Methods to identify valid data samples for ai / ML model training for positioning

Abstract

Various embodiments provide for a method for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning (AI / ML) model. A signaling mechanism can be provided where a network node specifies which positioning measurements data should be collected for training purposes and which should be discarded by providing the criteria for data collection to UEs or gNBs. A first network node can determine a tolerance for ground truth labeling (512), and then based on the tolerance, determine one or more reliability criteria for the positioning data (514). The first network node can provide the request for positioning data and reliability criteria to a second network node (516), which can collect the positioning data based on the criteria (518), and then provide the collected positioning data to the first network node (522), which can then use the positioning data as training data.

Description

METHODS TO IDENTIFY VALID DATA SAMPLES FOR AI / ML MODEL TRAINING FOR POSITIONINGRELATED APPLICATIONS

[0001] This application claims the benefit of provisional patent application serial number 63 / 570,445, filed March 27, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates to methods for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning (AI / ML) model in a wireless communication system.BACKGROUND

[0003] Artificial intelligence (Al) or machine learning (ML) technique comprises of one or more algorithms, which use a set of data as input for training one or more AI / ML models. The output of the AI / ML model is used by the device (e.g. user equipment (UE), base station (BS) or another node) for performing certain operations or taking certain decisions (e.g. handover etc.) fully or partially based on the prediction, which in turn depends on the trained model. The AI / ML model can be trained in the device online (or on-the-fly while processing the data) or offline in the background. More specifically:• Online training is an AI / ML training process where the model being used for inference is (typically continuously) trained in (near) real-time with the arrival of new training samples or data.• Offline training is an AI / ML training process where the model is trained based on collected samples or data, and where the trained model is later used or delivered for inference.

[0004] 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.

[0005] 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:• Case I: UE-side (AI / ML) model. It is an AI / ML model whose inference is performed entirely at the UE.• Case II: Network-side (AI / ML) model. It is an AI / ML model whose inference is performed entirely at the network.• Case III: One-sided (AI / ML) model. It is a UE-side (AI / ML) model or a Network-side (AI / ML) model.• Case IV: Two-sided (AI / ML) model. It is a paired AI / ML model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network, i.e., the first part of inference is firstly performed by UE and then the remaining part is performed by gNB, or vice versa.

[0006] 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.

[0007] The term lifecycle management (LCM) of an AI / ML model refers to the process of developing, deploying and maintaining the AI / ML model. An AI / ML model training pipeline includes several processing stages as gathering unprocessed input data from data repositories (data ingestion), finding high-quality input features (data pre-processing), finding the optimal mapping of the model input features to a desired model output target in a sense determined by a loss function (model training), evaluate model performance on unseen data from a functional level as well as from a system level when relevant (model evaluation). The training pipeline typically ends with a model registration stage, which may comprise of operations to make the ML model runnable via compilation to a specific HW and of steps like versioning and packaging of the model so that it can be executed. An example of the AI / ML model training pipeline illustrating different stages is shown in Figure 1.

[0008] According to Technical Specification Group Radio Access Network; Study on Artificial Intelligence (AI) / Machine Learning (ML) for NR air interface (Release 18), 3GPP TR 38.843, Dec. 2023, the LCM covers the following components:• Data collection• Model training• Functionality / model identification• Model inference• Functionality / model selection, activation, deactivation, switching, and fallback operation• Functionality / model monitoring• Model update• UE capability

[0009] In functionality-based LCM, network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signaling (e.g.,Radio Resource Control (RRC), Medium Access Control (MAC) Control Element (MAC-CE), or Downlink Control Information (DCI)). Models may not be identified at the Network (NW) and UE may perform model-level LCM. 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).

[0010] In model-based LCM, models are identified at the NW and NW / UE may activate / deactivate / select / switch individual AI / ML models vis model ID. A model may be associated with specific configurations / conditions and additional conditions (e.g., scenarios, sites, datasets) may be determined / identified between UE and NW.

[0011] 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.

[0012] AI / ML models can be used for UE positioning. A UE or a gNB, depending on capability, can have a trained model stored inside the device, or have an untrained AI / ML that can be trained on-the-fly to either produce measurements that are required to localize a UE within a radio access network (RAN) coverage area or directly predict / determine the UE location by exploiting the measurements performed by the UE or gNB on reference signals such as positioning reference signal (PRS), sounding reference signal (SRS) etc. within a RAN coverage area.

[0013] Measurements predicted / determined by the UE by exploiting an AI / ML model can be defined as, but not limited to• RSTD: It is Reference Signal Time Difference between the positioning node j and the reference positioning node i. It is measured on the DL PRS signals and always involves two cells (cell is interchangeably called as TRP).• UE Rx-Tx time difference: It is defined as TUE-RX -TUE-TX.• Where: o TUE-RX 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.

[0014] Measurements predicted / determined by the gNB by exploiting an AI / ML model can be defined as, but not limited to• gNB Rx-Tx time difference: It is defined as T§NB-RX - TgNB-rx.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-rx is the positioning node transmit timing of downlink subframe #j that is closest in time to the subframe #i received from the UE.• Timing advance (TADV): It is defined as the time difference TADV = (TgNB-Rx - TgNB-rx), Where: o TgNB-Rx is the Transmission and Reception Point (TRP) received timing of uplink subframe #i containing PRACH transmitted from UE, defined by the first detected path in time. o TgNB-rx 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.• 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.

[0015] In addition to these, UE or gNB may also perform power measurements such as reference signal received power (RSRP) and / or reference signal received path power (RSRPP). These measurements can be performed on reference signals such as PRS and SRS

[0016] Depending on the capability, a UE may also perform positioning measurements on the sidelink (SL) resources by exploiting AI / ML model, e.g. on the SL-PRS transmitted betweenthe target UE and one or more assisting or anchor UEs. The UE performing the positioning measurement is called as a target UE and the UE(s) assisting the target UE to perform the SL positioning measurements is called as the anchor or assisting UE.

[0017] In Assisted Positioning mode, the positioning measurements are performed by UE / gNB and used as input for AI / ML model reside at UE / gNB. After completion, the output (including measurement results) is then reported to the location server. The location server upon receiving AI / ML model output (including measurement results) determines the location of the UE within the RAN coverage area. The location server depending on the need may forward the UE location to another node within the network to facilitate provisioning of UE location information to the application layer or the third party that is interested or has requested the positioning of UE within the RAN coverage area for further action to be taken.• AI / ML model output: new measurement and / or enhancement of existing measurement• e.g., LOS / NLOS identification, timing and / or angle of measurement, likelihood of measurement

[0018] In direct positioning mode, the measurements performed by UE / gNB are input for positioning engine or the AI / ML model to predict or determine the UE location. AI / ML model resides within the UE node or the gNB node or the location server. The location server depending on the need may forward the UE location to another node within the network to facilitate provisioning of UE location information to the application layer or the third party that is interested or has requested the positioning of UE within the RAN coverage area for further action to be taken.• AI / ML model output: UE location• e.g., fingerprinting based on channel observation as the input of AI / ML model

[0019] In Positioning Measurement Procedure (PMP), the process includes 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 called 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.

[0020] To perform these measurements UE may make use of a trained model acquired before being deployed or 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 the cases, UE requires assistance information / data from the network to determine or identify how and whento train its AI / ML model and what information (positioning measurements or estimated position) to report to the network node such as location server.

[0021] Before Rel. 16, LTE based positioning was one the prevalent RAT based positioning solutions available. Starting from Rel. 16 specification, positioning is also supported in New Radio (NR). Positioning in NR is supported by the architecture shown in Figure 3. The interactions between the eNodeB 304-2 and the gNodeB 304-1 (referred to herein collectively as “base station device 304”) and the UE 306 is supported via the RRC protocol, while the location node interfaces with the UE via the LTE positioning protocol (LPP). LPP is a common protocol to both NR and LTE. Location Management Function (LMF) 302 is the location node in NR. There are also interactions between the LMF 302 and the base station device 304 via the NRPositioning Protocol a (NRPPa) protocol.

[0022] The positioning architecture in Figure 3 will also be used to support AI / ML based positioning. Rel. 19 work on introducing AI / ML based positioning will not only exploit the legacy protocol but will also rely on already defined / existing reference signals that are used for positioning.

[0023] A positioning output is provided by below IE TS 37.355 v 18.0.0:- ASN1 STARTEllipsoidPointWithAltitudeAndUncertaintyEllipsoid ::= SEQUENCE { latitudeSign ENUMERATED {north, south}, degreesLatitude INTEGER (0..8388607), - 23 bit field degreesLongitude INTEGER (-8388608..8388607), - 24 bit field altitudeDirection ENUMERATED {height, depth}, altitude INTEGER (0..32767), - 15 bit field uncertainty SemiMaj or INTEGER (0..127), uncertainty S emiMinor INTEGER (0..127), orientationMaj orAxis INTEGER (0..179), uncertainty Altitude INTEGER (0..127), confidence INTEGER (0..100)- ASN1STOP

[0024] A positioning Quality of Service (QoS) is typically defined by positioning accuracy and latency.

[0025] Integrity is the measure of trust that can be placed in the correctness of information supplied by a navigation / location system. For example, integrity includes the ability of a system to provide timely warnings to user receivers in case of a failure. An example of a failure can be taken from RAT independent positioning method such as Assisted GNSS: If a satellite is malfunctioning, it should be detected by the system and should be informed to the user saying do not use this satellite.

[0026] Example use cases and scenarios: Any use-case related to positioning in Ultra Reliable Low Latency Communication (URLLC) naturally requires high integrity performance. Some examples use-cases comprise V2X, autonomous driving, UAV (drones), eHealth, rail and maritime, emergency and mission critical. In use-cases in which large errors can lead to serious consequences, such as health-related impacts, wrong legal decisions or wrong charge computation, etc., the integrity reporting becomes crucial.

[0027] Integrity KPIs and events

[0028] There are below few examples Integrity KPIs defined that can help us in identifying different integrity events.

[0029] Alert Limit (AL): The maximum allowable positioning error for the purpose of integrity. If the positioning error is beyond this limit, the integrity results of the calculated location may not meet the integrity requirement.

[0030] Time to Alert (TTA): is the maximum allowable elapsed time from the onset of a positioning failure until the equipment announces the alert.

[0031] Integrity Risk (IR): is the maximum probability of providing a signal that is out of tolerance without warning the user in a given period of time.

[0032] Positioning integrity: A measure of the trust in the accuracy of the position-related data and the ability to provide associated alerts.

[0033] Protection Level (PL): A statistical upper-bound of the Positioning Error (PE) that ensures that, the probability per unit of time of the true error being greater than the AL and the PL being less than or equal to the AL, for longer than the TTA, is less than the TIR, i.e., the PL satisfies the following inequality:Prob per unit of time [(fPE>AL) & (PL<=AL)) for longer than TTA < TIR NOTE 1 : the PL inequality is valid for all values of the AL.NOTE 2: the TIR may correspond to the achievable TIR in the case that the requested TIR cannot be satisfied.

[0034] When the PL bounds the positioning error in the horizontal plane or on the vertical axis then it is called Horizontal Protection Level (HPL) or Vertical Protection Level (VPL)respectively. A specific equation for the PL is not specified as this is implementation-defined. For the PL to be considered valid, it must simply satisfy the inequality above.

[0035] Figure 4 shows an example with the Stanford plot in which all the possible integrity operation and events can be explained in its different regions.

[0036] Nominal Operation is when PE < PL < AL

[0037] System unavailable is when AL < PL

[0038] Misleading Operation is when PL < PE

[0039] Hazardously Operation is when PL < AL < PE

[0040] Integrity Failure is an integrity event that lasts for longer than the TTA and with no alarm raised within the TTA.

[0041] Misleading Information (MI) is an integrity event occurring when, being the system declared available, the position error exceeds the protection level but not the alert limit.

[0042] Hazardously Misleading Information (HMI) is an integrity event occurring when, being the system declared available, the position error exceeds the alert limit.SUMMARY

[0043] Various embodiments provide for a method for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning (AI / ML) model. A signaling mechanism can be provided where a network node specifies which positioning measurements data should be collected for training purposes and which should be discarded by providing the criteria for data collection to UEs or gNBs. Similarly, UEs or gNBs may request that it needs data collection with certain quality related criteria. A first network node can determine a tolerance for ground truth labeling, and then based on the tolerance, determine one or more reliability criteria for the positioning data. The first network node can provide the request for positioning data and reliability criteria to a second network node, which can collect the positioning data based on the criteria, and then provide the collected positioning data to the first network node, which can then use the positioning data as training data. The first network node and the second network node can be a UE, base station device, or a Location Management Function (LMF).

[0044] In an embodiment, a method performed by a second network node for facilitating collecting and using positioning data as training data to train an AI / ML model can include receiving, from a first network node, one or more reliability criteria for positioning data and collecting positioning data that satisfies the one or more reliability criteria, resulting in collected positioning data.

[0045] In another embodiment, the one or more reliability criteria are associated with a confidence level of the positioning data.

[0046] In another embodiment, the first network node is at least one of a base station, a User Equipment (UE) or a Location Management Function (LMF) and wherein the second network node is at least one of base station, UE, or LMF, and wherein the first network node and the second network node are different types.

[0047] In another embodiment, the one or more reliability criteria are determined based on a predefined configuration at one of the first network node or the second network node.

[0048] In another embodiment, the one or more reliability criteria define limits associated with an alert limit, a maximum positioning error, an uncertainty level and a confidence level of the positioning data.

[0049] In another embodiment, the alert limit, the maximum positioning error, the uncertainty level and the confidence level are based on a geographical area description associated with the positioning data.

[0050] In another embodiment, the one or more reliability criteria are based on measurement results obtained from uplink measurements from a UE with a known location.

[0051] In another embodiment, he one or more reliability criteria are based on measurement results obtained from downlink measurements at a UE with a known location.

[0052] In another embodiment, the UE is a positioning reference unit (PRU).

[0053] In another embodiment, the method can include obtaining the measurement results from the UE with the known location, wherein a difference between the measurement results from the UE and uplink measurement results from the second network node is less than predefined threshold.

[0054] In an embodiment, the method can include providing the collected positioning data to the first network node.

[0055] In another embodiment, a second network node for facilitating collecting and using positioning data as training data to train an AI / ML model comprises processing circuitry that causes the second network node to receive, from a first network node, one or more reliability criteria for positioning data; and collect positioning data that satisfies the one or more reliability criteria, resulting in collected positioning data. In another embodiment, the second network node can perform any of the embodiments described above.

[0056] In another embodiment, a method performed by a first network node for facilitating collecting and using positioning data as training data to train an AI / ML model is provided. The method includes determining a tolerance for ground truth labeling based on one or more Qualityof Service (QoS) criteria, determining one or more reliability criteria for positioning data based on the tolerance for ground truth labeling, providing the one or more reliability criteria for positioning data to a second network node, and receiving, from the second network node, collected positioning data.

[0057] In an embodiment, the first network node is at least one of a base station, a User Equipment, UE, or a Location Management Function, LMF, and wherein the second network node is at least one of base station, UE, or LMF, and wherein the first network node and the second network node are different types.

[0058] In an embodiment, the QoS criteria are received from a client application or from another network node.

[0059] In an embodiment, the one or more reliability criteria are determined based on a predefined configuration at one of the first network node or the second network node.

[0060] In an embodiment, the method further includes receiving capability information from the second network node, wherein the one or more reliability criteria are based at least in part on the capability information.

[0061] In an embodiment, the one or more reliability criteria define limits associated with an alert limit, a maximum positioning error, an uncertainty level and a confidence level of the positioning data.

[0062] In an embodiment, the alert limit, positioning error, uncertainty level and the confidence level are based on a geographical area description associated with the positioning data.

[0063] In an embodiment, the one or more reliability criteria are based on measurement results obtained from uplink measurements from a UE with a known location.

[0064] In an embodiment, the one or more reliability criteria are based on measurement results obtained from downlink measurements at a UE with a known location.

[0065] In an embodiment, the UE is a PRU.

[0066] In an embodiment, the second network node obtains the measurement results from theUE with the known location, wherein a difference between the measurement results from the UE and uplink measurement results from the second network node is less than predefined threshold.

[0067] In an embodiment, the collected positioning data comprises measurement data and label data, and wherein the measurement data of the collected positioning data satisfies a first minimum quality criteria, and wherein in response to the label data not satisfying a second minimum quality criteria, the collected positioning data is marked as unlabeled training data, andin response to the label data satisfying the second minimum quality criteria, the collected positioning data is marked as labelled training data.

[0068] In an embodiment, the method further includes checking a quality of the measurement data in response to an occurrence of one or more of a transmitter implementation error; a receiver implementation error; radio channel quality between a transmitter and receiver not meeting a predefined threshold; or a predefined radio signal configuration.

[0069] In an embodiment, a first network node is provided for facilitating collecting and using positioning data as training data to train an AI / ML model. The first network node includes processing circuitry to cause the network node to determine a tolerance for ground truth labeling based on one or more Quality of Service (QoS) criteria, determine one or more reliability criteria for positioning data based on the tolerance for ground truth labeling, provide the one or more reliability criteria for positioning data to a second network node, and receive, from the second network node, collected positioning data. In another embodiment, the first network node can perform any of the embodiments described above.BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0071] Figure 1 shows an example of an Artificial Intelligence / Machine Learning (AI / ML) model training pipeline illustrating different stages in accordance with some embodiments of the present disclosure;

[0072] Figure 2 shows an example of AI / ML model validation in accordance with some embodiments of the present disclosure;

[0073] Figure 3 shows an example of positioning architecture in accordance with some embodiments of the present disclosure;

[0074] Figure 4 shows an example of a Stanford Plot with Protection Level plotted against Position Error in accordance with some embodiments of the present disclosure;

[0075] Figure 5 shows a message sequence chart of a method for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning (AI / ML) model in accordance with some embodiments of the present disclosure;

[0076] Figure 6 shows an example of a communication system in accordance with some embodiments of the present disclosure;

[0077] Figure 7 shows a User Equipment (UE) in accordance with some embodiments of the present disclosure;

[0078] Figure 8 shows a network node in accordance with some embodiments of the present disclosure; and

[0079] Figure 9 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized in accordance with some embodiments of the present disclosure.DETAILED DESCRIPTION

[0080] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments.Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0081] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0082] There currently exist certain challenge(s). One of the main challenges in positioning is to identify ground truth. When positioning is computed there is always an uncertainty associated as positioning can be based upon triangulation process (measurement from at least 3 different Transmission and Reception Points (TRPs), Satellites etc.) and the intersection area obtained by time estimation can be considered as a position of a User Equipment (UE); i.e. the position is associated with an area defined by uncertainty and confidence factor.

[0083] A Rel-19 Work Item Description (WID) for Artificial Intelligence / Machine Learning (AI / ML) has below components for positioning:

[0084] Positioning accuracy enhancements, encompassing [Radio Access Network 1 (RAN 1 ) / RAN2 / RAN3] : a. Direct AI / ML positioning: i. (1stpriority) Case 1 : User Equipment (UE)-based positioning with UE- side model, direct AI / ML positioning ii. (2ndpriority) Case 2b: UE-assisted / Location Management Function (LMF)-based positioning with LMF-side model, direct AI / ML positioning iii. (1stpriority) Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning b. AI / ML assisted positioning(2ndpriority) Case 2a: UE-assisted / LMF -based positioning with UE-side model, AI / ML assisted positioning ii. (1stpriority) Case 3a: NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning

[0085] As the WID suggests there can be different combinations that can be possible; i.e., there are 5 different cases. It is possible that the model is in one Node (Location Management Function (LMF), UE, gNB) and that Node needs to rely on another node within the network for data collection to train its AI / ML model. For example, the AI / ML model is in LMF and the gNB and UE have to collect data and provide the collected data for LMF AI / ML model training purpose; the data reported by the UE / gNB may have a certain degree of error. Training the AI / ML at LMF with such data will result in erroneous inferencing while predicting the UE location.

[0086] For AI / ML based positioning, regardless of the node where the AI / ML model is deployed, it becomes particularly important to perform label data collection with higher precision. The label of data collected for AI / ML model training should maintain a certain degree of accuracy. More importantly if the data is collected for AI / ML model training, the label or the ground truth corresponding to the collected data should also meet a certain degree of precision.

[0087] It is unclear (especially) to UEs as which data can be safe to be collected for training purposes and which data should be discarded, or basically what can be acceptable label data that the UE should use it as label for the associated / obtained measurements.

[0088] For AI / ML assisted positioning use case, when the model output is an intermediate positioning measurement, the ground truth label is the true measurement output, for example Time of Arrival (TOA), Angle of Arrival (AO A), Angle of Departure (AOD), Reference Signal Time Difference (RSTD), Reference Signal Received Power (RSRP), Reference Signal Received Path Power (RSRPP), gNB Receive-Transmit (Rx-Tx) time difference, UE Rx-Tx time difference, timing advance, or the true LoS classification. When the model output is a position, the ground truth label is the true UE position. It is not clear which input data can be used and how it should be collected to obtain labels for true positions (direct positioning) or labels for true positioning measurements (assisted positioning).

[0089] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.

[0090] Various embodiments provide for a method for determining whether positioning data should be collected and used as training data to train an AI / ML model. A signaling mechanism can be provided where a network node specifies which positioning measurements data should be collected for training purposes and which should be discarded by providing the criteria for data collection to UEs or gNBs. Similarly, UEs or gNBs may request that it needs data collection withcertain quality related criteria. A first network node can determine a tolerance for ground truth labeling, and then based on the tolerance, determine one or more reliability criteria for the positioning data. The first network node can provide the request for positioning data and reliability criteria to a second network node, which can collect the positioning data based on the criteria, and then provide the collected positioning data to the first network node, which can then use the positioning data as training data. The first network node and the second network node can be a UE, base station device, or a Location Management Function (LMF).

[0091] In this disclosure, a signaling mechanism is provided where a NW node specifies which positioning measurements data (e.g.: CIR, power delay profile (PDP), RSRP, RSRPP, Time of arrival, RSTD, Location) should be collected for training purpose and which should be discarded by providing the criteria for data collection to UEs or gNBs. Similarly, UEs or gNBs may request that it needs data collection with certain quality related criteria.

[0092] A basic invention flow is presented where Nodel (e.g., a first network node) and Node2 (a second network node) can be a UE, gNB (base station device) or LMF.

[0093] Nodel from its own knowledge (e.g. pre-defined information, pre-configured information), or from the configuration information, the Positioning Quality of service or Integrity service requirements that it received from LCS client (or other node) may determine the tolerance for ground truth.

[0094] Nodel prepares the criteria to qualify for ground truth.

[0095] Node2 follows the logic to collect or discard the measurements depending upon the obtained positioning error or a measurement error and comparing with the threshold.

[0096] The collected results are stored by a training data collection entity. Many variants are possible for the training data collection entity, for example, either Nodel or Node2, or the node which hosts the model for model inference, or the model training entity of the given AI / ML model.

[0097] Certain embodiments may provide one or more of the following technical advantage(s). The positioning requests originate from different clients with their own requirements. Some may have very stringent requirements on positioning accuracy whereas some may have more relaxed tolerance on the resulting positioning accuracy. For AI / ML based positioning different sets of data can be collected depending upon the classification of positioning requirements. One set of data can be for high accuracy positioning whereas another set can be to improve positioning accuracy in a very cluttered environment to obtain decent accuracy. The mechanism in the present disclosure allows an entity to train its AI / ML modeldepending upon its preference (Quality of Service - QoS - requirements for positioning) and setting a criterion for data collection ground truth.

[0098] The terminology label data is primarily used here is ground truth for positioning in terms of true location coordinates or true location measurements without any errors.

[0099] When a network (NW) provides assistance data for Integrity the error can be bound. In such a scenario, NW specifies alert limit to configure the ground truth. It implies that, if the maximum positioning error (PE) is within the specified alert limit then the UE shall consume the information (use) for data collection else the UE shall discard the data (e.g.: measurements, location).

[0100] The below can be a part of the LPP assistance data when the AI / ML model is in the LMF and the LMF needs to configure UE for data collection, or can be in NR Positioning Protocol a (NRPPa) when TRP / gNB has to collect the data for the AI / ML model deployed in LMF, or can be in NRPPa when the AI / ML model is in LMF and LMF needs to configure TRP / gNB for data collection.

[0101] Similarly, the below can be a part of the LPP assistance data request coming from UE to LMF when AI / ML model is in the UE and UE requires the label data collected by LMF to train its AI / ML model. Then the UE may specify the criteria for label data collection. This is applicable for scenario when UE has LCS client and QoS is known to UE. It is also possible to set the data collection requirements to the LMF via supplementary request (Location services LCS message) or using LPP to LMF or via Radio Resource Control (RRC) to gNB.

[0102] The same is then applicable when gNB has the AI / ML model and needs label data to be collected from UE. It can be configured via RRC to the UE. It is also applicable when the gNB has the AI / ML model and needs label data (ground truth) to be collected from LMF, and in this case, it can be configured via NRPPa to LMF.

[0103] One signaling example for providing the location estimation threshold is shown below. It is understood that other variants can be used as well, for example, other maximum acceptable error than 20 meters.AIMLLocationInfo-rl9 ::= SEQUENCE { horizontal Al ertLimit-r 19 INTEGER (0..2000), verticalAlertLimit-r 19 INTEGER (0..2000)OPTIONAL,

[0104] AIMLLocationlnfo

[0105] This field provides the threshold for the acceptable location estimate error, where the location information is used in training data collection of an AI / ML model for positioning.• horizontalAlertLimit provides the threshold of horizontal location estimate error along the semi-major axis of the error ellipse. Scale factor 0.01 metre; range 0 - 20 metres.• verticalAlertLimit provides the threshold of vertical location estimate error. Scale factor 0.01 metre; range 0 - 20 metres.

[0106] Another approach or combined with the above is to specify whether uncertainty is within a certain limit or not and the associated measurements confidence factor is above a certain threshold or not.

[0107] UE may support different geographical shapes for location reporting format so the requesting entity may receive the capability and provide the uncertainty and confidence factor corresponding to the supported location format (geographical area description (GAD shapes).

[0108] An example below: Uncertainty ThresholdsForDataCollection-rl9 ::= CHOICE { ellipsoidPointWithUncertaintyCircle-rl9 INTEGER (0..127), ellipsoidPointWithUncertaintyEllipse-rl9 SEQUENCE { uncertaintySemiMajor-r!9 INTEGER (0..127), uncertainty S emiMinor-r 19 INTEGER (0..127) ellipsoidPointWithAltitudeAndUncertaintyEllipsoid-rl9 SEQUENCE { uncertaintySemiMajor-rl9 INTEGER (0..127), uncertainty S emiMinor-r 19 INTEGER(0..127), uncertainty Altitude-r 19 INTEGER (0..127) highAccuracyEllipsoidPointWithUncertaintyEllipse-rl9 SEQUENCE { uncertaintySemiMajor-rl9 INTEGER (0..255), uncertainty S emiMinor-r 19 INTEGER (0..255), uncertainty Altitude-r 19 INTEGER (0..255),ConfidenceThresholdsForDataCollection-rl9 ::= SEQUENCE { horizontalConfidence-r 19 INTEGER (0..100),verticalConfidence-r 19 INTEGER (0..100),Conlidence-r 19 INTEGER (0.. 100)

[0109] The field in IE Uncertainty ThresholdsForDataCollection corresponds to one of the supported location reporting format (geographical shape) of the Node (UE, gNB, LMF) and the uncertainty thresholds for the corresponding location reporting format. Similarly, the IE ConfidenceThresholdsForDataCollection corresponds to confidence or confidence specified for horizontal and vertical dependent up on the Node Capability

[0110] Two possible options for setting the criteria for checking if measurements can be used as ground truth:• Criteria check to be applied to the intermediate measurement’s accuracy: in this case the threshold is set for measurement error (such TOA, AO A, AOD, LOS / NLOS probability, CIR, PDP, RSRP, RSRPP, RSRD, UE Rx-Tx time difference, gNB Rx-Tx time difference, TA).• Criteria check to be applied to the positioning accuracy: in this case the threshold is set for positioning error.

[0111] In some embodiment, the criteria for ground truth measurements (stored at the network node 1 in the main figure) is obtained based on the measurement results obtained from UL measurements from a UE with a known location (e.g., PRU, or other). Since the location of the UE can be defined with uncertainty / confidence parameters, the ground truth UL measurements may be associated with uncertainty / confidence interval.

[0112] In some other embodiment, the criteria for ground truth measurements are obtained based on the measurement results obtained from DL measurements at the UE with a known location (e.g., PRU).

[0113] (From TS 38.305 (Section 5.4.5), the PRU measurements can be compared by a location server with the measurements expected at the known PRU location to determine correction terms for other nearby target devices. The DL- and / or UL location measurements for other target devices can then be corrected based on the previously determined correction terms. Hence, if a UE is not a PRU, the DL- and / or UL location measurements for other target devices can then be corrected based on the previously determined correction terms.)

[0114] In some other embodiment, the network Node 2 is a UE located nearby a UE with known location within a predefined confidence interval. The UL measurements can be considered as ground truth if the difference between these measurements and the measurementsobtained from the UE with known location are less than a predefined threshold by the network Node 1.

[0115] In general, when supervised learning is used in model training, one training data sample is composed of two parts, [Xsampie( ), Ysam ie(j ) I:(a) measurements data Xsampie(j) which correspond to model input X; (b) label data Ysampie(j) which corresponds to model output Y. When label data is not available or not acceptable, the training data sample is only composed of measurements data Xsampie(j) corresponding to model input X, and this is an unlabeled training data sample [Xsampie(j)]- Unlabeled training data samples can be used in semi-supervised learning or unsupervised learning in the training process. Note that here Xsampie(j) and Ysampie(j) are typically vectors or matrices (i.e. , not limited to a single value), and their element can be real or complex values, depending on the model input and output design choices.

[0116] When collecting training data samples, both measurement data Xsampie(j) and label data Ysampie(j) need to be examined, and only data of desired quality should be stored as a part of training dataset.

[0117] If the measurement data Xsampie(j) of a sample does not satisfy the minimum quality criteria, then the sample should be rejected, even if the corresponding label data Ysampie(j) is of sufficient quality.

[0118] If the measurement data Xsampie(j) of a sample satisfies the minimum quality criteria for X, then the sample can be accepted as a training data sample. If the corresponding label data Ysampie(j) is available and satisfies the minimum quality criteria for Y, then a labelled training data sample can be stored: [Xsampie(j), YsamPie(j)] ; otherwise, an unlabeled training data sample can be stored: [Xsampie(j)] •

[0119] The criteria for checking the quality of measurement data Xsampie(j) may include one or more of the following aspects:• Transmitter implementation error: c. For example, if the synchronization error between TRPs is too large, then the measurement data of the downlink signal may not have sufficiently good quality. d. In another example, if the UE transmit timing error is too large, then the measurement data of the uplink signal may not have sufficiently good quality.• Receiver implementation error: e. For example, if the channel estimation error at the receiver is too large, then the measurement data may not have sufficiently good quality.• Radio channel quality between the transmitter and receiver:f. For example, the SINR of the received signal for generating the measurement data may be too low to be acceptable;• The radio signal configuration, where the radio signal is used to obtain the measurement data: g. For example, if the reference signal bandwidth is configured too small, the measurement data obtained from the reference signal cannot have sufficient quality. Typical reference signal for positioning includes DL-PRS for downlink, positioning SRS for uplink.

[0120] In another embodiment, Node 2 may report its capability to Node 1 for the data collection regarding the potential accuracy of ground truth measurements, and this report may be based on the capability request from Node 1. In this case, Node 2 may be PRU or may be not PRU. For example, if Node 2 is not PRU but the accuracy of its ground truth measurement meets the requirements of Node 1 (or roughly meets the requirements of Node 1), then Node 1 may configure Node 2 to report the corresponding ground truth measurement.

[0121] It is also possible that NW node (e.g. : LMF) puts a requirement with set of capabilities to ensure only those UEs that can meet or has the capabilities can be then considered as data source (i.e. to be able to provide location measurements and / or estimated location or able to transmit SRS) for AIML purpose. Example: If the UE supports.• Timing Error Group feature (as defined in TS 38.305 v 18.0.0): one definition is as below: h. Tx Timing Error: Result of Tx time delay involved in the transmission of a signal.It is the uncalibrated Tx time delay, or the remaining delay after the TRP / UE internal calibration / compensation of the Tx time delay, involved in the transmission of the DL-PRS / UL SRS signals. The calibration / compensation may also include the calibration / compensation of the relative time delay between different RF chains in the same TRP / UE and may also possibly consider the offset of the Tx antenna phase center to the physical antenna center.• has GNSS Chip set (i.e. able to use High accuracy positioning HA-GNSS (TS 37.355 V18.0.0) as location source).• The UE knows its known location (example: If UE is stationary / fixed UE) or moves / hops in known locations.• If UE can operate in UE-Based Positioning mode (as defined in TS 37.355v 18.0.0)

[0122] In one method, before configuring the UE with the SRS resources for data collection at gNB-side, the gNB may determine whether the UE can provide the ground truth related to the estimated location with a certain accuracy. This information can for example be provided by theUE in a UE capability signaling, or in response to a gNB or LMF request. If the accuracy indicated by the UE is above a certain threshold, then the gNB can configure the UE with the said SRS resource for the purpose of data collection. The UE may determine if it can meet the accuracy fulfillment based upon previous measurements performed from same location (beam, cell) and indicate to the NW whether it can meet the requirement or not. The UE will then transmit together with the UL also the ground truth estimation (e.g. location coordinates in Geographical Area Description GAD shape) to the gNB. The gNB may also indicate an expected accuracy level, so that in case, the UE detects successively that the ground truth estimation does not fall any longer within the configured accuracy the UE indicates that to the gNB. The gNB may then de-configure the UE with the transmission of UL SRS, or it may indicate to the UE to temporarily deactivate the transmission of UL SRS.

[0123] In another example, the gNB may determine whether the UE can provide the ground truth related to the estimated location with a certain accuracy from the LMF. For example, the gNB may request the LMF whether the UE can provide the ground truth with a certain accuracy. Or the LMF can inform the gNB on whether the ground truth can be provided by the UE with a certain accuracy. In such a case, the gNB may configure the UE with the UL SRS resources needed for data collection at NW-side.

[0124] Similarly, if LMF has to collect data from the UE for AIML data collection for training purpose, it may request to gNB to transmit PRS only if it knows that the UE can act as data source, i.e., the UE is capable of producing the measurements and location estimation meeting the ground truth criteria.

[0125] When the criteria for ground truth from one node to another node has been provided and if the recipient node cannot fulfil the requestor ground truth criteria, it may provide an error or failure message with cause code as an example below:

[0126] “LocationEstimateWithGivenAccuracyNotPossible”, “failedToEstimateLocation”, “groundTruthNotDetermined”

[0127] Further, a node may include a flag that certain location measurements and achieved location meets the criteria and can be stored for future use. This can be done also for the case when normal positioning has been configured towards a target UE and if the target UE is a cooperative UE (willing to provide both location and measurements) and meets the ground truth criteria.

[0128] Figure 5 shows a message sequence chart of a method for determining whether positioning data should be collected and used as training data to train an ArtificialIntelligence / Machine Learning (AI / ML) model in accordance with some embodiments of the present disclosure.

[0129] The method of Figure 5 can begin at step 508, where a first network node (e.g., Node 1) sends a capability request message to a second network node 504 (e.g., Node 2). In response, at step 510, the second network node 504 can respond back with a capability report to the first network node 502.

[0130] At step 512, the first network node 502 can determine a tolerance for ground truth labeling based on one or more QoS criteria. In an embodiment, the QoS criteria are received from a client application or from another network node.

[0131] At step 514, the first network node 502 determines one or more reliability criteria for positioning data based on the tolerance for ground truth labeling. In an embodiment, the one or more reliability criteria define limits associated with an uncertainty level and a confidence level of the positioning data. In another embodiment, the one or more reliability criteria are determined based on a predefined configuration at one of the first network node 502 or the second network node 504. In an embodiment, the uncertainty level and the confidence level are based on a geographical area description associated with the positioning data. In another embodiment, the one or more reliability criteria are based on measurement results obtained from uplink measurements from a UE 506 with a known location. In an embodiment, the one or more reliability criteria are based on measurement results obtained from downlink measurements at a UE 506 with a known location. In an embodiment, the UE could be a PRU.

[0132] In an embodiment, the second network node obtains the measurement results from the UE 506 at step 520 with the known location, wherein a difference between the measurement results from the UE and uplink measurement results from the second network node (504) is less than predefined threshold.

[0133] At step 516, the first network node 502 provides to the second network node 504 the one or more reliability criteria for positioning data.

[0134] At step 518, the second network node 504 collects positioning data that satisfies the one or more reliability criteria, resulting in collected positioning data. Positioning data that does not satisfy the one or more reliability criteria can be discarded. The second network node 504 can also optionally obtain measurement data from UE 506 that can be collected as positioning data.

[0135] At step 522, the second network node 504 can provide the positioning data to the first network node 502.

[0136] At step 524, the first network node 502 can optionally check a quality of the measurement data in response to an occurrence of one or more of: a transmitter implementation error; a receiver implementation error; radio channel quality between a transmitter and receiver not meeting a predefined threshold; or a predefined radio signal configuration.

[0137] At step 526, the first network node 502 can use the positioning data, including both the measurement data and label data as either unlabeled training data or labeled training data to train the AI / ML model at the first network node 502.

[0138] Figure 6 shows an example of a communication system 600 in accordance with some embodiments.

[0139] In the example, the communication system 600 includes a telecommunication network 602 that includes an access network 604, such as a Radio Access Network (RAN), and a core network 606, which includes one or more core network nodes 608. The access network 604 includes one or more access network nodes, such as network nodes 610A and 610B (one or more of which may be generally referred to as network nodes 610), or any other similar Third Generation Partnership Project (3 GPP) access nodes or non-3GPP Access Points (APs). Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 602 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 602 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 602, including one or more network nodes 610 and / or core network nodes 608. The network node 610 could be an example of a gNB implementation of the first network node 502 or second network node 504. Likewise, the core network node 608 can implement a Location Management Function when it is an implementation of the first network node 502 or second network node 504.

[0140] Examples of an ORAN network node include an Open Radio Unit (O-RU), an Open Distributed Unit (O-DU), an Open Central Unit (O-CU), including an O-CU Control Plane (O- CU-CP) or an O-CU User Plane (O-CU-UP), a RAN intelligent controller (near-real time or non- real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node maysupport a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the 0-RAN Alliance or comparable technologies. The network nodes 610 facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 612A, 612B, 612C, and 612D (one or more of which may be generally referred to as UEs 612) to the core network 606 over one or more wireless connections. The UE 612 could be an implementation of the first network node 502 or second network node 504 when they are UEs.

[0141] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 600 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 600 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0142] The UEs 612 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 610 and other communication devices. Similarly, the network nodes 610 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 612 and / or with other network nodes or equipment in the telecommunication network 602 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 602.

[0143] In the depicted example, the core network 606 connects the network nodes 610 to one or more hosts, such as host 616. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 606 includes one more core network nodes (e.g., core network node 608) that are structured with hardware and software components. Features of these componentsmay be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 608. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-Concealing Function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0144] The host 616 may be under the ownership or control of a service provider other than an operator or provider of the access network 604 and / or the telecommunication network 602 and may be operated by the service provider or on behalf of the service provider. The host 616 may host a variety of applications to provide one or more services. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0145] As a whole, the communication system 600 of Figure 6 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 600 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable Second, Third, Fourth, or Fifth Generation (2G, 3G, 4G, or 5G) standards, or any applicable future generation standard (e.g., Sixth Generation (6G)); Wireless Local Area Network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any Low Power Wide Area Network (LPWAN) standards such as LoRa and Sigfox.

[0146] In some examples, the telecommunication network 602 is a cellular network that implements 3 GPP standardized features. Accordingly, the telecommunication network 602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 602. For example, the telecommunication network 602 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs,while providing enhanced Mobile Broadband (eMBB) services to other UEs, and / or massive Machine Type Communication (mMTC) / massive Internet of Things (loT) services to yet further UEs.

[0147] In some examples, the UEs 612 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 604. Additionally, a UE may be configured for operating in single- or multi -Radio Access Technology (RAT) or multi-standard mode. For example, a UE may operate with any one or combination of WiFi, New Radio (NR), and LTE, i.e. being configured for Multi -Radio Dual Connectivity (MR-DC), such as Evolved UMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).

[0148] In the example, a hub 614 communicates with the access network 604 to facilitate indirect communication between one or more UEs (e.g., UE 612C and / or 612D) and network nodes (e.g., network node 610B). In some examples, the hub 614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 614 may be a broadband router enabling access to the core network 606 for the UEs. As another example, the hub 614 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 610, or by executable code, script, process, or other instructions in the hub 614. As another example, the hub 614 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 614 may be a content source. For example, for a UE that is a Virtual Reality (VR) headset, display, loudspeaker or other media delivery device, the hub 614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 614 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 614 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0149] The hub 614 may have a constant / persistent or intermittent connection to the network node 610B. The hub 614 may also allow for a different communication scheme and / or schedule between the hub 614 and UEs (e.g., UE 612C and / or 612D), and between the hub 614 and the core network 606. In other examples, the hub 614 is connected to the core network 606 and / or one or more UEs via a wired connection. Moreover, the hub 614 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 604 and / or to anotherUE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 610 while still connected via the hub 614 via a wired or wireless connection. In some embodiments, the hub 614 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 61 OB. In other embodiments, the hub 614 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and the network node 61 OB, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0150] Figure 7 shows a UE 700 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged, and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, Voice over Internet Protocol (VoIP) phone, wireless local loop phone, desktop computer, Personal Digital Assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, Laptop Embedded Equipment (LEE), Laptop Mounted Equipment (LME), smart device, wireless Customer Premise Equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0151] A UE may support Device-to-Device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), or Vehicle- to-Everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller).Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0152] The UE 700 includes processing circuitry 702 that is operatively coupled via a bus 704 to an input / output interface 706, a power source 708, memory 710, a communication interface 712, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 7. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multipleinstances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0153] The processing circuitry 702 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 710. The processing circuitry 702 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general purpose processors, such as a microprocessor or Digital Signal Processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 702 may include multiple Central Processing Units (CPUs).

[0154] In the example, the input / output interface 706 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 700. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0155] In some embodiments, the power source 708 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 708 may further include power circuitry for delivering power from the power source 708 itself, and / or an external power source, to the various parts of the UE 700 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging the power source 708. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 708 to make the power suitable for the respective components of the UE 700 to which power is supplied.

[0156] The memory 710 may be or be configured to include memory such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 710 includes one or more application programs 714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 716. The memory 710 may store, for use by the UE 700, any of a variety of various operating systems or combinations of operating systems.

[0157] The memory 710 may be configured to include a number of physical drive units, such as Redundant Array of Independent Disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, High Density Digital Versatile Disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, Holographic Digital Data Storage (HDDS) optical disc drive, external mini Dual In-line Memory Module (DIMM), Synchronous Dynamic RAM (SDRAM), external micro-DIMM SDRAM, smartcard memory such as a tamper resistant module in the form of a Universal Integrated Circuit Card (UICC) including one or more Subscriber Identity Modules (SIMs), such as a Universal SIM (USIM) and / or Internet Protocol Multimedia Services Identity Module (ISIM), other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as a ‘SIM card.’ The memory 710 may allow the UE 700 to access instructions, application programs, and the like stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system, may be tangibly embodied as or in the memory 710, which may be or comprise a device-readable storage medium.

[0158] The processing circuitry 702 may be configured to communicate with an access network or other network using the communication interface 712. The communication interface 712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 722. The communication interface 712 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 718 and / or a receiver 720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 718 and receiver 720 may be coupled to one or more antennas (e.g., the antenna 722) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0159] In the illustrated embodiment, communication functions of the communication interface 712 may include cellular communication, WiFi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, NFC, location-based communication such as the use of the Global Positioning System (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband CDMA (WCDMA), GSM, LTE, NR, UMTS, WiMax, Ethernet, Transmission Control Protocol / Intemet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth.

[0160] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 712, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected, an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0161] 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.

[0162] A UE, when in the form of an loT device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application, and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a television, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle chargingstation, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or VR, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 700 shown in Figure 7.

[0163] 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 3 GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0164] 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.

[0165] Figure 8 shows a network node 800 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged, and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment in a telecommunication network. Examples of network nodes include, but are not limited to, APs (e.g., radio APs), Base Stations (BSs) (e.g., radio BSs, Node Bs, evolved Node Bs (eNBs), NR Node Bs (gNBs)), and O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O- CU).

[0166] 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 arelay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node), and / or Remote Radio Units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such RRUs may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a Distributed Antenna System (DAS).

[0167] Other examples of network nodes include multiple Transmission Point (multi-TRP) 5G access nodes, Multi-Standard Radio (MSR) equipment such as MSR BSs, network controllers such as Radio Network Controllers (RNCs) or BS Controllers (BSCs), Base Transceiver Stations (BTSs), transmission points, transmission nodes, Multi-Cell / Multicast Coordination Entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0168] The network node 800 includes processing circuitry 802, memory 804, a communication interface 806, and a power source 808. The network node 800 may be composed of multiple physically separate components (e.g., aNodeB component and an RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 800 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair may in some instances be considered a single separate network node. In some embodiments, the network node 800 may be configured to support multiple RATs. In such embodiments, some components may be duplicated (e.g., separate memory 804 for different RATs) and some components may be reused (e.g., a same antenna 810 may be shared by different RATs). The network node 800 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 800, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, Long Range Wide Area Network (LoRaWAN), Radio Frequency Identification (RFID), or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within the network node 800.

[0169] The processing circuitry 802 may comprise a combination of one or more of a microprocessor, controller, microcontroller, CPU, DSP, ASIC, FPGA, or any other suitable computing device, resource, or combination of hardware, software, and / or encoded logicoperable to provide, either alone or in conjunction with other network node 800 components, such as the memory 804, to provide network node 800 functionality.

[0170] In some embodiments, the processing circuitry 802 includes a System on a Chip (SOC). In some embodiments, the processing circuitry 802 includes one or more of Radio Frequency (RF) transceiver circuitry 812 and baseband processing circuitry 814. In some embodiments, the RF transceiver circuitry 812 and the baseband processing circuitry 814 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of the RF transceiver circuitry 812 and the baseband processing circuitry 814 may be on the same chip or set of chips, boards, or units.

[0171] The memory 804 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid state memory, remotely mounted memory, magnetic media, optical media, RAM, ROM, mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD), or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable, and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 802. The memory 804 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 802 and utilized by the network node 800. The memory 804 may be used to store any calculations made by the processing circuitry 802 and / or any data received via the communication interface 806. In some embodiments, the processing circuitry 802 and the memory 804 are integrated.

[0172] The communication interface 806 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 806 comprises port(s) / terminal(s) 816 to send and receive data, for example to and from a network over a wired connection. The communication interface 806 also includes radio front-end circuitry 818 that may be coupled to, or in certain embodiments a part of, the antenna 810. The radio front-end circuitry 818 comprises filters 820 and amplifiers 822. The radio front-end circuitry 818 may be connected to the antenna 810 and the processing circuitry 802. The radio front-end circuitry 818 may be configured to condition signals communicated between the antenna 810 and the processing circuitry 802. The radio front-end circuitry 818 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 818 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of thefilters 820 and / or the amplifiers 822. The radio signal may then be transmitted via the antenna 810. Similarly, when receiving data, the antenna 810 may collect radio signals which are then converted into digital data by the radio front-end circuitry 818. The digital data may be passed to the processing circuitry 802. In other embodiments, the communication interface 806 may comprise different components and / or different combinations of components.

[0173] In certain alternative embodiments, the network node 800 does not include separate radio front-end circuitry 818; instead, the processing circuitry 802 includes radio front-end circuitry and is connected to the antenna 810. Similarly, in some embodiments, all or some of the RF transceiver circuitry 812 is part of the communication interface 806. In still other embodiments, the communication interface 806 includes the one or more ports or terminals 816, the radio front-end circuitry 818, and the RF transceiver circuitry 812 as part of a radio unit (not shown), and the communication interface 806 communicates with the baseband processing circuitry 814, which is part of a digital unit (not shown).

[0174] The antenna 810 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 810 may be coupled to the radio front-end circuitry 818 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 810 is separate from the network node 800 and connectable to the network node 800 through an interface or port.

[0175] The antenna 810, the communication interface 806, and / or the processing circuitry 802 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node 800. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 810, the communication interface 806, and / or the processing circuitry 802 may be configured to perform any transmitting operations described herein as being performed by the network node 800. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0176] The power source 808 provides power to the various components of the network node 800 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 808 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 800 with power for performing the functionality described herein. For example, the network node 800 may be connectable to an external power source (e.g., the power grid or an electricity outlet) via input circuitry or an interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 808. As a further example, the power source 808may 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.

[0177] Embodiments of the network node 800 may include additional components beyond those shown in Figure 8 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 800 may include user interface equipment to allow input of information into the network node 800 and to allow output of information from the network node 800. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 800. In some embodiments providing a core network node, such as core network node 108 of FIG. 6, some components, such as the radio front-end circuitry 818 and the RF transceiver circuitry 812 may be omitted.

[0178] Figure 9 is a block diagram illustrating a virtualization environment 900 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices, and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more Virtual Machines (VMs) implemented in one or more virtualization environments 900 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, a UE, a core network node, or a host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 900 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface. Virtualization may facilitate distributed implementations of a network node, a UE, a core network node, or a host.

[0179] Applications 902 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 900 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0180] Hardware 904 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, an input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 906 (also referred to as hypervisors or Virtual Machine Monitors (VMMs)), provide VMs 908A and 908B (one or more of which may be generally referred to as VMs 908), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 906 may present a virtual operating platform that appears like networking hardware to the VMs 908.

[0181] The VMs 908 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 906. Different embodiments of the instance of a virtual appliance 902 may be implemented on one or more of VMs 908, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as Network Function Virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers and customer premise equipment.

[0182] In the context of NFV, a VM 908 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 908, and that part of the hardware 904 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 908 on top of the hardware 904 and corresponds to the application 902.

[0183] The hardware 904 may be implemented in a standalone network node with generic or specific components. The hardware 904 may implement some functions via virtualization. Alternatively, the hardware 904 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 910, which, among others, oversees lifecycle management of the applications 902. In some embodiments, the hardware 904 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or abase station. In some embodiments, some signaling can be provided with the use of a control system 912 which may alternatively be used for communication between hardware nodes and radio units.

[0184] Although the computing devices described herein (e.g., UEs, network nodes) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Determining, calculating, obtaining, or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0185] In certain embodiments, some or all of the functionalities described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionalities may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device but are enjoyed by the computing device as a whole and / or by end users and a wireless network generally.

[0186] Some of the references referred to herein as background are described below:1. Technical Specification Group Radio Access Network; Study on Artificial Intelligence (AI) / Machine Learning (ML) for NR air interface (Release 18), 3GPP TR 38.843, Dec. 2023.2. Ferrand, Paul, Maxime Guillaud, Christoph Studer, and Olav Tirkkonen. "Wireless Channel Charting: Theory, Practice, and Applications. "IEEE Communications Magazine 61, no. 6 (2023): 124-130.3. NG Radio Access Network (NG-RAN); Stage 2 functional specification of User Equipment (UE) positioning in NG-RAN, 3GPP TR 38.305, Dec. 2023.

[0187] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0188] Some of the embodiments of the present disclosure include:

[0189] Embodiment 1 : A method performed by a first network node (502) for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning, AI / ML, model, the method comprising: determining (512) a tolerance for ground truth labeling based on one or more Quality of Service, QoS, criteria; determining (514) one or more reliability criteria for positioning data based on the tolerance for ground truth labeling; providing (516) the one or more reliability criteria for positioning data to a second network node (504) and receiving (522), from the second network node (504), collected positioning data.

[0190] Embodiment 2: The method embodiment 1, wherein the first network node (502) is at least one of a base station device, a User Equipment device, UE, or a Location Management Function, LMF, and wherein the second network node (504) is at least one of base station device, UE, or LMF, and wherein the first network node (502) and the second network node (504) are different types.

[0191] Embodiment 3 : The method of any of embodiments 1 to 2, wherein the QoS criteria are received from a client application or from another network node.

[0192] Embodiment 4: The method of any of embodiments 1 to 3, wherein the one or more reliability criteria are determined based on a predefined configuration at one of the first network node (502) or the second network node (504).

[0193] Embodiment 5 : The method any of embodiments 1 to 4, further comprising: receiving (510) capability information from the second network node (504), wherein the one or more reliability criteria are based at least in part on the capability information.

[0194] Embodiment 6: The method of any of embodiments 1 to 5, wherein the one or more reliability criteria define limits associated with an alert limit, a maximum positioning error, an uncertainty level and a confidence level of the positioning data.

[0195] Embodiment 7 : The method of embodiment 6, wherein the alert limit, positioning error, uncertainty level and the confidence level are based on a geographical area description associated with the positioning data.

[0196] Embodiment 8: The method of any of embodiments 1 to 7, wherein the one or more reliability criteria are based on measurement results obtained from uplink measurements from a UE (506) with a known location.

[0197] Embodiment 9: The method of any of embodiments 1 to 7, wherein the one or more reliability criteria are based on measurement results obtained from downlink measurements at a UE with a known location.

[0198] Embodiment 10: The method of any of embodiments 8 to 9, wherein the UE is a positioning reference unit, PRU.

[0199] Embodiment 11 : The method of embodiment 9, wherein the second network node(504) obtains the measurement results from the UE with the known location, wherein a difference between the measurement results from the UE and uplink measurement results from the second network node (504) is less than predefined threshold.

[0200] Embodiment 12: The method of any of embodiments 1 to 11, wherein the collected positioning data comprises measurement data and label data, and wherein the measurement data of the collected positioning data satisfies a first minimum quality criteria, and wherein in response to the label data not satisfying a second minimum quality criteria, the collected positioning data is marked as unlabeled training data, and in response to the label data satisfying the second minimum quality criteria, the collected positioning data is marked as labelled training data.

[0201] Embodiment 13: The method of embodiment 12, further comprising: checking (524) a quality of the measurement data in response to an occurrence of one or more of: a transmitter implementation error; a receiver implementation error; radio channel quality between a transmitter and receiver not meeting a predefined threshold; or a predefined radio signal configuration.

[0202] Embodiment 14: A first network node (502) for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning, AI / ML, model, comprising processing circuitry to perform any of the methods of embodiments 1 to 13.

[0203] Embodiment 15: A method performed by a second network node (504) for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning, AI / ML, model, the method comprising: receiving (516), from a first network node (502), one or more reliability criteria for positioning data; collecting (518) positioning data that satisfies the one or more reliability criteria, resulting in collected positioning data; and providing (522), the collected positioning data to the first network node (502).

[0204] Embodiment 16: The method of embodiment 15, further comprising: providing (510) capability information to the first network node (502).

[0205] Embodiment 17: The method of any of embodiments 15 to 16, wherein the first network node (502) is at least one of a base station device, a User Equipment device, UE, or a Location Management Function, LMF, and wherein the second network node (504) is at least one of base station device, UE, or LMF, and wherein the first network node (502) and the second network node (504) are different types.

[0206] Embodiment 18: The method of any of embodiments 15 to 17, wherein the one or more reliability criteria are determined based on a predefined configuration at one of the first network node (502) or the second network node (504).

[0207] Embodiment 19: The method of any of embodiments 15 to 18, wherein the one or more reliability criteria define limits associated with an alert limit, a maximum positioning error, an uncertainty level and a confidence level of the positioning data.

[0208] Embodiment 20: The method of embodiment 19, wherein the alert limit, the maximum positioning error, the uncertainty level and the confidence level are based on a geographical area description associated with the positioning data.

[0209] Embodiment 21: The method of any of embodiments 15 to 20, wherein the one or more reliability criteria are based on measurement results obtained from uplink measurements from a UE with a known location.

[0210] Embodiment 22: The method of any of embodiments 15 to 20, wherein the one or more reliability criteria are based on measurement results obtained from downlink measurements at a UE (506) with a known location.

[0211] Embodiment 23: The method of any of embodiments 21 to 22, wherein the UE (506) is a positioning reference unit, PRU.

[0212] Embodiment 24: The method of any of embodiments 21 to 23, further comprising: obtaining (520) the measurement results from the UE (506) with the known location, wherein adifference between the measurement results from the UE and uplink measurement results from the second network node (504) is less than predefined threshold.

[0213] Embodiment 25: A second network node (504) for determining whether positioning data should be collected and used as training data to train an Artificial Intelligence / Machine Learning, AI / ML, model, comprising processing circuitry to perform any of the methods of embodiments 1 to 13.

Claims

CLAIMS1. A method performed by a second network node (504) for facilitating collecting and using positioning data as training data to train an Artificial Intelligence / Machine Learning, AI / ML, model, the method comprising: receiving (516), from a first network node (502), one or more reliability criteria for positioning data; and collecting (518) positioning data that satisfies the one or more reliability criteria, resulting in collected positioning data.

2. The method of claim 1, wherein the one or more reliability criteria are associated with a confidence level of the positioning data.

3. The method of any of claims 1 to 2, wherein the first network node (502) is at least one of a base station, a User Equipment, UE, or a Location Management Function, LMF, and wherein the second network node (504) is at least one of base station, UE, or LMF, and wherein the first network node (502) and the second network node (504) are different types.

4. The method of any of claims 1 to 3, wherein the one or more reliability criteria are determined based on a predefined configuration at one of the first network node (502) or the second network node (504).

5. The method of any of claims 1 to 4, wherein the one or more reliability criteria define limits associated with an alert limit, a maximum positioning error, an uncertainty level and a confidence level of the positioning data.

6. The method of claim 5, wherein the alert limit, the maximum positioning error, the uncertainty level and the confidence level are based on a geographical area description associated with the positioning data.

7. The method of any of claims 1 to 6, wherein the one or more reliability criteria are based on measurement results obtained from uplink measurements from a UE with a known location.

8. The method of any of claims 1 to 6, wherein the one or more reliability criteria are basedon measurement results obtained from downlink measurements at a UE (506) with a known location.

9. The method of any of claims 7 to 8, wherein the UE (506) is a positioning reference unit, PRU.

10. The method of any of claims 7 to 9, further comprising: obtaining (520) the measurement results from the UE (506) with the known location, wherein a difference between the measurement results from the UE and uplink measurement results from the second network node (504) is less than predefined threshold.

11. The method of any of claims 1 to 10, further comprising: providing (522) the collected positioning data to the first network node (502).

12. A second network node (504) for facilitating collecting and using positioning data as training data to train an Artificial Intelligence / Machine Learning, AI / ML, model, comprising processing circuitry that causes the second network node (504) to: receive (516), from a first network node (502), one or more reliability criteria for positioning data; and collect (518) positioning data that satisfies the one or more reliability criteria, resulting in collected positioning data.

13. The second network node (504) of claim 12, wherein the one or more reliability criteria are quality indications associated with the positioning data.

14. The second network node (504) of any of claims 12 to 13, wherein the first network node (502) is at least one of a base station, a User Equipment, UE, or a Location Management Function, LMF, and wherein the second network node (504) is at least one of base station, UE, or LMF, and wherein the first network node (502) and the second network node (504) are different types.

15. The second network node (504) of any of claims 12 to 14, wherein the one or more reliability criteria are determined based on a predefined configuration at one of the first network node (502) or the second network node (504).

16. The second network node (504) of any of claims 12 to 15, wherein the one or more reliability criteria define limits associated with an alert limit, a maximum positioning error, an uncertainty level and a confidence level of the positioning data.

17. The second network node (504) of claim 16, wherein the alert limit, the maximum positioning error, the uncertainty level and the confidence level are based on a geographical area description associated with the positioning data.

18. The second network node (504) of any of claims 12 to 17, wherein the one or more reliability criteria are based on measurement results obtained from uplink measurements from a UE with a known location.

19. The second network node (504) of any of claims 12 to 17, wherein the one or more reliability criteria are based on measurement results obtained from downlink measurements at a UE (506) with a known location.

20. The second network node (504) of any of claims 18 to 19, wherein the UE (506) is a positioning reference unit, PRU.

21. The second network node (504) of any of claims 18 to 20, wherein the processing circuitry further causes the second network node (504) to: obtain (520) the measurement results from the UE (506) with the known location, wherein a difference between the measurement results from the UE and uplink measurement results from the second network node (504) is less than predefined threshold.

22. The second network node (504) of any of claims 12 to 21, wherein the processing circuitry further causes the second network node (504) to: provide (522) the collected positioning data to the first network node (502).

23. A method performed by a first network node (502) for facilitating collecting and using positioning data as training data to train an Artificial Intelligence / Machine Learning, AI / ML, model, the method comprising: determining (512) a tolerance for ground truth labeling based on one or more Quality of Service, QoS, criteria;determining (514) one or more reliability criteria for positioning data based on the tolerance for ground truth labeling; providing (516) the one or more reliability criteria for positioning data to a second network node (504) and receiving (522), from the second network node (504), collected positioning data.

24. The method claim 23, wherein the first network node (502) is at least one of a base station, a User Equipment, UE, or a Location Management Function, LMF, and wherein the second network node (504) is at least one of base station, UE, or LMF, and wherein the first network node (502) and the second network node (504) are different types.

25. The method of any of claims 23 to 24, wherein the QoS criteria are received from a client application or from another network node.

26. The method of any of claims 23 to 25, wherein the one or more reliability criteria are determined based on a predefined configuration at one of the first network node (502) or the second network node (504).

27. The method any of claims 23 to 26, further comprising: receiving (510) capability information from the second network node (504), wherein the one or more reliability criteria are based at least in part on the capability information.

28. The method of any of claims 23 to 27, wherein the one or more reliability criteria define limits associated with an alert limit, a maximum positioning error, an uncertainty level and a confidence level of the positioning data.

29. The method of claim 28, wherein the alert limit, positioning error, uncertainty level and the confidence level are based on a geographical area description associated with the positioning data.

30. The method of any of claims 23 to 29, wherein the one or more reliability criteria are based on measurement results obtained from uplink measurements from a UE (506) with a known location.

31. The method of any of claims 23 to 29, wherein the one or more reliability criteria are based on measurement results obtained from downlink measurements at a UE with a known location.

32. The method of any of claims 30 to 31, wherein the UE is a positioning reference unit, PRU.

33. The method of claim 31, wherein the second network node (504) obtains the measurement results from the UE with the known location, wherein a difference between the measurement results from the UE and uplink measurement results from the second network node (504) is less than predefined threshold.

34. The method of any of claims 23 to 33, wherein the collected positioning data comprises measurement data and label data, and wherein the measurement data of the collected positioning data satisfies a first minimum quality criteria, and wherein in response to the label data not satisfying a second minimum quality criteria, the collected positioning data is marked as unlabeled training data, and in response to the label data satisfying the second minimum quality criteria, the collected positioning data is marked as labelled training data.

35. The method of claim 34, further comprising: checking (524) a quality of the measurement data in response to an occurrence of one or more of: a transmitter implementation error; a receiver implementation error; radio channel quality between a transmitter and receiver not meeting a predefined threshold; or a predefined radio signal configuration.

36. A first network node (502) for facilitating collecting and using positioning data as training data to train an Artificial Intelligence / Machine Learning, AI / ML, model, comprising processing circuitry to: determine (512) a tolerance for ground truth labeling based on one or more Quality of Service, QoS, criteria; determine (514) one or more reliability criteria for positioning data based on the tolerancefor ground truth labeling; provide (516) the one or more reliability criteria for positioning data to a second network node (504) and receive (522), from the second network node (504), collected positioning data37. The first network node (502) of claim 36, wherein the processing circuitry is further configured to perform any of the methods of claims 24 to 35.

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