Network assisted ai / ML model validation for positioning

Abstract

Methods, apparatuses, and systems for validating or training a first Artificial Intelligence / Machine Learning, AI / ML, model for positioning a user equipment, UE, in a wireless communications network. An example method, as carried out by a first node in a wireless network, comprises the steps of obtaining (1310) one or more expected output values for the first AI / ML model and validating (1320) the performance of and / or training the AI / ML model, using the expected output values. In some embodiments or instances, the expected output values for the first AI / ML model are generated by a second AI / ML model or sub-model of the first AI / ML, where the second AI / ML model or sub-model of the first AI / ML is trained using input data corresponding to different measurements from measurements used by first AI / ML model for inference or training.

Description

[0001] NETWORK ASSISTED AI / ML MODEL VALIDATION FOR POSITIONING

[0002] TECHNICAL FIELD

[0003] The present disclosure is generally related to positioning of wireless devices in wireless communications networks and is more particularly related to improved techniques for the use of artificial intelligence / machine-leaming models in positioning.

[0004] BACKGROUND

[0005] AI / ML modeling and associated principles

[0006] Artificial intelligence (Al) or machine learning (ML) techniques utilize one or more algorithms to model a certain system and make inferences, or predictions of certain system parameters or outputs, based on measurements from that system. These algorithms use a set of data as input for training one or more AI / ML models. The output of the AI / ML model may be used by a device in a wireless communications system, such as a user equipment (UE), base station (BS) or another node, for performing certain operations or taking certain decisions, such as handover decisions, whether fully or partially based on the model’s predictions, which in turn depend on the trained model. The AI / ML model can be trained in the device online (or on-the-fly while processing the data) or offline in the background. More specifically:

[0007] • 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.

[0008] • 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.

[0009] 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.

[0010] As noted above, the AL / ML models can be trained in a device, which can be a UE, a network node, or another node. In this respect the AI / ML modes can be broadly classified as:

[0011] • Case I: UE-side (AI / ML) model, i.e., an AI / ML model whose inference is performed entirely at the UE.

[0012] • Case II: Network-side (AI / ML) model, i.e., an AI / ML model whose inference is performed entirely at the network.

[0013] • Case III: One-sided (AI / ML) model, i.e., a UE-side (AEML) model or a Network-side (AI / ML) model.

[0014] • Case IV: Two-sided (AI / ML) model, a paired AI / ML model(s) over which joint inference is performed, where joint inference comprises AI / ML inference performed jointly across the UE and the network, i.e., the first part of inference is performed by UE and then the remaining part is performed by gNB, or vice versa.

[0015] An AI / ML model can be transferred or delivered over the air interface either in terms of one or more parameters of a model structure known at the receiving end or a new model with parameters. The model delivery may contain a full model or a partial model.

[0016] 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, such as gathering unprocessed input data from data repositories (data ingestion), finding high-quality input features (data pre-processing), finding the optimal mapping of the model input features to a desired model output target in a sense determined by a loss function (model training), 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 of operations to make the ML model executable via compilation to a specific hardware and of steps like versioning and packaging of the model so that it can be executed. An example of a AI / ML model training pipeline illustrating all of these different stages is shown in figure 1.

[0017] According to a technical report issued by 3GPP, “Technical Specification Group Radio Access Network; Study on Artificial Intelligence (AI)ZMachine Learning (ML) for NR air interface (Release 18),” 3GPP TR 38.843, Dec. 2023, AI / ML LCM covers the following components:

[0018] ■ Data collection

[0019] ■ Model training

[0020] ■ Functionality / model identification

[0021] ■ Model inference

[0022] ■ Functionality / model selection, activation, deactivation, switching, and fallback operation

[0023] ■ Functionality / model monitoring

[0024] ■ Model update

[0025] ■ UE capability.

[0026] In functionality-based LCM, the network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signaling, e.g., using Radio Resource Control (RRC) signaling, Medium Access Control Control Elements (MAC-CEs), or Downlink Control Information (DCI), as specified by 3GPP standards. Models might not be specifically identified at the network, in some cases, and a UE may perform model-level LCM. A UE may have one AI / ML model per functionality or multiple AI / ML models per functionality. Functionality refers to an AI / ML-enabled feature / feature group enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by UE capability. In addition to functionality identification, the UE can also report updates on applicable functionality(ies) among functionality(ies).

[0027] In model-based LCM, models are identified at the network and the network and / or UE may activate / deactivate / select / switch individual AI / ML models using a model ID. A model may be associated with specific configurations / conditions and additional conditions (e.g., scenarios, sites, datasets) may be determined / identified between a UE and the network.

[0028] AI / ML model validation is a subprocess of training as depicted in Figure 2, to evaluate the quality of an AI / ML model using a dataset different from the one used for model training, that helps selecting model parameters that generalize beyond the dataset used for model training.

[0029] AI / ML model-based positioning

[0030] An AI / ML model can be used for UE positioning. A UE or a gNB (3GPP terminology for a 5G base station), depending on capability, can have a trained model stored inside the device, or have an untrained AI / ML that can be trained on-the-fly to either produce measurements that are required to localize a UE within a radio access network (RAN) coverage area or directly predict / determine the UE location, by exploiting the measurements performed by the UE or gNB on reference signals such as positioning reference signal (PRS), sounding reference signal (SRS) etc. within a RAN coverage area.

[0031] Measurements predicted / determined by the UE by exploiting an AI / ML model can be defined as, but are not limited to:

[0032] • RSTD: It is reference signal time difference between the positioning node j and the reference positioning node i. It is measured on the DL PRS signals and always involve two cells (cell is interchangeably called as TRP).

[0033] • UE Rx-Tx time difference: It is defined as TUE-RX -TUE-TX.

[0034] Where: o TUE-R is the UE received timing of downlink subframe #i from a positioning node, defined by the first detected path in time. It is measured on PRS signals received from the gNB. o TUE-TX is the UE transmit timing of uplink subframe #j that is closest in time to the subframe #i received from the positioning node.

[0035] Measurements predicted / determined by the gNB by exploiting an AI / ML model can be defined as, but not limited to:

[0036] • gNB Rx-Tx time difference: It is defined as T8NB-RX - TgNB-Tx.

[0037] 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.

[0038] • Timing advance (TADV): It is defined as the time difference TADV = (TgNB-Rx - TgNB-rx),

[0039] Where: o TgNB-Rx is the Transmission and Reception Point (TRP)

[0018] 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 PRACH is used to determine the start of one subframe containing that PRACH.

[0040] • UL Relative Time of Arrival (UL RTOA): It is defined as the beginning of subframe i containing SRS received in positioning node j, relative to the configurable reference time. For example, nodel (e.g., base station etc) measures the reception time of signals transmitted by the UE with respect to a reference time.

[0041] In addition to these, a UE or gNB may also perform power measurements such as reference signal received power (RSRP) measurements and / or reference signal received path power (RSRPP) measurements. These measurements can be performed on reference signals such as a Positioning Reference Signal (PRS) and / or a Sounding Reference Signal (SRS).

[0042] Depending on the capability, a UE may also perform positioning measurements on sidelink (SL) resources by exploiting an 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 in this case is called a target UE and the UE(s) assisting the target UE to perform the SL positioning measurements is called the anchor UE or assisting UE.

[0043] Two approaches to positioning with AI / ML models may be referred to as Direct AI / ML for Positioning and Assisted AI / ML For Positioning.

[0044] The following are selected as representative sub-use cases:

[0045] • Direct AI / ML positioning : o AI / ML model output: UE location o e.g., fingerprinting based on channel observation as the input of AI / ML model

[0046] • AI / ML assisted positioning: o AI / ML model output: new measurement and / or enhancement of existing measurement o e.g., LOS / NLOS identification, timing and / or angle of measurement, likelihood of measurement

[0047] In 3GPP TR 38.848, for AI / ML positioning, different TRP (transmission point) model constructions are being considered for evaluations. A first construction may be referred to as Single-TRP construction with the same model for ATRPs. This construction is illustrated in Figure 3. A second construction may be referred to as Single-TRP construction with N models for ATRPs. This is illustrated in Figure 4. A third construction may be referred to as multi-TRP construction with one model for ATRPs, as shown in Figure 5. As seen in the figure, in a multi-TRP construction, measurements from multiple TRPs are fed into a single AI / ML. model.

[0048] Positioning measurement procedure (PMP)

[0049] The PMP comprises performing one or more positioning measurements on DL RS (e.g. PRS) and / or uplink (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.

[0050] To perform these measurements, the UE may make use of a trained model acquired before being deployed, or it needs to train its model on-the-fly before performing AI / ML based positioning measurements to be reported to the network node such as location server. In either of these cases, UE requires 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 a location server.

[0051] Positioning architecture

[0052] Before Release 16 of the 3GPP standards, LTE based positioning was one of the more prevalent RAT- based positioning solutions available. Starting with the Release 16 specification, positioning is also supported in New Radio (NR). Positioning in NR is supported by the architecture shown in Figure 6. The interactions between the gNodeB and the device is supported via the Radio Resource Control (RRC) protocol, while the location node interfaces with the UE via the LTE positioning protocol (LPP). LPP is a common protocol to both NR and LTE. LMF is the location node in NR. There are also interactions between the location node and the gNodeB via the NRPPa protocol. The positioning architecture in Figure 6 will also be used to support AI / ML based positioning. Release 19 work on introducing AI / ML based positioning will not only exploit the legacy protocol but will also rely on already defined / existing reference signals that are used for positioning.

[0053] SUMMARY

[0054] For AI / ML-based positioning, it is generally expected that the AI / ML model management need to be done by the network. The AI / ML models will have to be handled in a life cycle management procedure where a model has to be regularly validated and may have to be updated or switched off or another model needs to be activated.

[0055] Previously, there has been no understanding of how a UE positioning model can be validated efficiently and with low cost (e.g., less signaling overhead or with less computational complexity).

[0056] Embodiments of the techniques, apparatuses, and systems disclosed herein address this problem by providing a solution where an AI / ML model for positioning is tied to a specific area, with a set of TRPs, and is therefore valid for that specific area. Model validity is ensured by providing an expected measurement that the model should generate. That is, each model that generates output has an associated expected measurement value.

[0057] A model may be divided into two sub-models, or two different models can be used where one model generates the output and the other model or sub-model or the second model verifies the output of the first model.

[0058] Additionally, or alternatively, a network node such as a Location Management Function (LMF) may signal the necessary expected measurement to the gNB and to UE.

[0059] Example embodiments described herein thus include methods, apparatuses, and systems for validating or training a first Artificial Intelligence / Machine Learning model for positioning a user equipment (UE) in a wireless communications network. An example method, as carried out by a first node in a wireless network, comprises the steps of obtaining one or more expected output values for the first AI / ML model and validating the performance of and / or training the AI / ML model, using the expected output values. In some embodiments or instances, the expected output values for the first AI / ML model are generated by a second AI / ML model or sub-model of the first AI / ML, where the second AI / ML model or sub-model of the first AI / ML is trained using input data corresponding to different measurements from measurements used by first AI / ML model for inference or training.

[0060] Mechanisms described herein thus have one model for AI / ML-based positioning and another for validating that model, where both the models are trained with different data sets (training data, ground truth, labelled data). Thus, the data sets are not corelated, allowing for cross-verification. Advantages of the techniques disclosed herein include that they preserve the measurement output integrity (trustworthiness) for AI / ML generated output using the property of classical methods. A model can be validated with the model property itself, without external signaling. It is also possible that a network provides, via signaling, the expected measurement value for every model.

[0061] BRIEF DESCRIPTION OF THE FIGURES

[0062] Figure 1 shows an example of a AI / ML model training pipeline.

[0063] Figure 2 illustrates model training and validation.

[0064] Figure 3 shows assisted positioning with single-TRP construction and one model for JVTRPs.

[0065] Figure 4 shows assisted positioning with single-TRP construction and N models for / VTRPs.

[0066] Figure 5 illustrates assisted positioning with multi -TRP construction and one model for / VTRPs.

[0067] Figure 6 illustrates the NR positioning architecture.

[0068] Figure 7 shows the use of two models or sub-models, with a second of the two models or sub-models being used to validate the first.

[0069] Figure 8 illustrates the validation process.

[0070] Figure 9 is an illustration of an example where model 1 generates predicted RSTD from CIR while model2 verifies if it is within the expected range (value).

[0071] Figure 10 illustrates another example, where model 1 generates predicted LOS / NLOS from CIR whereas model2 verifies whether it is correct, based upon a different data set.

[0072] Figure 11 and Figure 12 are signaling diagrams illustrating two example methods, according to several embodiments.

[0073] Figure 13 is a process flow diagram illustrating an example method for validating an AI / ML model for positioning a UE, according to various embodiments.

[0074] Figure 14 shows a communication system according to various embodiments of the present disclosure.

[0075] Figure 15 shows a UE according to various embodiments of the present disclosure.

[0076] Figure 16 shows a network node according to various embodiments of the present disclosure. Figure 17 shows a host computing system according to various embodiments of the present disclosure.

[0077] Figure 18 is a block diagram of a virtualization environment in which functions implemented by some embodiments of the present disclosure may be virtualized.

[0078] Figure 19 illustrates communication between a host computing system, a network node, and a UE via multiple connections, at least one of which is wireless, according to various embodiments of the present disclosure.

[0079] DETAILED DESCRIPTION

[0080] As discussed above, it is generally expected that the AI / ML model management for AI / ML-based positioning needs to be done by the network. The AI / ML models will have to be handled in a life cycle management procedure where a model has to be regularly validated and may have to be updated or switched off or another model needs to be activated. Previously, there has been no understanding of how a UE positioning model can be validated efficiently and with low cost (e.g., less signaling overhead or with less computational complexity).

[0081] Embodiments of the techniques, apparatuses, and systems disclosed herein address this problem by providing a solution where an AI / ML model for positioning is tied to a specific area, with a set of TRPs, and is therefore valid for that specific area. Model validity is ensured by providing an expected measurement that the model should generate. That is, each model that generates output has an associated expected measurement value.

[0082] A model may be divided into two sub-models, or two different models can be used where one model generates the output and the other model or sub-model or the second model verifies the output of the first model.

[0083] Additionally, or alternatively, a network node such as a Location Management Function (LMF) may signal the necessary expected measurement to the gNB and to UE.

[0084] Mechanisms described herein thus have one model for AI / ML-based positioning and another for validating that model, where both the models are trained with different data sets (training data, ground truth, labelled data). Thus, the data sets are not corelated, allowing for cross-verification.

[0085] A general approach may be understood using a scenario that includes:

[0086] • A UE / gNB capable of supporting AI / ML based positioning.

[0087] • UE / gNB performs AI / ML-based positioning with UE-side or gNB-side model.

[0088] • AI / ML-based positioning measurements: e.g: CIR, PDP, DP. • Models are in UE / gNB which generates positioning measurements such as TDOA, LOS, NLOS, TOA, Rx-Tx based upon input such as CIR, PDP, DP; i.e., assisted AI / ML.

[0089] Regarding terminology and specific examples, the discussion below may explain certain concepts with respect to a certain measurement or predicted output, such as RSTD. It should be understood, however, that the concept is valid for other measurements as well, where NW can judge the expected measurement with the UE coarse location. The expected measurement is valid for measurements performed by both gNB and UE, which is dependent upon prior knowledge of UE.

[0090] According to the approach described herein, AI / ML models are validated for positioning either using a second (separate or sub model) just for the purpose of validation, or the UE / gNB requests certain specific assistance data for model validation, which in this case is expected measurement value.

[0091] The expected measurement value that is existing in LPP specifications (3GPP TS 37.355 specification) may be reused for the model validation purpose.

[0092] An example specification of an expected measurement is provided below, for expected RSTD.

[0093] - begin 3 GPP specification excerpt - r-DL-PRS-ExpectedRSTD-r! 6 INTEGER ( -3841 . . 3841 ) , nr-DL-PRS-ExpectedRSTD-Uncertainty-r! 6

[0094] INTEGER ( 0 . . 246 ) ,

[0095] -cnc| 3 Gpp specification excerpt -

[0096] Another example below is for AoD

[0097] - begin 3 GPP specification excerpt - expectedAoD-r!7 SEQUENCE { expectedDL-AzimuthAoD-rl7 INTEGER ( 0 . . 359 ) , expectedDL-AzimuthAoD-Unc-rl7 INTEGER ( 0 . . 60 ) OPTIONAL, — Need OP expectedDL-ZenithAoD-r!7 INTEGER ( 0 . . 180 ) , expectedDL-ZenithAoD-Unc-r!7 INTEGER ( 0 . . 30 ) OPTIONAL — Need OP

[0098] } , nr-DL-PRS-ExpectedRSTD

[0099] This field indicates the RSTD value that the target device is expected to measure between this TRP and the assistance data reference TRP. The nr-DL-PRS-ExpectedRSTD field takes into account the expected propagation time difference as well as transmit time difference of PRS positioning occasions between the two TRPs. The resolution is 4xTs, with Ts=1 Z(15000*2048) seconds.

[0100] NR-DL-PRS-ExpectedLOS-NLOS-Assistance The IE NR-DL-PRS-ExpectedLOS-NLOS-Assistance is used by the location server to provide the expected likelihood of a LOS propagation path from a TRP to the target device, or for all DL-PRS Resources of the TRP to the target device.

[0101] — ASN1START

[0102] NR-DL-PRS-ExpectedL0S-NL0S-Assistance-rl7 : := SEQUENCE (SIZE ( 1. . nrMaxFreqLayers-r!6) ) OF

[0103] NR-DL-PRS-ExpectedLOS-NLOS- AssistancePerFreqLayer-rl7

[0104] NR-DL-PRS-ExpectedL0S-NL0S-AssistancePerFreqLayer-rl7 : :=

[0105] SEQUENCE (SIZE ( 1. . nrMaxTRPsPerFreq-r!6) ) OF NR-DL-PRS-ExpectedLOS-NLOS- AssistancePerTRP-rl7

[0106] NR-DL-PRS-ExpectedL0S-NL0S-AssistancePerTRP-rl7 : := SEQUENCE { dl-PRS-ID-r!7 INTEGER (0..255) , nr-PhysCellID-r!7 NR-PhysCellID-rl6 OPTIONAL, — Need

[0107] ON nr-CellGlobalID-rl7 NCGI-rl5 OPTIONAL, — Need ON nr-ARFCN-r!7 ARFCN-ValueNR-rl5 OPTIONAL, — Need ON nr-los-nlos-indicator-r!7 CHOICE { perTrp-r!7 L0S-NL0S-Indicator-rl7 , perResource-r!7 SEQUENCE (SIZE

[0108] (1. . nrMaxSetsPerTrpPerFreqLayer-r!6) ) OF

[0109] NR-DL-PRS-ExpectedLOS-NLOS- AssistancePerResource-rl7 },

[0110] }

[0111] NR-DL-PRS-ExpectedL0S-NL0S-AssistancePerResource-rl7 : :=

[0112] SEQUENCE (SIZE ( 1. . nrMaxResourcesPerSet- r!6) ) OF

[0113] L0S-NL0S-Indicator-rl7

[0114] — ASN1STOP end 3 GPP specification excerpt These expected measurement values are associated with an AI / ML model where the UE compares the output it received with the expected value to identify if the model is correct or if there is any outlier.

[0115] This provides a technique to ensure integrity (trustworthiness) of AI / ML generated output by reusing the outlier detection used by classical methods.

[0116] A model may be divided into two sub-models, or two different models can be used, where one model generates the output and the other model or sub-model verifies the output of the 1stmodel. This is shown in Figure 7.

[0117] Model 1 may be trained with CIR, PDP, DP, RSRP, etc. measurements, for example, with the output being predicted positioning measurements, e.g., TOA / TDOA or expected AoA / AoD or expected LOS / NLOS, etc. In this case the ground truth is labeled with accurate / true TOA / TDOA, AOA / AoD, LOS / NLOS, etc.

[0118] Model2, meanwhile, is trained with UE (coarse) location (e.g: cell ID) and measured TRPs with ground truth of expectedTOA, expected LOS / NLOS, expectedTDOA(RSTD), expectedAoA / AoD values.

[0119] The output of model 1 ’s predicted positioning measurements along with indications of which TRPs and from which cell the UE performed the positioning is fed to model2 (validator), which then provides the expected positioning measurements. The predicted and expected positioning measurements are compared to validate the performance of Model 1.

[0120] In more detail, the scenario assumes a UE that can perform positioning measurements based on AI / ML model. The UE is also capable of performing positioning measurements by exploiting non- AI / ML methods such as methods defmed / specified up until rel. 18 NR positioning specification.

[0121] The scenario comprises: a UE; a network node 1 (NW1), which can be a serving TRP, a reference TR, or a neighbor TRP transmitting reference signal for positioning measurements such as PRS; and a network node 2 (NW2), which can be a location server in the network that provides assistance data to the UE for positioning measurements and can configure network node 1 (NW1) to perform positioning measurements on reference signals transmitted by UE in UL. In the considered scenario, the network node NW2 or the location server is aware of the TRP deployment. The UE positioning measurements can be one or more of the RSTD, UE Rx-Tx time difference, RSTD performed with carrier phase difference, UE Rx-Tx time difference performed with carrier phase measurement, etc. The positioning measurements performed by the network node NW 1 can be one or more of the gNB Rx-Tx difference, UL RToA, gNB Rx-Tx difference performed with carrier phase measurement, UL RToA performed with carrier phase difference measurement etc. Both the UE and the network node NW 1 have capability to perform one or more positioning measurements by exploiting an AI / ML model. NW2 is aware of the UE / NW1 capability to perform singular positioning measurements and or multiple positioning measurements, e.g., jointly performing and reporting the positioning measurements.

[0122] The UE referred to herein 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 called as a positioning node, Serving Mobile Location Centre (SMLC), Evolved Serving Mobile Location Centre (E-SMLC), LMF etc. NW2 provides assistance data to the UE via higher layer signalling e.g., LPP messages. The UE reports positioning measurement results to NW2 via higher layer signalling (e.g., LPP messages) after performing the positioning measurement based on the configuration / assistance data provided to the UE by NW2. The UE can be in any of the RRC states (e.g., either 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 of RRC state of the UE while the UE is performing positioning measurements configured by NW2.

[0123] Model Validation Technique

[0124] For AI / ML-assisted positioning, the input can be measurements whereas the output is predicted positioning measurement. The input can be, for example: CIR. When passed via AI / ML, this would provide a more accurate estimation of the positioning measurements.

[0125] A key idea is that the predicted measurement (e.g., predicted RSTD) is validated with a separate model, or a sub-model within the main model, which generates or obtains an expected measurement, e.g., expected RSTD, and compares the predicted RSTD with the expected RSTD. For every main model ID, there can be a model validator ID that may be associated or provisioned by the NW node (such as LMF). If the values for the predicted measurement and the expected measurement are similar or differ by an amount within a certain threshold, the model is considered to be valid. This approach is shown in Figure 8, for an example in which the input measurements are CIR and the predicted output is predicted RSTD. As seen in the figure, if predicted RSTD is similar enough to the expected RSTD, the model is deemed to be valid. Otherwise, it is deemed to be invalid. In this case, a different model may be used, or the system may revert to conventional (non-AI / ML-assisted) positioning techniques.

[0126] This approach can be generalized by extending it to a UE (or a second wireless node in general) using a range of values signalled by the LMF / gNB (or a first wireless node in general) to validate the output of an algorithm. In one example, the output of the model is a measurement (such as RSTD), and the range of values for validation corresponds to the expected RSTD. In another example, the output of the model can be a different quantity from the range of values transmitted for validation. For example, the UE can produce the UE location out of the ML model, and use expected RSTD together with RSTD measurements for validation.

[0127] In another example, a Channel Charting (CC) model can be developed as the validation model or model 2. CC uses Channel State Information (CSI) to assign a quasi-positional value to each sample in the training dataset within a low-dimensional space. Essentially, CC is designed to capture the local spatial structure of the area, ensuring that points that are close in physical space are also closely positioned on the channel chart, and vice versa. The results from the CC validation model can then serve as a confidence metric for the predictions made by the initial model.

[0128] In another example, Model 2 could consist of various ML and non-ML solutions. In this scenario, Model 2's output would be a collective or aggregated result from these different solutions.

[0129] How the NW generates the expected measurement for the AI / ML model:

[0130] • The NW while generating the model takes expected measurement (e.g.: expected RSTD / AoD / TOA) also into account and trains the model so that the expected measurement is also generated as an output with the model. The model may require additional input parameters such as the TRPs (UE coarse, locations which is used for measurement along with the coarse UE location given by the cell ID to generate the expected measurement value.

[0131] • The validation occurs occasionally or when configured (i.e asked) by the NW node (such as LMF, gNB, Network Data Analytics Function (NWDAF)) or by the UE itself for UE side model.

[0132] • The validation may also occur at gNB for any model generated or available at gNB.

[0133] • The expected measurement value can also be provided within a range (min, max) or / and with confidence factor or associated uncertainty value. That is the model may generate lower or upper bounds.

[0134] • The expected measurement values can be provided via signaling such as using LCS (location service message), LPP, NRPPa.

[0135] • The expected measurement value for LOS / NLOS can be within a given range of values for LOS / NLOS probability.

[0136] • The model validation is associated with a given model ID which has its corresponding submodel for validation.

[0137] Training data sets: For model 1, the training data set may be based on, for example, TRPs’ CIR and ground truth positioning measurements (e.g.: RSTD, TDOA, UE Rx-Tx , gNB Rx-Tx). For model2, the training data sets may be sub-frame time difference between TRPs, propagation delay based upon cell size (or coarse UE location: RSRP, pathloss, celllD, cell Sector), and ground truth expected RSTD. This is shown in Figure 9, which also shows the validation of the predicted RSTD output from Model 1 by the Model2 validator. Figure 10 shows another example, where model 1 generates predicted LOS / NLOS from CIR, while model2 verifies whether this predicted LOS / NLOS measurement is correct, based upon a different data set.

[0138] Following are descriptions of two example implementations of the general concepts described herein. First is a description of a method, in a UE, for validating a AI / ML model for positioning. Second is a method in a network node, for providing assistance data for AI / ML model validation for positioning.

[0139] Method in UE to validate AI / ML model for positioning

[0140] According to some embodiments of the techniques described herein, a UE receives one or more values as the expected outcome of an AI / ML model implemented at the UE side. The AI / ML model at the UE side can either be a trained model or a model that is provisioned to the UE by the network node NW 1 or NW2 to be used for the positioning measurements, or an untrained AI / ML model that the UE intends to use to perform positioning measurements. The UE uses the values received from the network node as the expected outcome of the AI / ML model to either validate the trained AI / ML model it has or the trained AI / ML model that has been provisioned to UE by the network node or to train the AI / ML model it intends to use for positioning measurements.

[0141] In a first example, the UE already has a trained AI / ML model. The UE uses values received from the network node NW2 as expected outcome of the AI / ML model to validate the performance of the trained model. The UE may receive a list of values that the UE can use to validate its AI / ML model, e.g., for one specific positioning measurements such as RSTD, or the UE may receive a set of values that UE can use to validate its AI / ML model for more than one positioning measurements, such as RSTD and UE Rx-Tx time difference measurement, RSTD and RSCPD measurement, UE Rx-Tx with RS CP measurement, etc.

[0142] In a second example, the UE has an untrained model and makes use of the values received from the network node NW2 as expected outcomes of the AI / ML model to train the AI / ML model stored in the UE or the AI / ML model provisioned by the network node to be used for positioning measurements. The UE in this case trains the AI / ML model to predict the one or more positioning measurements to be within expected outcome as indicated by the network node NW2.

[0143] In a third example, the UE receives a set of values that can be used by the UE to validate its AI / ML model for positioning measurements. The UE may consider one or more values from the set of values provided by the network node NW2 to validate performance of its AI / ML model for positioning measurements. In a fourth example, the UE uses values received from the network node NW2 as expected outcomes of the AI / ML model to validate the performance of the trained model. In this example if the UE determines the AI / ML model or models it has access to cannot achieve the expected outcome set by the network node, the UE may fall back to non-AI / ML method to perform positioning measurements. In this case, the positioning measurements performed or reported by the UE may or may not be the measurements requested by the network node NW2.

[0144] Method in network node to provide assistance data for AI / ML model validation for positioning. According to some embodiments, a network node, e.g., NW2, determines value(s) as expected outcome s of the AI / ML model(s) implemented at the UE, e.g., where the AI / ML model(s) is / are associated with given model ID(s). The network node NW2 can determine values as expected outcomes of the AI / ML models at the UE on a per-positioning-measurement basis or a set of values as expected outcome of the AI / ML models at the UE valid for a set of measurements UE is capable of predicting based on AI / ML models. Examples of such measurements are RSTD, Rx-Tx time difference measurement, RSTD with RSCPD measurement, Rx-Tx time difference with RSCP measurement.

[0145] In one example, a network node NW2 determines the expected outcome of the AI / ML model considering one of the TRPs in the deployment as a reference TRP. The reference TRP being considered by network node NW2 is the same TRP that is indicated as reference TRP in the assistance data provisioned to the UE. In another example, network node NW2 determines a list of expected outcomes of the AI / ML model considering all possible TRPs as reference TRP. In this example if there are N TRPs with single construction model with the same model at all N TRPs in the deployment, the network node NW2 provisions N — 1 sets of expected outcomes to UE.

[0146] In one embodiment, the same model is used at a set of TRPs (for multi -TRP construction model as and a reference TRPs is defined per set of TRPs with the same model. In this case, the model validation for the a given set of TRPs is done using the expected outcome from the corresponding reference TRP.

[0147] Figure 11 and Figure 12 illustrate signaling flows for related methods, i.e., for obtaining expected positioning measurement values for model validation, and for signaling capabilities of training AI / ML models for positioning and receiving validation models or sub-models with expected positioning measurement values for model validation.

[0148] As seen in Figure 11, in one example method, a UE or gNB inquires, of another network node such as an LMF, of expected positioning measurement values for model validation. As also seen in the figure, the other network node, e.g., the LMF, responds by providing positioning measurement values, which may be the expected outcomes of the AI / ML model run by the UE or gNB.

[0149] As seen in Figure 12, in some example methods, a UE or gNB signals another network node, such as an LMF, with information indicating the UE’s or gNB’s capability to train a model with expected measurement values for model validation. In response, the other network node (e.g., the LMF) responds with model validation models or sub-models, with expected positioning measurement values for model validation.

[0150] In view of the detailed examples and explanations provided above, it will be appreciated that the process flow diagram shown in Figure 13 illustrates an example method for validating or training a first Artificial Intelligence / Machine Learning model for positioning a user equipment (UE) in a wireless communications network, as carried out by a first node in a wireless network. The illustrated method, as described below, is intended to be a generalization of the various techniques described above. Thus, where the terminology differs slightly from that used above, the terminology used to describe Figure 13 should be understood to be synonymous with or, more generally, to at least encompass similar terminology used above.

[0151] The method, as shown at blocks 1310 and 1320, comprises the steps of obtaining one or more expected output values for the first AI / ML model and validating the performance of and / or training the AI / ML model, using the expected output values. As was discussed above, this validating comprises comparing the output of the AI / ML model, or some derivative of the output of the AI / ML model, with the expected output values or some derivative of the expected output values. The expected output values may comprise a range of expected output, in some instances or embodiments. In others, the comparison may comprise determining the actual output value (or some derivative of that value) falls within a predetermined tolerance of the expected output value (or derivative of the expected output value), where that predetermined tolerance may be specified by the model and / or by assistance data received from a second node of the wireless network. In some embodiments or instances, the expected output values for the first AI / ML model are generated by a second AI / ML model or sub-model of the first AI / ML, where the second AI / ML model or sub-model of the first AI / ML is trained using input data corresponding to different measurements from measurements used by first AI / ML model for inference or training.

[0152] In some embodiments or instances of the illustrated method, the first node is the UE, and the obtaining step comprises receiving the expected output values from a second node of the wireless network, e.g., a gNB or node implementing an LMF. In other embodiments, the first node is the UE, and the obtaining step comprises receiving a second AI / ML model or sub-model of the first AI / ML, from a second node of the wireless network, and generating the expected output values using the second AI / ML model or sub-model of the first AI / ML. Again, the second node may a gNB or a node implementing an LMF, for example. In any of these or in other embodiments or instances, the obtaining step may be preceded by a step of requesting the expected output values or other positioning assistance from the second node, as shown at block 1305 of Figure 13.

[0153] In some embodiments or instances, the first node, carrying out the illustrated method, is a gNB serving the UE, where the obtaining step comprises receiving the expected output values from a second node of the wireless network other than the UE, such as an LMF. In other embodiments or instances, the first node is the gNB, and the obtaining step comprises receiving a second AI / ML model or sub-model of the first AI / ML, from a second node of the wireless network, e.g., an LMF, and generating the expected output values using the second AI / ML model or sub-model of the first AI / ML. As was the case with the example UE-based embodiments described above, the gNB may request the expected output values or other positioning assistance from the second node, as shown at block 1305 of Figure 13.

[0154] Embodiments of the presently disclosed techniques includes apparatuses, such as UE apparatuses and gNB apparatuses, configured to carry out any of the methods describe above. Below is a detailed description of an example wireless system and several of its nodes - it should be understood that the method described above may be implemented in one or more of these nodes, in various embodiments and instances.

[0155] Figure 14 shows an example of a communication system 1400 in accordance with some embodiments. This provides a context for the techniques described herein, which can be implemented by devices operating in such a communication system 1400. In this example, communication system 1400 includes telecommunication network 1402 that includes access network 1404 (e.g., RAN) and a core network 1406, which includes one or more core network nodes 1408. Access network 1404 includes one or more access network nodes, such as network nodes 1410a-b (one or more of which may be generally referred to as network nodes 1410), or any other similar 15GPP access node or non-3GPP access point. Network nodes 1410 facilitate direct or indirect connection of UEs, such as by connecting UEs 1412a-d (one or more of which may be generally referred to as UEs 1412) to core network 1406 over one or more wireless connections.

[0156] 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, communication system 1400 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. Communication system 1400 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0157] UEs 1412 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with network nodes 1410 and other communication devices. Similarly, network nodes 1410 are arranged, capable, configured, and / or operable to communicate directly or indirectly with UEs 1412 and / or with other network nodes or equipment in telecommunication network 1402 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in telecommunication network 1402.

[0158] In the depicted example, core network 1406 connects network nodes 1410 to one or more hosts, such as host 1416. 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. Core network 1406 includes one or more core network nodes (e.g., 1408) 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 core network node 1408. 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 Deconcealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0159] Host 1416 may be under the ownership or control of a service provider other than an operator or provider of access network 1404 and / or telecommunication network 1402, and may be operated by the service provider or on behalf of the service provider. Host 1416 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.

[0160] As a whole, communication system 1400 of Figure 14 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.

[0161] In some examples, telecommunication network 1402 is a cellular network that implements 15GPP standardized features. Accordingly, telecommunication network 1402 may support network slicing to provide different logical networks to different devices that are connected to telecommunication network 1402. For example, telecommunication network 1402 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.

[0162] In some examples, UEs 1412 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to access network 1404 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from access network 1404. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e., being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0163] In the example, hub 1414 communicates with access network 1404 to facilitate indirect communication between one or more UEs (e.g., UE 1412c and / or 1412d) and network nodes (e.g., network node 1410b). In some examples, hub 1414 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, hub 1414 may be a broadband router enabling access to core network 1406 for the UEs. As another example, hub 1414 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 1410, or by executable code, script, process, or other instructions in hub 1414. As another example, hub 1414 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, hub 1414 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, hub 1414 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which hub 1414 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, hub 1414 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.

[0164] Hub 1414 may have a constant / persistent or intermittent connection to network node 1410b. Hub 1414 may also allow for a different communication scheme and / or schedule between hub 1414 and UEs (e.g., UE 1412c and / or 1412d), and between hub 1414 and core network 1406. In other examples, hub 1414 is connected to core network 1406 and / or one or more UEs via a wired connection. Moreover, hub 1414 may be configured to connect to an M2M service provider over access network 1404 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with network nodes 1410 while still connected via hub 1414 via a wired or wireless connection. In some embodiments, hub 1414 may be a dedicated hub - that is, a hub whose primary function is to route communications to / ffom the UEs from / to network node 1410b. In other embodiments, hub 1414 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1410b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0165] Figure 15 shows a UE 1500 in accordance with some embodiments. 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 15GPP, including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0166] A UE may support device-to-device (D2D) communication, for example by implementing a 15GPP 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).

[0167] UE 1500 includes processing circuitry 1502 that is operatively coupled via bus 1504 to input / output interface 1506, power source 1508, memory 1510, communication interface 1512, and possibly other components not explicitly shown. Certain UEs may utilize all or a subset of the components shown in Figure 15. 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.

[0168] Processing circuitry 1502 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 memory 1510. Processing circuitry 1502 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, processing circuitry 1502 may include multiple central processing units (CPUs).

[0169] In the example, input / output interface 1506 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 UE 1500. 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.

[0170] In some embodiments, power source 1508 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. Power source 1508 may further include power circuitry for delivering power from power source 1508 itself, and / or an external power source, to the various parts of UE 1500 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging power source 1508. Power circuitry may perform any formatting, converting, or other modification to the power from power source 1508 to make the power suitable for the respective components of UE 1500 to which power is supplied. Memory 1510 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 readonly 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, memory 1510 includes one or more application programs 1514, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1516. Memory 1510 may store, for use by UE 1500, any of a variety of various operating systems or combinations of operating systems.

[0171] Memory 1510 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.’ Memory 1510 may allow UE 1500 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 memory 1510, which may be or comprise a device-readable storage medium.

[0172] Processing circuitry 1502 may be configured to communicate with an access network or other network using communication interface 1512. Communication interface 1512 may comprise one or more communication subsystems and may include or be communicatively coupled to antenna 1522. Communication interface 1512 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 transmitter 1518 and / or receiver 1520 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, transmitter 1518 and receiver 1520 may be coupled to one or more antennas (e.g., 1522) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0173] In the illustrated embodiment, communication functions of communication interface 1512 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 (W CDMA), 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.

[0174] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1512, 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 19 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., an alert is sent when moisture is detected), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0175] 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.

[0176] 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 item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to UE 1500 shown in Figure 15.

[0177] 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 15GPP context be referred to as an MTC device. As one particular example, the UE may implement the 15GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0178] 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.

[0179] Figure 16 shows a network node 1600 in accordance with some embodiments. Examples of network nodes include, but are not limited to, access points (e.g., radio access points) and base stations (e.g., radio base stations, Node Bs, eNBs, and gNBs).

[0180] 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).

[0181] Other examples of network nodes include multiple transmission point (multi-TRP) 17G 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).

[0182] Network node 1600 includes processing circuitry 1602, memory 1604, communication interface 1606, and power source 1608. Network node 1600 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which network node 1600 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, network node 1600 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1604 for different RATs) and some components may be reused (e.g., a same antenna 1610 may be shared by different RATs). Network node 1600 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1600, 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 1600.

[0183] Processing circuitry 1602 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 1600 components, such as memory 1604, to provide network node 1600 functionality.

[0184] In some embodiments, processing circuitry 1602 includes a system on a chip (SOC). In some embodiments, processing circuitry 1602 includes one or more of radio frequency (RF) transceiver circuitry 1612 and baseband processing circuitry 1614. In some embodiments, RF transceiver circuitry 1612 and baseband processing circuitry 1614 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 1612 and baseband processing circuitry 1614 may be on the same chip or set of chips, boards, or units.

[0185] Memory 1604 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non- transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by processing circuitry 1602. Memory 1604 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 (collectively denoted computer program product 1604a) capable of being executed by processing circuitry 1602 and utilized by network node 1600. Memory 1604 may be used to store any calculations made by processing circuitry 1602 and / or any data received via communication interface 1606. In some embodiments, processing circuitry 1602 and memory 1604 is integrated.

[0186] Communication interface 1606 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, communication interface 1606 comprises port(s) / terminal(s) 1616 to send and receive data, for example to and from a network over a wired connection. Communication interface 1606 also includes radio front-end circuitry 1618 that may be coupled to, or in certain embodiments a part of, antenna 1610. Radio front-end circuitry 1618 comprises filters 1620 and amplifiers 1622. Radio front-end circuitry 1618 may be connected to antenna 1610 and processing circuitry 1602. The radio front-end circuitry may be configured to condition signals communicated between antenna 1610 and processing circuitry 1602. Radio frontend circuitry 1618 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. Radio front-end circuitry 1618 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1620 and / or amplifiers 1622. The radio signal may then be transmitted via antenna 1610. Similarly, when receiving data, antenna 1610 may collect radio signals which are then converted into digital data by radio front-end circuitry 1618. The digital data may be passed to processing circuitry 1602. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0187] In certain alternative embodiments, network node 1600 does not include separate radio front-end circuitry 1618, instead, processing circuitry 1602 includes radio front-end circuitry and is connected to antenna 1610. Similarly, in some embodiments, all or some of RF transceiver circuitry 1612 is part of communication interface 1606. In still other embodiments, communication interface 1606 includes one or more ports or terminals 1616, radio front-end circuitry 1618, and RF transceiver circuitry 1612, as part of a radio unit (not shown), and communication interface 1606 communicates with the baseband processing circuitry 1614, which is part of a digital unit (not shown).

[0188] Antenna 1610 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. Antenna 1610 may be coupled to radio front-end circuitry 1618 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, antenna 1610 is separate from network node 1600 and connectable to network node 1600 through an interface or port.

[0189] Antenna 1610, communication interface 1606, and / or processing circuitry 1602 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, antenna 1610, communication interface 1606, and / or processing circuitry 1602 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.

[0190] Power source 1608 provides power to the various components of network node 1600 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). Power source 1608 may further comprise, or be coupled to, power management circuitry to supply the components of network node 1600 with power for performing the functionality described herein. For example, network node 1600 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 power source 1608. As a further example, power source 1608 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.

[0191] Embodiments of network node 1600 may include additional components beyond those shown in Figure 16 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, network node 1600 may include user interface equipment to allow input of information into network node 1600 and to allow output of information from network node 1600. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for network node 1600.

[0192] Figure 17 is a block diagram of a host 1700, which may be an embodiment of host 1416 of Figure 14, in accordance with various aspects described herein. Host 1700 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud- implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. Host 1700 may provide one or more services to one or more UEs. Host 1700 includes processing circuitry 1702 that is operatively coupled via a bus 1704 to an input / output interface 1706, a network interface 1708, a power source 1710, and a memory 1712. 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 15 and 16, such that the descriptions thereof are generally applicable to the corresponding components of host 1700.

[0193] Memory 1712 may include one or more computer programs including one or more host application programs 1714 and data 1716, which may include user data, e.g., data generated by a UE for host 1700 or data generated by host 1700 for a UE. Embodiments of host 1700 may utilize only a subset or all of the components shown. Host application programs 1714 may be implemented in a containerbased 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). Host application programs 1714 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, host 1700 may select and / or indicate a different host for over-the-top services for a UE. Host application programs 1714 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.

[0194] Figure 18 is a block diagram illustrating a virtualization environment 1800 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1800 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0195] Applications 1802 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1800 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0196] Hardware 1804 includes processing circuitry, memory that stores software and / or instructions (collectively denoted computer program product 1804a) 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 1806 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1808a-b (one or more of which may be generally referred to as VMs 1808), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1806 may present a virtual operating platform that appears like networking hardware to VMs 1808.

[0197] VMs 1808 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1806. Different embodiments of the instance of a virtual appliance 1802 may be implemented on one or more of VMs 1808, and the implementations may differ. 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.

[0198] In the context of NFV, each VM 1808 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 VMs 1808, and that part of hardware 1804 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 1808 on top of hardware 1804 and corresponds to application 1802.

[0199] Hardware 1804 may be implemented in a standalone network node with generic or specific components. Hardware 1804 may implement some functions via virtualization. Alternatively, hardware 1804 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1810, which, among others, oversees lifecycle management of applications 1802. In some embodiments, hardware 1804 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 control system 1812 which may alternatively be used for communication between hardware nodes and radio units.

[0200] Figure 19 shows a communication diagram of a host 1902 communicating via a network node 1904 with a UE 1906 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 1412a of Figure 14 and / or UE 1500 of Figure 15), network node (such as network node 1410a of Figure 14 and / or network node 1600 of Figure 16), and host (such as host 1416 of Figure 14 and / or host 1700 of Figure 17) discussed in the preceding paragraphs will now be described with reference to Figure 19.

[0201] Like host 1700, embodiments of host 1902 include hardware, such as a communication interface, processing circuitry, and memory. Host 1902 also includes software, which is stored in or accessible by host 1902 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 UE 1906 connecting via an over-the- top (OTT) connection 1950 extending between UE 1906 and host 1902. In providing the service to the remote user, a host application may provide user data which is transmitted using OTT connection 1950.

[0202] Network node 1904 includes hardware enabling it to communicate with host 1902 and UE 1906. Connection 1960 may be direct or pass through a core network (like core network 1406 of Figure 14) 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.

[0203] UE 1906 includes hardware and software, which is stored in or accessible by UE 1906 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 1906 with the support of host 1902. In host 1902, an executing host application may communicate with the executing client application via OTT connection 1950 terminating at UE 1906 and host 1902. 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. OTT connection 1950 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 OTT connection 1950.

[0204] OTT connection 1950 may extend via a connection 1960 between host 1902 and network node 1904 and via wireless connection 1970 between network node 1904 and UE 1906 to provide the connection between host 1902 and UE 1906. Connection 1960 and wireless connection 1970, over which OTT connection 1950 may be provided, have been drawn abstractly to illustrate the communication between host 1902 and UE 1906 via network node 1904, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0205] As an example of transmitting data via OTT connection 1950, in step 1908, host 1902 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 UE 1906. In other embodiments, the user data is associated with a UE 1906 that shares data with host 1902 without explicit human interaction. In step 1910, host 1902 initiates a transmission carrying the user data towards UE 1906. Host 1902 may initiate the transmission responsive to a request transmitted by UE 1906. The request may be caused by human interaction with UE 1906 or by operation of the client application executing on UE 1906. The transmission may pass via network node 1904, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1912, network node 1904 transmits to UE 1906 the user data that was carried in the transmission that host 1902 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1914, UE 1906 receives the user data carried in the transmission, which may be performed by a client application executed on UE 1906 associated with the host application executed by host 1902.

[0206] In some examples, UE 1906 executes a client application which provides user data to host 1902. The user data may be provided in reaction or response to the data received from host 1902. Accordingly, in step 1916, UE 1906 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 UE 1906. Regardless of the specific manner in which the user data was provided, UE 1906 initiates, in step 1918, transmission of the user data towards host 1902 via network node 1904. In step 1920, in accordance with the teachings of the embodiments described throughout this disclosure, network node 1904 receives user data from UE 1906 and initiates transmission of the received user data towards host 1902. In step 1922, host 1902 receives the user data carried in the transmission initiated by UE 1906.

[0207] One or more of the various embodiments improve the performance of OTT services provided to UE 1906 using OTT connection 1950, in which wireless connection 1970 forms the last segment. More precisely, embodiments can reduce and / or prevent undesired recovery actions by UEs. For example, due to the conditions for considering an LTM cell switch procedure successful (causing UE to stop a supervision timer), undesired recovery actions due to supervision timer expiration are prevented at the UE. This is especially an issue in the scenarios where LTM cell switch needs to be performed without a RA procedure (i.e., “RACH-less”). Preventing undesired recovery actions makes LTM RACH-less solutions more efficient, which reduces the delay to access an LTM candidate cell. Embodiments can facilitate predictable UE behavior in LTM execution failures and can reduce and / or eliminate ambiguity for UE actions in the event of LTM failures that are concurrent other failures such as radio link failure (RLF). By improving operation of UEs and RANs in this manner, embodiments increase the value of OTT services delivered to / from the UE via the RAN, to both end users and service providers.

[0208] In an example scenario, factory status information may be collected and analyzed by host 1902. As another example, host 1902 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, host 1902 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, host 1902 may store surveillance video uploaded by a UE. As another example, host 1902 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, host 1902 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.

[0209] 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 OTT connection 1950 between host 1902 and UE 1906, 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 host 1902 and / or UE 1906. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which OTT connection 1950 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 OTT connection 1950 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of network node 1904. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by host 1902. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using OTT connection 1950 while monitoring propagation times, errors, etc.

[0210] The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. Various embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art.

[0211] The term unit, as used herein, can have conventional meaning in the field of electronics, electrical devices and / or electronic devices and can include, for example, electrical and / or electronic circuitry, devices, modules, processors, memories, logic solid state and / or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and / or displaying functions, and so on, as such as those that are described herein.

[0212] Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special-purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and / or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according to one or more embodiments of the present disclosure.

[0213] As described herein, device and / or apparatus can be represented by a semiconductor chip, a chipset, or a (hardware) module comprising such chip or chipset; this, however, does not exclude the possibility that a functionality of a device or apparatus, instead of being hardware implemented, be implemented as a software module such as a computer program or a computer program product comprising executable software code portions for execution or being run on a processor. Furthermore, functionality of a device or apparatus can be implemented by any combination of hardware and software. A device or apparatus can also be regarded as an assembly of multiple devices and / or apparatuses, whether functionally in cooperation with or independently of each other. Moreover, devices and apparatuses can be implemented in a distributed fashion throughout a system, so long as the functionality of the device or apparatus is preserved. Such and similar principles are considered as known to a skilled person.

[0214] Furthermore, functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and / or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.

[0215] In addition, certain terms used in the present disclosure, including the specification, drawings and embodiments thereof, can be used synonymously in certain instances, including, but not limited to, e.g., data and information. It should be understood that, while these words and / or other words that can be synonymous to one another, can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.

[0216] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0217] In addition, certain terms used in the present disclosure, including the specification and drawings, can be used synonymously in certain instances (e.g., “data” and “information”). It should be understood, that although these terms (and / or other terms that can be synonymous to one another) can be used synonymously herein, there can be instances when such words can be intended to not be used synonymously.

[0218] EXAMPLE EMBODIMENTS

[0219] Embodiments of the techniques, apparatuses, and systems described herein include, but are not limited to, the following enumerated examples:

[0220] 1. A method, in a first node in a wireless network, for validating or training a first Artificial Intelligence / Machine Learning model for positioning a user equipment (UE), the method comprising: obtaining one or more expected output values for the first AI / ML model; and validating the performance of and / or training the AI / ML model, using the expected output values.

[0221] 2. The method of example embodiment 1, wherein the expected output values for the first AI / ML model are generated by a second AI / ML model or sub-model of the first AI / ML, and wherein the second AI / ML model or sub-model of the first AI / ML is trained using input data corresponding to different measurements from measurements used by first AI / ML model for inference or training. 3. The method of example embodiment 1 or 2, wherein the first node is the UE, and wherein said obtaining comprises receiving the expected output values from a second node of the wireless network.

[0222] 4. The method of example embodiment 1 or 2, wherein the first node is the UE, and wherein said obtaining comprises receiving a second AI / ML model or sub-model of the first AI / ML, from a second node of the wireless network, and generating the expected output values using the second AI / ML model or sub-model of the first AI / ML.

[0223] 5. The method of example embodiment 3 or 4, wherein the second node is a gNB or a node implementing a Location Management Eunction (LMF).

[0224] 6. The method of any of example embodiments 3-5, wherein the method comprises requesting the expected output values or other positioning assistance from the second node.

[0225] 7. The method of example embodiment 1 or 2, wherein the first node is a gNB serving the UE, and wherein said obtaining comprises receiving the expected output values from a second node of the wireless network other than the UE.

[0226] 8. The method of example embodiment 1 or 2, wherein the first node is the gNB, and wherein said obtaining comprises receiving a second AI / ML model or sub-model of the first AI / ML, from a second node of the wireless network, and generating the expected output values using the second AI / ML model or sub-model of the first AI / ML.

[0227] 9. The method of example embodiment 7 or 8, wherein the second node is a node implementing a Location Management Function (LMF).

[0228] 10. The method of any of example embodiments 7-9, wherein the method comprises requesting the expected output values or other positioning assistance from the second node.

[0229] 11. An apparatus for use as a node in a wireless network, the apparatus comprising being adapted to carry out a method according to any one of example embodiments 1-10.

[0230] 12. An apparatus for use as a node in a wireless network, the apparatus comprising: interface circuitry configured to communicate with one or more other nodes in the wireless network; and processing circuitry operatively coupled to the interface circuitry and configured to carry out a method according to any one of example embodiments 1-10.

[0231] 13. A computer program product comprising computer-executable instructions configured so that, when executed by processing circuitry of a node in a wireless network, the instructions cause the node to perform operations corresponding to any of the methods of example embodiments 1-10.

[0232] 14. A computer-readable medium comprising, stored thereupon, a computer program product according to example embodiment 13.

[0233] REFERENCES

[0234] Technical Specification Group Radio Access Network; Study on Artificial Intelligence (AI)ZMachine Learning (ML) for NR air interface (Release 18), 3GPP TR 38.843, Dec. 2023. Ferrand, Paul, Maxime Guillaud, Christoph Studer, and Olav Tirkkonen. "Wireless Channel Charting: Theory, Practice, and Applications." IEEE Communications Magazine 61, no. 6 (2023): 124-130.

Claims

CLAIMS1. A method, in a first node in a wireless network, for validating or training a first Artificial Intelligence / Machine Learning, AI / ML, model for positioning a user equipment, UE, the method comprising: obtaining (1310) one or more expected output values for the first AI / ML model; and validating (1320) the performance of and / or training the AI / ML model, using the expected output values.

2. The method of claim 1, wherein the expected output values for the first AI / ML model are generated by a second AI / ML model or sub-model of the first AI / ML, and wherein the second AI / ML model or sub-model of the first AI / ML is trained using input data corresponding to different measurements from measurements used by first AI / ML model for inference or training.

3. The method of claim 1 or 2, wherein the first node is the UE, and wherein said obtaining (1310) comprises receiving the expected output values from a second node of the wireless network.

4. The method of claim 1 or 2, wherein the first node is the UE, and wherein said obtaining (1310) comprises receiving a second AI / ML model or sub-model of the first AI / ML, from a second node of the wireless network, and generating the expected output values using the second AI / ML model or sub-model of the first AI / ML.

5. The method of claim 3 or 4, wherein the second node is a gNB or a node implementing a Location Management Eunction, LMF.

6. The method of any of claims 3-5, wherein the method comprises requesting (1305) the expected output values or other positioning assistance from the second node.

7. The method of claim 1 or 2, wherein the first node is a gNB serving the UE, and wherein said obtaining (1310) comprises receiving the expected output values from a second node of the wireless network other than the UE.

8. The method of claim 1 or 2, wherein the first node is the gNB, and wherein said obtaining (1310) comprises receiving a second AI / ML model or sub-model of the first AI / ML, from a second node of the wireless network, and generating the expected output values using the second AI / ML model or sub-model of the first AI / ML.

9. The method of claim 7 or 8, wherein the second node is a node implementing a Location Management Function, LMF.

10. The method of any of claims 7-9, wherein the method comprises requesting (1305) the expected output values or other positioning assistance from the second node.

11. An apparatus (1500, 1600) for use as a node in a wireless network, the apparatus comprising being adapted to carry out a method according to any one of claims 1-10.

12. An apparatus (1500, 1600) for use as a first node in a wireless network, the apparatus (1500, 1600) comprising: interface circuitry (1512, 1606) configured to communicate with one or more other nodes in the wireless network; and processing circuitry (1502, 1602) operatively coupled to the interface circuitry (1512, 1606) and configured to obtain one or more expected output values for the first AI / ML model and validate the performance of and / or training the AI / ML model, using the expected output values.

13. The apparatus (1500, 1600) of claim 12, wherein the expected output values for the first AI / ML model are generated by a second AI / ML model or sub-model of the first AI / ML, and wherein the second AI / ML model or sub-model of the first AI / ML is trained using input data corresponding to different measurements from measurements used by first AI / ML model for inference or training.

14. The apparatus (1500, 1600) of claim 12 or 13, wherein the first node is a user equipment, UE, and wherein the processing circuitry (1502, 1602) is configured to receive the expected output values from a second node of the wireless network, via the interface circuitry (1512, 1606).

15. The apparatus (1500, 1600) of claim 12 or 13, wherein the first node is a user equipment, UE, and wherein the processing circuitry (1502, 1602) is configured to receive a second AI / ML model or submodel of the first AI / ML via the interface circuitry (1512, 1606), from a second node of the wireless network, and to generate the expected output values using the second AI / ML model or sub-model of the first AI / ML.

16. The apparatus (1500, 1600) of claim 14 or 15, wherein the second node is a gNB or a node implementing a Location Management Function, LMF.

17. The apparatus (1500, 1600) of any of claims 14-16, wherein the processing circuitry (1502, 1602) is configured to request the expected output values or other positioning assistance from the second node, via the interface circuitry (1512, 1606).

18. The apparatus (1500, 1600) of claim 12 or 13, wherein the first node is a gNB serving the UE, and wherein the processing circuitry (1502, 1602) is configured to receive the expected output values from a second node of the wireless network other than the UE, via the interface circuitry (1512, 1606).

19. The apparatus (1500, 1600) of claim 12 or 13, wherein the first node is the gNB, and wherein the processing circuitry (1502, 1602) is configured to receive a second AI / ML model or sub-model of the first AI / ML from a second node of the wireless network, via the interface circuitry (1512, 1606), and to generate the expected output values using the second AI / ML model or sub-model of the first AI / ML.

20. The apparatus (1500, 1600) of claim 18 or 19, wherein the second node is a node implementing a Location Management Eunction, LMF.

21. The apparatus (1500, 1600) of any of claims 18-20, wherein the processing circuitry (1502, 1602) is configured to request the expected output values or other positioning assistance from the second node, via the interface circuitry (1512, 1606).

22. A computer program product comprising computer-executable instructions configured so that, when executed by processing circuitry (1502, 1602) of a node in a wireless network, the instructions cause the node to perform operations corresponding to any of the methods of claims 1-10.

23. A computer-readable medium comprising, stored thereupon, a computer program product according to claim 13.

24. The computer-readable medium of claim 23, wherein the computer-readable medium is non- transitory.

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