurements cannot be provided. In one embodiment, the response message comprises at least one of: an indication that a failure due to which the one or more of the at least one inferred measurement cannot be provided is temporary, an indication that the DU does not support the one or more of the at least one inferred measurement. In one embodiment, the at least one value associated with the at least one predicted measurement is associated with at least one of: Uplink (UL) Angle of Arrival, (UL-AoA); UL Relative Time of Arrival (UL-RTOA), UL Sounding Reference Signal (SRS) Reference Signal Received Power (UL-SRS-RSRP), UL SRS Reference Signal Received Path Power (UL-SRS- RSRPP). gNodeB (gNB) Receive-Transmit (Rx-Tx) time difference, and UL Reference Signal Carrier Phase (UL-RSCP). In one embodiment, the method further comprises transmitting, to the CU of the RAN node, capability information indicating a capability of the DU to generate at least one inferred measurement based on the DU-side AI / ML model. In one embodiment, the method further comprises receiving, from the CU of the RAN node, a request for the capability information, and wherein transmitting the capability information is in response to the receiving the request for the capability information. In one embodiment, transmitting the capability information comprises transmitting the capability information to the CU via an IE in a F1AP TRP INFORMATION RESPONSE message. In one embodiment, the capability information comprises at least one of: information about one or more inferred measurements supported by the DU and that can be provided as output of the DU-side AI / ML model, information about an accuracy level and / or uncertainty level of inferred measurements supported by the DU, information about training data used to train the DU-side AI / ML model, information about a time horizon for inferred measurements supported at the DU, information about a processing delay for the DU-side AI / ML model at the DU, information about a maximum inferred measurement resolution supported by the DU, information indicating at least one TRP hosted by the DU that supports inferred measurements, and information about a maximum update rate for a particular inferred measurement resolution the DU supports. Corresponding embodiments of a network node for implementing a DU of a RAN node comprising one or more DUs and a CU are also disclosed. In one embodiment, the network node comprises processing circuitry configured to cause the network node to receive, from the CU of the RAN node, a measurement request and transmit, to the CU of the RAN node, at least one value associated with at least one inferred measurement generated using a DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request. Embodiments of a method performed by a CU of a RAN node for supporting inferred measurements based on a DU-side AI / ML model for AI / ML assisted positioning are disclosed. In one embodiment, the method performed by the UC of the RAN node comprises transmitting, to a DU of the RAN node, a measurement request and receiving, from the DU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request. In one embodiment, the DU-side AI / ML model is hosted at the DU. In one embodiment, the at least one inferred measurement is at least one inferred measurement related to AI / ML assisted positioning of a User Equipment, UE. In one embodiment, the measurement request comprises a request for at least one inferred measurement. In one embodiment, the at least one value associated with the at least one inferred measurement is received in a report or message comprising an indication that the at least one inferred measurement is an inferred measurement. In one embodiment, the DU is associated to one or more TRPs, and receiving the at least one value associated with the at least one inferred measurement comprises receiving the at least one value associated with the at least one inferred measurement in a TRP measurement result IE. In one embodiment, receiving the at least one value associated with the at least one inferred measurement comprises receiving the at least one value associated with the at least one inferred measurement via an F1 interface between the DU and the CU. In one embodiment, receiving the at least one value associated with the at least one inferred measurement comprises receiving the at least one value associated with the at least one inferred measurement in a prediction report. In one embodiment, the method further comprises receiving, from the DU, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement, time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement. In one embodiment, the method further comprises receiving the measurement request from an LMF, and wherein the transmitting the measurement request to the DU comprises forwarding the measurement request to the DU. In one embodiment, the measurement request comprises at least one condition related to the at least one inferred measurement. In one embodiment, the at least one condition comprises at least one of: a condition that the at least one value associated with the at least one inferred measurement is associated with an uncertainty or accuracy level that is equal to or greater than a threshold, a condition that the at least one value associated with the at least one inferred measurement is calculated for a specific point in time in the future or within a time window, a condition that values associated with the at least one inferred measurement are to be reported periodically, a condition that the at least one value associated with the at least one inferred measurement is to be inferred and reported only once, a condition that the at least one value associated with the at least one inferred measurement is to be reported periodically at a given periodicity for a given time duration, a condition that the at least one value associated with the at least one inferred measurement is associated with a quality level that is equal to or greater than a threshold. In one embodiment, the method further comprises, in response to transmitting the measurement request, receiving a response message from the DU, the response message comprising an indication that one or more of the at least one inferred measurements can be provided. In one embodiment, the method further comprises, in response to transmitting the measurement request, receiving a response message from the DU, the response message comprising an indication that one or more of the at least one inferred measurements cannot be provided. In one embodiment, the response message comprises at least one of: an indication that a failure due to which the one or more of the at least one inferred measurement cannot be provided is temporary, and / or an indication that the DU does not support the one or more of the at least one inferred measurement. In one embodiment, the at least one value associated with the at least one inferred measurement is associated with at least one of: UL-AoA, UL-RTOA, UL-SRS-RSRP, UL-SRS- RSRPP, gNB Rx-Tx time difference, and UL-RSCP. In one embodiment, the method further comprises transmitting a message comprising the at least one value associated with the at least one inferred measurement to an LMF. In one embodiment, the method further comprises receiving, from the DU of the network node, capability information indicating a capability of the DU to generate at least one predicted measurement based on the DU-side model. In one embodiment, the method further comprises transmitting, to the DU, a request for the capability information, and wherein receiving the capability information from the DU comprises receiving the capability information from the DU in response to the request. In one embodiment, receiving the capability information from the DU comprises receiving the capability from the DU via an IE in a F1AP TRP INFORMATION RESPONSE message. In one embodiment, the capability information comprises at least one of: information about one or more inferred measurements supported by the DU and that can be provided as output of the DU-side AI / ML model, information about an accuracy level and / or uncertainty level of inferred measurements supported by the DU, information about training data used to train the DU-side AI / ML model, information about a time horizon for inferred measurements supported at the DU, information about a processing delay for the DU-side AI / ML model at the DU, information about a maximum inferred measurement resolution supported by the DU, information indicating at least one TRP hosted by the DU that supports inferred measurements, and information about a maximum update rate for a particular inferred measurement resolution the DU supports. In one embodiment, the method further comprises transmitting the capability information to the LMF. Corresponding embodiments of a network node for implementing a CU of a RAN node for supporting inferred measurement based on a DU-side AI / ML model for AI / ML assisted positioning are also disclosed. In one embodiment, a network node for implementing the CU of the RAN node comprises processing circuitry configured to cause the network node to transmit, to a DU of the RAN node, a measurement request and receive, from the DU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request. Embodiments of a method performed by an LMF for supporting inferred measurements based on a DU-side AI / ML model for AI / ML assisted positioning are also disclosed. In one embodiment, the method performed by the LMF comprises transmitting, to a CU of the RAN node, a measurement request and receiving, from the CU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request. In one embodiment, the RAN node comprises a DU associated with the CU, and the DU- side AI / ML model is hosted at the DU. In one embodiment, the at least one inferred measurement is an inferred measurement for AI / ML assisted positioning of a UE. In one embodiment, the measurement request comprises a request for at least one inferred measurement. In one embodiment, the at least one value associated with the at least one inferred measurement is received in a report or message comprising an indication that the at least one inferred measurement is an inferred measurement. In one embodiment, the method further comprises performing at least one positioning operation for at least one UE based on the at least one value associated with at least one inferred measurement. In one embodiment, receiving the at least one value associated with the at least one inferred measurement comprises receiving the at least one value associated with the at least one inferred measurement from the CU in a NRPPa Measurement Response message. In one embodiment, the NRPPa Measurement Response message comprises at least one additional value associated with a TRP Measurement performed by a UE and the at least one value associated with the at least one inferred measurement. In one embodiment, the method further comprises receiving, from the CU, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement, time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement. In one embodiment, a characteristic of the at least one value received from the at least one of the CU and the DU fulfills at least one condition, and wherein the at least one condition comprises: the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold, the at least one value is calculated for a specific point in time in the future or within a time window, the at least one value is associated with a quality level that is equal to or greater than a threshold. In one embodiment, the method further comprises transmitting, to the CU, the at least one condition. In one embodiment, the method further comprises receiving, from the CU, a response message comprising an indication that one or more of the at least one inferred measurements can be provided. In one embodiment, the method further comprises receiving, from the CU, a response message comprising an indication that one or more of the at least one inferred measurements cannot be provided. In one embodiment, the response message comprises at least one of: an indication that a failure due to which the one or more of the at least one inferred measurement cannot be provided is temporary, and an indication that the DU does not support the one or more of the at least one inferred measurement. In one embodiment, the at least one value associated with the at least one predicted measurement is associated with at least one of: UL-AoA, UL-RTOA, UL-SRS-RSRP, UL-SRS- RSRPP, gNB Rx-Tx time difference, and UL-RSCP. In one embodiment, the method further comprises receiving, from the CU of the RAN node, capability information indicating a capability of the DU to generate the at least one inferred measurement based on the DU-side AI / ML model. In one embodiment, the method further comprises transmitting, to the CU of the RAN node, a request for the capability information, and wherein the capability information is received in response to the request. In one embodiment, the request is transmitted to the CU via NRPPa in a TRP information request message. In one embodiment, the capability information is received from the CU in a NRPPa message. In one embodiment, the capability information comprises at least one of: information about one or more inferred measurements supported by the DU and that can be provided as output of the DU-side AI / ML model, information about an accuracy level and / or uncertainty level of inferred measurements supported by the DU, information about training data used to train the DU-side AI / ML model, information about a time horizon for inferred measurements supported at the DU, information about a processing delay for the DU-side AI / ML model at the DU, information about a maximum inferred measurement resolution supported by the DU, information indicating at least one TRP hosted by the DU that supports inferred measurements, and information about a maximum update rate for a particular inferred measurement resolution the DU supports. Corresponding embodiments of a network for implementing an LMF for supporting inferred measurements based on a DU-side AI / ML model for AI / ML assisted positioning are also disclosed. In one embodiment, the network node comprises processing circuitry configured to cause the network node to transmit, to a CU of the RAN node, a measurement request and receive, from the CU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure. FIGURE 1 illustrates a Next Generation (NG) Radio Access Network (RAN) node as specified by the 3rdGeneration Partnership Project (3GPP). FIGURE 2 illustrates the User Equipment (UE) positioning architecture applicable to NG-RAN. FIGURE 3 illustrates an example procedure for AI / ML positioning capability declaration, in accordance with embodiments of the present disclosure. FIGURE 4 illustrates a procedure for NG-RAN node assisted positioning with gNB-DU- side model, in accordance with embodiments of the present disclosure. FIGURE 5 illustrates a method and signaling diagram for indicating new elements of predicted measurements based on gNodeB (gNB)-Distributed United (DU) side model for inference. FIGURE 6 shows an example of a communication system in accordance with some embodiments. FIGURE 7 shows a User Equipment (UE), which may be an embodiment of the UE of FIGURE 6, in accordance with some embodiments. FIGURE 8 shows a network node, which may be an embodiment of the network node 110 of FIGURE 7, in accordance with some embodiments. FIGURE 9 is a block diagram of a host, which may be an embodiment of the host of FIGURE 6, in accordance with various aspects described herein. FIGURE 10 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized. FIGURE 11 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments. FIGURE 12 illustrates an example method by a DU of a network node for generating predicted measurements based on a DU-side model, according to certain embodiments. FIGURE 13 illustrates an example method by a Centralized Unit (CU) of a network node for supporting predicted measurements based on a DU-side model, according to certain embodiments. FIGURE 14 illustrates an example method by a Location Management Function (LMF) of a network node for supporting predicted measurements based on a DU-side model, according to certain embodiments. 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. There currently exist certain challenge(s) in relation to 3rdGeneration Partnership Project (3GPP) positioning solutions and methods. For example, until recently, the 3GPP positioning solutions and methods relied on measurement reports by the User Equipment (UE) and gNodeB (gNB) to the Location Management Function (LMF). The measured quantities are typically the result of processing at the UE / gNB, e.g. the time of arrival or power from a reference signal. These reports were thus reasonably small in size. In Release 19 (Rel-19), 3GPP will support so-called “direct AI / ML based positioning” in network nodes (LMF, gNB Central Unit (CU), or gNB Distributed Unit (DU)), where the network collects a much rawer set of measurements which are in fact channel impulse responses, consisting of multipath complex-valued channel response. In the case of Next Generation Radio Access Network (NG-RAN) node assisted positioning with gNB-DU-side model for data collection, Artificial Intelligence (AI) / Machine Learning (ML) assisted positioning, the input data is generated by the Transmission and Reception Points (TRPs) hosted by each gNB-DU, which will provide new measurements and / or enhancement of existing measurements. For Rel-19 work, under this option, signaling support from the entity hosting the AI / ML model (i.e., gNB-DU) to other network nodes is needed so that the new positioning information is received by the central entity (i.e., gNB-CU), which will forward the new positioning information to the positioning server (i.e., LMF) to calculate the User Equipment (UE) position. Due to this distributed architecture with gNB-DU side AI / ML model (e.g., model for data collection training and inference), many interfaces are impacted (F1, and NRPPa). Thus, a first problem with current implementations and specifications is that they do not support the transferring of data needed to support AI / ML based positioning. Moreover, in current systems, it is not possible to determine whether a gNB-DU supports NG-RAN node assisted positioning with gNB-DU-side model. This may result in the problem of triggering requests for NG-RAN node assisted positioning with gNB-DU-side model, which may end up in failure cases due to lack of feature support at the gNB-DU. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, methods and systems are disclosed that may include one or more of the following: • The gNB-DU provides its capability of supporting AI / ML based positioning to generate new measurement and / or enhancement of existing measurement based on input data. • The positioning server enquires the new type of measurements from the network entity (i.e., gNB-DU) hosting the model. o As one alternative to the gNB-DU declaring its capabilities to support new measurements needed for AI / ML based positioning, the gNB-DU may reply to the positioning server´s request with information concerning its capabilities support new measurements needed for AI / ML based positioning. • The gNB-DU provides the predicted AI / ML positioning measurements for positioning accuracy improvement with a new indication. • A quality indication is signaled from the gNB-DU, consisting of the quality of the predicted AI / ML measurements as output from the model • The gNB-DU groups the measurements to be signaled so that the size of the signaling messages does not exceed a certain limit. Such grouping involves legacy and new inferred measurements. Certain embodiments may provide one or more of the following technical advantage(s). For example, certain embodiments may provide a technical advantage of supporting AI / ML based positioning in the gNB-DU and / or providing signaling support over the F1 and NRPPa interfaces to signal the predicted positioning measurements. Other advantages may be readily apparent to one having skill in the art. Certain embodiments may have none, some, or all of the recited advantages. As used herein, “node” can be a network node or a UE. Examples of network nodes are NodeB, base station (BS), Multi-Standard Radio (MSR) radio node such as MSR BS, eNodeB (eNB), gNodeB (gNB), Master eNB (MeNB), Secondary eNB (SeNB), Integrated Access Backhaul (IAB) node, network controller, Radio Network Controller (RNC), Base Station Controller (BSC), relay, donor node controlling relay, Base Transceiver Station (BTS), Central Unit (e.g. in a gNB), Distributed Unit (e.g. in a gNB), Baseband Unit, Centralized Baseband, Cloud RAN (C-RAN), Access Point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU), Remote Radio Head (RRH), nodes in Distributed Antenna System (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc.), Operations & Maintenance (O&M), Operations Support System (OSS), Self Organizing Network (SON), positioning node (e.g. E-SMLC), etc. Another example of a node is User Equipment (UE), which is a non-limiting term and refers to any type of wireless device communicating with a network node and / or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, Vehicular to Vehicular (V2V), machine type UE, Machine Type Communication (MTC) UE or UE capable of Machine To Machine (M2M) communication, Personal Digital Assistant (PDA), Tablet, mobile terminals, smart phone, Laptop Embedded Equipment (LEE), Laptop Mounted Equipment (LME), Universal Serial Bus (USB) dongles, etc. In some embodiments, generic terminology, “radio network node” or simply “network node (NW node)”, is used. It can be any kind of network node which may comprise base station, radio base station, base transceiver station, base station controller, network controller, evolved Node B (eNB), Node B, gNodeB (gNB), relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH), Central Unit (e.g. in a gNB), Distributed Unit (e.g. in a gNB), Baseband Unit, Centralized Baseband, C-RAN, access point (AP), etc. The term Radio Access Technology (RAT) may refer to any RAT such as, for example, Universal Terrestrial Radio Access Network (UTRA), Evolved Universal Terrestrial Radio Access Network (E-UTRA), Narrow Band Internet of Things (NB-IoT), WiFi, Bluetooth, next generation RAT, New Radio (NR), 4thGeneration (4G), 5thGeneration (5G), etc. Any of the equipment denoted by the terms node, network node or radio network node may be capable of supporting a single or multiple RATs. The term “signal” or “radio signal” used herein can be any physical signal or physical channel. Examples of downlink (DL) physical signals are reference signal (RS) such as Primary Synchronization Signal (PSS), Secondary Synchronization Signal (SSS), Channel State Information (CSI) Reference Signal (CSI-RS), Demodulation Reference Signal (DMRS) signals in Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) Block (SSB), Discovery Reference Signal (DRS), Cell-specific Reference Signal (CRS), Positioning Reference Signal (PRS), etc. A Reference Signal (RS) may be periodic e.g. RS occasion carrying one or more RSs may occur with certain periodicity e.g. 20 milliseconds (ms), 40 ms, etc. The RS may also be aperiodic. Each SSB carries NR-PSS, NR-SSS and NR-PBCH in 4 successive symbols. One or multiple SSBs are transmit in one SSB burst which is repeated with certain periodicity e.g.5 ms, 10 ms, 20 ms, 40 ms, 80 ms and 160 ms. The UE is configured with information about SSB on cells of certain carrier frequency by one or more SS / PBCH block Measurement Timing Configuration (SMTC) configurations. The SMTC configuration comprising parameters such as SMTC periodicity, SMTC occasion length in time or duration, SMTC time offset with respect to reference time (e.g. serving cell’s System Frame Number (SFN)), etc. Therefore, SMTC occasion may also occur with certain periodicity e.g. 5 ms, 10 ms, 20 ms, 40 ms, 80 ms and 160 ms. Examples of uplink (UL) physical signals are reference signal such as Sounding Reference Signal (SRS), DMRS etc. The term physical channel refers to any channel carrying higher layer information e.g. data, control etc. Examples of physical channels are PBCH, Narrowband PBCH (NPBCH), Physical Downlink Control Channel (PDCCH), Physical Downlink Shared Channel (PDSCH), Physical Uplink Control Channel (PUCCH), Physical Uplink Shared Channel (PUSCH), short PDCCH (sPDCCH), short PDSCH (sPDSCH), short PUCCH (sPUCCH), short PUSCH (sPUSCH), MTC PDCCH (MPDCCH), Narrowband PDCCH (NPDCCH), Narrowband PDSCH (NPDSCH), Enhanced PDCCH (E-PDCCH), Narrowband PUSCH (NPUSCH), etc. The term “time resource” used herein may correspond to any type of physical resource or radio resource expressed in terms of length of time. Examples of time resources are: symbol, time slot, subframe, radio frame, Transmission Time Interval (TTI), interleaving time, slot, sub-slot, mini-slot, SFN cycle, hyper-SFN (H-SFN) cycle, etc. Certain embodiments described herein can be applicable to, but are not limited to, 3GPP NR. Thus, a network node can be a NR gNB or 6thGeneration (6G)-RAT base station or any other network node or device with similar functions. Embodiments of the present disclosure related to AI / ML positioning capability declaration include the following. In this regard, FIGURE 3 illustrates an example procedure for AI / ML positioning capability declaration, in accordance with embodiments of the present disclosure. According to certain embodiments, the LMF sends a request indicator via NRPPa to enquire (e.g., request) the network nodes to provide information about which TRPs are hosted by a gNB-DU that supports AI / ML assisted positioning (step 300). Namely, the request is aimed at gathering information concerning the TRPs supported by a gNB-DU and whether the gNB-DU supports AI / ML assisted positioning. For example, in a particular embodiment, the request may be sent via a new codepoint in the NRPPa TRP INFORMATION REQUEST message to NG-RAN node. In other words, the request may be sent via a codepoint in an enhanced, or modified, version of the NRPPa TRP INFORMATION REQUEST message that has been enhanced to support this codepoint. In a particular embodiment, once the gNB-CU receives the request indicator from the LMF, the gNB-CU sends a request indicator to the gNB-DU to enquire whether it supports AI / ML assisted positioning (step 302). For example, in a particular embodiment, the gNB-CU may send the request indicator to the gNB-DU via a new codepoint in the F1AP TRP INFORMATION REQUEST message to gNB-DU. In other words, the gNB-CU may send the request via a codepoint in an enhanced, or modified, version of the F1AP TRP INFORMATION REQUEST message that has been enhanced to support this codepoint. In a particular embodiment, the gNB-DU signals its capability for supporting AI / ML assisted positioning to the gNB-CU (step 304). For example, the gNB-DU may signal its capability via a new Information Element (IE) in the F1AP TRP INFORMATION RESPONSE message to gNB-CU. In other words, the gNB-DU may signal its capability via an IE in an enhanced, or modified, version of the F1AP TRP INFORMATION RESPONSE message that has been enhanced to support this IE. Such capability may consist of details about the inference outputs supported and optionally the characteristics of such outputs. Examples of such information may consist of one or more of the following information: i) Information concerning the inferred measurements supported (i.e., the types of inferred measurements supported) and that can be provided as output of an AI / ML model by the gNB-DU; ii) Information concerning the accuracy or the uncertainty of the predictions the gNB-DU can infer for the requested measurements. Such uncertainty / accuracy information may be provided on a per measurement basis or as an overall parameter for all the measurements that may be inferred; iii) Information concerning the training data used to derive the one or more AI / ML model supported at the gNB-DU and used to derive the inferred measurements that might be requested. Example of such information may be ranges of data used for training, provided for each data type, e.g. for each measurement used as a training data; iv) Information concerning the time horizon for which the inferred measurements that could potentially be requested may be generated. Namely, information about how far into the future and for which points in time into the future the one or more models in use at the gNB-DU may provide measurements predictions; v) Information about the processing delay the one or more gNB-DU side models are affected by when producing inferred measurements. Such delay may be measured from the time inputs are all received to the time inferred measurements are produced. vi) Information concerning the maximum measurement resolution the gNB-DU is able to provide. For example, the maximum number of path in a multiple report. vii) Information concerning the maximum update rate for a given measurement resolution the gNB-DU is able to support. For example, the gNB-DU may be able to provide a measurement every 5ms for a very accurate (many paths complex impulse response), but also a faster update rate (say 1ms) for cruder (less paths in the complex response) measurements. viii) information indicating at least one TRP hosted by the DU that supports inferred measurements. In a particular embodiment, the gNB-CU signals the information on which TRPs are hosted by a gNB-DU supporting AI / ML assisted positioning to the LMF (step 306). The gNB-CU signals also information concerning the gNB-DU capabilities described above to the LMF. For example, in a particular embodiment, the gNB-CU may signal the information via a new IE in the NRPPa TRP INFORMATION RESPONSE message to LMF. In other words, the gNB-CU may signal this information via an IE in an enhanced, or modified, version of the NRPPa TRP INFORMATION RESPONSE message that has been enhanced to support this IE. In certain of these and other embodiments, the LMF and gNB-CU know which TRPs are hosted by a gNB-DU that supports AI / ML assisted positioning via pre-configuration from an external node / function / system such as the OAM, as well as the capabilities of the gNB-DU. Embodiments related to NG-RAN node assisted positioning with gNB-DU-side model for AI / ML assisted positioning include the following. FIGURE 4 illustrates a procedure for NG-RAN node assisted positioning with gNB-DU- side model, in accordance with embodiments of the present disclosure. Optional steps are represented by dashed lines. As illustrated, in a particular embodiment, the gNB-DU receives a request to provide predicted measurements (e.g., to be inferred) for the purpose of AI / ML assisted positioning (steps 400 and 402). As a consequence of receiving such request, the gNB-DU may positively reply confirming that the predicted measurements can be provided (step 404). Alternatively, if the gNB-DU is not able to produce some or all of the predicted measurements, the gNB-DU may reply in step 404 with a message stating which measurements for AI / ML assisted positioning failed to be admitted and cannot be reported and which measurements for AI / ML assisted positioning were successfully admitted and can be reported. In a further particular embodiment, if the gNB-DU fails to admit all the predicted measurements requested, the gNB-DU signals back (e.g., in step 404) a failure message including information about why the measurements were not admitted. In a further particular embodiment, any of the messages above describing that some or all of the requested predicted measurements could not be admitted may contain information concerning whether the measurements could not be derived due to temporary issues, such as shortage of processing power, or whether the measurements are not supported, namely the gNB- DU is not capable of inferring such measurements. In a further particular embodiment, any of the messages above describing that some or all of the requested predicted measurements could not be admitted may contain information concerning the capabilities of the gNB-DU, where such capabilities may follow the same description provided above relating to AI / ML positioning capability declaration. Note that the message(s) above sent from the gNB-DU to the gNB-CU may be relayed to the LMF (step 405). In a particular embodiment, the gNB-DU receives the input data from the air interface (e.g., fingerprinting based on channel observation) to be used to generate predicted positioning measurements (step 406). The gNB-DU applies the gNB-DU-side AI / ML model(s) to the received input data to obtain predicted measurements (step 406). In a particular embodiment, the predicted measurements are considered enhancement positioning measurements generated by the one or more AI / ML model hosted at the gNB-DU. In one example embodiment, the gNB-DU provides the measurements resulting from the AI / ML model inference in the TRP measurement result IE present in 3GPP TS 38.473, with a new indication of the Predicted measurement IE over F1 message to the gNB-CU (step 408). In a particular embodiment, the gNB-DU includes, in the measurement results from AI / ML model inference provided to the gNB-CU in step 408, additional information concerning the predicted measurements. Such additional information enables the LMF to better understand how the predictions were derived and therefore how they can be used for the calculation of UE location. Such additional information may consist of one or more of the following: i) Information concerning the accuracy or the uncertainty of the predicted measurements. Such uncertainty / accuracy information may be provided on a per measurement basis or as an overall parameter for all the measurements inferred. ii) Information concerning the training data used to derive the one or more AI / ML model used at the gNB-DU to derive the inferred measurements that might be requested. Example of such information may be ranges of data used for training, provided for each data type, e.g. for each measurement used as a training data iii) Information concerning the time horizon for which the inferred measurements were calculated. Namely, information about how far into the future and for which points in time into the future the one or more models in use at the gNB-DU has calculated the measurements predictions. iv) Information about the whether one or more of the predicted measurements were subject to epistemic uncertainty, namely whether the one or more predicted measurements were derived from inputs for which the model in use at the gNB-DU was not trained or it was not sufficiently trained. v) Information concerning the quality of the predicted measurements, in terms of time, angle and phase quality compared to the non-predicted measurements. Such quality score may be provided as a percentage of error , an accuracy or an uncertainty. In a particular embodiment, the gNB-CU relays the received report from the gNB-DU with a new indication of the output TRP predicted measurement IE to the LMF over NRPPa message (step 410). In a particular embodiment, the gNB-DU provides the predicted measurement based on input data only for a specific positioning measurement, or for all positioning measurements, or based on request from LMF indicating for which positioning measurement a predicted measurement should be generated for accuracy improvement. In a particular embodiment the gNB-DU provides predicted measurements that follow specific requests from the LMF. Such specific requests may consist of one or more of the following: i) The requested predicted measurements shall have an uncertainty or accuracy better or equal than a threshold specified by the LMF ii) The requested predicted measurements should be calculated for a specific point in time in the future. This point in time may be specified by the LMF as a time window, e.g. in seconds or ms, starting at the time of the reception of the predicted measurements request at the gNB-DU iii) The requested predicted measurements shall be inferred and reported periodically, at a specific reporting period T specified in e.g. ms. iv) The requested predicted measurements shall be inferred and reported only once, namely not periodically. v) The requested predicted measurements shall be inferred and reported periodically, at a specific reporting period T specified in e.g. ms, and for a specific time duration Td. Namely, the gNB-DU shall generate predicted measurements periodically, with periodicity T, during a time window of duration Td starting at the time the request for predicted measurements is received at the gNB-DU. After the expiration of the time window Td, the gNB-DU shall no longer infer and report predicted measurements. vi) The requested predicted measurements shall be inferred with quality levels equal of better than requested thresholds. Such quality levels may be applied to parameters such as time, angle and phase quality. Quality level as may be calculated with respect to the non-predicted measurements. Such quality thresholds / requirements may be provided as a percentage of error , an accuracy or an uncertainty. In a particular embodiment, the predicted measurements resulting from AI / ML model inference can cover predicted measurements of existing positioning measurement , e.g., Uplink (UL) Angle of Arrival (UL-AoA), UL Relative Time of Arrival (UL-RTOA), UL SRS Reference Signal Received Power (UL-SRS-RSRP), UL SRS Reference Signal Received Path Power (UL- SRS-RSRPP), gNB Rx-Tx, UL Reference Signal Carrier Phase (UL-RSCP), and can be sent per TRP measurement report IE (option 1). In a particular embodiment, the predicted measurements can be bundled and sent in a separate Prediction report from TRP to gNB-CU, and from gNB-CU to LMF (option 2). FIGURE 5 illustrates a method and signaling diagram for indicating new elements of predicted measurements based on gNB-DU side model for inference. Specifically, FIGURE 5 illustrates the following messages and operations: • Step 500: TRP Information capability exchange between gNB-DU and LMF indicating AI / ML inference model support. See, for example, the procedure of FIGURE 3. • Step 502: Configures the UE SRS transmission • Step 504: The LMF signals the NRPPa Measurement Request to the gNB-CU. Such request includes an indication of which measurement(s) to provided predicted measurement. Note that steps 504-512 correspond one example embodiment of steps 400, 402, 406, 408, and 410 of FIGURE 4. • Step 506: The gNB-CU signals a F1AP Measurement Request to the gNB-DU. The request includes an indication of which measurement(s) to provide predicted measurement. • Step 508: The gNB-DU receives input data and applies inference model. • Step 510: The gNB-DU transmits, to the gNB-CU, a F1AP Measurement Response that reports the TRP Measurement result with predicted measurements. • Step 512: The gNB-CU transmits, to the LMF, a NRPPa Measurement Response that reports the TRP Measurement result with predicted measurements. An example implementation of an embodiment of the present disclosure is shown below as changes applied to both NRPPa and F1AP 3GPP specifications. Additions are shown via underlined text. Changes to TS 38.455 / 38.473 (predicted measurement per measurement – option 1) 9.2.37 TRP Measurement Result This information element contains the measurement result. IE / Group Name Presence Range IE Type and Semantics Criticality Assigned Reference Description Criticality Measured Result 1.. - Item <maxnoPosMeas> >CHOICE M - Measured Results Value >>UL Angle of 9.2.38 Arrival >>Predicted 9.2.38 YES reject UL Angle of Arrival >>UL SRS- INTEGER RSRP (0..126) >>Predicted YES reject UL SRS-RSRP >>UL RTOA 9.2.39 >>Predicted 9.2.39 YES reject UL RTOA >>gNB Rx-Tx 9.2.40 Time Difference >>Predicted 9.2.40 YES reject gNB Rx-Tx Time Difference >>Z-AoA 9.2.67 YES reject >>Predicted 9.2.67 YES reject Z-AoA >>Multiple 9.2.71 YES reject UL-AoA >>Predicted 9.2.71 YES reject Multiple UL- AoA >>UL SRS- 9.2.72 YES reject RSRPP >>Predicted 9.2.72 YES reject UL SRS- RSRPP >Time Stamp M 9.2.42 - >Measurement O 9.2.43 - Quality >Prediction O • Time - Quality prediction quality • Angle prediction quality • Phase prediction quality, • Margin of error >Measurement O 9.2.57 - Beam Information >SRS Resource O 9.2.73 YES ignore type >ARP ID O 9.2.75 YES ignore >LoS / NLoS O 9.2.77 YES ignore Information Range bound Explanation maxnoPosMeas Maximum no. of measured quantities that can be configured and reported with one positioning measurement message. Value is 16384. Changes to TS 38.455 / 38.473 (predicted measurement per TRP– option 1) 9.1.4.2 MEASUREMENT RESPONSE This message is sent by the NG-RAN node to report positioning measurements for the target UE. Direction: NG-RAN node → LMF. IE / Group Name Presence Range IE type and Semantics Criticality Assigned reference description Criticality Message Type M 9.2.3 YES reject NRPPa Transaction ID M 9.2.4 - LMF Measurement ID M INTEGER YES reject (1..65536, …) RAN Measurement ID M INTEGER YES reject (1..65536, …) TRP Measurement 0..1 YES reject Response List >TRP 1..<maxnoofMeasTRPs> EACH reject Measurement Response Item >>TRP ID M 9.2.24 - >>TRP M 9.2.37 - Measurement Result >>Predicted TRP M 9.2.37 YES ignore Measurement Result >PredictionO• Time- Quality prediction quality • Angle prediction quality • Phase prediction quality, Margin of error >>Cell ID O NR CGI The Cell ID of YES ignore 9.2.9 the TRP identified by the TRP ID IE. Criticality Diagnostics O 9.2.2 YES ignore 9.1.4.4 MEASUREMENT REPORT This message is sent by the NG-RAN node to report positioning measurements for the target UE. Direction: NG-RAN node → LMF. IE / Group Name Presence Range IE type and Semantics Criticality Assigned reference description Criticality Message Type M 9.2.3 YES reject NRPPa Transaction ID M 9.2.4 - LMF Measurement ID M INTEGER YES reject (1..65536, …) RAN Measurement ID M INTEGER YES reject (1..65536, …) TRP Measurement 1 YES reject Response List >TRP 1..<maxnoofMeasTRPs> EACH reject Measurement Response Item >>TRP ID M 9.2.24 - >>TRP M 9.2.37 - Measurement Result >>Predicted TRP M 9.2.37 YES ignore Measurement Result >PredictionO• Time- Quality prediction quality • Angle prediction quality • Phase prediction quality, Margin of error >>Cell ID O NR CGI The Cell ID of YES ignore 9.2.9 the TRP identified by the TRP ID IE. Range bound Explanation maxnoofMeasTRPs Maximum no. of TRPs that can be included within one message. Value is 64. Changes to TS 38.455 / 38.473 (request from sender) 9.2.81 Measurement Characteristics Request Indicator This IE contains the measurement characteristic information requested by LMF. IE / Group Name Presence Range IE Type and Semantics Description Reference Measurement M BIT STRING Each position in the bitmap characteristic request (SIZE(16)) represents a requested indicator measurement characteristic: first bit: Measurement Beam Information Second bit: Extended Additional Path List Third bit: Additional Path Power Fourth Bit: Multiple UL AoA of Additional Path Fifth bit: LoS / NLoS Information Sixth bit: TRP Rx TEG association for UL-TDOA Seventh bit: TRP RxTxTEG- ID information for DL+UL positioning. Eighth bit: SRS Resource Type Ninth bit: Multiple Measurement Instances XY bit: report predicted measurements from the inference model XX: report prediction quality Other bits reserved for future use. Value ‘1’ indicates ‘requested measurement characteristic’, Value ‘0’ indicates ‘not requested’. 9.2.25 TRP Information The TRP Information IE contains information for one TRP within an NG-RAN node. IE / Group Name Presence Range IE Type and Semantics Criticality Assigned Reference Description Criticality TRP ID M 9.2.24 - TRP Information Type 1.. - <maxnoTRPInfoTypes> >CHOICE TRP M - Information Item >>NR PCI M INTEGER NR Physical - (0..1007) Cell ID >>NR CGI M 9.2.9 - >>NR ARFCN M INTEGER - (0..3279165) >>PRS M 9.2.44 - Configuration >>SSB Information M 9.2.54 - >>SFN M Relative Time - Initialisation Time 1900 9.2.36 >>Spatial Direction M 9.2.45 - Information >>Geographical M 9.2.46 - Coordinates >>TRP type M ENUMERATED TS 38.305

[0018] YES reject (prs-only-tp, srs- only-rp, tp, rp, trp…) >>On-demand PRS M 9.2.65 YES reject TRP Information >>TRP Tx TEG M 9.2.79 YES reject Association >>TRP Beam M 9.2.82 YES reject Antenna Information >>Predicted M ENUMERATED Indicates the YES reject measurements (true,…) TRP supports reporting receiving input data and hosted by a gNB-DU side inference model Range bound Explanation maxnoTRPInfoTypes Maximum no of TRP information types that can be requested and reported with one message. Value is 64. FIGURE 6 shows an example of a communication system 600 in accordance with some embodiments. In the example, the communication system 600 includes a telecommunication network 602 that includes an access network 604, such as a radio access network (RAN), and a core network 606, which includes one or more core network nodes 608. The access network 604 includes one or more access network nodes, such as network nodes 610a and 610b (one or more of which may be generally referred to as network nodes 610), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 610 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 612a, 612b, 612c, and 612d (one or more of which may be generally referred to as UEs 612) to the core network 606 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 600 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 600 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system. The UEs 612 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 610 and other communication devices. Similarly, the network nodes 610 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 612 and / or with other network nodes or equipment in the telecommunication network 602 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 602. In the depicted example, the core network 606 connects the network nodes 610 to one or more hosts, such as host 616. 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 606 includes one more core network nodes (e.g., core network node 608) 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 608. 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 616 may be under the ownership or control of a service provider other than an operator or provider of the access network 604 and / or the telecommunication network 602, and may be operated by the service provider or on behalf of the service provider. The host 616 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 600 of FIGURE 6 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 602 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 602. For example, the telecommunications network 602 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 612 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 604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 604. 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 614 communicates with the access network 604 to facilitate indirect communication between one or more UEs (e.g., UE 612c and / or 612d) and network nodes (e.g., network node 610b). In some examples, the hub 614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 614 may be a broadband router enabling access to the core network 606 for the UEs. As another example, the hub 614 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 610, or by executable code, script, process, or other instructions in the hub 614. As another example, the hub 614 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 614 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 614 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 614 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy IoT devices. The hub 614 may have a constant / persistent or intermittent connection to the network node 610b. The hub 614 may also allow for a different communication scheme and / or schedule between the hub 614 and UEs (e.g., UE 612c and / or 612d), and between the hub 614 and the core network 606. In other examples, the hub 614 is connected to the core network 606 and / or one or more UEs via a wired connection. Moreover, the hub 614 may be configured to connect to an M2M service provider over the access network 604 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 610 while still connected via the hub 614 via a wired or wireless connection. In some embodiments, the hub 614 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 610b. In other embodiments, the hub 614 may be a non- dedicated hub – that is, a device which is capable of operating to route communications between the UEs and network node 610b, but which is additionally capable of operating as a communication start and / or end point for certain data channels. FIGURE 7 shows a UE 700, which may be an embodiment of the UE 112 of FIGURE 6, in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-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 700 includes processing circuitry 702 that is operatively coupled via a bus 704 to an input / output interface 706, a power source 708, a memory 710, a communication interface 712, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIGURE 7. 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 702 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 710. The processing circuitry 702 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 702 may include multiple central processing units (CPUs). In the example, the input / output interface 706 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 700. 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 708 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 708 may further include power circuitry for delivering power from the power source 708 itself, and / or an external power source, to the various parts of the UE 700 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 708. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 708 to make the power suitable for the respective components of the UE 700 to which power is supplied. The memory 710 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 710 includes one or more application programs 714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 716. The memory 710 may store, for use by the UE 700, any of a variety of various operating systems or combinations of operating systems. The memory 710 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 710 may allow the UE 700 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 710, which may be or comprise a device-readable storage medium. The processing circuitry 702 may be configured to communicate with an access network or other network using the communication interface 712. The communication interface 712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 722. The communication interface 712 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 718 and / or a receiver 720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 718 and receiver 720 may be coupled to one or more antennas (e.g., antenna 722) and may share circuit components, software or firmware, or alternatively be implemented separately. In the illustrated embodiment, communication functions of the communication interface 712 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 712, 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 head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item- tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an 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 700 shown in FIGURE 7. 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 8 shows a network node 800, which may be an embodiment of the network node 110 of FIGURE 7, 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)). 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 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 800 includes a processing circuitry 802, a memory 804, a communication interface 806, and a power source 808. The network node 800 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 800 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 800 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 804 for different RATs) and some components may be reused (e.g., a same antenna 810 may be shared by different RATs). The network node 800 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 800, 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 800. The processing circuitry 802 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 800 components, such as the memory 804, to provide network node 800 functionality. In some embodiments, the processing circuitry 802 includes a system on a chip (SOC). In some embodiments, the processing circuitry 802 includes one or more of radio frequency (RF) transceiver circuitry 812 and baseband processing circuitry 814. In some embodiments, the radio frequency (RF) transceiver circuitry 812 and the baseband processing circuitry 814 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 812 and baseband processing circuitry 814 may be on the same chip or set of chips, boards, or units. The memory 804 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 802. The memory 804 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 802 and utilized by the network node 800. The memory 804 may be used to store any calculations made by the processing circuitry 802 and / or any data received via the communication interface 806. In some embodiments, the processing circuitry 802 and memory 804 is integrated. The communication interface 806 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 806 comprises port(s) / terminal(s) 816 to send and receive data, for example to and from a network over a wired connection. The communication interface 806 also includes radio front- end circuitry 818 that may be coupled to, or in certain embodiments a part of, the antenna 810. Radio front-end circuitry 818 comprises filters 820 and amplifiers 822. The radio front-end circuitry 818 may be connected to an antenna 810 and processing circuitry 802. The radio front- end circuitry may be configured to condition signals communicated between antenna 810 and processing circuitry 802. The radio front-end circuitry 818 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 818 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 820 and / or amplifiers 822. The radio signal may then be transmitted via the antenna 810. Similarly, when receiving data, the antenna 810 may collect radio signals which are then converted into digital data by the radio front-end circuitry 818. The digital data may be passed to the processing circuitry 802. In other embodiments, the communication interface may comprise different components and / or different combinations of components. In certain alternative embodiments, the network node 800 does not include separate radio front-end circuitry 818, instead, the processing circuitry 802 includes radio front-end circuitry and is connected to the antenna 810. Similarly, in some embodiments, all or some of the RF transceiver circuitry 812 is part of the communication interface 806. In still other embodiments, the communication interface 806 includes one or more ports or terminals 816, the radio front-end circuitry 818, and the RF transceiver circuitry 812, as part of a radio unit (not shown), and the communication interface 806 communicates with the baseband processing circuitry 814, which is part of a digital unit (not shown). The antenna 810 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 810 may be coupled to the radio front-end circuitry 818 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 810 is separate from the network node 800 and connectable to the network node 800 through an interface or port. The antenna 810, communication interface 806, and / or the processing circuitry 802 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 810, the communication interface 806, and / or the processing circuitry 802 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 808 provides power to the various components of network node 800 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 808 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 800 with power for performing the functionality described herein. For example, the network node 800 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 808. As a further example, the power source 808 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 800 may include additional components beyond those shown in FIGURE 8 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 800 may include user interface equipment to allow input of information into the network node 800 and to allow output of information from the network node 800. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 800. FIGURE 9 is a block diagram of a host 900, which may be an embodiment of the host 616 of FIGURE 6, in accordance with various aspects described herein. As used herein, the host 900 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 900 may provide one or more services to one or more UEs. The host 900 includes processing circuitry 902 that is operatively coupled via a bus 904 to an input / output interface 906, a network interface 908, a power source 910, and a memory 912. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as FIGURES 7 and 8, such that the descriptions thereof are generally applicable to the corresponding components of host 900. The memory 912 may include one or more computer programs including one or more host application programs 914 and data 916, which may include user data, e.g., data generated by a UE for the host 900 or data generated by the host 900 for a UE. Embodiments of the host 900 may utilize only a subset or all of the components shown. The host application programs 914 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 914 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 900 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 914 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc. FIGURE 10 is a block diagram illustrating a virtualization environment 1000 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 1000 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. Applications 1002 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1000 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein. Hardware 1004 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 1006 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1008a and 1008b (one or more of which may be generally referred to as VMs 1008), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1006 may present a virtual operating platform that appears like networking hardware to the VMs 1008. The VMs 1008 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1006. Different embodiments of the instance of a virtual appliance 1002 may be implemented on one or more of VMs 1008, 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 1008 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 1008, and that part of hardware 1004 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 1008 on top of the hardware 1004 and corresponds to the application 1002. Hardware 1004 may be implemented in a standalone network node with generic or specific components. Hardware 1004 may implement some functions via virtualization. Alternatively, hardware 1004 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 1010, which, among others, oversees lifecycle management of applications 1002. In some embodiments, hardware 1004 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 1012 which may alternatively be used for communication between hardware nodes and radio units. FIGURE 11 shows a communication diagram of a host 1102 communicating via a network node 1104 with a UE 1106 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 612a of FIGURE 6 and / or UE 700 of FIGURE 7), network node (such as network node 610a of FIGURE 6 and / or network node 800 of FIGURE 8), and host (such as host 616 of FIGURE 6 and / or host 900 of FIGURE 9) discussed in the preceding paragraphs will now be described with reference to FIGURE 11. Like host 900, embodiments of host 1102 include hardware, such as a communication interface, processing circuitry, and memory. The host 1102 also includes software, which is stored in or accessible by the host 1102 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 1106 connecting via an over-the-top (OTT) connection 1150 extending between the UE 1106 and host 1102. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1150. The network node 1104 includes hardware enabling it to communicate with the host 1102 and UE 1106. The connection 1160 may be direct or pass through a core network (like core network 606 of FIGURE 6) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet. The UE 1106 includes hardware and software, which is stored in or accessible by UE 1106 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 1106 with the support of the host 1102. In the host 1102, an executing host application may communicate with the executing client application via the OTT connection 1150 terminating at the UE 1106 and host 1102. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 1150 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 1150. The OTT connection 1150 may extend via a connection 1160 between the host 1102 and the network node 1104 and via a wireless connection 1170 between the network node 1104 and the UE 1106 to provide the connection between the host 1102 and the UE 1106. The connection 1160 and wireless connection 1170, over which the OTT connection 1150 may be provided, have been drawn abstractly to illustrate the communication between the host 1102 and the UE 1106 via the network node 1104, without explicit reference to any intermediary devices and the precise routing of messages via these devices. As an example of transmitting data via the OTT connection 1150, in step 1108, the host 1102 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 1106. In other embodiments, the user data is associated with a UE 1106 that shares data with the host 1102 without explicit human interaction. In step 1110, the host 1102 initiates a transmission carrying the user data towards the UE 1106. The host 1102 may initiate the transmission responsive to a request transmitted by the UE 1106. The request may be caused by human interaction with the UE 1106 or by operation of the client application executing on the UE 1106. The transmission may pass via the network node 1104, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1112, the network node 1104 transmits to the UE 1106 the user data that was carried in the transmission that the host 1102 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1114, the UE 1106 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1106 associated with the host application executed by the host 1102. In some examples, the UE 1106 executes a client application which provides user data to the host 1102. The user data may be provided in reaction or response to the data received from the host 1102. Accordingly, in step 1116, the UE 1106 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 1106. Regardless of the specific manner in which the user data was provided, the UE 1106 initiates, in step 1118, transmission of the user data towards the host 1102 via the network node 1104. In step 1120, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1104 receives user data from the UE 1106 and initiates transmission of the received user data towards the host 1102. In step 1122, the host 1102 receives the user data carried in the transmission initiated by the UE 1106. One or more of the various embodiments improve the performance of OTT services provided to the UE 1106 using the OTT connection 1150, in which the wireless connection 1170 forms the last segment. More precisely, the teachings of these embodiments may improve one or more of, for example, data rate, latency, and / or power consumption and, thereby, provide benefits such as, for example, reduced user waiting time, relaxed restriction on file size, improved content resolution, better responsiveness, and / or extended battery lifetime. In an example scenario, factory status information may be collected and analyzed by the host 1102. As another example, the host 1102 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1102 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1102 may store surveillance video uploaded by a UE. As another example, the host 1102 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 1102 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data. In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 1150 between the host 1102 and UE 1106, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 1102 and / or UE 1106. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1150 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 1150 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 1104. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 1102. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1150 while monitoring propagation times, errors, etc. FIGURE 12 illustrates an example method by a DU of a network node for generating predicted measurements based on a DU-side model, according to certain embodiments. In the illustrated embodiment, the method includes at least one of a transmitting step at 1202, a receiving step at 1204, and a transmitting step at 1206. For example, at step 1202, the DU may transmit, to a CU of the network node, capability information indicating a capability of the DU to generate at least one predicted measurement based on the DU-side model. At step 1204, for example, the DU may receive, from the CU of the network node, a measurement request comprising a request for at least one predicted measurement generated using the DU-side model. At step 1206, for example, the DU may transmit, to the CU of the network node, at least one value associated with the at least one predicted measurement generated using the DU-side model FIGURE 13 illustrates an example method by a CU of a network node for supporting predicted measurements based on a DU-side model, according to certain embodiments. In the illustrated embodiment, the method includes at least one of a receiving step at 1302, a transmitting step at 1304, and a receiving step at 1306. For example, at step 1302, the CU may receive, from the DU of the network node, capability information indicating a capability of the DU to generate at least one predicted measurement based on the DU-side model. At step 1304, for example, the CU may transmit, to the DU of the network node, a measurement request comprising a request for at least one predicted measurement generated using the DU-side model. At step 1306, the CU may receive, from the DU of the network node, at least one value associated with the at least one predicted measurement generated using the DU-side model. FIGURE 14 illustrates an example method by an LMF of a network node for supporting predicted measurements based on a DU-side model, according to certain embodiments. In the illustrated embodiment, the method includes at least one of a receiving step at 1402, a transmitting step at 1404, and a receiving step at 1406. For example, at step 1402, the LMF may receive, from at least one of the CU and the DU of a network node, capability information indicating a capability of the DU to generate at least one predicted measurement based on the DU-side model. At step 1404, for example, the LMF may transmit, to at least one of the CU and the DU, a measurement request comprising a request for at least one predicted measurement generated using the DU-side model. At step 1406, the LMF may receive, from at least one of the CU and the DU, at least one value associated with the at least one predicted measurement generated using the DU-side model. Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be 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. Some example embodiments of the present disclosure are as follows: Group A Example Embodiments Example Embodiment A1. A method performed by a user equipment comprising: any of the user equipment steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above. Example Embodiment A2. The method of the previous embodiment, further comprising one or more additional user equipment steps, features or functions described above. Example Embodiment A3. The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host computer via the transmission to the network node. Group B Example Embodiments Example Embodiment B1. A method performed by a network node for predicted measurements based on a gNB-DU side model, the method comprising: any of the network node steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above. Example Embodiment B2. The method of the previous embodiment, further comprising one or more additional network node steps, features or functions described above. Example Embodiment B3. The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment. Group C Example Embodiments Example Embodiment C1. A method performed by a Distributed Unit (DU) of a network node for generating predicted measurements based on a DU-side model, the method comprising at least one of: • transmitting, to a Centralized Unit (CU) of the network node, capability information indicating a capability of the DU to generate at least one predicted measurement based on the DU-side model; and / or • receiving, from the CU of the network node, a measurement request comprising a request for at least one predicted measurement generated using the DU-side model; and / or • transmitting, to the CU of the network node, at least one value associated with the at least one predicted measurement generated using the DU-side model. Example Embodiment C2. The method of Example Embodiment C1, wherein the DU- side model comprises an AI and / or ML model stored at the DU. Example Embodiment C3. The method of any one of Example Embodiments C1 to C2, comprising using the DU-side model to generate the at least one predicted measurement. Example Embodiment C4. The method of any one of Example Embodiments C1 to C3, wherein the at least one predicted measurement is generated for AI and / or ML assisted positioning. Example Embodiment C5. The method of any one of Example Embodiments C1 to C4, wherein the at least one value associated with the at least one predicted measurement is transmitted in a TRP measurement result IE. Example Embodiment C6. The method of any one of Example Embodiments C1 to C4, wherein the at least one value associated with the at least one predicted measurement is transmitted via the F1. Example Embodiment C7. The method of any one of Example Embodiments C1 to C6, comprising transmitting, to the CU, additional information with the at least one value associated with the at least one predicted measurement, wherein the additional information comprises at least one of: • information indicating an accuracy level or uncertainty level of the at least one predicted measurement, • information associated with training data used to generate the at least one value associated with the at least one predicted measurement, • time information associated with the at least one value associated with the at least one predicted measurement, • information indicating how far into the future the at least one predicted measurement is associated, • information indicating a quality and / or quality level of the at least one value associated with the at least one predicted measurement. Example Embodiment C8. The method of any one of Example Embodiments C1 to C7, wherein the measurement request comprises an indication of at least one type of predicted measurement that is requested, and wherein the at least one value associated with the at least one predicted measurement is of the at least one type requested. Example Embodiment C9. The method of any one of Example Embodiments C1 to C8, wherein the measurement request is associated with a LMF and is forwarded to the DU via the CU. Example Embodiment C10. The method of any one of Example Embodiments C1 to C9, comprising determining that at least one condition is fulfilled prior to transmitting the at least one value associated with the at least one predicted measurement, and wherein the at least one condition comprises: • the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold; • the at least one value is calculated for a specific point in time in the future or within a time window; • the DU is configured to report the predicted measurements periodically and a time duration since a last reported predicted measurement is greater than a threshold; and / or • the at least one value is associated with a quality level that is equal to or greater than a threshold. Example Embodiment C11. The method of any one of Example Embodiments C1 to C10, comprising: in response to receiving the measurement request, transmitting a response message comprising an indication that one or more of the at least one predicted measurements can / will / may / is to be provided. Example Embodiment C12. The method of any one of Example Embodiments C1 to C11, comprising: in response to receiving the measurement request, transmitting a response message comprising an indication that one or more of the at least one predicted measurements cannot / will not / may not / is not to be provided. Example Embodiment C13. The method of any one of Example Embodiments C11 to C12, wherein the response message indicates at least one of: • at least one type of predicted measurement that can / will / may / is to be provided, and • at least one type of predicted measurement that cannot / will not / may not / is not to be provided Example Embodiment C14. The method of any one of Example Embodiments C12 to C13, wherein the response message comprises at least one of: • a cause and / or reason that the one or more predicted measurements cannot / will not / may not / is not to be provided, • an indication that the failure to provide the one or more predicted measurements is temporary, and / or • an indication that the DU does not support the one or more predicted measurements. Example Embodiment C15. The method of any one of Example Embodiment C1 to C14, wherein the at least one value associated with the at least one predicted measurement is associated with at least one of: UL-AoA, UL-RTOA, UL-SRS-RSRP, UL-SRS-RSRPP, gNB Rx-Tx, and UL-RSCP. Example Embodiment C16. The method of any one of Example Embodiments C1 to C15, wherein the at least one value associated with the at least one predicted measurement is transmitted to the CU via a TRP measurement report IE. Example Embodiment C17. The method of any one of Example Embodiments C1 to C16, wherein the at least one value associated with the at least one predicted measurement is transmitted to the CU via a prediction report. Example Embodiment C18. The method of any one of Example Embodiments C1 to C17, wherein the at least one value associated with the at least one predicted measurement is transmitted to the CU for forwarding to a LMF. Example Embodiment C19. The method of any one of Example Embodiments C1 to C18, comprising receiving a request for the capability information, and wherein the capability information is transmitted in response to the request. Example Embodiment C20. The method of any one of Example Embodiments C1 to C19, wherein the capability information is transmitted to the CU via an IE in a F1AP TRP INFORMATION RESPONSE message. Example Embodiment C21. The method of any one of Example Embodiments C1 to C20, wherein the capability information comprises at least one of: • an indication of at least one measurement prediction, inference input, and / or inference output supported by the DU, • an indication of at least one type of inferred measurement supported by the model, • information associated with an accuracy level and / or uncertainty level of measurement predictions supported by the model at the DU, • information associated with training data used to train the model at the DU, • information associated with a time horizon for prediction measurements supported at the DU, • information associated with a processing delay for the model at the DU, • information associated with a maximum measurement resolution the model can provide and / or the DU can provide, • information indicating at least one TRP hosted by the DU that support measurement prediction, and • information associated with a maximum update rate for a particular measurement resolution the DU supports. Example Embodiment C22. The method of any of the previous Example Embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment. Example Embodiment C23. A network node comprising processing circuitry configured to perform any of the methods of Example Embodiments C1 to C22. Example Embodiment C24. A network node configured and / or adapted to perform any of the methods of Example Embodiments C1 to C22. Example Embodiment C25. A computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments C1 to C22. Example Embodiment C26. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments C1 to C22. Example Embodiment C27. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the methods of Example Embodiments C1 to C22. Group D Example Embodiments Example Embodiment D1. A method performed by a Centralized Unit (CU) of a network node for supporting predicted measurements based on a Distributed Unit (DU)-side model, the method comprising at least one of: • receiving, from the DU of the network node, capability information indicating a capability of the DU to generate at least one predicted measurement based on the DU- side model; • transmitting, to the DU of the network node, a measurement request comprising a request for at least one predicted measurement generated using the DU-side model; and • receiving, from the DU of the network node, at least one value associated with the at least one predicted measurement generated using the DU-side model Example Embodiment D2. The method of Example Embodiment D1, wherein the DU- side model comprises an AI and / or ML model stored at the DU. Example Embodiment D3. The method of any one of Example Embodiments D1 to D2, wherein the at least one predicted measurement is generated for AI and / or ML assisted positioning. Example Embodiment D4. The method of any one of Example Embodiments D1 to D3, wherein the at least one value associated with the at least one predicted measurement is received in a TRP measurement result IE. Example Embodiment D5. The method of any one of Example Embodiments D1 to D3, wherein the at least one value associated with the at least one predicted measurement is received via the F1. Example Embodiment D6. The method of any one of Example Embodiments D1 to D5, comprising receiving, from the DU, additional information with the at least one value associated with the at least one predicted measurement, wherein the additional information comprises at least one of: • information indicating an accuracy level or uncertainty level of the at least one predicted measurement, • information associated with training data used to generate the at least one value associated with the at least one predicted measurement, • time information associated with the at least one value associated with the at least one predicted measurement, • information indicating how far into the future the at least one predicted measurement is associated, • information indicating a quality and / or quality level of the at least one value associated with the at least one predicted measurement. Example Embodiment D7. The method of any one of Example Embodiments D1 to D6, wherein the measurement request comprises an indication of at least one type of predicted measurement that is requested, and wherein the at least one value associated with the at least one predicted measurement is of the at least one type requested. Example Embodiment D8. The method of any one of Example Embodiments D1 to D7, comprising receiving the measurement request from a LMF, and wherein the CU forwards to the measurement request to the DU. Example Embodiment D9. The method of any one of Example Embodiments D1 to D8, wherein at least one condition is fulfilled prior to the at least one value associated with the at least one predicted measurement being transmitted to the CU, and wherein the at least one condition comprises: • the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold; • the at least one value is calculated for a specific point in time in the future or within a time window; • the DU is configured to report the predicted measurements periodically and a time duration since a last reported predicted measurement is greater than a threshold; and / or • the at least one value is associated with a quality level that is equal to or greater than a threshold. Example Embodiment D10. The method of Example Embodiment D9, comprising receiving the at least one condition from the LMF and transmitting the at least one condition to the DU. Example Embodiment D11. The method of any one of Example Embodiments D1 to D10, comprising: in response to transmitting the measurement request, receiving a response message from the DU, the response message comprising an indication that one or more of the at least one predicted measurements can / will / may / is to be provided. Example Embodiment D12. The method of any one of Example Embodiments D1 to D11, comprising: in response to transmitting the measurement request, receiving a response message from the DU, the response message comprising an indication that one or more of the at least one predicted measurements cannot / will not / may not / is not to be provided. Example Embodiment D13. The method of any one of Example Embodiments D11 to D12, wherein the response message indicates at least one of: • at least one type of predicted measurement that can / will / may / is to be provided, and • at least one type of predicted measurement that cannot / will not / may not / is not to be provided Example Embodiment D14. The method of any one of Example Embodiments D12 to D13, wherein the response message comprises at least one of: • a cause and / or reason that the one or more predicted measurements cannot / will not / may not / is not to be provided, • an indication that the failure to provide the one or more predicted measurements is temporary, and / or • an indication that the DU does not support the one or more predicted measurements. Example Embodiment D15. The method of any one of Example Embodiment D1 to D14, wherein the at least one value associated with the at least one predicted measurement is associated with at least one of: UL-AoA, UL-RTOA, UL-SRS-RSRP, UL-SRS-RSRPP, gNB Rx-Tx, and UL-RSCP. Example Embodiment D16. The method of any one of Example Embodiments D1 to D15, wherein the at least one value associated with the at least one predicted measurement is received from the DU via a TRP measurement report IE. Example Embodiment D17. The method of any one of Example Embodiments D1 to D16, wherein the at least one value associated with the at least one predicted measurement is received from the DU via a prediction report. Example Embodiment D18. The method of any one of Example Embodiments D1 to D17, comprising forwarding and / or transmitting the at least one value associated with the at least one predicted measurement to a LMF. Example Embodiment D19. The method of any one of Example Embodiments D1 to D18, comprising transmitting, to the DU, a request for the capability information, and wherein the capability information is received in response to the request. Example Embodiment D20. The method of any one of Example Embodiments D1 to D19, wherein the capability information is received from the DU via an IE in a F1AP TRP INFORMATION RESPONSE message. Example Embodiment D21. The method of any one of Example Embodiments D1 to D20, wherein the capability information comprises at least one of: • an indication of at least one measurement prediction, inference input, and / or inference output supported by the DU, • an indication of at least one type of inferred measurement supported by the model, • information associated with an accuracy level and / or uncertainty level of measurement predictions supported by the model at the DU, • information associated with training data used to train the model at the DU, • information associated with a time horizon for prediction measurements supported at the DU, • information associated with a processing delay for the model at the DU, • information associated with a maximum measurement resolution the model can provide and / or the DU can provide, • information indicating at least one TRP hosted by the DU that support measurement prediction, and • information associated with a maximum update rate for a particular measurement resolution the DU supports. Example Embodiment D23. The method of any one of Example Embodiments D1 to D21, comprising transmitting the capability information to the LMF. Example Embodiment D24. The method of any one of Example Embodiments D1 to D23, comprising performing at least one positioning operation for at least one UE based on the at least one value associated with at least one predicted measurement. Example Embodiment D25. The method of any of the previous Example Embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment. Example Embodiment D26. A network node comprising processing circuitry configured to perform any of the methods of Example Embodiments D1 to D25. Example Embodiment D27. A network node configured to perform any of the methods of Example Embodiments D1 to D25. Example Embodiment D28. A computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments D1 to D25. Example Embodiment D29. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments D1 to D25. Example Embodiment D30. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the methods of Example Embodiments D1 to D25. Group E Example Embodiments Example Embodiment E1. A method performed by a Location Management Function (LMF) for supporting predicted measurements based on a Distribured Unit (DU)-side model, the method comprising at least one of: • receiving, from at least one of the CU and the DU of a network node, capability information indicating a capability of the DU to generate at least one predicted measurement based on the DU-side model; and / or • transmitting, to at least one of the CU and the DU, a measurement request comprising a request for at least one predicted measurement generated using the DU-side model; and / or • receiving, from at least one of the CU and the DU, at least one value associated with the at least one predicted measurement generated using the DU-side model. Example Embodiment E2. The method of Example Embodiment E1, wherein the DU- side model comprises an AI and / or ML model at the DU. Example Embodiment E3. The method of any one of Example Embodiments E1 to E2, wherein the at least one predicted measurement is generated for AI and / or ML assisted positioning. Example Embodiment E4. The method of any one of Example Embodiments E1 to E3, comprising performing at least one positioning operation for at least one UE based on the at least one value associated with at least one predicted measurement. Example Embodiment E5. The method of any one of Example Embodiments E1 to E4, wherein the at least one value associated with the at least one predicted measurement is received from the CU in a NRPPa Measurement Response message. Example Embodiment E6. The method of Example Embodiment E5, wherein the NRPPa Measurement Response message comprises at least one additional value associated with a TRP Measurement performed by a UE and the at least one value associated with the at least one predicted measurement. Example Embodiment E7. The method of any one of Example Embodiments E1 to E6, comprising receiving, from the CU, additional information with the at least one value associated with the at least one predicted measurement, wherein the additional information comprises at least one of: • information indicating an accuracy level or uncertainty level of the at least one predicted measurement, • information associated with training data used to generate the at least one value associated with the at least one predicted measurement, • time information associated with the at least one value associated with the at least one predicted measurement, • information indicating how far into the future the at least one predicted measurement is associated, • information indicating a quality and / or quality level of the at least one value associated with the at least one predicted measurement. Example Embodiment E8. The method of any one of Example Embodiments E1 to E7, wherein the measurement request transmitted to the at least one of the CU and the DU comprises an indication of at least one type of predicted measurement that is requested from the at least one of the CU and the DU, and wherein the at least one value associated with the at least one predicted measurement received from the at least one of the CU and the DU is of the at least one type requested. Example Embodiment E9. The method of any one of Example Embodiments E1 to E8, wherein a characteristic of the at least one value received from the at least one of the CU and the DU fulfills at least one condition, and wherein the at least one condition comprises: • the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold; • the at least one value is calculated for a specific point in time in the future or within a time window; • the DU is configured to report the predicted measurements periodically and a time duration since a last reported predicted measurement is greater than a threshold; and / or • the at least one value is associated with a quality level that is equal to or greater than a threshold. Example Embodiment E10. The method of Example Embodiment E9, comprising transmitting, to the at least one of the CU and the DU, the at least one condition. Example Embodiment E11. The method of any one of Example Embodiments E1 to E10, comprising: receiving, from the at least one of the CU and the DU, a response message comprising an indication that one or more of the at least one predicted measurements can / will / may / is to be provided. Example Embodiment E12. The method of any one of Example Embodiments E1 to E11, comprising: receiving, from the at least one of the CU and the DU, a response message comprising an indication that one or more of the at least one predicted measurements cannot / will not / may not / is not to be provided. Example Embodiment E13. The method of any one of Example Embodiments E11 to E12, wherein the response message indicates at least one of: • at least one type of predicted measurement that can / will / may / is to be provided, and • at least one type of predicted measurement that cannot / will not / may not / is not to be provided. Example Embodiment E14. The method of any one of Example Embodiments E12 to E13, wherein the response message comprises at least one of: • a cause and / or reason that the one or more predicted measurements cannot / will not / may not / is not to be provided, • an indication that the failure to provide the one or more predicted measurements is temporary, and / or • an indication that the DU does not support the one or more predicted measurements. Example Embodiment E15. The method of any one of Example Embodiments E1 to E14, wherein the at least one value associated with the at least one predicted measurement is associated with at least one of: UL-AoA, UL-RTOA, UL-SRS-RSRP, UL-SRS-RSRPP, gNB Rx-Tx, and UL-RSCP. Example Embodiment E16. The method of any one of Example Embodiments E1 to E15, wherein the at least one value associated with the at least one predicted measurement is received from the at least one of the CU and the DU via a TRP measurement report IE. Example Embodiment E17. The method of any one of Example Embodiments E1 to E16, wherein the at least one value associated with the at least one predicted measurement is received from the at least one of the CU and the DU via a prediction report. Example Embodiment E18. The method of any one of Example Embodiments E1 to E17, comprising transmitting, to the at least one of the CU and the DU, a request for the capability information, and wherein the capability information is received in response to the request. Example Embodiment E19. method of Example Embodiment E18, wherein the requests is transmitted to the CU via NRPPa in a TRP information request message. Example Embodiment E20. The method of any one of Example Embodiments E1 to E19, wherein the capability information is received from the CU in a NRPPa message . Example Embodiment E21. The method of any one of Example Embodiments E1 to E20, wherein the capability information comprises at least one of: • an indication of at least one measurement prediction, inference input, and / or inference output supported by the DU, • an indication of at least one type of inferred measurement supported by the model, • information associated with an accuracy level and / or uncertainty level of measurement predictions supported by the model at the DU, • information associated with training data used to train the model at the DU, • information associated with a time horizon for prediction measurements supported at the CU, • information associated with a processing delay for the model at the DU, • information associated with a maximum measurement resolution the model can provide and / or the DU can provide, • information indicating at least one TRP hosted by the DU that support measurement prediction, and • information associated with a maximum update rate for a particular measurement resolution the DU supports. Example Embodiment E22. The method of any of the previous Example Embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment. Example Embodiment E23. A network node comprising processing circuitry configured to perform any of the methods of Example Embodiments E1 to E22. Example Embodiment E24. A network node configured to and / or adapted to perform any of the methods of Example Embodiments E1 to E22. Example Embodiment E25. A computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments E1 to E22. Example Embodiment E26. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments E1 to E22. Example Embodiment E27. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the methods of Example Embodiments E1 to E22. Group F Example Embodiments Example Embodiment F1. A user equipment comprising: processing circuitry configured to perform any of the steps of any of the Group A Example Embodiments; and power supply circuitry configured to supply power to the processing circuitry. Example Embodiment F2. A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B, C, D, and E Example Embodiments; power supply circuitry configured to supply power to the processing circuitry. Example Embodiment F3. A user equipment (UE) comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A Example Embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE. Example Embodiment E4. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A Example Embodiments to receive the user data from the host. Example Embodiment F5. The host of the previous Example Embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host. Example Embodiment F6. The host of the previous 2 Example Embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application. Example Embodiment F7. A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host. Example Embodiment F8. The method of the previous Example Embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE. Example Embodiment F9. The method of the previous Example Embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application. Example Embodiment F10. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A Example Embodiments to transmit the user data to the host. Example Embodiment F11. The host of the previous Example Embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host. Example Embodiment F12. The host of the previous 2 Example Embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application. Example Embodiment F13. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, receiving user data transmitted to the host via the network node by the UE, wherein the UE performs any of the steps of any of the Group A Example Embodiments to transmit the user data to the host. Example Embodiment F14. The method of the previous Example Embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE. Example Embodiment F15. The method of the previous Example Embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application. Example Embodiment F16. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B, C, D, and E Example Embodiments to transmit the user data from the host to the UE. Example Embodiment F17. The host of the previous Example Embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host. Example Embodiment F18. A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B, C, D, and E Example Embodiments to transmit the user data from the host to the UE. Example Embodiment F19. The method of the previous Example Embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE. Example Embodiment F20. The method of any of the previous 2 Example Embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application. Example Embodiment F21. A communication system configured to provide an over-the- top service, the communication system comprising a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B, C, D, and E Example Embodiments to transmit the user data from the host to the UE. Example Embodiment F22. The communication system of the previous Example Embodiment, further comprising: the network node; and / or the user equipment. Example Embodiment F23. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B, C, D, and E Example Embodiments to receive the user data from a user equipment (UE) for the host. Example Embodiment F24. The host of the previous 2 Example Embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application. Example Embodiment F25. The host of the any of the previous 2 Example Embodiments, wherein the initiating receipt of the user data comprises requesting the user data. Example Embodiment F26. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B, C, D, and E Example Embodiments to receive the user data from the UE for the host. Example Embodiment F27. The method of the previous Example Embodiment, further comprising at the network node, transmitting the received user data to the host.

Claims

CLAIMS 1. A method performed by a Distributed Unit, DU, of a Radio Access Network, RAN, node for generating inferred measurements based on a DU-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, the method comprising: receiving (402; 506), from a Centralized Unit, CU, of the RAN node, a measurement request; and transmitting (408; 510), to the CU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request.

2. The method of claim 1, wherein the DU-side AI / ML model is hosted at the DU.

3. The method of any one of claims 1 to 2, further comprising using (408; 508) the DU-side AI / ML model to generate the at least one inferred measurement.

4. The method of any one of claims 1 to 3, wherein the at least one inferred measurement is at least one inferred measurement related to AI / ML assisted positioning of a User Equipment, UE.

5. The method of any one of claims 1 to 4, wherein the measurement request comprises a request for at least one inferred measurement.

6. The method of any one of claims 1 to 5, wherein the at least one value associated with the at least one inferred measurement is transmitted in a report or message comprising an indication that the at least one inferred measurement is an inferred measurement.

7. The method of any one of claims 1 to 6, wherein the DU is associated to one or more Transmission and Reception Points, TRPs, and transmitting (408; 510) the at least one value associated with the at least one inferred measurement comprises transmitting (408; 510) the at least one value associated with the at least one inferred measurement in a TRP measurement result Information Element, IE.

8. The method of any one of claims 1 to 6, wherein transmitting (408; 510) the at least one value associated with the at least one inferred measurement comprises transmitting (408; 510) the at least one value associated with the at least one inferred measurement to the CU via an F1 interface between the DU and the CU.

9. The method of any one of claims 1 to 6, wherein transmitting (408; 510) the at least one value associated with the at least one inferred measurement comprises transmitting (408; 510) the at least one value associated with the at least one inferred measurement to the CU in a prediction report.

10. The method of any one of claims 1 to 9, further comprising transmitting (408; 510), to the CU, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement, time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement.

11. The method of any one of claims 1 to 10, wherein the measurement request is associated with a Location Management Function, LMF, and is forwarded to the DU via the CU.

12. The method of any one of claims 1 to 11, wherein transmitting (408; 510).the at least one value associated with the at least one inferred measurement comprises transmitting (408; 510) the at least one value associated with the at least one inferred measurement in accordance with at leastone condition indicated in association with the measurement request, the at least one condition comprising any one or more of: the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold; the at least one value is calculated for a specific future point in time or within a time window; the at least one predicted measurement is to be reported periodically at a given periodicity; the at least one inferred measurement is to be inferred and reported only once; the at least one predicted measurement is to be reported periodically at a given periodicity for a given time duration; the at least one value is associated with a quality level that is equal to or greater than a threshold.

13. The method of any one of claims 1 to 12, further comprising, in response to receiving (402) the measurement request, transmitting (404) a response message comprising an indication that one or more of the at least one inferred measurements can be provided.

14. The method of any one of claims 1 to 12, further comprising, in response to receiving (402) the measurement request, transmitting (404) a response message comprising an indication that one or more of the at least one inferred measurements cannot be provided.

15. The method of claim 14, wherein the response message comprises at least one of: an indication that a failure due to which the one or more of the at least one inferred measurement cannot be provided is temporary, an indication that the DU does not support the one or more of the at least one inferred measurement.

16. The method of any one of claims 1 to 15, wherein the at least one value associated with the at least one predicted measurement is associated with at least one of: Uplink, UL, Angle of Arrival, UL-AoA; UL Relative Time of Arrival, UL-RTOA; UL Sounding Reference Signal, SRS, Reference Signal Received Power, UL-SRS-RSRP; UL SRS Reference Signal Received PathPower, UL-SRS-RSRPP; gNodeB, gNB, Receive-Transmit, Rx-Tx, time difference; and UL Reference Signal Carrier Phase, UL-RSCP.

17. The method of any one of claims 1 to 16, further comprising transmitting (304; 500), to the CU of the RAN node, capability information indicating a capability of the DU to generate at least one inferred measurement based on the DU-side AI / ML model.

18. The method of claim 17, further comprising receiving (302; 500), from the CU of the RAN node, a request for the capability information, and wherein transmitting (304; 500) the capability information is in response to the receiving (302; 500) the request for the capability information.

19. The method of any one of claims 17 to 18, wherein transmitting (304; 500) the capability information comprises transmitting (304; 500) the capability information to the CU via an Information Element, IE, in a F1AP TRP INFORMATION RESPONSE message.

20. The method of any one of claims 17 to 19, wherein the capability information comprises at least one of: information about one or more inferred measurements supported by the DU and that can be provided as output of the DU-side AI / ML model, information about an accuracy level and / or uncertainty level of inferred measurements supported by the DU, information about training data used to train the DU-side AI / ML model, information about a time horizon for inferred measurements supported at the DU, information about a processing delay for the DU-side AI / ML model at the DU, information about a maximum inferred measurement resolution supported by the DU, information indicating at least one TRP hosted by the DU that supports inferred measurements, and information about a maximum update rate for a particular inferred measurement resolution the DU supports.

21. A network node for implementing a Distributed Unit, DU, of a Radio Access Network, RAN, node comprising one or more DUs and a Centralized Unit, CU, wherein the network node comprises processing circuitry configured to cause the network node to: receive (402; 506), from a Centralized Unit, CU, of the RAN node, a measurement request; and transmit (408; 510), to the CU of the RAN node, at least one value associated with at least one inferred measurement generated using a DU-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, in accordance with the measurement request.

22. The network node of claim 21, wherein the processing circuitry is further configured to cause the network node to perform the method of any of claims 2 to 20.

23. A network node for implementing a Distributed Unit, DU, of a Radio Access Network, RAN, node comprising one or more DUs and a Centralized Unit, CU, wherein the network node is configured to perform the method of any of claims 1 to 20.

24. A computer program comprising instructions which when executed by processing circuitry of a network node for implementing a Distributed Unit, DU, of a Radio Access Network, RAN, node comprising one or more DUs and a Centralized Unit, CU, cause the network node to perform the method of claims 1 to 20.

25. A computer program product comprising computer program, the computer program comprising instructions which when executed by processing circuitry of a network node for implementing a Distributed Unit, DU, of a Radio Access Network, RAN, node comprising one or more DUs and a Centralized Unit, CU, cause the network node to perform the method of any of claims 1 to 20.

26. A non-transitory computer readable medium storing instructions which when executed by processing circuitry of a network node for implementing a Distributed Unit, DU, of a Radio Access Network, RAN, node comprising one or more DUs and a Centralized Unit, CU, cause the network node to:receive (402; 506), from a Centralized Unit, CU, of the RAN node, a measurement request; and transmit (408; 510), to the CU of the RAN node, at least one value associated with at least one inferred measurement generated using a DU-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, in accordance with the measurement request.

27. A method performed by a Centralized Unit, CU, of a Radio Access Network, RAN, node for supporting inferred measurements based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, the method comprising: transmitting (402; 506), to a DU of the RAN node, a measurement request; and receiving (408; 510), from the DU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request.

28. The method of claim 27, wherein the DU-side AI / ML model is hosted at the DU.

29. The method of any one of claims 27 to 28, wherein the at least one inferred measurement is at least one inferred measurement related to AI / ML assisted positioning of a User Equipment, UE.

30. The method of any one of claims 27 to 29, wherein the measurement request comprises a request for at least one inferred measurement.

31. The method of any one of claims 27 to 30, wherein the at least one value associated with the at least one inferred measurement is received in a report or message comprising an indication that the at least one inferred measurement is an inferred measurement.

32. The method of any one of claims 27 to 31, wherein the DU is associated to one or more Transmission and Reception Points, TRPs, and receiving (408; 510) the at least one value associated with the at least one inferred measurement comprises receiving (408; 510) the at leastone value associated with the at least one inferred measurement in a TRP measurement result Information Element, IE.

33. The method of any one of claims 27 to 31, wherein receiving (408; 510) the at least one value associated with the at least one inferred measurement comprises receiving (408; 510) the at least one value associated with the at least one inferred measurement via an F1 interface between the DU and the CU.

34. The method of any one of claims 27 to 31, wherein receiving (408; 510) the at least one value associated with the at least one inferred measurement comprises receiving (408; 510) the at least one value associated with the at least one inferred measurement in a prediction report.

35. The method of any one of claims 27 to 34, further comprising receiving (408; 510), from the DU, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement, time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement.

36. The method of any one of claims 27 to 35, further comprising receiving (400; 504) the measurement request from a Location and Management Function, LMF, and wherein the transmitting (402; 506) the measurement request to the DU comprises forwarding (402; 506) the measurement request to the DU.

37. The method of claim 36, wherein the measurement request comprises at least one condition related to the at least one inferred measurement.

38. The method of claim 37, wherein the at least one condition comprises at least one of: a condition that the at least one value associated with the at least one inferred measurement is associated with an uncertainty or accuracy level that is equal to or greater than a threshold; a condition that the at least one value associated with the at least one inferred measurement is calculated for a specific point in time in the future or within a time window; a condition that values associated with the at least one inferred measurement are to be reported periodically; a condition that the at least one value associated with the at least one inferred measurement is to be inferred and reported only once; a condition that the at least one value associated with the at least one inferred measurement is to be reported periodically at a given periodicity for a given time duration; a condition that the at least one value associated with the at least one inferred measurement is associated with a quality level that is equal to or greater than a threshold.

39. The method of any one of claims 27 to 38, further comprising, in response to transmitting the measurement request (402), receiving (404) a response message from the DU, the response message comprising an indication that one or more of the at least one inferred measurements can be provided.

40. The method of any one of claims 27 to 38, further comprising, in response to transmitting (402) the measurement request, receiving (404) a response message from the DU, the response message comprising an indication that one or more of the at least one inferred measurements cannot be provided.

41. The method of claim 40, wherein the response message comprises at least one of: an indication that a failure due to which the one or more of the at least one inferred measurement cannot be provided is temporary, and / oran indication that the DU does not support the one or more of the at least one inferred measurement.

42. The method of any one of claims 27 to 41, wherein the at least one value associated with the at least one inferred measurement is associated with at least one of: Uplink, UL, Angle of Arrival, UL-AoA; UL Relative Time of Arrival, UL-RTOA; UL Sounding Reference Signal, SRS, Reference Signal Received Power, UL-SRS-RSRP; UL SRS Reference Signal Received Path Power, UL-SRS-RSRPP; gNodeB, gNB, Receive-Transmit, Rx-Tx, time difference; and UL Reference Signal Carrier Phase, UL-RSCP.

43. The method of any one of claims 27 to 42, further comprising transmitting (410; 512) a message comprising the at least one value associated with the at least one inferred measurement to a Location Management Function, LMF.

44. The method of any one of claims 27 to 43, further comprising receiving (304; 500), from the DU of the network node, capability information indicating a capability of the DU to generate at least one predicted measurement based on the DU-side model.

45. The method of claim 44, further comprising transmitting (302), to the DU, a request for the capability information, and wherein receiving (304) the capability information from the DU comprises receiving (304) the capability information from the DU in response to the request.

46. The method of any one of claims 44 to 45, wherein receiving (304; 500) the capability information from the DU comprises receiving (304; 500) the capability from the DU via an Information Element, IE, in a F1AP TRP INFORMATION RESPONSE message.

47. The method of any one of claims 44 to 46, wherein the capability information comprises at least one of: information about one or more inferred measurements supported by the DU and that can be provided as output of the DU-side AI / ML model,information about an accuracy level and / or uncertainty level of inferred measurements supported by the DU, information about training data used to train the DU-side AI / ML model, information about a time horizon for inferred measurements supported at the DU, information about a processing delay for the DU-side AI / ML model at the DU, information about a maximum inferred measurement resolution supported by the DU, information indicating at least one TRP hosted by the DU that supports inferred measurements, and information about a maximum update rate for a particular inferred measurement resolution the DU supports.

48. The method of any one of claims 44 to 47, further comprising transmitting (306) the capability information to the LMF.

49. A network node for implementing a Centralized Unit, CU, of a Radio Access Network, RAN, node for supporting inferred measurement based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, the network node comprising processing circuitry configured to cause the network node to: transmit (402; 506), to a DU of the RAN node, a measurement request; and receive (408; 510), from the DU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request.

50. The network node of claim 49, wherein the processing circuitry is further configured to cause the network node to perform the method of any of claims 28 to 48.

51. A network node for implementing a Centralized Unit, CU, of a Radio Access Network, RAN, node for supporting inferred measurement based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, the network node configured to perform the method of any of claims 27 to 48.

52. A computer program comprising instructions which when executed by processing circuitry of a network node for implementing a Centralized Unit, CU, of a Radio Access Network, RAN, node for supporting inferred measurement based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, cause the network node to perform the method of any of claims 27 to 48.

53. A computer program product comprising a computer program, the computer program comprising instructions which when executed by processing circuitry of a network node for implementing a Centralized Unit, CU, of a Radio Access Network, RAN, node for supporting inferred measurement based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, cause the network node to perform the method of any of claims 27 to 48.

54. A non-transitory computer readable medium storing instructions which when executed by processing circuitry of a network node for implementing a Centralized Unit, CU, of a Radio Access Network, RAN, node for supporting inferred measurement based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, cause the network node to perform the method of any of claims 27 to 48.

55. A method performed by a Location Management Function, LMF, for supporting inferred measurements based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, the method comprising: transmitting (400; 504), to a Centralized Unit, CU, of the RAN node, a measurement request; and receiving (410; 512), from the CU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request.

56. The method of claim 55, wherein the RAN node comprises a DU associated with the CU, and the DU-side AI / ML model is hosted at the DU.

57. The method of any one of claims 55 to 56, wherein the at least one inferred measurement is an inferred measurement for AI / ML assisted positioning of a User Equipment, UE.

58. The method of any one of claims 55 to 57, wherein the measurement request comprises a request for at least one inferred measurement.

59. The method of any one of claims 55 to 58, wherein the at least one value associated with the at least one inferred measurement is received in a report or message comprising an indication that the at least one inferred measurement is an inferred measurement.

60. The method of any one of claims 55 to 59, further comprising performing at least one positioning operation for at least one UE based on the at least one value associated with at least one inferred measurement.

61. The method of any one of claims 55 to 60, wherein receiving (410; 512) the at least one value associated with the at least one inferred measurement comprises receiving (410; 512) the at least one value associated with the at least one inferred measurement from the CU in a NRPPa Measurement Response message.

62. The method of claim 61, wherein the NRPPa Measurement Response message comprises at least one additional value associated with a Transmission and Reception Point, TRP, Measurement performed by a User Equipment, UE, and the at least one value associated with the at least one inferred measurement.

63. The method of any one of claims 55 to 62, comprising receiving (512), from the CU, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement,time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement.

64. The method of any one of claims 55 to 63, wherein a characteristic of the at least one value received from the at least one of the CU and the DU fulfills at least one condition, and wherein the at least one condition comprises: the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold; the at least one value is calculated for a specific point in time in the future or within a time window; the at least one value is associated with a quality level that is equal to or greater than a threshold.

65. The method of claim 64, further comprising transmitting (504), to the CU, the at least one condition.

66. The method of any one of claims 55 to 65, further comprising receiving (505), from the CU, a response message comprising an indication that one or more of the at least one inferred measurements can be provided.

67. The method of any one of claims 55 to 65, further comprising receiving (405), from the CU, a response message comprising an indication that one or more of the at least one inferred measurements cannot be provided.

68. The method of claim 67, wherein the response message comprises at least one of: an indication that a failure due to which the one or more of the at least one inferred measurement cannot be provided is temporary,an indication that the DU does not support the one or more of the at least one inferred measurement.

69. The method of any one of claims 55 to 68, wherein the at least one value associated with the at least one predicted measurement is associated with at least one of: Uplink, UL, Angle of Arrival, UL-AoA; UL Relative Time of Arrival, UL-RTOA; UL Sounding Reference Signal, SRS, Reference Signal Received Power, UL-SRS-RSRP; UL SRS Reference Signal Received Path Power, UL-SRS-RSRPP; gNodeB, gNB, Receive-Transmit, Rx-Tx, time difference; and UL Reference Signal Carrier Phase, UL-RSCP.

70. The method of any one of claims 55 to 69, further comprising receiving (306), from the CU of the RAN node, capability information indicating a capability of the DU to generate the at least one inferred measurement based on the DU-side AI / ML model.

71. The method of claim 70, further comprising transmitting (300), to the CU of the RAN node, a request for the capability information, and wherein the capability information is received in response to the request.

72. The method of claim 71, wherein the request is transmitted to the CU via NRPPa in a Transmission and Reception Point, TRP, information request message.

73. The method of any one of claims 70 to 72, wherein the capability information is received from the CU in a NRPPa message .

74. The method of any one of claims 70 to 73, wherein the capability information comprises at least one of: information about one or more inferred measurements supported by the DU and that can be provided as output of the DU-side AI / ML model, information about an accuracy level and / or uncertainty level of inferred measurements supported by the DU, information about training data used to train the DU-side AI / ML model,information about a time horizon for inferred measurements supported at the DU, information about a processing delay for the DU-side AI / ML model at the DU, information about a maximum inferred measurement resolution supported by the DU, information indicating at least one TRP hosted by the DU that supports inferred measurements, and information about a maximum update rate for a particular inferred measurement resolution the DU supports.

75. A network node for implementing a Location Management Function, LMF, for supporting inferred measurements based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, the network node comprising processing circuitry configured to cause the network node to: transmit (400; 504), to a Centralized Unit, CU, of the RAN node, a measurement request; and receive (410; 512), from the CU of the RAN node, at least one value associated with at least one inferred measurement generated using the DU-side AI / ML model for AI / ML assisted positioning, in accordance with the measurement request.

76. The network node of claim 75, wherein the processing circuitry is further configured to cause the network node to perform the method of any of claims 56 to 74.

77. A network node for implementing a Location Management Function, LMF, for supporting inferred measurements based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, the network node configured to perform the method of any of claims 55 to 74.

78. A computer program comprising instructions which when executed by processing circuitry of a network node for implementing a Location Management Function, LMF, for supporting inferred measurements based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, cause the network node to perform the method of any of claims 55 to 74.

79. A computer program product comprising a computer program, the computer program comprising instructions which when executed by processing circuitry of a network node for implementing a Location Management Function, LMF, for supporting inferred measurements based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, cause the network node to perform the method of any of claims 55 to 74.

80. A non-transitory computer readable medium storing instructions which when executed by processing circuitry of a network node for implementing a Location Management Function, LMF, for supporting inferred measurements based on a Distributed Unit, DU,-side Artificial Intelligence, AI, / Machine Learning, ML, model for AI / ML assisted positioning, cause the network node to perform the method of any of claims 55 to 74.

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