Positioning measurement based on ai / ML model

Abstract

A method is performed by a user equipment (600) for performing a positioning measurement. The method comprises receiving a request to perform one or more positioning measurements based at least on an artificial intelligence or machine learning (AI / ML) model. The method further comprises determining whether the AI / ML model is applicable for the requested positioning measurements. The method further comprises, in response to determining that the AI / ML model is not applicable for at least one of the one or more positioning measurements, transmitting, to a network node (700), information comprising: an indication that the UE (600) could not perform the at least one positioning measurement based on the AI / ML model.

Description

POSITIONING MEASUREMENT BASED ON AI / ML MODELTECHNICAL FIELD

[0001] Embodiments of the disclosure relate to positioning, and particularly to methods, apparatus and computer-readable media for performing a positioning measurement.BACKGROUNDAI / ML modeling and associated principles

[0002] Artificial intelligence (Al) or machine learning (ML) techniques comprise one or more algorithms, which use a set of data as input for training one or more AI / ML models. The output of the AI / ML model is used by a device (e.g. user equipment (UE), a base station (BS), or another node) for performing certain operations or taking certain decisions (e.g. handover, etc.) fully or partially based on the prediction, which in turn depends on the trained model. The AI / ML model may be trained in the device online (or on-fly while processing the data) or offline in the background. More specifically:• Online training is an AI / ML training process where the model being used for inference is (typically continuously) trained in (near) real-time with the arrival of new training samples or data.• Offline training is an AI / ML training process where the model is trained based on collected samples or data, and where the trained model is later used or delivered for inference.

[0003] AI / ML model inference refers to a process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0004] The AI / ML models may be trained in a device, which may be a UE, a network node, or another node. In this respect, the AI / ML modes may be broadly classified as:• UE-side (AI / ML) model - an AI / ML model whose inference is performed entirely at the UE.• Network-side (AI / ML) model - an AI / ML model whose inference is performed entirely at the network.• One-sided (AI / ML) model - a UE-side (AI / ML) model or a Network-side (AI / ML) model.• Two-sided (AI / ML) model - a paired AI / ML model(s) over which j oint inference is performed, where joint inference comprises AI / ML inference whose inference is performed jointly across the UE and the network, i.e., the first part of inference is firstly performed by UE and then the remaining part is performed by Next Generation Node B (gNB), or vice versa.

[0005] An AI / ML model may be transferred or delivered over the air interface either in terms of one or more parameters of a model structure known at the receiving end or a new model with parameters. The model delivery may contain a full model or a partial model.

[0006] The term “lifecycle management (LCM)” of an AI / ML model refers to the process of developing, deploying, and maintaining the AI / ML model. An AI / ML model training pipeline includes several processing stages, including gathering unprocessed input data from data repositories (data ingestion), finding high-quality input features (data pre-processing), finding the optimal mapping of the model input features to a desired model output target in a sense determined by a loss function (model training), and evaluating model performance on unseen data from a functional level as well as from a system level when relevant (model evaluation). The training pipeline typically ends with a model registration stage, which may comprise operations to make the ML model runnable via compilation to a specific hardware (HW) and of steps like versioning and packaging of the model so that it may be executed. An example of the AI / ML model training pipeline illustrating different stages is shown in Figure 1.AI / ML model based positioning

[0007] AI / ML models may be used for UE positioning. A UE or a gNB, depending on capability, may have a trained model stored inside the device, or have an untrained AI / ML that may be trained on-the-fly to either produce measurements that are required to localize a UE within a radio access network (RAN) coverage area or directly predict / determine the UE location by using the measurements performed by the UE or gNB on reference signals such as positioning reference signal (PRS), sounding reference signal (SRS) etc. within a RAN coverage area.

[0008] Measurements predicted / determined by the UE by using an AI / ML model may include, but are not limited to, one or more of the following:• received signal time difference (RSTD): This is the reference signal time difference between the positioning node j and the reference positioning node i. It is measured on the downlink (DL) PRS signals and always involves two cells (cell is interchangeably referred to as transmission reception point (TRP)).• UE Receive - Transmit (Rx-Tx) time difference: This is defined as TUE-RX -TUE-TX, where: o TUE-RX is the UE received timing of downlink subframe # / from a positioning node, defined by the first detected path in time. It is measured on PRS signals received from the gNB. o TUE-TX is the UE transmit timing of uplink subframe #j that is closest in time to the subframe #i received from the positioning node.

[0009] Measurements predicted / determined by the gNB by using an AI / ML model may include, but are not limited to, one or more of the following:• gNB Rx-Tx time difference: This is defined as TgNB-Rx - TSNB-TX, where: o TgNB-Rx is the positioning node received timing of uplink subframe #i containing SRS associated with UE, defined by the first detected path in time. It is measured on SRS signals received from the UE. o TgNB-Tx is the positioning node transmit timing of downlink subframe #j that is closest in time to the subframe #i received from the UE.• Timing advance (TADV): This is defined as the time difference TADV = (TgNB-Rx - TSNB-TX), here: o TgNB-Rx is the Transmission and Reception Point (TRP) received timing of uplink subframe #i containing PRACH transmitted from UE, defined by the first detected path in time. o TgNB-Tx is the TRP transmit timing of downlink subframe #j that is closest in time to the subframe #i received from the UE. o The detected physical random access channel (PRACH) is used to determine the start of one subframe containing that PRACH.• Uplink (UL) relative time of arrival (RTOA): This is defined as the beginning of subframe i containing SRS received in positioning node j, relative to the configurable reference time. For example, anode (e.g., base station, etc.) measures the reception time of signals transmitted by the UE with respect to a reference time.

[0010] In addition to these, UE or gNB may also perform power measurements such as reference signal received power (RSRP) and / or reference signal received path power (RSRPP). These measurements may be performed on reference signals such as PRS and SRS.

[0011] Depending on its capability, a UE may also perform positioning measurements on the sidelink (SL) resources by using AI / ML model, e.g. on the SL-PRS transmitted between the target UE and one or more assisting or anchor UEs. The UE performing the positioning measurement is referred to as a target UE and the UE(s) assisting the target UE to perform the SL positioning measurements is referred to as the anchor or assisting UE.Assisted positioning

[0012] In this mode, positioning measurements are performed by UE / gNB by using the AI / ML model. After completion, the positioning measurements are then reported to the location server. The location server upon receiving measurements determines the location of the UE within the RAN coverage area. Depending on need, the location server may forward the UE location toanother node within the network to facilitate the provisioning of UE location information to the application layer or the third party that is interested or has requested the positioning of the UE within the RAN coverage area for further action to be taken.Direct positioning

[0013] In this mode, measurements performed by the UE or the gNB are not reported to the location server. The positioning engine or the AI / ML model to predict or determine the UE location within the RAN coverage area may reside within the UE, the gNB node, or the location server. After determining the UE position within the RAN coverage area, the estimated UE positioning is then reported to the location server in a deployment, or in a scenario where a location management function (LMF) is not deployed with AI / ML capability to localize a UE. Depending on need, the location server may forward the UE location to another node within the network to facilitate the provisioning of UE location information to the application layer or the third party that is interested or has requested the positioning of the UE within the RAN coverage area for further action to be taken.Positioning measurement procedure (PMP):

[0014] The PMP comprises performing one or more positioning measurements on DL Reference Signal (RS) (e.g. PRS) and / or UL RS (e.g. SRS) transmitted between the UE and one or more cells. The cell may also be referred to as a transmission-reception point (TRP) or node. Examples of the positioning measurements performed on DL and / or UL signals are RSTD, PRS- RSRP, PRS-RSRPP, UE Rx-Tx time difference, round trip time (RTT), time of arrival (TOA), channel impulse response (CIR), timing advance (TA), angle of departure (AoD), angle of arrival (AoA), power delay profile (PDP), delay profile (DP) etc.

[0015] To perform these measurements, the UE may make use of a trained model acquired before being deployed, or may need to train its model on-the-fly before performing AI / ML based positioning measurements to be reported to the network node such as the location server. In either of the cases, the UE may require assistance information / data from the network to determine or identify how and when to train its AI / ML model and what information (positioning measurements or estimated position) to report to the network node such as the location server.Positioning architecture

[0016] Before Rel. 16, Long Term Evolution (LTE) based positioning was one of the prevalent Radio Access Technology (RAT) based positioning solutions available. Starting fromthe Rel. 16 specification, positioning is also supported in New Radio (NR). Positioning in NR is supported by the architecture shown in Figure 2. The interactions between the gNodeB and the device are supported via the Radio Resource Control (RRC) protocol, while the location node interfaces with the UE via the LTE positioning protocol (LPP). LPP is a common protocol for both NR and LTE. LMF is the location node in NR. There are also interactions between the location node and the gNodeB via the NR Positioning Protocol A (NRPPa) protocol.SUMMARY

[0017] There currently exist certain challenge(s). For example, according to Rel. 19 specifications, the positioning architecture in Figure 2 will also be used to support AI / ML based positioning. In its current state, the LPP protocol does not support configuring a UE to perform positioning measurements based on an AI / ML model. Rel. 19 specifications will therefore have to define LPP based configuration for AI / ML based positioning. It will then be the case that the LMF will send an explicit request to UE to perform positioning measurements based on an AI / ML model either for assisted mode positioning or direct mode positioning. The LMF will make such a request to the UE, given that the UE has reported its capability to support AI / ML based positioning to the network or the network is aware of the UE capability of supporting AI / ML based positioning. In practice, when the LMF requests the UE to perform positioning measurements based on an AI / ML model, the LMF is not aware if the AI / ML model at the UE is trained for the scenario it is in, and whether or not it may therefore produce reliable measurements based on AI / ML for high accuracy UE positioning.

[0018] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, if the AI / ML model at the UE is not trained for the scenario it is in, the UE may not or should not use its AI / ML model to produce positioning measurements because these measurements are not reliable enough to localize the UE within the RAN coverage area with higher accuracy. Thus, the AI / ML model needs to be trained before it may be used to predict reliable measurements that may be used for UE positioning or to predict the UE position directly if the AI / ML model is used to predict the UE location directly.

[0019] According to a first aspect of the disclosure, there is provided a method performed by a user equipment (UE) for performing a positioning measurement. The method comprises receiving a request to perform one or more positioning measurements based at least on an artificial intelligence or machine learning (AI / ML) model. The method further comprises determining whether the AI / ML model is applicable for the requested positioning measurements. The method further comprises, in response to determining that the AI / ML model is not applicable for at leastone of the one or more positioning measurements: transmitting, to a network node, information comprising: an indication that the UE could not perform the at least one positioning measurement based on the AI / ML model.

[0020] According to a second aspect of the disclosure, there is provided a method performed by a network node for facilitating a positioning measurement for a user equipment (UE). The method comprises transmitting, to the UE, a request to perform one or more positioning measurements based at least on an artificial intelligence or machine learning (AI / ML) model. The method further comprises receiving, from the UE, information comprising: an indication that the UE could not perform at least one of the one or more positioning measurements based on the AI / ML model.

[0021] According to a third aspect of the disclosure, there is provided a user equipment (UE) for performing a positioning measurement. The UE comprises processing circuitry configured to cause the UE to perform the method according to the first aspect of the disclosure.

[0022] According to a fourth aspect of the disclosure, there is provided a network node for facilitating a positioning measurement for a user equipment (UE). The network node comprises processing circuitry configured to cause the network node to perform the method according to the second aspect of the disclosure.

[0023] In particular embodiments, the UE, upon receiving a request from a network node (e.g. LMF) to perform one or more positioning measurements or estimate the UE location based on AI / ML model, may perform those requested positioning measurements based on an AI / ML model which is valid for the scenario in which the UE is currently located. The UE may further use the positioning measurement results for determining its position and / or transmit the positioning measurement results to the network node (e.g., NW2). The UE may further transmit information about one or more requested positioning measurements that have not been performed by the UE based on the AI / ML model, e.g., if the UE is not operating in the applicable scenario for that AI / ML model. In particular embodiments, if UE determines that its AI / ML model is not valid or trained for the scenario in which the UE is currently located, the UE may perform one or more of the following actions:• request the network node to transmit assistance information for enabling the UE to perform one or more positioning measurements based on a non-AI / ML method. In case of measurement based on the non-AI / ML model, the UE may perform the measurements on the downlink (DL) reference signal (e.g., PRS) transmitted by a network node and / or on the uplink (UL) reference signal (e.g., SRS) transmitted by the UE;• perform positioning measurements based on a non-AI / ML method and report them to the network with an indication that the measurements are not based on an AI / ML model; and / or• respond to a request from the LMF with an indication that the UE needs to train its AI / ML model before performing positioning measurements and request assistance data for model training.

[0024] In particular embodiments, the UE may determine whether it is operating in the scenario applicable for certain AI / ML models for inferring one or more requested positioning measurements based on one or more of the following: historical data / past history, an indication received from a network node (e.g. serving base station), an estimation / measurements (e.g. radio channel characteristics such as delay spread, Doppler frequency, etc.), preconfigured information (e.g. stored on subscriber identity module (SIM), embedded subscriber identity module (eSIM), or universal subscriber identity module (USIM)), etc.

[0025] In particular embodiments, the network node (e.g., NW2 such as LMF) may send a request to the UE to perform one or more positioning measurements or to estimate the UE location based on an AI / ML model. The network node may receive the results of all or a subset of the requested positioning measurements performed by the UE based on the AI / ML model in the scenario in which the AI / ML model is valid. The network node may further receive information (e.g. identifiers etc.) about one or more requested positioning measurements that are not performed by the UE based on the AI / ML model e.g. due to an invalid AI / ML model, because the UE is not operating in the scenario in which the AI / ML model is trained for, etc.

[0026] In the above embodiments, the applicable scenarios for different AI / ML models may differ in terms of the radio environment characteristics, e.g., based on one or more of a channel coherence time, a delay spread, a Doppler spread, a Doppler frequency, a presence of line of sight (LOS) path, presence of a number of dominant Non-LOS (NLOS) paths, etc. For example, a factory or workshop with densely deployed machines may comprise a scenario in which the number of dominant multipaths is above a certain threshold, the delay spread is larger than a certain threshold, etc. In the same or another example, a factory or workshop with sparsely deployed machines may comprise a scenario in which the number of dominant multipaths is below a certain threshold, the delay spread is below a certain threshold, etc. The UE may reliably perform / infer the positioning measurement based on a certain AI / ML model while the UE is operating in the densely deployed factory environment / scenario if that AI / ML model is trained for that scenario (i.e. densely deployed factory). On the other hand, the UE may reliably perform / infer the positioning measurement based on a certain AI / ML model while the UE is operating in the sparselydeployed factory environment / scenario if that AI / ML model is trained for that scenario (i.e. sparsely deployed factory).

[0027] Particular embodiments provide a solution that (upon receiving a request from LMF to report AI / ML based positioning measurements) enables the UE to determine if its AI / ML model(s) is valid and trained for the scenario it is in, to perform the positioning measurements based on AI / ML model only if its AI / ML model for positioning is valid and trained for the scenario it is in, and to report the performed positioning measurements. In particular embodiments, if these conditions do not hold true, UE may either report positioning measurements based on non-AI / ML methods and / or request assistance data from the network node that it may use to generate samples to train its AI / ML model for positioning before performing positioning measurements based on the AI / ML model.

[0028] Certain embodiments may provide one or more of the following technical advantage(s). Particular embodiments provide methods that enable the UE to use its AI / ML model for positioning if the AI / ML model is valid and trained for the scenario the UE is in and is requested to perform AI / ML based measurements for positioning by a network node. In particular embodiments, if the AI / ML model for positioning in UE is not valid or not trained for the scenario the UE is in, the UE may perform measurements for positioning without using its AI / ML model for positioning such that high accuracy positioning may still be achieved. In particular embodiments, if the AI / ML model for positioning in UE is not valid or not trained for the scenario it is in, the UE may indicate to the network that it is not possible for the UE to produce measurements by its AI / ML model for positioning and that the UE requires assistance data to produce samples that the UE may use to train its AI / ML model for positioning before performing and reporting measurements that may be used for UE positioning. Particular embodiments provide methods that improve the positioning measurement performance, for example by increasing reliability of the positioning measurements, improving accuracy of the positioning measurements, and reducing delay in performing positioning measurements, among others. This in turn improves UE positioning accuracy.Brief Description of the Drawings

[0029] For a better understanding of the embodiments of the disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

[0030] Figure 1 illustrates an example of the AI / ML model training pipeline;

[0031] Figure 2 illustrates an example of the positioning architecture in NR;

[0032] Figure 3 is a schematic flowchart showing a method in accordance with some embodiments;

[0033] Figure 4 is a schematic flowchart showing a method in accordance with some embodiments;

[0034] Figure 5 shows an example of a communication system in accordance with some embodiments;

[0035] Figure 6 shows a UE in accordance with some embodiments;

[0036] Figure 7 shows a network node in accordance with some embodiments;

[0037] Figure 8 is a block diagram of a host in accordance with various aspects described herein;

[0038] Figure 9 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized; and

[0039] Figure 10 is a block diagram showing a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.DETAILED DESCRIPTION

[0040] 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.Generalization and Terminology

[0041] In this disclosure a term “node” is used which may 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, 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 (CU) (e.g. in a gNB), distributed unit (DU) (e.g. in a gNB), baseband unit (BU), centralized baseband (CB), cloud radio access network (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), operational support system (OSS), self-organizing network (SON), positioning node (e.g. evolved serving mobile location center (E-SMLC), etc.

[0042] Another example of a node is a 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 a UE are a target device, a device to device (D2D) UE, a vehicular to vehicular (V2V) UE, a machine type UE, a machine type communication (MTC) UE or a UE capable of machine to machine (M2M) communication, a personal digital assistant (PDA), a tablet, mobile terminals, a smart phone, laptop embedded equipment (LEE), laptop mounted equipment (LME), Universal Serial Bus (USB) dongles, etc.

[0043] In some embodiments, generic terminology “radio network node” or simply “network node (NW node)”, is used. It may be any kind of network node which may comprise a base station, a radio base station, a base transceiver station, a base station controller, a network controller, an evolved Node B (eNB), a Node B, a gNodeB (gNB), a relay node, an access point, a radio access point, a remote radio unit (RRU), a remote radio head (RRH), a central unit (e.g. in a gNB), a distributed unit (e.g. in a gNB), a baseband unit, a centralized baseband, C-RAN, etc.

[0044] The term “radio access technology”, or RAT, may refer to any RAT e.g. universal terrestrial radio access (UTRA), evolved universal terrestrial radio access (E-UTRA), narrow band internet of things (NB-IoT), Wi-Fi, Bluetooth, next generation RAT, new radio (NR), 4th generation (4G), 5th generation (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.

[0045] The term “signal” used herein may be any physical signal or physical channel. Examples of physical signals are reference signals such as primary synchronization signal (PSS), secondary synchronization signal (SSS), channel state information-reference signal (CSI-RS), demodulation reference signal (DMRS), signals in synchronization signal block (SSB), cellspecific reference signal (CRS), positioning reference signal (PRS), sounding reference signal (SRS), etc. The term “physical channel” used herein is also referred to as ‘channel’, which contains higher-layer information, e.g. logical channel, transport channel etc. Examples of physical channels are master information block (MIB), physical sidelink broadcast channel (PSBCH), physical sidelink control channel (PSCCH), physical sidelink shared channel (PSSCH), physical broadcast channel (PBCH), physical downlink control channel (PDCCH), physical downlink shared channel (PDSCH), physical uplink shared channel (PUSCH), physical uplink control channel (PUCCH), etc.

[0046] 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 symbols, time slots, subframes, radio frames, transmission time intervals (TTIs), interleaving time, etc. The term “TTI” used herein may correspond to any time period over which a physical channel may beencoded and optionally interleaved for transmission. The physical channel is decoded by the receiver over the same time period over which it was encoded. The TTI may also interchangeably referred to as short TTI (sTTI), transmission time, slot, sub-slot, mini-slot, mini-subframe, etc.

[0047] The term “time-frequency resource” is used herein for any radio resource defined in any time-frequency resource grid in a cell. Examples of time-frequency resources are resource blocks, subcarriers, resource blocks (RBs), etc. The RB may also be interchangeably referred to as physical RB (PRB), virtual RB (VRB), etc.System description

[0048] Embodiments of the disclosure assume the presence of a UE that may perform positioning measurements based on an AI / ML model. The UE is also capable of performing positioning measurements by using non- AI / ML methods such as methods defined / specified up until the Rel. 18 NR positioning specification.

[0049] The system comprises a UE, a network node 1 (NW1) which may be serving a TRP, reference TRP, or neighbor TRP transmitting reference signal for positioning measurements such as PRS, and a network node 2 (NW2) which may be a location server in the network which provides assistance data to the UE for positioning measurements and may configure network node 1 (NW1) to perform positioning measurements on reference signals transmitted by the UE in UL. In some examples, the UE positioning measurements may be one or more of RSTD, UE Rx-Tx time difference, RSTD performed with carrier phase difference, UE Rx-Tx time difference performed with carrier phase measurement, etc. In some examples, the NW1 positioning measurements may be one or more of gNB Rx-Tx difference, UL RToA, gNB Rx-Tx difference performed with carrier phase measurement, UL RToA performed with carrier phase difference measurement etc. The UE may also interchangeably be referred to as a target device, wireless device, etc. The TRP may also interchangeably be referred to as a base station, access point, gNB, eNB, satellite access node (SAN), high altitude platform station (HAPS), integrated access and backhaul (IAB) node, etc. The location server may also interchangeably be referred to as a positioning node, serving mobile location center (SMLC), E-SMLC, LMF, etc. NW2 provides assistance data to the UE via higher layer signaling e.g., LPP messages. The UE may report positioning measurement results to NW2 via higher layer signaling (e.g., LPP messages) after performing the positioning measurement based on the configuration / assistance data provided to the UE by NW2. The UE may be in any of the RRC states (e.g., any of RRC_CONNECTED, RRC INACTIVE, and RRC IDLE states) when performing the positioning measurements configured by NW2. The assistance data provided by NW2 to the UE remains valid regardless ofthe RRC state of the UE while the UE is performing positioning measurements configured by NW2.

[0050] The UE is capable of performing singular positioning measurements such as RSTD, PRS-RSRP, PRS-RSRPP, UE Rx-Tx time difference measurement, and / or measuring and reporting multiple measurements such as reference signal carrier phase difference (RSCPD) with RSTD, and reference signal carrier phase (RSCP) with UE Rx-Tx time difference measurement. NW2 is aware of the UE’s capability to perform singular positioning measurements and / or multiple positioning measurements, e.g. jointly performing and reporting the positioning measurements.Embodiments #1: Method in a UE after reception of a request to perform positioning measurements using an AUML model from the network

[0051] In particular embodiments, the UE is configured by the network node NW2 via signaling (e.g. LPP message) to perform one or more positioning measurements based on at least one AI / ML model, which has been trained for inferring or obtaining the results of one or more requested positioning measurements. The UE may further determine the one or more requested positioning measurements which may be performed / inferred by the UE:• based on at least one AI / ML model applicable to the one or more requested positioning measurements, and• based on whether the UE is also operating in the scenario that its AI / ML model is trained for.

[0052] The UE may perform / infer one or more requested positioning measurements based on at least one AI / ML model, provided that the UE determines that it is also operating in the scenario applicable to that AI / ML model. On the other hand, if the UE cannot or does not meet the above two conditions for any of the requested positioning measurements, then the UE may not perform the positioning measurements based on any AI / ML model. The concept of the applicable scenario is described further below.

[0053] If the UE cannot or does not perform any requested positioning measurements based on any AI / ML model, the UE may perform one or more of the following tasks:• The UE may inform the network node (e.g., NW2) that it has not performed or cannot perform any of the requested positioning measurements based on any AI / ML model. The UE may further provide a reason for being unable to perform any of the requested positioning measurements based on the AI / ML model. For example, the UE may inform the network node that the UE is not operating in the scenario applicable for inferring any of the requested positioning measurements based on the AI / ML model, the UE does not contain any AI / MLmodel for performing the requested positioning measurements, the applicable AI / ML model is not currently fully trained, etc.• The UE may request the network node (e.g. NW2) to transmit assistance information to the UE for enabling the UE to perform the one or more positioning measurements based on a non- AI / ML method.• The UE may perform and report the results of one or more positioning measurements based on non-AI / ML methods to the network node (e.g. NW2). The UE may further report the network node (e.g. NW2) with an indication that the reported measurements are not based on the AI / ML model.• In some examples, the UE may report the measurements based on non-AI / ML methods and request network node NW2 to provide assistance data valid for the scenario it is in to train its AI / ML model. In the later examples, the UE receives assistance data from the network node NW2, and generates the samples based on the assistance data from the network node NW2 to train its AI / ML model to perform AI / ML-based positioning measurements for future positioning measurement instances.

[0054] But if the UE performs any requested positioning measurements based on any AI / ML model, then the UE further uses the performed / inferred one or more requested positioning measurements for performing one or more operational tasks. Examples of the tasks are:• transmitting the positioning measurement results to a network node (e.g. NW2 such as the location server),• informing the network node of information about any requested positioning measurement that could not be performed by the UE based on the AI / ML model. The information may comprise one or more of: identifier(s) of the one or more requested positioning measurements, reason(s) due to which one or more requested positioning measurements were not performed (e.g. the UE was not operating in the scenario applicable for inferring that measurement based on the AI / ML model, the applicable AI / ML model is not currently fully trained, etc.), etc.• using the positioning measurement results for determining the UE location, etc.

[0055] The UE may determine whether it is operating in any of the scenarios which are applicable for a certain AI / ML model, which may be used by the UE for obtaining / inferring one or more requested positioning measurements based on one or more of the following principles:• historical data / past history. For example, the UE has used the certain AI / ML model for performing positioning measurements in the scenario the UE is in or in the scenario similar to the one in which the UE is located.• indications received from a network node (e.g. NW1 such as serving base station). For example, the network node is aware of the approximate UE location and / or the radio channel with respect to the UE. Based on this information, the network node informs the UE via signaling message (e.g. RRC, Media Access Control-Control Element (MAC-CE), Downlink Control Information (DCI), etc.) about the scenario in which it is currently operating.• estimations / measurements performed by the UE. For example, the UE estimates the radio channel characteristics such as delay spread, Doppler frequency, channel coherence time, etc., on the signal received from the serving cell. The UE, based on this estimation, determines the scenario in which it is currently operating.• preconfigured information stored in the UE. For example, subscription information stored in the UE (e.g. on the SIM, eSIM, USIM, etc.) may include the scenario in which it is currently operating, the scenario in which the UE is expected to operate, and / or the scenario in which the UE typically operates. The subscription information about the applicable scenario(s) may change over time (e.g. temporary) or may remain static (e.g. permanently stored).

[0056] The requested positioning measurements may be performed by the UE on the DL reference signal transmitted by the network node NW1 and / or on both the DL reference signal transmitted by the network node NW1 and the UL reference signal transmitted by the UE. Examples of the DL reference signal transmitted by the network node NW1 for positioning measurements are CSI-RS, SSB, PRS, etc. Examples of the UL reference signal transmitted by the UE for positioning measurements are SRS, positioning SRS, etc. The UE may be configured by NW2 to perform and use the positioning measurements such as, but not limited to, RSTD, UE Rx- Tx time difference measurement, PRS-RSRP, PRS-RSRPP, etc. by using the AI / ML model stored at the UE or the AI / ML model that is pro visioned / configured by the network node to the UE.

[0057] The one or more AI / ML models used for performing one or more positioning measurements are stored in the UE. The UE may also inform the network node (e.g. NW1, NW2, etc.) of information about the AI / ML models applicable for one or more positioning measurements and available at the UE for inferring the one or more positioning measurements. Each AI / ML model may have been trained by the UE and / or by another node (e.g. network node such as NW1, NW2 etc., another UE etc.). Each AI / ML model is valid for inferring one or more positioning measurements in at least one applicable scenario. The UE obtains information about any AI / ML model trained by another node by receiving the information from the other node (e.g. network node). For example, the information may comprise a set of trained measurement samples, an identifier of the AI / ML model, information about the one or more positioning measurements forwhich the AI / ML model is applicable, information about the one or more scenarios under which the AI / ML model is applicable for, etc.

[0058] Upon receiving a positioning measurement request, the UE may determine the scenario it is in and validate if the AI / ML model stored in it or provisioned by the network node NW1 or NW2 is valid for the scenario it is in. If the UE determines its AI / ML model is valid for the scenario it is in or the scenario where it has been requested to perform measurements based on the AI / ML model, the UE may use the AI / ML model to perform measurements such as, but not limited to, RSTD and UE Rx-Tx time difference measurements, and reports them to the network node NW2. The model validity in this context corresponds to determining if the model output is relevant to the measurements to be reported and the training status of the AI / ML model it is expected to use to produce the measurements to be reported to the network node NW2. If the model is determined to be invalid by the UE, the UE then performs positioning measurements by using non-AI / ML based methods and reports them to the network node NW2. In an example, if the UE determines that the AI / ML model is not valid for the scenario it is in, the UE may request assistance data from the network node NW2 that is required by the UE to perform positioning measurements without using an AI / ML model.Details about applicable scenarios for inference based on an AI / ML model

[0059] The applicable scenario described herein may also be referred to as the environment, setting, framework, paradigm, etc. Some specific examples of the applicable scenarios are different types of settings in certain physical zones, localities, or areas such as factories, warehouses, production lines, etc. For example, the applicable scenarios for different AI / ML models may depend on the factory setting which is characterized by one or more configuration or setup parameters. Examples of the configuration parameters are factory size (e.g. height of the roof, length, breadth, etc.), the density of the installed machines (e.g. number of machines installed per unit area), a minimum distance between the installed machines, a maximum distance between the installed machines, a size or dimension of the installed machines (e.g. volume, height, length, breadth), etc. From the radio communication perspective, different sets of configuration parameters may result in different radio environments, which are also referred to as the radio propagation channel or radio channel. The radio environment is characterized by one or more parameters such as the presence of LOS path and / or NLOS path(s) / signal components, multipath delay, delay spread, coherence time, Doppler frequency, Doppler shift, etc. Therefore, in one example, the applicable scenario from the radio communication perspective may be represented by the radio environment. This is described with examples:• In a first example (Example #1), the AI / ML model is trained for one or more positioning measurements in a very dense factory environment in which machine density, in terms of a number of machines, is above a certain threshold (Hl l) and / or the minimum distance between the machines is below a certain threshold (H12). In this case, the radio environment of the applicable scenario may consist of only NLOS paths and / or delay spread above a certain threshold.• In a second example (Example #2), the AI / ML model is trained for one or more positioning measurements in a sparse factory environment in which machine density, in terms of a number of machines, is above a certain threshold (H21) and / or the minimum distance between the machines is above a certain threshold (H22). In this case, the radio environment of the applicable scenario may consist of at least the LOS path and / or the delay spread below a certain threshold.• In a third example (Example #3), the AI / ML model is trained for one or more positioning measurements in a moderately dense factory environment in which the machine density, in terms of number of machines, is larger than a certain threshold (H31) but smaller than another threshold (H32) and / or the minimum distance between the machines is above a certain threshold (H33) but below another threshold (H34). In this case, the radio environment of the applicable scenario may consist of at least a LOS path and one or a few NLOS paths whose power is not more than XI dB below the power of the LOS path, and / or the delay spread is larger than a certain threshold (H35) but below another threshold (H36).

[0060] In the above examples (#1, #2, #3), the mapping may be defined between the applicable scenario and one or more positioning measurements which may be reliably inferred, obtained, or estimated by the UE when the UE operates in that applicable scenario. As an example, the positioning measurement is considered reliable if one or more of the following conditions are met: positioning measurement is accurate with respect to a reference value within a certain margin (e.g. ±81 ns), positioning measurement quality is above a certain quality level (e.g. level # 10 in ranges between 0 to 31) etc. The mapping is further described below with 5 different examples: a) In one example, assume that AI / ML model #1 (AMI) is trained in a very dense factory environment (e.g. as explained in example #1 above) for those UE timing measurements which may be reliably inferred by the UE based on AMI when operating in that factory environment. As an example, the UE may reliably estimate only UE transmit timing-related timing measurements based on AMI while the UE is operating in the corresponding radio environment (i.e. with only NLOS paths). Examples of the UE transmit timing-related measurements are UE Rx-Tx time difference, timingadvance, etc. However, the UE may not estimate the UE receive timing related measurements based on AMI while the UE is operating in the corresponding radio environment (i.e., with only NLOS paths). Examples of the UE receive timing related measurements are RSTD, time of arrival of the reference signal, channel impulse response (CIR), channel delay profile, channel power delay profile, etc. i. For example, assume that the UE is requested by NW2 to perform UE Rx-Tx time difference measurement and RSTD based on the AI / ML model. However, since currently the UE has only AMI trained and valid for the scenario it is in, therefore the UE only performs the UE Rx-Tx time difference measurement based on AMI. But it does not or may not perform the requested RSTD measurement. The UE therefore transmits the UE Rx-Tx time difference measurement result to the NW2 and may further inform NW2 that it cannot (or may not) and has not performed the RSTD measurement requested by the network node (NW2). b) In another example, assume that AI / ML model #2 (AM2) is trained in the sparse factory environment (e.g. as explained in example #2 above) for those UE timing measurements which may be reliably inferred by the UE based on AM2 when operating in that factory environment. As an example, the UE may reliably estimate the UE transmit timing related measurements as well as the UE receive timing related measurements based on AM2 while the UE is operating in the corresponding radio environment (i.e. with at least a LOS path). i. For example, assume that the UE is requested by NW2 to perform UE Rx-Tx time difference measurement and RSTD based on the AI / ML model. Since currently the UE has AM2 trained and is valid for the scenario it is in, the UE performs both UE Rx-Tx time difference and the RSTD measurements based on AM2. The UE therefore transmits the UE Rx-Tx time difference measurement and RSTD results to the network node NW2. c) In another example, assume that AI / ML model #3 (AM3) is trained in the very dense factory environment (e.g. as explained in example #1 above) for those UE signal measurements which may be reliably inferred by the UE based on AM3 when operating in that factory environment. As an example, the UE may reliably estimate the signal measurements related to the power measurement of the i-th path based on AM3 while the UE is operating in the corresponding radio environment (i.e. with only NLOS paths). Examples of the signal power measurement of the i-th path are reference signalreceived path power, PRS-RSRPP, etc. However, the UE may not reliably estimate the signal power measurement of multiple paths based on AM3. Examples of the signal power measurement of multiple paths are reference signal received power, PRS-RSRP, etc. i. For example, assume that the UE is requested by NW2 to perform PRS-RSRP and PRS-RSRPP based on the AI / ML model. Since currently the UE has AM3 trained and valid for the scenario it is in, the UE performs only the PRS-RSRPP based on AM3. However, the UE may not perform the PRS-RSRP. The UE therefore transmits the PRS-RSRPP measurement result to the NW2 and may further inform NW2 that it cannot (or may not) and has not performed the PRS- RSRP. d) In another example, assume that AI / ML model #4 (AM4) is trained in the sparse factory environment (e.g. as explained in example #2 above) for those UE signal measurements which may be reliably inferred by the UE based on AM4 when operating in that factory environment. As an example, the UE may reliably estimate the signal power measurement of multiple paths (e.g. PRS-RSRP) based on AM4 while the UE is operating in the corresponding radio environment (i.e. with at least a LOS path). However, the UE may not reliably estimate the signal measurements related to the power measurement of the i-th path (e.g. PRS-RSRPP) based on AM4 while the UE is operating in the corresponding radio environment (i.e. with at least one NLOS path). i. For example, assume that the UE is requested by NW2 to perform PRS-RSRPP and PRS-RSRP based on the AI / ML model. Since currently the UE has AM4 trained and valid, the UE performs only the PRS-RSRP based on AM4. But the UE may not perform the PRS-RSRPP. The UE therefore transmits the PRS- RSRP measurement result to the NW2 and may further inform NW2 that it cannot (or may not) and has not performed the PRS-RSRPP. e) In another example, assume that AI / ML model #5 (AM5) is trained in a moderately dense factory environment (e.g. as explained in example #3 above) for those UE signal measurements which may be reliably inferred by the UE based on AM5 when operating in that factory environment. As an example, the UE may reliably estimate both the signal measurements related to the power measurement of the i-th path (e.g. PRS- RSRPP) as well as the signal power measurement of multiple paths (e.g. PRS-RSRP) based on AM5 while the UE is operating in the corresponding radio environment (i.e. with LOS and few dominant NLOS paths).i. For example, assume that the UE is requested by NW2 to perform PRS-RSRP and PRS-RSRPP based on the AI / ML model. Since currently the UE has AM4 trained and valid for the scenario it is in, the UE performs both the PRS-RSRPP and PRS-RSRP based on AM5. The UE transmits both the PRS-RSRPP and PRS-RSRP measurement results to the NW2.Embodiments #2: Method in a network node configuring a UE to perform positioning measurements using an AI / ML model and reception of a measurement report

[0061] In particular embodiments, the network node NW2 configures the UE to perform positioning measurements using the AI / ML model. The network node NW2 does so via signaling (e.g. LPP message) and configures the UE to perform one or more positioning measurements based on at least one AI / ML model. Examples of such positioning measurements are DL measurements such as, but not limited to, RSTD, RSTD with carrier phase measurement to be performed on DL reference signals transmitted by the network node NW1, and / or a combination of DL and UL measurements such as, but not limited to, Rx-Tx time difference measurement, UE Rx-Tx time difference with carrier phase measurement to be performed on UL reference signals transmitted by the UE and on both downlink (DL) and uplink (UL) reference signals transmitted for positioning.

[0062] In such embodiments, it is assumed that the network node NW2 receives the positioning measurement results based on the AI / ML model reported or indicated by the UE to be valid for the positioning measurement. For example, NW2 receives the results if the AI / ML model at the UE is validated by the UE for the scenario in which it was operating during the positioning measurement procedure or if the UE was operating in the scenario for which it was requested to perform one or more positioning measurements based on the AI / ML model. If the AI / ML model is validated by the UE only for some of the measurements requested by the network node NW2, then the network node NW2 receives information about the requested measurement(s) that could not be performed by the UE based on the AI / ML model. The information received by the network node NW2 may comprise one or more of the following:- identifier(s) of the one or more requested positioning measurements,- reason(s) due to which one or more requested positioning measurements were not performed. Examples of reasons due to which the UE may not be able to perform / predict one or more of the positioning measurements requested by the network node NW2 are provided in embodiment #1.

[0063] The following text sets out some the examples of the UE reporting after being configured to perform positioning measurements based on an AI / ML model.

[0064] In one example, the network node NW2 receives results of all positioning measurements that were configured to the UE by the network node NW2 to be performed by the UE based on an AI / ML model. This is the scenario where the AI / ML model at the UE is trained and is applicable to the scenario / environment where the UE is located and in which the UE is configured to perform the one or more AI / ML based positioning measurements.

[0065] In the same or another example, the network node NW2 receives results of a sub-set of positioning measurements out of all the configured positioning measurements based on AI / ML methods, reported by the UE. This is the scenario where the UE has determined the validity of its AI / ML model for performing one or a few (but not all) positioning measurements configured at the UE by the network node NW2.

[0066] In the same or another example, the network node NW2 receives one or more positioning measurements based on non-AI / ML methods. This is the scenario where the UE has determined that its AI / ML model is invalid for the scenario in which the UE is operating or currently located. In this example, the network node NW2 also receives an indication from the UE that the measurements are performed by the UE based on the non-AI / ML method e.g. based on the actual measurements of the positioning reference signals (e.g. PRS, SRS, etc.).

[0067] In the same or another example, the network node NW2 receives an indication from the UE that the AI / ML model at the UE is not valid or applicable for the scenario in which the UE is operating or currently located or the scenario for which it has been configured to perform one or more positioning measurements. With such an indication, the network node NW2 may also receive a request from the UE for obtaining assistance data for enabling the UE to perform positioning measurements based on any non-AI / ML method, e.g. by measuring the positioning reference signals transmitted between the UE and a network node (e.g. NW1). The network node NW2 in this scenario may send assistance data to the UE that is valid for the UE to perform one or more positioning measurements based on the non-AI / ML method.

[0068] Figure 3 depicts a method in accordance with particular embodiments. The method of Figure 3 may be performed by a UE or wireless device (e.g. the UE 512 or UE 600 as described later with reference to Figures 5 and 6 respectively) for performing a positioning measurement. In some respects, the method shown in Figure 3 may correspond to the embodiments discussed in the section above entitled ‘‘Embodiments #1: Method in UE after reception of a request to performpositioning measurements using AI / ML model from the network” . For example, the method shown in Figure 3 may correspond to steps performed by the UEs discussed in these sections.

[0069] The method 300 begins at step 310, with the UE receiving a request to perform one or more positioning measurements based at least on an artificial intelligence or machine learning (AI / ML) model. For example, the one or more positioning measurements may comprise at least one of a received signal time difference (RSTD), a UE receive-transmit (Rx-Tx) time difference, a RSTD performed with a carrier phase difference, or a UE Rx-Tx time difference performed with a carrier phase measurement. In step 320, the UE determines whether the AI / ML model is applicable for the requested positioning measurements. For example, the UE may determine whether the AI / ML model is trained for a scenario which the UE is currently in. In one example, the scenario indicates an operational condition in a location in which the UE is located. The operational condition may comprise a network traffic pattern and / or a signal quality. The location may comprise a factory, a warehouse, an office building, a residential place, or an open space. In one example, the UE determines whether the AI / ML model is trained to perform the one or more positioning measurements.

[0070] In response to determining that the AI / ML model is not applicable for at least one of the one or more positioning measurements, the UE, in step 340, transmits, to a network node (e.g. the network node 510 or network node 700 as described later with reference to Figures 5 and 7 respectively), information comprising an indication that the UE could not perform the at least one positioning measurement based on the AI / ML model. In one example, in response to determining that the AI / ML model is not applicable for at least one of the one or more positioning measurements, the UE also transmits, to the network node, information comprising an indication of a reason why the UE could not perform the at least one positioning measurement based on the AI / ML model. For example, the reason why the UE could not perform the at least one positioning measurement based on the AI / ML model may comprise one or more of: the UE is not operating in the scenario applicable for inferring the at least one positioning measurement based on the AI / ML model; the AI / ML model is not fully trained; and the UE does not contain any AI / ML model for performing the at least one positioning measurement.

[0071] In one example, the UE determines whether the AI / ML model is trained for the scenario which the UE is currently in based at least on one or more of: historical data; an indication received from the network node, comprising an indication of a serving base station; an estimation of radio channel characteristics comprising a delay spread or a doppler frequency; or a preconfigured information stored on the UE, comprising a subscriber identity module (SIM), embedded SIM (eSIM) or universal sim (USIM).

[0072] In one example, the UE determining that the AI / ML model is not trained for the scenario in which the UE is currently in for at least one of the one or more positioning measurements comprises the UE identifying a second portion of the one or more positioning measurements for which the AI / ML model is not trained. In one example, the UE transmitting information to the network node comprises the UE transmitting information about the second portion of the one or more positioning measurements that have not been performed by the UE due to the AI / ML model not being applicable to the second portion of the one or more positioning measurements.

[0073] In one example, the UE transmits a response message indicating that the AI / ML model needs to be trained before performing the one or more positioning measurements. The request message may comprise a request for assistance data for training the AI / ML model.

[0074] Optionally, in step 342, the UE transmits a request for assistance information to enable the UE to perform the one or more positioning measurements based on a non-AI / ML model. Optionally, in step 344, the UE receives the assistance information. Optionally, in step 346, the UE performs the one or more positioning measurements using the non-AI / ML model. For example, the UE may perform a measurement on a downlink reference signal and / or an uplink reference signal. Optionally, in step 348, the UE transmits a second result of the one or more positioning measurements with an indication that the second result is determined based on the non- AI / ML model.

[0075] In response to determining that the AI / ML model is applicable for the requested positioning measurements (e.g., trained for the scenario which the UE is currently in), optionally, in step 350, the UE identifies a first portion of the one or more positioning measurements for which the AI / ML model is trained. Optionally, in step 352, the UE performs the first portion of the one or more positioning measurements using the AI / ML model. Optionally, in step 354, the UE transmits a first result of the first portion of the one or more positioning measurements to a network node. Optionally, in step 356, the UE determines a position of the UE based at least on the first result.

[0076] In one example, the method 300 further comprises the UE evaluating performance of the AI / ML model based at least on the first result of the first portion of the one or more positioning measurements. The method may also comprise the UE transmitting feedback to the network node regarding the performance of the AI / ML model. The feedback may comprise discrepancies between expected and actual measurement outcomes.

[0077] In one example, the method 300 further comprises the UE dynamically adapting a positioning measurement process based at least on an applicability of the AI / ML model for thescenario which the UE is currently in. The UE dynamically adapting the positioning measurement process may comprise switching between an AI / ML model and a non-AI / ML model as per available model training and the applicability of the AI / ML model.

[0078] In one example, the method 300 further comprises the UE proactively requesting an update for the AI / ML model from the network node, based at least on a periodic assessment of performance of the AI / ML model in various scenarios and / or in response to a change in an operational environment. The change in the operational environment may comprise the change in a physical location of the UE and / or the change in a serving cell of the UE. For example, the various scenarios may comprise different operational conditions in physical locations. For example, the different operational conditions may comprise a network traffic pattern and a signal quality. For example, the physical locations may comprise a factory, a warehouse, an office building, a residential place, and an open space.

[0079] Figure 4 depicts a method in accordance with particular embodiments. The method of Figure 4 may be performed by a network node (e.g. the network node 508, the network node 510 or network node 700 as described later with reference to Figures 5 and 7 ) of a communications network for facilitating a positioning measurement for a user equipment (UE) (e.g. the UE 512 or UE 600 as described later with reference to Figures 5 and 6 respectively). The method may be performed by a network node responsible for positioning, such as an LMF. In some respects, the method shown in Figure 4 may correspond to the embodiments discussed in the Embodiments #2: Method in network node configuring UE to perform positioning measurements using AI / ML model and reception of measurement report section above. For example, the method shown in Figure 4 may correspond to steps performed by a network node discussed in this section. The method of Figure 4 may also correspond in certain respects to the method described above with respect to Figure 3, but from the network’s perspective.

[0080] The method 400 begins at step 410, with the network node transmitting, to the UE, a request to perform the one or more positioning measurements based at least on an artificial intelligence or machine learning (AI / ML) model. For example, the one or more positioning measurements may comprise at least one of a received signal time difference (RSTD), a UE receive-transmit (Rx-Tx) time difference, a RSTD performed with a carrier phase difference, or a UE Rx-Tx time difference performed with a carrier phase measurement. In step 420, the network node receives, from the UE, information comprising an indication that the UE could not perform at least one of the one or more positioning measurements based on the AI / ML model. In one example, the network node also receives, from the UE, information comprising an indication of areason why the UE could not perform the at least one positioning measurement based on the AI / ML model.

[0081] In one example, the reason why the UE could not perform the at least one positioning measurement based on the AI / ML model comprises one or more of: the UE is not operating in the scenario applicable for inferring the at least one positioning measurement based on the AI / ML model; the AI / ML model is not fully trained; and the UE does not contain any AI / ML model for performing the at least one positioning measurement. For example, the scenario may indicate an operational condition in a location in which the UE is located. For example, the operational condition may comprise a network traffic pattern and / or a signal quality. For example, the location may comprise a factory, a warehouse, an office building, a residential place, or an open space.

[0082] In one example, the method 400 further comprises the network node receiving a first result of a first portion of the one or more positioning measurements with an indication that the first portion of the one or more positioning measurements is performed by the AI / ML model. For example, the network node receiving the first result may be in response to a determination that: the AI / ML model is trained for a scenario in which the UE is currently in; and the AI / ML model is trained to perform the first portion of the one or more positioning measurements.

[0083] The method may also comprise the network node receiving a second result of a second portion of the one or more positioning measurements with an indication that the second portion of the one or more positioning measurements is performed by a non-AI / ML model. For example, the network node receiving the second result may be in response to a determination that: the AI / ML model is not trained for a scenario in which the UE is currently in; and / or the AI / ML model is not trained to perform the first portion of the one or more positioning measurements.

[0084] The method may also comprise the network node determining a location of the UE based at least on one or more of the first result or the second result.

[0085] The method may also comprise the network node receiving feedback on performance of the AI / ML model on performing the first portion of the one or more positioning measurements. The method may also comprise the network node transmitting the assistance information. The assistance information may be tailored based at least on the feedback. The method may also comprise the network node receiving a third result of the subsequent positioning measurement. The method may also comprise the network node determining a location of the UE based at least on the third result.

[0086] In one example, the method 400 further comprises the network node receiving a request for assistance information to enable the UE to perform a subsequent positioning measurement based on the non-AI / ML model. The network node may receive the request inresponse to a determination that the AI / ML model is not trained for the scenario which the UE is currently in. For example, the assistance information may comprise at least one of: training data specific to the feedback for training the AI / ML model; information about applicable scenarios for the AI / ML model; identifiers of positioning measurements for which the AI / ML model is trained.

[0087] In one example, the method 400 further comprises the network node updating the assistance information based at least on the first result and / or the second result. The network node updating the network configuration and / or the assistance information may comprise refining the AI / ML model. Refining the AI / ML model may comprise updating weight and bias values of a neural network of the AI / ML model. The method may also comprise the network node transmitting a recommendation to the UE, the recommendation indicating the updated assistance information, a use of the AI / ML model for a certain scenario, and / or update measurement parameters of the one or more positioning measurements based at least on the updated assistance information.

[0088] In one example, the method 400 further comprises the network node dynamically reconfiguring settings of the one or more positioning measurements based at least on the feedback, comprising: adjusting: a type of reference signal to be used for the one or more positioning measurements; and the one or more positioning measurements.

[0089] In one example, the method 400 further comprises the network node configuring the UE to perform the one or more positioning measurements on a particular downlink reference signal comprising a channel state information-reference signal (CSI-RS), a synchronization signal block (SSB), or a positioning reference signal (PRS); and configuring the UE to perform the one or more positioning measurements on a particular uplink reference signal comprising sounding reference signal (SRS).

[0090] In one example, the method 400 further comprises the network node, based at least on the indication that the UE could not perform at least one of the one or more positioning measurements based on the AI / ML model, determining whether the AI / ML model requires refinement or if additional training data should be provided to the UE.

[0091] In one example, the method 400 further comprises the network node receiving aggregated results of positioning measurements from multiple UEs. The method may also comprise the network node proactively refining the AI / ML model based at least on the aggregated results of the positioning measurements reported by the multiple UEs.

[0092] Figure 5 shows an example of a communication system 500 in accordance with some embodiments.

[0093] In the example, the communication system 500 includes a telecommunication network 502 that includes an access network 504, such as a radio access network (RAN), and a core network 506, which includes one or more core network nodes 508. The access network 504 includes one or more access network nodes, such as network nodes 510a and 510b (one or more of which may be generally referred to as network nodes 510), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 510 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 512a, 512b, 512c, and 512d (one or more of which may be generally referred to as UEs 512) to the core network 506 over one or more wireless connections.

[0094] 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 500 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 500 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0095] The UEs 512 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 510 and other communication devices. Similarly, the network nodes 510 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 512 and / or with other network nodes or equipment in the telecommunication network 502 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 502.

[0096] In the depicted example, the core network 506 connects the network nodes 510 to one or more hosts, such as host 516. 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 506 includes one more core network nodes (e.g., core network node 508) 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 508. 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).

[0097] The host 516 may be under the ownership or control of a service provider other than an operator or provider of the access network 504 and / or the telecommunication network 502, and may be operated by the service provider or on behalf of the service provider. The host 516 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.

[0098] As a whole, the communication system 500 of Figure 5 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.

[0099] In some examples, the telecommunication network 502 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 502 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 502. For example, the telecommunications network 502 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)ZMassive loT services to yet further UEs.

[0100] In some examples, the UEs 512 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 504 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 504. Additionally, a UE may beconfigured 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).

[0101] In the example, the hub 514 communicates with the access network 504 to facilitate indirect communication between one or more UEs (e.g., UE 512c and / or 512d) and network nodes (e.g., network node 510b). In some examples, the hub 514 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 514 may be a broadband router enabling access to the core network 506 for the UEs. As another example, the hub 514 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 510, or by executable code, script, process, or other instructions in the hub 514. As another example, the hub 514 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 514 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 514 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 514 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 514 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0102] The hub 514 may have a constant / persistent or intermittent connection to the network node 510b. The hub 514 may also allow for a different communication scheme and / or schedule between the hub 514 and UEs (e.g., UE 512c and / or 512d), and between the hub 514 and the core network 506. In other examples, the hub 514 is connected to the core network 506 and / or one or more UEs via a wired connection. Moreover, the hub 514 may be configured to connect to an M2M service provider over the access network 504 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 510 while still connected via the hub 514 via a wired or wireless connection. In some embodiments, the hub 514 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 510b. In other embodiments, the hub 514 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 510b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0103] Figure 6 shows a UE 600 in accordance with some embodiments.

[0104] 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- loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0105] 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).

[0106] The UE 600 includes processing circuitry 602 that is operatively coupled via a bus 604 to an input / output interface 606, a power source 608, a memory 610, a communication interface 612, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 6. 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.

[0107] The processing circuitry 602 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 610. The processing circuitry 602 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 602 may include multiple central processing units (CPUs).

[0108] In the example, the input / output interface 606 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 600. 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.

[0109] In some embodiments, the power source 608 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 608 may further include power circuitry for delivering power from the power source 608 itself, and / or an external power source, to the various parts of the UE 600 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 608. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 608 to make the power suitable for the respective components of the UE 600 to which power is supplied.

[0110] The memory 610 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 610 includes one or more application programs 614, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 616. The memory 610 may store, for use by the UE 600, any of a variety of various operating systems or combinations of operating systems.

[0111] The memory 610 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 610 may allow the UE 600 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 610, which may be or comprise a device-readable storage medium.

[0112] The processing circuitry 602 may be configured to communicate with an access network or other network using the communication interface 612. The communication interface 612 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 622. The communication interface 612 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 618 and / or a receiver 620 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 618 and receiver 620 may be coupled to one or more antennas (e.g., antenna 622) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0113] In the illustrated embodiment, communication functions of the communication interface 612 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 / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0114] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 612, 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).

[0115] 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.

[0116] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a 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 itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 600 shown in Figure 6.

[0117] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 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 equipmentthat is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0118] 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.

[0119] Figure 7 shows a network node 700 in accordance with some embodiments.

[0120] 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)).

[0121] 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).

[0122] 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).

[0123] The network node 700 includes a processing circuitry 702, a memory 704, a communication interface 706, and a power source 708. The network node 700 may be composedof multiple physically separate components (e.g., aNodeB 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 700 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 700 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 704 for different RATs) and some components may be reused (e.g., a same antenna 710 may be shared by different RATs). The network node 700 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 700, 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 700.

[0124] The processing circuitry 702 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 700 components, such as the memory 704, to provide network node 700 functionality.

[0125] In some embodiments, the processing circuitry 702 includes a system on a chip (SOC). In some embodiments, the processing circuitry 702 includes one or more of radio frequency (RF) transceiver circuitry 712 and baseband processing circuitry 714. In some embodiments, the radio frequency (RF) transceiver circuitry 712 and the baseband processing circuitry 714 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 712 and baseband processing circuitry 714 may be on the same chip or set of chips, boards, or units.

[0126] The memory 704 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 thatstore information, data, and / or instructions that may be used by the processing circuitry 702. The memory 704 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 702 and utilized by the network node 700. The memory 704 may be used to store any calculations made by the processing circuitry 702 and / or any data received via the communication interface 706. In some embodiments, the processing circuitry 702 and memory 704 is integrated.

[0127] The communication interface 706 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 706 comprises port(s) / terminal(s) 716 to send and receive data, for example to and from a network over a wired connection. The communication interface 706 also includes radio front-end circuitry 718 that may be coupled to, or in certain embodiments a part of, the antenna 710. Radio front-end circuitry 718 comprises filters 720 and amplifiers 722. The radio front-end circuitry 718 may be connected to an antenna 710 and processing circuitry 702. The radio front-end circuitry may be configured to condition signals communicated between antenna 710 and processing circuitry 702. The radio front-end circuitry 718 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 718 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 720 and / or amplifiers 722. The radio signal may then be transmitted via the antenna 710. Similarly, when receiving data, the antenna 710 may collect radio signals which are then converted into digital data by the radio front-end circuitry 718. The digital data may be passed to the processing circuitry 702. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0128] In certain alternative embodiments, the network node 700 does not include separate radio front-end circuitry 718, instead, the processing circuitry 702 includes radio front-end circuitry and is connected to the antenna 710. Similarly, in some embodiments, all or some of the RF transceiver circuitry 712 is part of the communication interface 706. In still other embodiments, the communication interface 706 includes one or more ports or terminals 716, the radio front-end circuitry 718, and the RF transceiver circuitry 712, as part of a radio unit (not shown), and the communication interface 706 communicates with the baseband processing circuitry 714, which is part of a digital unit (not shown).

[0129] The antenna 710 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 710 may be coupled to the radio front-endcircuitry 718 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 710 is separate from the network node 700 and connectable to the network node 700 through an interface or port.

[0130] The antenna 710, communication interface 706, and / or the processing circuitry 702 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 710, the communication interface 706, and / or the processing circuitry 702 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.

[0131] The power source 708 provides power to the various components of network node 700 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 708 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 700 with power for performing the functionality described herein. For example, the network node 700 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 708. As a further example, the power source 708 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.

[0132] Embodiments of the network node 700 may include additional components beyond those shown in Figure 7 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 700 may include user interface equipment to allow input of information into the network node 700 and to allow output of information from the network node 700. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 700.

[0133] Figure 8 is a block diagram of a host 800, which may be an embodiment of the host 516 of Figure 5, in accordance with various aspects described herein.

[0134] As used herein, the host 800 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, adistributed server, a virtual machine, container, or processing resources in a server farm. The host 800 may provide one or more services to one or more UEs.

[0135] The host 800 includes processing circuitry 802 that is operatively coupled via a bus 804 to an input / output interface 806, a network interface 808, a power source 810, and a memory 812. 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 6 and 7, such that the descriptions thereof are generally applicable to the corresponding components of host 800.

[0136] The memory 812 may include one or more computer programs including one or more host application programs 814 and data 816, which may include user data, e.g., data generated by a UE for the host 800 or data generated by the host 800 for a UE. Embodiments of the host 800 may utilize only a subset or all of the components shown. The host application programs 814 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 814 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 800 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 814 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.

[0137] Figure 9 is a block diagram illustrating a virtualization environment 900 in which functions implemented by some embodiments may be virtualized.

[0138] 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 900 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, corenetwork 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.

[0139] Applications 902 (which may alternatively be referred to software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0140] Hardware 904 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 906 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 908a and 908b (one or more of which may be generally referred to as VMs 908), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 906 may present a virtual operating platform that appears like networking hardware to the VMs 908.

[0141] The VMs 908 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 906. Different embodiments of the instance of a virtual appliance 902 may be implemented on one or more of VMs 908, 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.

[0142] In the context of NFV, a VM 908 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 908, and that part of hardware 904 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 908 on top of the hardware 904 and corresponds to the application 902.

[0143] Hardware 904 may be implemented in a standalone network node with generic or specific components. Hardware 904 may implement some functions via virtualization. Alternatively, hardware 904 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 andorchestration 910, which, among others, oversees lifecycle management of applications 902. In some embodiments, hardware 904 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 912 which may alternatively be used for communication between hardware nodes and radio units.

[0144] Figure 10 shows a communication diagram of a host 1002 communicating via a network node 1004 with a UE 1006 over a partially wireless connection in accordance with some embodiments.

[0145] Example implementations, in accordance with various embodiments, of the UE (such as a UE 512a of Figure 5 and / or UE 600 of Figure 6), network node (such as network node 510a of Figure 5 and / or network node 700 of Figure 7), and host (such as host 516 of Figure 5 and / or host 800 of Figure 8) discussed in the preceding paragraphs will now be described with reference to Figure 10.

[0146] Like host 800, embodiments of host 1002 include hardware, such as a communication interface, processing circuitry, and memory. The host 1002 also includes software, which is stored in or accessible by the host 1002 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 1006 connecting via an over-the-top (OTT) connection 1050 extending between the UE 1006 and host 1002. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1050.

[0147] The network node 1004 includes hardware enabling it to communicate with the host 1002 and UE 1006. The connection 1060 may be direct or pass through a core network (like core network 506 of Figure 5) 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.

[0148] The UE 1006 includes hardware and software, which is stored in or accessible by UE 1006 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 1006 with the support of the host 1002. In the host 1002, an executing host application may communicate with the executing client application via the OTT connection 1050 terminating at the UE 1006 and host 1002. 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 1050 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 1050.

[0149] The OTT connection 1050 may extend via a connection 1060 between the host 1002 and the network node 1004 and via a wireless connection 1070 between the network node 1004 and the UE 1006 to provide the connection between the host 1002 and the UE 1006. The connection 1060 and wireless connection 1070, over which the OTT connection 1050 may be provided, have been drawn abstractly to illustrate the communication between the host 1002 and the UE 1006 via the network node 1004, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0150] As an example of transmitting data via the OTT connection 1050, in step 1008, the host 1002 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 1006. In other embodiments, the user data is associated with a UE 1006 that shares data with the host 1002 without explicit human interaction. In step 1010, the host 1002 initiates a transmission carrying the user data towards the UE 1006. The host 1002 may initiate the transmission responsive to a request transmitted by the UE 1006. The request may be caused by human interaction with the UE 1006 or by operation of the client application executing on the UE 1006. The transmission may pass via the network node 1004, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1012, the network node 1004 transmits to the UE 1006 the user data that was carried in the transmission that the host 1002 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1014, the UE 1006 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1006 associated with the host application executed by the host 1002.

[0151] In some examples, the UE 1006 executes a client application which provides user data to the host 1002. The user data may be provided in reaction or response to the data received from the host 1002. Accordingly, in step 1016, the UE 1006 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 1006. Regardless of the specific manner in which the user data was provided, the UE 1006 initiates, in step 1018, transmission of the user data towards the host 1002 via the network node 1004. In step 1020, in accordance with the teachings of the embodiments described throughout thisdisclosure, the network node 1004 receives user data from the UE 1006 and initiates transmission of the received user data towards the host 1002. In step 1022, the host 1002 receives the user data carried in the transmission initiated by the UE 1006.

[0152] One or more of the various embodiments improve the performance of OTT services provided to the UE 1006 using the OTT connection 1050, in which the wireless connection 1070 forms the last segment. More precisely, the teachings of these embodiments may improve the accuracy and reliability of positioning measurements and thereby provide benefits such as enhanced user experience, improved safety and security, optimized resource allocation, and more efficient navigation and tracking services.

[0153] In an example scenario, factory status information may be collected and analyzed by the host 1002. As another example, the host 1002 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1002 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1002 may store surveillance video uploaded by a UE. As another example, the host 1002 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 1002 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.

[0154] 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 1050 between the host 1002 and UE 1006, 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 1002 and / or UE 1006. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1050 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 1050 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 1004. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurementsof throughput, propagation times, latency and the like, by the host 1002. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1050 while monitoring propagation times, errors, etc.

[0155] 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.

[0156] 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.

[0157] The following numbered statements set out embodiments of the disclosure:Group A Embodiments1. A method performed by a user equipment (UE) for performing a positioning measurement, the method comprising: receiving a request to perform one or more positioning measurements based at least on a machine learning (ML) model; determining whether the ML model is trained for a scenario in which the UE is currently located; in response to determining that the ML model is trained for the scenario in which the UE is currently located: identifying a first portion of the one or more positioning measurements for which the ML model is trained; performing the first portion of the one or more positioning measurements using the ML model; transmitting a first result of the first portion of the one or more positioning measurements to a network node; and determining a position of the UE based at least on the first result.2. The method of embodiment 1, wherein the scenario indicates an operational condition in a location in which the user equipment (UE) is located; wherein the operational condition comprises a network traffic pattern and / or a signal quality; and wherein the location comprises a factory, a warehouse, an office building, a residential place, or an open space.3. The method of any one of the embodiments 1-2, further comprising: identifying a second portion of the one or more positioning measurements for which the ML model is not trained; transmitting information about the second portion of the one or more positioning measurements that have not been performed by the UE due to the ML model not being applicable to the second portion of the one or more positioning measurements.4. The method of any one of the embodiments 1-3, wherein determining whether the MLmodel is trained for the scenario in which the UE is currently located comprises determining whether the ML model is trained to perform the one or more positioning measurements.5. The method of any one of the embodiments 1-4, wherein determining whether the ML model is trained for the scenario in which the UE is currently located is based at least on one or more of: historical data; an indication received from the network node, comprising an indication of a serving base station; an estimation of radio channel characteristics comprising a delay spread or a doppler frequency, or a preconfigured information stored on the UE, comprising a subscriber identity module (SIM), embedded SIM (eSIM), or universal SIM (USIM).6. The method of any one of the embodiments 1-5, further comprising: in response to determining that the ML model is not trained for the scenario in which the UE is currently located: transmitting a request for assistance information to enable the UE to perform the one or more positioning measurements based on the non-ML model; receiving the assistance information; performing the one or more positioning measurements using the non-ML model; transmitting a second result of the one or more positioning measurements with an indication that the second result is determined based on the non-ML model.7. The method of any one of the embodiments 1-6, wherein performing the one or more positioning measurements using the non-ML model comprises performing a measurement on a downlink reference signal and / or an uplink reference signal.8. The method of any one of the embodiments 1-7, in response to determining that the ML model is not trained for the scenario in which the UE is currently located: transmitting a response message indicating that the ML model needs to be trained before performing the one or more positioning measurements, wherein the request message comprises a request for assistance data for training the ML model.9. The method of any one of the embodiments 1-8, further comprising: evaluating performance of the ML model based at least on the first result of the first portion of the one or more positioning measurements; transmitting feedback to the network node regarding the performance of the ML model, the feedback comprising any discrepancies between expected and actual measurement outcomes.10. The method of any one of the embodiments 1-9, further comprising: dynamically adapting a positioning measurement process based at least on an applicability of the ML model for the scenario in which the UE is located, wherein dynamically adapting the positioning measurement process comprises switching between an ML model and a non-ML model as per available model training and the applicability of the ML model.11. The method of any one of the embodiments 1-10, further comprising: proactively requesting an update for the ML model from the network node, based at least on a periodic assessment of performance of the ML model in various scenarios and / or in response to a change in an operational environment, wherein the change in the operational environment comprises the change in a physical location of the UE and / or the change in a serving cell of the UE, wherein the various scenarios comprise different operational conditions in physical locations; wherein the different operational conditions comprise a network traffic pattern and a signal quality. wherein the physical locations comprise a factory, a warehouse, an office building, a residential place, and an open space.12. The method of any one of the embodiments 1-11, wherein the one or more positioning measurements comprise at least one of a received signal time difference (RSTD), a UE receivetransmit (Rx-Tx) time difference, a RSTD performed with a carrier phase difference, or a UE Rx- Tx time difference performed with a carrier phase measurement.13. A method performed by a wireless device, the method comprising:- any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.14. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.15. 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 base station.Group B Embodiments16. A method performed by a network node for facilitating a positioning measurement for a user equipment (UE), the method comprising: transmitting, to the UE, a request to perform one or more positioning measurements based at least on a machine learning (ML) model; receiving a first result of a first portion of the one or more positioning measurements with an indication that the first portion of the one or more positioning measurements is performed by the ML model; receiving a second result of a second portion of the one or more positioning measurements with an indication that the second portion of the one or more positioning measurements is performed by a non-ML model; and determining a location of the UE based at least on one or more of the first result or the second result.17. The method of embodiment 16, wherein receiving the first result is in response to a determination that: the ML model is trained for a scenario in which the UE is currently located; and the ML model is trained to perform the first portion of the one or more positioning measurements.18. The method of any one of the embodiments 16-17, wherein receiving the second result is in response to a determination that: the ML model is not trained for a scenario in which the UE is currently located; and / or the ML model is not trained to perform the first portion of the one or more positioning measurements.19. The method of any one of the embodiments 16-18, further comprising:receiving a request for assistance information to enable the UE to perform a subsequent positioning measurement based on the non-ML model, wherein receiving the request is in response to a determination that the ML model is not trained for the scenario in which the UE is currently located; receiving feedback on performance of the ML model on performing the first portion of the one or more positioning measurements; transmitting the assistance information, wherein the assistance information is tailored based at least on the feedback; receiving a third result of the subsequent positioning measurement; determining a location of the UE based at least on the third result.20. The method the embodiment 19, wherein the assistance information comprises at least one of: a training data specific to the feedback for training the ML model; information about applicable scenarios for the ML model; identifiers of positioning measurements for which the machine learning is trained.21. The method of any one of the embodiments 16-20, further comprising: configuring the UE to perform the one or more positioning measurements on a particular downlink reference signal comprising a channel state information-reference signal (CSI-RS), a synchronization signal block (SSB), or a positioning reference signal (PRS); and configuring the UE to perform the one or more positioning measurements on a particular uplink reference signal comprising sounding reference signal (SRS).22. The method of the embodiments 19-21, further comprising: updating the assistance information based at least on the first result and / or the second result, wherein updating the network configuration and / or the assistance information comprises refining the ML model, wherein refining the ML model comprises updating weight and bias values of a neural network of the ML model; transmitting a recommendation to the UE, the recommendation indicating the updated assistance information, a use of the ML model for a certain scenario, and / or update measurement parameters of the one or more positioning measurements based at least on the updated assistance information.23. The method of the embodiments 16-22, further comprising: receiving an indication from the UE that a certain requested positioning measurement could not be performed based on the ML model, wherein the indication comprises a reason for the certain requested positioning measurement not being performed; based at least on the indication, determining whether the ML model requires refinement or if additional training data should be provided to the UE.24. The method of any one of the embodiments 16-23, further comprising: receiving aggregated results of positioning measurements from multiple UEs; proactively refining the ML model based at least on the aggregated results of the positioning measurements reported by the multiple UEs.25. The method of any one of the embodiments 16-24, further comprising: dynamically reconfiguring settings of the one or more positioning measurements based at least on the feedback, comprising: adjusting a type of reference signal to be used for the one or more positioning measurements; and the one or more positioning measurements to be attempted by the UE26. The method of any one of the embodiments 16-25, wherein the one or more positioning measurements comprise at least one of a received signal time difference (RSTD), a UE receivetransmit (Rx-Tx) time difference, a RSTD performed with a carrier phase difference, or a UE Rx- Tx time difference performed with a carrier phase measurement.27. The method of any one of the embodiments 16-26, wherein the scenario indicates an operational condition in a location in which the user equipment (UE) is located; wherein the operational condition comprises a network traffic pattern and / or a signal quality; and wherein the location comprises a factory, a warehouse, an office building, a residential place, or an open space.28. A method performed by a base station, the method comprising:- any of the steps, features, or functions described above with respect to base station, either alone or in combination with other steps, features, or functions describedabove.29. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.30. The method of any of the previous embodiments, further comprising:- obtaining user data; and- forwarding the user data to a host computer or a wireless device.Group C Embodiments31. A user equipment for performing a positioning measurement comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.32. A network node for facilitating a positioning measurement for a user equipment (UE), the network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry.33. A user equipment (UE) for performing a positioning measurement the UE comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.34. 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 embodiments to receive the user data from the host.35. The host of the previous 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.36. The host of the previous 2 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.37. 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.38. The method of the previous 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.39. The method of the previous 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.40. 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 embodiments to transmit the user data to the host.41. The host of the previous 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.42. The host of the previous 2 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.43. 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 embodiments to transmit the user data to the host.44. The method of the previous 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.45. The method of the previous 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.46. 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 embodiments to transmit the user data from the host to the UE.47. The host of the previous 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.48. 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 embodiments to transmit the user data from the host to the UE.49. The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.50. The method of any of the previous 2 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.51. 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 embodiments to transmit the user data from the host to the UE.52. The communication system of the previous embodiment, further comprising: the network node; and / or the user equipment.53. 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 embodiments to receive the user data from a user equipment (UE) for the host.54. The host of the previous 2 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.55. The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data.56. 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 nodeperforms any of the steps of any of the Group B embodiments to receive the user data from the UE for the host.57. The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host.

Claims

CLAIMS1. A method performed by a user equipment, UE, (600) for performing a positioning measurement, the method comprising: receiving (310) a request to perform one or more positioning measurements based at least on an artificial intelligence or machine learning, AI / ML, model; determining (320) whether the AI / ML model is applicable for the requested positioning measurements; in response to determining (330) that the AI / ML model is not applicable for at least one of the one or more positioning measurements: transmitting (340), to a network node (700), information comprising: an indication that the UE (600) could not perform the at least one positioning measurement based on the AI / ML model.

2. The method of claim 1, wherein determining whether the AI / ML model is applicable for the requested positioning measurements comprises determining whether the AI / ML model is trained for a scenario which the UE (600) is currently in.

3. The method of claim 2, further comprising, in response to determining that the AI / ML model is not trained for the scenario which the UE is currently in: transmitting a response message indicating that the AI / ML model needs to be trained before performing the one or more positioning measurements, wherein the request message comprises a request for assistance data for training the AI / ML model.

4. The method of any one of claims 2-3, wherein the scenario indicates an operational condition in a location in which the UE is located; wherein the operational condition comprises a network traffic pattern and / or a signal quality; and wherein the location comprises a factory, a warehouse, an office building, a residential place, or an open space.

5. The method of any one of claims 2-4, wherein determining whether the AI / ML model is trained for the scenario which the UE is currently in is based at least on one or more of: historical data;an indication received from the network node, comprising an indication of a serving base station; an estimation of radio channel characteristics comprising a delay spread or a doppler frequency, or a preconfigured information stored on the UE, comprising a subscriber identity module, SIM, embedded SIM, eSIM, or universal SIM, USIM.

6. The method of any one of claims 1-5, further comprising, in response to determining that the AI / ML model is not applicable for at least one of the one or more positioning measurements, transmitting, to the network node, information comprising: an indication of a reason why the UE (600) could not perform the at least one positioning measurement based on the AI / ML model.

7. The method of claim 6, wherein the reason why the UE could not perform the at least one positioning measurement based on the AI / ML model comprises one or more of: the UE is not operating in the scenario applicable for inferring the at least one positioning measurement based on the AI / ML model; the AI / ML model is not fully trained; and the UE does not contain any AI / ML model for performing the at least one positioning measurement.

8. The method of any one of claims 1-7, wherein determining that the AI / ML model is not applicable for at least one of the one or more positioning measurements comprises: identifying a second portion of the one or more positioning measurements for which the AI / ML model is not trained; and wherein transmitting information to the network node comprises: transmitting information about the second portion of the one or more positioning measurements that have not been performed by the UE due to the AI / ML model not being applicable to the second portion of the one or more positioning measurements.

9. The method of any one of claims 1-8, further comprising: in response to determining that the AI / ML model is not applicable for the at least one positioning measurement: transmitting (342) a request for assistance information to enable the UE to perform the one or more positioning measurements based on a non- AI / ML model; receiving (344) the assistance information;performing (346) the one or more positioning measurements using the non- AI / ML model; transmitting (348) a second result of the one or more positioning measurements with an indication that the second result is determined based on the non-AI / ML model.

10. The method of claim 9 wherein performing the one or more positioning measurements using the non-AI / ML model comprises performing a measurement on a downlink reference signal and / or an uplink reference signal.

11. The method of any one of claims 1-10, wherein determining whether the AI / ML model is applicable for the one or more requested positioning measurements comprises determining whether the AI / ML model is trained to perform the one or more positioning measurements.

12. The method of any one of claims 1-11, further comprising: in response to determining that the AI / ML model is applicable for at least one of the requested positioning measurements: identifying (350) a first portion of the one or more positioning measurements for which the AI / ML model is trained; performing (352) the first portion of the one or more positioning measurements using the AI / ML model; transmitting (354) a first result of the first portion of the one or more positioning measurements to a network node; and determining (356) a position of the UE based at least on the first result.

13. The method of claim 12, further comprising: evaluating performance of the AI / ML model based at least on the first result of the first portion of the one or more positioning measurements; transmitting feedback to the network node regarding the performance of the AI / ML model, the feedback comprising any discrepancies between expected and actual measurement outcomes.

14. The method of any one of claims 1-13, further comprising: dynamically adapting a positioning measurement process based at least on an applicability of the AI / ML model for the scenario which the UE is currently in, whereindynamically adapting the positioning measurement process comprises switching between an AI / ML model and a non-AI / ML model as per available model training and the applicability of the AI / ML model.

15. The method of any one of claims 1-14, further comprising: proactively requesting an update for the AI / ML model from the network node, based at least on a periodic assessment of performance of the AI / ML model in various scenarios and / or in response to a change in an operational environment, wherein the change in the operational environment comprises the change in a physical location of the UE and / or the change in a serving cell of the UE, wherein the various scenarios comprise different operational conditions in physical locations; wherein the different operational conditions comprise a network traffic pattern and a signal quality; wherein the physical locations comprise a factory, a warehouse, an office building, a residential place, and an open space.

16. The method of any one of claims 1-15, wherein the one or more positioning measurements comprise at least one of a received signal time difference (RSTD), a UE receive-transmit (Rx-Tx) time difference, a RSTD performed with a carrier phase difference, or a UE Rx-Tx time difference performed with a carrier phase measurement.

17. A method performed by a network node (700) for facilitating a positioning measurement for a user equipment, UE, (600) the method comprising: transmitting (410), to the UE (600), a request to perform one or more positioning measurements based at least on an artificial intelligence or machine learning, AI / ML, model; and receiving (420), from the UE (600), information comprising: an indication that the UE (600) could not perform at least one of the one or more positioning measurements based on the AI / ML model.

18. The method of claim 17, further comprising receiving, from the UE, information comprising: an indication of a reason why the UE could not perform the at least one positioning measurement based on the AI / ML model.

19. The method of claim 18 wherein the reason why the UE could not perform the at least one positioning measurement based on the AI / ML model comprises one or more of: the UE is not operating in the scenario applicable for inferring the at least one positioning measurement based on the AI / ML model; the AI / ML model is not fully trained; and the UE does not contain any AI / ML model for performing the at least one positioning measurement.

20. The method of any one of claims 17 to 19, further comprising: receiving a first result of a first portion of the one or more positioning measurements with an indication that the first portion of the one or more positioning measurements is performed by the AI / ML model; receiving a second result of a second portion of the one or more positioning measurements with an indication that the second portion of the one or more positioning measurements is performed by a non- AI / ML model; and determining a location of the UE based at least on one or more of the first result or the second result.

21. The method of claim 20, wherein receiving the first result is in response to a determination that: the AI / ML model is trained for a scenario which the UE is currently in; and the AI / ML model is trained to perform the first portion of the one or more positioning measurements.

22. The method of any one of claims 20-21, wherein receiving the second result is in response to a determination that: the AI / ML model is not trained for a scenario which the UE is currently in; and / or the AI / ML model is not trained to perform the first portion of the one or more positioning measurements.

23. The method of any one of claims 17-22, further comprising: receiving a request for assistance information to enable the UE to perform a subsequent positioning measurement based on the non-AI / ML model, wherein receiving the request is in response to a determination that the AI / ML model is not trained for the scenario which the UE is currently in;receiving feedback on performance of the AI / ML model on performing the first portion of the one or more positioning measurements; transmitting the assistance information, wherein the assistance information is tailored based at least on the feedback; receiving a third result of the subsequent positioning measurement; determining a location of the UE based at least on the third result.

24. The method of claim 23, wherein the assistance information comprises at least one of: training data specific to the feedback for training the AI / ML model; information about applicable scenarios for the AI / ML model; identifiers of positioning measurements for which the AI / ML model is trained.

25. The method of any of claims 23-24, further comprising: updating the assistance information based at least on the first result and / or the second result, wherein updating the network configuration and / or the assistance information comprises refining the AI / ML model, wherein refining the AI / ML model comprises updating weight and bias values of a neural network of the AI / ML model; transmitting a recommendation to the UE, the recommendation indicating the updated assistance information, a use of the AI / ML model for a certain scenario, and / or update measurement parameters of the one or more positioning measurements based at least on the updated assistance information.

26. The method of any one of the claims 22-25, further comprising: dynamically reconfiguring settings of the one or more positioning measurements based at least on the feedback, comprising: adjusting: a type of reference signal to be used for the one or more positioning measurements; and the one or more positioning measurements.

27. The method of any one of claims 17-26, further comprising: configuring the UE to perform the one or more positioning measurements on a particular downlink reference signal comprising a channel state information-reference signal (CSI-RS), a synchronization signal block (SSB), or a positioning reference signal (PRS); and configuring the UE to perform the one or more positioning measurements on a particular uplink reference signal comprising sounding reference signal (SRS).

28. The method of the claims 17-27, further comprising: based at least on the indication that the UE could not perform at least one of the one or more positioning measurements based on the AI / ML model, determining whether the AI / ML model requires refinement or if additional training data should be provided to the UE.

29. The method of any one of claims 17-28, further comprising: receiving aggregated results of positioning measurements from multiple UEs; proactively refining the AI / ML model based at least on the aggregated results of the positioning measurements reported by the multiple UEs.

30. The method of any one of claims 17-29, wherein the one or more positioning measurements comprise at least one of a received signal time difference (RSTD), a UE receive-transmit (Rx-Tx) time difference, a RSTD performed with a carrier phase difference, or a UE Rx-Tx time difference performed with a carrier phase measurement.

31. The method of any one of claims 17-30, wherein the scenario indicates an operational condition in a location in which the UE is located; wherein the operational condition comprises a network traffic pattern and / or a signal quality; and wherein the location comprises a factory, a warehouse, an office building, a residential place, or an open space.

32. A user equipment, UE, (600) for performing a positioning measurement comprising: processing circuitry configured to cause the user equipment to: receive (310) a request to perform one or more positioning measurements based at least on an artificial intelligence or machine learning, AI / ML, model; determine (320) whether the AI / ML model is applicable for the requested positioning measurements; in response to determining (330) that the AI / ML model is not applicable for at least one of the one or more positioning measurements: transmit (340), to a network node (700), information comprising: an indication that the UE (600) could not perform the at least one positioning measurement based on the AI / ML model.

33. The UE of claim 32, wherein the processing circuitry is further configured to cause the UE to perform the method of any one of claims 2 to 16.

34. A UE configured to perform the method of any one of claims 1 to 16.

35. A network node (700) for facilitating a positioning measurement for a user equipment, UE, (600) the network node comprising: processing circuitry configured to cause the network node to: transmit (410), to the UE (600), a request to perform one or more positioning measurements based at least on an artificial intelligence or machine learning, AI / ML, model; and receive (420), from the UE (600), information comprising: an indication that the UE (600) could not perform at least one of the one or more positioning measurements based on the AI / ML model.

36. The network node according to claim 35, wherein the processing circuitry is further configured to cause the network node to perform the method of any one of claims 18 to 31.

37. A network node configured to perform the method of any one of claims 17 to 31.

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