Measurement report for ai / ML assisted positioning

Abstract

Systems and methods are disclosed for measurement reporting in a cellular communication system for Artificial Intelligence (AI) / Machine Learning (ML) assisted positioning. In one embodiment, a method performed by a User Equipment (UE) comprises obtaining a positioning- related measurement for to a link between the UE and an anchor node and sending, to a network node, the positioning-related measurement and information that indicates whether the positioning- related measurement was obtained using an AI / ML based technique or a non-AI / ML technique. In this manner, information about whether the positioning-related measurement was obtained using an AI / ML based technique or a non-AI / ML technique is known to the network, which enables improved performance of the system.

Description

MEASUREMENT REPORT FOR AUML ASSISTED POSITIONINGRELATED APPLICATIONS

[0001] This application claims the benefit of provisional patent application serial number 63 / 554,465, filed February 16, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates to measurement reporting in a cellular communications system for Artificial Intelligence (Al) / Machine Learning (ML) assisted positioning.BACKGROUND

[0003] Artificial Intelligence (Al) and Machine Learning (ML) have been investigated as promising tools to optimize the design of air-interface in wireless communication networks in both academia and industry. Example use cases include using autoencoders for Channel State Information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy, using deep neural networks for classifying Line-of Sight (LOS) and Non-LOS (NLOS) conditions to enhance the positioning accuracy, using reinforcement learning for beam selection at the network side and / or the User Equipment (UE) side to reduce the signaling overhead and beam alignment latency, and using deep reinforcement learning to learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.

[0004] In 3RDGeneration Partnership Project (3 GPP) New Radio (NR) standardization work, the release 18 study item on AI / ML for NR air interface has been completed. This study item explores the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), this study item aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques.

[0005] Building an AI / ML model includes several development steps where the actual training of the AI / ML model is just one step in a training pipeline. An important part in AI / ML development is the AI / ML model lifecycle management. In this regard, Figure 1 is an illustration of training and inference pipelines, and their interactions within a model lifecycle management procedure. The AI / ML model lifecycle management typically consists of• A training (re-training) pipeline,o With data ingestion referring to gathering raw (training) data from a data storage. After data ingestion, there may also be a step that controls the validity of the gathered data. o With data pre-processing referring to some feature engineering applied to the gathered data, e.g., it may include data normalization and possibly a data transformation required for the input data to the AI / ML model. o With the actual model training steps where a model is obtained using the training dataset. o With model evaluation referring to benchmarking the performance to some baseline. The iterative steps of model training and model evaluation continues until the acceptable level of performance is achieved. o With model registration referring to registering the AI / ML model, including any corresponding AI / ML-metadata that provides information on how the AI / ML model was developed, and possibly AI / ML model evaluations performance outcomes.• A deployment stage to make the trained (or re-trained) AI / ML model part of the inference pipeline.• An inference pipeline, o With data ingestion referring to gathering raw (inference) data from a data storage. o With data pre-processing stage that is typically identical to corresponding processing that occurs in the training pipeline. o With model operational referring to using the trained and deployed model in an operational mode. o With data & model monitoring referring to validating that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts.• A drift detection stage that informs about any drifts in the model operations.

[0006] One important AI / ML Physical layer (PHY) use case is the positioning of a target UE. Both positioning approaches below have been shown to be effective in obtaining a target UE's location.Direct AI / ML positioning, where the AI / ML model output is UE location. Direct AI / ML positioning typically refers to radio fingerprinting, where channel observation is used as the input of AI / ML model.• AI / ML assisted positioning, where the AI / ML model output is a new measurement and / or enhancement of existing measurement. The model output can be, for example, LOS / NLOS identification, timing and / or angle measurement, likelihood or reliability of the measurement. The model input is also channel observations.

[0007] When applying the direct and assisted AI / ML positioning to an NR wireless communication network, the following cases are further identified for investigation:• Case 1 : UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioning;• Case 2a: UE-assisted / Location Management Function (LMF)-based positioning with UE- side model, AI / ML assisted positioning;• Case 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning;• Case 3a: Next Generation Radio Access Network (NG-RAN) node assisted positioning with gNodeB (gNB)-side model, AI / ML assisted positioning; and• Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning.

[0008] For radio signal based positioning methods, conventional positioning methods rely on a sufficient number of LOS links, typically at least three to five LOS links depending on the positioning method, and whether vertical position is estimated in addition to horizontal position.

[0009] In a cluttered environment, there is often a low probability of LOS for a radio link between a UE and a Transmission-Reception Point (TRP). For example, for InF-DH (Indoor Factory with Dense clutter and High base station height (transmitter (Tx) or receiver (Rx) elevated above the clutter)) environment, Table 1 shows the LOS probabilities of a radio link between TRP and UE when assuming different InF-DH clutter parameter settings. It is observed that the LOS probability ranges from 44.9% in a mildly cluttered environment to only 0.8% in a heavily cluttered environment.Table 1. LOS probabilities of a radio link between TRP and UE when assuming different InF- DH clutter parameter settings.

[0010] In a highly NLOS environment such as the InF-DH {60%, 6m, 2m} scenario, the observable first path Time of Arrival (ToA) does not correctly reflect the true distance between the UE and TRP. Using these observable first path ToAs in a conventional positioning algorithm will lead to inaccurate UE position estimates. Thus, conventional positioning methods struggle to locate a target UE in a heavily cluttered environment. Evaluations show that the 90%-tile positioning accuracy of conventional positioning methods is more than 15 meters in an InF-DH {60%, 6m, 2m} environment, due to the unavailability of sufficient LOS links. This motivates the application of AI / ML based positioning in such challenging deployment environments.

[0011] With AI / ML-assisted positioning, the AI / ML model uses as input the measurements of the channel between TRPs and the target UE. The model outputs are enhanced measurements (e.g., LOS / NLOS identification, timing and / or angle measurement) for each TRP-UE link. For Case 2a (UE-side model and AI / ML assisted positioning), the model input is based on measurement of downlink reference signal, typically the Positioning Reference Signal (PRS). For Case 3a (gNB-side model, AI / ML assisted positioning), the model input is based on measurement of uplink reference signal, typically the Sounding Reference Signal (SRS).SUMMARY

[0012] Systems and methods are disclosed for measurement reporting in a cellular communication system for Artificial Intelligence (Al) / Machine Learning (ML) assisted positioning. In one embodiment, a method performed by a User Equipment (UE) comprises obtaining a positioning-related measurement for to a link between the UE and an anchor node and sending, to a network node, the positioning-related measurement and information that indicates whether the positioning-related measurement was obtained using an AI / ML based technique or a non-AI / ML technique. In this manner, information about whether the positioning-related measurement was obtained using an AI / ML based technique or a non-AI / ML technique is known to the network, which enables improved performance of the system.

[0013] In one embodiment, the positioning-related measurement comprises timing information. In one embodiment, the timing information comprises a Time of Arrival (ToA), Reference Signal Timing Difference (RSTD), Relative Time of Arrival (RTOA), UE Recevie- Transmit Time Difference (RxTxTimeDiff),, or gNodeB (gNB) RxTxTimeDiff.

[0014] In one embodiment, the positioning-related measurement comprises angle information. In one embodiment, the angle information comprises Angle of Arrival (AoA) or Angle of Departure (AoD).

[0015] In one embodiment, sending the positioning-related measurement and the information that indicates whether the positioning-related measurement was obtained using an AI / ML based technique or a non-AI / ML technique comprises sending a measurement report comprising the measurement and sending an indication of whether the positioning-related measurement was obtained using an AI / ML based technique or a non-AI / ML technique. In one embodiment, the indication of whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement is separate from the measurement report. In another embodiment, the indication of whether an AI / ML based technique or non-AI / ML based technique was used to generate the positioning-related measurement is comprised in the measurement report.

[0016] In one embodiment, sending the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises sending a measurement report comprising the measurement and the information that indicates whether an AI / ML-based technique or a non- AI / ML based technique was used to generate the positioning-related measurement. In one embodiment, the measurement report further comprises a Line-of-Sight (LOS) / Non-LOS (NLOS) indicator, and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises a type indicator for the LOS / NLOS indicator that indicates whether the LOS / NLOS indicator and the measurement are for a physical Line-of-Sight, LOS, path or a virtual LOS path. In another embodiment, the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises a virtual LOS indicator that indicates whether or not the measurement is for a virtual LOS path. In another embodiment, the measurement report comprises a measurement information element that comprises the measurement and the information that indicates whether an AI / ML-based technique or a non- AI / ML based technique was used to generate the positioning-related measurement. In another embodiment, the measurement report comprises a first information element that contains the measurement if the measurement was obtained using a non-AI / ML based technique and a second information element that contains the measurement if the measurement was obtained using an AI / ML-based technique, and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement is implicitinformation about whether the measurement is comprised in the first information element or the second information element.

[0017] In one embodiment, the measurement report further comprises a LOS / NLOS indicator.

[0018] In one embodiment, the anchor node is a radio network node or another UE.

[0019] Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE comprises a communication interface comprising a transmitter and a receiver, and processing circuitry associated with the communication interface. The processing circuitry is configured to cause the UE to obtain a positioning-related measurement for to a link between the UE and an anchor node and send, to a network node, the measurement and information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning- related measurement.

[0020] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node comprises receiving, from a UE, a positioning-related measurement for to a link between the UE and an anchor node and information that indicates whether an AI / ML based technique or a non-AI / ML based technique was used to generate the positioning-related measurement and performing one or more actions using the measurement and the information that indicates whether an AI / ML-based technique or a non- AI / ML based technique was used to generate the positioning-related measurement.

[0021] In one embodiment, performing the one or more actions comprise using the measurement and the information that indicates whether an AI / ML-based technique or a non- AI / ML based technique was used to generate the positioning-related measurement for generating a positioning estimate for the UE.

[0022] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node comprises processing circuitry configured to cause the network node to receive, from a UE, a positioning-related measurement for to a link between the UE and an anchor node and information that indicates whether an AI / ML based technique or a non-AI / ML based technique was used to generate the positioning-related measurement and perform one or more actions using the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement,BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 is an illustration of training and inference pipelines, and their interactions within a model lifecycle management procedure.

[0025] Figure 2 is an illustration of the wireless link between a Transmission-Reception Point(TRP) and a User Equipment (UE), where there exists a Line-Of-Sight (LOS) path between TRP and UE, i.e., a physical LOS path and the LOS / Non-LOS (NLOS) indicator is set to 'TRUE'. In this case, the virtual LOS path is the same as the physical LOS path.

[0026] Figure 3 is an illustration of the wireless link between a TRP and a UE, where there is NLOS path between TRP and UE, i.e., there is no physical LOS path, such that the LOS / NLOS indicator is set to 'FALSE. The virtual LOS path is shown.

[0027] Figure 4 is a diagram of Artificial Intelligence (AI) / Machine Learning (ML) assisted positioning with measurements from N TRPs. A single AI / ML model or multiple AI / ML models can be designed to support the N TRPs.

[0028] Figure 5 illustrates the operation of a UE and a network node in accordance with an example embodiment of the present disclosure.

[0029] Figure 6 shows an example of a communication system in which embodiments of the present disclosure may be implemented.

[0030] Figure 7 shows a UE in accordance with some embodiments.

[0031] Figure 8 shows a network node in accordance with some embodiments.

[0032] Figure 9 is a block diagram of a host, which may be an embodiment of the host ofFigure 6, in accordance with various aspects described herein.

[0033] Figure 10 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0034] Figure 11 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.DETAILED DESCRIPTION

[0035] 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 theseconcepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0036] 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.

[0037] There currently exist certain challenge(s). The existing Line of Sight (LOS)-Non-LOS (NLOS)-Indicator in 3rdGeneration Partnership Project (3GPP) New Radio (NR) represents the property of the physical link between two nodes. Furthermore, the LOS-NLOS-Indicator is used by the Location Management Function (LMF) to remove or reduce the influence of NLOS link(s). While the above signaling works well for conventional positioning methods, it is not adequate when Artificial Intelligence (AI) / Machine Learning (ML) models are used to estimate the measurements for the unobserved direct path of the NLOS link.

[0038] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. In this disclosure, embodiments of systems and methods are provided so that measurements (e.g., timing or angle information) can be reported for both a physical LOS link(s) and a virtual LOS link(s). Furthermore, the characteristics of the link associated with the measurements are also clearly and correctly reported.

[0039] For AI / ML assisted positioning, it is proposed to differentiate two types of paths in a measurement report for a Transmission-Reception Point (TRP)-User Equipment (UE) link: physical LOS path versus (vs) virtual LOS path. The differentiation is carried in the signaling from an AI / ML model inference entity to another entity. For example, for Case 2a, the signaling carries the differentiation from User Equipment (UE) to LMF; for Case 3a, the signaling carries the differentiation from gNodeB (gNB) to LMF. With the virtual LOS paths identified, virtual LOS paths are used in triangulating a UE's location, the same way as a physical LOS path, regardless of whether the physical link is considered LOS or NLOS according to the LOS / NLOS indicator. When a Transmission-Reception Point (TRP)-UE link is identified to use virtual LOS path, no 'additionalPath' measurements need to be reported by the model inference entity.

[0040] Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the disclosed systems and methods support the reporting of accurate information on a measured link, which include characteristics of the link being measured (e.g., measured with physical LOS path or measured virtual LOS path), as well as measurement values (e.g., Time of Arrival (ToA)) of the link. When such information is disseminated and shared with entities other than the model inference entity, the information can be used as reliable assistance information by other entities, thus improving the performance of the system.

[0041] Now, a more detailed description of embodiments of the present disclosure will be provided.

[0042] In some embodiments the non-limiting terms “UE” and a “wireless device” are used interchangeably. The UE herein can be any type of wireless device capable of communicating with a network node or another UE over radio signals. The UE may also be a radio communication device, target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine communication (M2M), low-cost and / or low-complexity UE, a sensor equipped with UE, tablet, mobile terminals, smart phone, laptop embedded equipment (LEE), laptop mounted equipment (LME), Universal Serial Bus (USB) dongle, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.

[0043] In some embodiments, the generic term “anchor node” is used, where anchor nodes are used as reference points for determining the location of a target UE. In general, the anchor nodes for positioning can be a variety of nodes in the wireless network. For positioning using the radio link between the target UE and a radio network node, the anchor node can be any kind of a radio network node which may comprise any of a base station, radio base station, base transceiver station, Node B, evolved Node B (eNB), Next-Generation Node B (gNodeB or gNB), Next Generation Radio Access Network (NG-RAN) node, Transmission Point (TP), Transmission- Reception Point (TRP), Multi -cell / multicast Coordination Entity (MCE), relay node, access point (AP), Antenna Reference Point (ARP), radio access point, Remote Radio Unit (RRU), Remote Radio Head (RRH), or the like. For positioning using sidelink between two UEs, the anchor node is a UE or a wireless device.

[0044] For ease of discussion, embodiments of the present disclosure are described using the radio links between a UE and an anchor node. TRP is used as a representative example of the anchor node, where the TRP is connected to a gNB. It is understood by those skilled in the art that the same methodology can be easily applied to many other wireless communication scenarios, e.g., sidelink-based positioning.

[0045] In general, the model output can provide a variety of measurements, including timing information and angle information of links between two wireless nodes. Without losing generality, timing information is used below as a representative measurement for discussion. Furthermore, Time of Arrival (ToA) is used as a representative example of the timing information. It is understood by those skilled in the art that other types of measurements can be reported as well, and the same principle applies. For example, variants of timing information include: Reference Signal Timing Difference (RSTD), Relative Time of Arrival (RTOA), UE RxTxTimeDiff, gNBRxTxTimeDiff, and variants of angle information include Angle of Arrival (AoA), Angle of Departure (AoD).

[0046] For AI / ML assisted positioning, two types of "line-of-sight" paths are considered.

[0047] The first type is physical LOS paths, or true LOS paths. In positioning methods up to 3GPP Release (Rel)-18 (i.e., conventional non-AI methods), the LOS / NLOS indicator provides information on the physical path only. Up to Rel-18, LOS / NLOS indicator is not relevant to any virtual path or virtual link, and in general there was no concept of virtual LOS path in a measurement report. Up to Rel-18, the interpretation of LOS / NLOS indicator is as follows:• If there exists a physical LOS propagation path between the TRP and the UE, then LOS / NLOS indicator = TRUE, for example, the wireless link illustrated in Figure 2;• Otherwise (i.e., there is no physical LOS propagation path between the TRP and the UE), LOS / NLOS indicator = FALSE, for example, the wireless link illustrated in Figure 3.

[0048] In the above, it is assumed that LOS / NLOS indicator is represented with hard values.

[0049] The second type is virtual LOS paths. When AI / ML assisted positioning is applied, virtual LOS paths are introduced in AI / ML positioning design, since only virtual LOS paths can provide accurate positioning information for a target UE, if no direct path exists between a TRP and a UE. This is shown in Figure 3.• If there exists a physical LOS propagation path between the TRP and the UE, then it can be understood that virtual LOS path = physical LOS path;• Otherwise (i.e., there is no physical LOS propagation path between the TRP and the UE), the virtual LOS path is the artificial path that directly connects the TRP and the UE, irrespective of any blockage between them.

[0050] Virtual LOS paths are utilized by AI / ML models to obtain positioning related measurement information for a target UE, for example, timing information or angle information. At the model output of AI / ML assisted positioning, positioning related measurements (e.g., timing or angle) is generated for virtual LOS paths. This is necessary since conventional methods (e.g., triangulation or tri-lateration) can only determine accurate UE location using information of virtual LOS paths, when the direct path between TRP and UE is blocked. This is particularly important for UEs in highly cluttered environment since physical LOS paths are often absent.

[0051] With AI / ML-assisted positioning, the model outputs are forwarded to the LMF to obtain UE positions using conventional positioning algorithms. This process is illustrated in Figure 4. In particular, Figure 4 illustrates AI / ML assisted positioning with measurements from N TRPs. A single AI / ML model, or multiple AI / ML models, can be designed to support the N TRPs. If the model output includes positioning related measurements beyond LOS / NLOS indicator, suchintermediate measurement values (e.g., timing info, angle info) are for the unobserved direct path (aka, virtual LOS path).

[0052] It is noted that Figure 4 provides high-level abstraction of the procedure and some detailed steps are ignored for simplicity and clarity. For example, raw channel measurements may go through a pre-processing stage before feeding as input to the AI / ML model, and the model output may go through a post-processing stage before being reported to LMF. Detailed steps such as pre-processing and post-processing are ignored in Figure 4 and in the discussion below.

[0053] In the output of AI / ML assisted positioning, two types of paths are used: physical LOS path (also called physical LOS link) and virtual LOS path (also called virtual LOS link). The two types of paths can be used simultaneously in a measurement report for AI / ML assisted positioning, e.g., when a measurement report contains both LOS / NLOS indicator for physical path as in 3GPP specifications up to Rel-18, and positioning related measurements (e.g., TOA or angle measurements produced by the model) for the virtual LOS path. Thus, there is a confusion as to whether the model output (e.g., LOS / NLOS indicator, timing information, angle information) refers to the physical LOS path only (as in conventional methods), or the virtual LOS path, or both depending on the configuration or indication. The two types of paths need to be clearly distinguished when information elements are sent between entities for AI / ML assisted positioning.

[0054] For AI / ML assisted positioning, numerous variants have been built and evaluated. To send the model output from the model inference entity to a different entity (e.g., LMF), the measurement report can be reported at least in the following ways:(a) Report positioning related measurements only (i.e., without LOS / NLOS indicator).• That is, LOS / NLOS indicator is optional, and the model inference entity may choose not to report LOS / NLOS indicator.• Positioning related measurements include, for example: timing measurements (e.g., RSTD for downlink (DL), RTOA for uplink (UL), UE RxTxTimeDiff and gNB RxTxTimeDiff for multi -RTT), angle measurements (e.g., AoD, AoA).(b) Report both LOS / NLOS indicator and positioning related measurements, where LOS / NLOS indicator is reported using hard values (TRUE, FALSE).(c) Report both LOS / NLOS indicator and positioning related measurements, where the LOS / NLOS indicator is reported using soft values (i.e., a probability value such as, e.g. : 0, 0.1, 0.2, ..., 1.0).

[0055] The option of reporting LOS / NLOS indicator only (i.e., without positioning related measurements) is not considered above since LOS / NLOS indicator alone does not allow the LMF to determine the location of target UE. On the other hand, if LOS / NLOS indicator is indeedreported by itself, the interpretation should be the same as in conventional methods up to Rel-18, i.e., it provides information on the presence / absence / likelihood of a physical line-of-sight propagation path between TRP and UE.

[0056] When AI / ML model is used to generate positioning related measurements as model output, there is no need to send information for additional paths to describe the multipath channel. This is because the effect of multipath has been resolved by the AI / ML model. Also, as explained earlier, the positioning related measurements are for virtual LOS paths. When the first path of the TRP-UE link is indeed LOS (i.e., physical line-of-sight path), then the positioning related measurement is for the physical LOS path. Otherwise (when the first path of the TRP-UE link is non-line-of-sight), then the positioning related measurement is for a virtual LOS path, which directly connects the TRP and the target UE.

[0057] When the measurement reports are sent from the model inference entity to LMF, it is assumed that the LMF can execute the conventional positioning methods, with only minor modification where necessary. The model inference entity may be UE as in Case 2a or NG-RAN node (i.e., gNB) as in Case 3a. One modification is, when it is indicated that the positioning related measurements are for virtual LOS paths, then they are not removed from the calculation in the conventional positioning methods, even if the physical link is indicated as NLOS by the LOS / NLOS indicator. A virtual LOS path is intended to be used in triangulating a UE's location, the same way as a physical LOS path. This is in contrast to the existing procedure, where measurement associated with a link indicated as NLoS is excluded or discounted when triangulating a UE's location.

[0058] To support the above, methods are needed for distinguishing the two different types of paths when information elements are sent between entities for AI / ML assisted positioning. Several candidate methods are provided in the following sections.

[0059] The methods have the following advantages:• This allows the indication of whether the first path ToA value is provided for physical or virtual line-of-sight propagation path.• This is useful for providing assistance information on LOS / NLOS likelihood to another entity. For example, if a gNB (or UE) explicitly indicate a TRP-UE link as 'virtual' LOS, LMF knows that 'virtual' means the link is not necessarily a physical LOS link as observed by the gNB (or UE). This is useful when LMF needs to provide LOS / NLOS indication as assistance info to the UE (or gNB).• The knowledge of physical vs virtual indication allows the LMF to further weigh different LOS / NLOS reports from different TRP. When a TRP-UE link is indicated as 'virtual' LOS,this enables the LMF to know that the ToA reported is already at “path level”, i.e. no interpath interference need to be cleaned up, and there is no need to ask for additional path info from gNB or UE.• It can be clearly specified that whenever the link has type = 'virtual', no 'additionalPath' measurements are reported.Method 1: Enhanced Interpretation of Information Elements in Measurement Report to Support AI / ML Assisted Positioning

[0060] In Method 1, information elements for a measurement report can be reused. The positioning related measurements (e.g., ToA) can be used by LMF the same way as in the existing 3 GPP specification to determine UE location.

[0061] However, without enhanced interpretation, when the model generates positioning related measurements (e.g., ToA) and sends these positioning related measurements to LMF, the following two scenarios are not distinguishable by LMF.• Scenario (1): The positioning related measurement is for virtual LOS path since the physical link is non-line-of-sight; or• Scenario (2): The positioning related measurement is for physical LOS path since the physical link is line-of-sight.For example, the same ToA values can be reported to LMF for a pair of TRP-UE links, while the value is under Scenario (1) when the direct link is blocked, and the same value is under Scenario (2) when the blockage is removed. Thus, there is a need to define a mechanism to distinguish Scenario (1) and (2).

[0062] One option is to rely on the indication whether Al or non-AI based method is used to generate the reported measurements (e.g., ToA). The indication is called "measurement-report- generation-method " in the following discussion. This indication is not specific for measurement report of AI / ML assisted positioning, but it can be leveraged to distinguish between physical LOS and virtual LOS as shown below. Additionally, when measurement-report-generation-method = "AI / ML based" and a conventional method (e.g., downlink (DL)-RTOA, multi-Round-Trip-Time (RTT)) is activated, then it is understood that AI / ML assisted positioning is applied.• If Al-based method is used, then the positioning related measurement is for virtual LOS path (including when virtual LOS path = physical LOS path). o Since no measurements on additional paths are expected to be generated by the AI / ML model, only measurement for the "first path" (=virtual LOS path) is needed, i.e., no need to include additional paths in the measurement report.• Otherwise (i.e., non-AI-based method is used), the positioning related measurements are for physical paths, including the first path and potentially additional paths as in existing specification.

[0063] It is noted that in this Method 1, when LOS / NLOS indicator is reported, it maintains the same meaning as in existing specification. Thus, Scenario (1) and (2) can be distinguished by the following combinations:• For Scenario (1) [physical NLOS]: LOS / NLOS indicator = FALSE, measurement-report- generation-method = "AI / ML based".• For Scenario (2) [physical LOS]: LOS / NLOS indicator = TRUE, measurement-report- generation-method can be "AI / ML based" or "non -AI / ML based".

[0064] Furthermore, the meaning needs to be specified for the report with (a) measurement- report-generation-method = "AI / ML based" and (b) LOS / NLOS indicator is not reported. Since AI / ML based method is used, the positioning related measurement should be interpreted as for virtual LOS path (i.e., Scenario (1)).

[0065] In other words, the information elements can be understood as follows.• measurement-report-generation-method determines the interpretation of positioning related measurements (e.g., ToA, RTOA, AoA, AoD). o If "AI / ML based" : The positioning related measurements (e.g., ToA) are for virtual LoS links; o If "non-AI / ML based": The positioning related measurements (e.g., ToA) follow the same interpretation as in existing specification. The positioning related measurements are for physical links, where a line-of-sight path may or may not exist between the TRP and UE.• LOS / NLOS indicator provides characteristic information on a physical link, the same meaning as in existing specification.

[0066] It is noted that while the information elements (IES) in the measurement report are not changed, a new IE (measurement-report-generation-method) is signaled outside the measurement report. This new IE switches between two categories of positioning methods: Al or non-AI based. When it indicates "AI / ML based", this is applicable to all links between a multitude of TRPs and the target UE. That is, it is not a per-link or per- TRP indicator. This is in contrast to per- TRP information elements such as LOS / NLOS indicator, ToA, etc.Method 2: Introduce New Information Elements to Indicate the Characteristics of the Link Being Reported

[0067] In Method 2, new IES are introduced in the measurement report to indicate whether the report is for a physical LOS path or a virtual LOS path

[0068] For Method 2, the LOS-NLOS -Indicator can be still optional (as in existing specification) and can be absent in the measurement report. However, the model inference entity does not have to avoid reporting LOS-NLOS -Indicator to avoid confusion.Method 2-A. Measurement Reports with Differentiation of Virtual vs Physical LOS Paths in LOS- NLOS-Indicator

[0069] In Method 2-A, the LOS-NLOS-Indicator and positioning related measurement (e.g., timing or angle) are for the same type of path. Either both are for physical path as in conventional (i.e., non-AI) methods, or both are for virtual paths.

[0070] In one exemplary specification change, a 'type' attribute is added to LOS / NLOS indicator.

[0071] If LOS / NLOS indicator has type 'physical', then the timing information for the first path is for a physical line-of-sight propagation path; if LOS / NLOS indicator has type 'virtual', then the timing information for the first path is for a virtual line-of-sight propagation path, e.g., generated by an AI / ML model.- ASN1 STARTLOS-NLOS-Indicator-rl7 ::= SEQUENCE { indicator-rl7 CHOICE { soft-rl 7 INTEGER (0..10), hard-rl7 BOOLEAN}, type ENUMERATED { physical, virtual } OPTIONAL,}- ASN1ST0P

[0072] Equivalently, instead of signal 'type' with two possible values, the new field (' virtual- indicator' as shown below) is added to signal one value 'true' (i.e., virtual LOS), with the understanding that absence of the new field means 'false' (i.e., physical LOS).- ASN1 STARTLOS-NLOS-Indicator-rl7 ::= SEQUENCE { indicator-rl7 CHOICE { soft-rl 7 INTEGER (0..10), hard-rl7 BOOLEAN }, virtual-LOS-indicator ENUMERATED { true } OPTIONAL,}- ASN1STOP

[0073] In the discussion above, the indicators ('type', 'virtual-LOS-indicator') to differentiate physical vs virtual LOS path are assumed to take binary values (also known as hard values). It is understood by those skilled in the art that other formats can be used. For example, soft values can be used to provide information on likelihood or confidence level. For instance, soft values in the range of {0.0, 0.1, 0.2, . . . , 1.0} may be reported, with 0.0 representing lowest probability or lowest confidence, with 1.0 representing highest probability or highest confidence. The soft values can be reported in place of, or in addition to, the hard values.Method 2-B. Measurement Reports with Differentiation of Virtual vs Physical LOS Links of the Associated Channel Measurement

[0074] In Method 2-B, the LOS-NLOS -Indicator is unchanged, i.e., it refers to physical path as in conventional (i.e., non-AI) methods. The positioning related measurement (e.g., timing orangle) can be for physical path or virtual path, depending on the new information added in the measurement report.

[0075] One method is to add a field to indicate the characteristic of the link (physical LOS or virtual LOS) that the positioning related measurement is for. This is illustrated below using RSTD reporting in DL-TDOA as an example. In the following, 'direct-path' refers to a virtual path that directly connects the TRP and the UE.- ASN1 STARTNR-DL-TDOA-MeasElement-rl6 ::= SEQUENCE { nr-RSTD-rl6 CHOICE { k0-rl6 INTEGER (0..1970049), kl-rl6 INTEGER (0..985025), k2-rl6 INTEGER (0..492513), k3-rl6 INTEGER (0..246257), k4-rl6 INTEGER (0..123129), k5-rl6 INTEGER (0 .61565), nr-los-nlos-Indicator-rl7 CHOICE { perTRP-rl7 LOS-NLOS-Indicator-rl7, perResource-rl7 LOS-NLOS-Indicator-rl7 } nr-direct-path-toa-r!9 ENUMERATED { true } OPTIONAL,- ASN1STOP

[0076] Similar to the indicators ('type', 'virtual-LOS-indicator') described in Method 2 -A, for 'nr-direct-path-toa', soft values can be used to provide information on likelihood or confidence level on the classification of physical vs virtual LOS path. The soft values can be reported in place of, or in addition to, the hard values.

[0077] In another method, an optional field is added to report direct path measurement.

[0078] This is illustrated below using RSTD reporting in DL-TDOA as an example. Thus, if conventional method is used to generate RSTD, the existing nr-RSTD can be used for reporting. On the other hand, if AI / ML is used to generate direct path RSTD, then the new nr-RSTD-direct- path can be used for reporting. Both fields can be reported, if so desired, when the model inference entity run both the conventional method and the AI / ML method.

[0079] Note that existing timing information fields (e.g., nr-RSTD) are mandatorily reported. Thus, when it is desired to report the enhanced measurement (e.g., nr-RSTD-direct-path) from AI / ML only, then a dummy value can be reported for the existing field nr-RSTD, for example, report a dummy value of 0. Then LMF can ignore the dummy value of nr-RSTD and only use the reported value of nr-RSTD-direct-path in determining the location of the target UE.

[0080] Additionally or alternatively, it can be specified that whenever nr-RSTD-direct-path is reported, then nr-RSTD field is absent or ignored.- ASN1 STARTNR-DL-TDOA-MeasElement-rl6 ::= SEQUENCE { nr-RSTD-rl6 CHOICE { k0-rl6 INTEGER (0..1970049), kl-rl6 INTEGER (0..985025), k2-rl6 INTEGER (0..492513), k3-rl6 INTEGER (0..246257), k4-rl6 INTEGER (0..123129), k5-rl6 INTEGER (0 .61565),}, nr-RSTD-direct-path-r 19 CHOICE { k0-rl6 INTEGER (0..1970049), kl-rl6 INTEGER (0..985025), k2-r!6 INTEGER (0..492513),k3-rl6 INTEGER (0..246257), k4-rl6 INTEGER (0..123129), k5-rl6 INTEGER (0..61565),}, OPTIONAL, nr-los-nlos-Indicator-rl7 CHOICE { perTRP-rl7 LOS-NLOS-Indicator-rl7, perResource-rl7 LOS-NLOS-Indicator-rl7 }}- ASN1STOPFurther Description

[0081] Figure 5 illustrates the operation of a UE 500 and a network node 502 in accordance with an example embodiment of the present disclosure. Note that details of various embodiments related to each of the steps of Figure 5 can be found in the corresponding description above. As illustrated, the UE 500 obtains a measurement related to a link between the UE and another node (e.g., an anchor node, a network node, a TRP, another UE, or the like), wherein the measurement is for either a physical LOS path between the UE and the other node or a virtual LOS path between the UE and the other node, as described above (step 504). In other words, the measurement is either obtained in the conventional manner (i.e., obtained using a non-AI / ML based technique and is thus for a physical LOS path) or obtained as an output of an AI / ML model (i.e., obtained using an AI / ML based technique and is thus for a virtual LOS path). The UE sends, to the network node (502), the measurement and information that indicates whether the measurement is for a physical LOS path or a virtual LOS path (step 506). In other words, this information indicates whether the measurement was obtained using a non-AI / ML based technique or an AI / ML based technique.

[0082] In one embodiment, the measurement is a positioning related measurement. In one embodiment, the measurement comprises timing information (e.g., Time of Arrival, ToA, or Reference Signal Timing Difference, RSTD, or Relative Time of Arrival, RTOA, or UE RxTxTimeDiff, or gNB RxTxTimeDiff) and / or angle information (e.g., Angle of Arrival, AoA, or Angle of Departure, AoD).

[0083] In one embodiment, the UE sends the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path in step 506 by sending a measurement report comprising the measurement and sending an indication of whether Al or non-AI based technique is used to generate the measurement, as described above. The information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises the indication of whether an Al or non-AI based technique is used to generate the measurement. In one embodiment, the indication of whether an Al or non-AI based technique is used to generate the measurement is separate from the measurement report. In another embodiment, the indication of whether an Al or non-AI based technique is used to generate the measurement is comprised in the measurement report. As described above, in one embodiment, if the indication of whether an Al or non-AI based technique is used to generate the measurement indicates that an Al-based technique is used to generate the measurement, then the indication indicates that the measurement is for a virtual LOS path; otherwise, if the indication of whether an Al or non-AI based technique is used to generate the measurement indicates that a non-ALbased technique is used to generate the measurement, then the indication indicates that the measurement is for a physical LOS path.

[0084] In another embodiment, the UE sends the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path in step 506 by sending a measurement report comprising the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path. In one embodiment, the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises a type indicator for the LOS / NLOS indicator that indicates whether the LOS / NLOS indicator and the measurement are for a physical LOS path or a virtual LOS path. In another embodiment, the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises a virtual LOS indicator that indicates whether the measurement is for a physical LOS path or a virtual LOS path. . In another embodiment, the measurement report comprises a measurement information element that comprises the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path. In another embodiment, the measurement report comprises a first information element that contains the measurement if the measurement is for a physical LOS path and a second information element that contains the measurement if the for a virtual LOS path, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path is implicit information about whether the measurement is comprised in the first information element or the second informationelement. Each of the aforementioned embodiments is described above in more detail, and those details are equally applicable here.

[0085] The network node 502 receives the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path from the UE 500 in step 506. The network node 502 performs one or more actions based on the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path (step 508). As described above, this may include, for example, using the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path for generating a positioning estimate for the UE, e.g., using an AI / ML model.

[0086] Figure 6 shows an example of a communication system 600 in which embodiments of the present disclosure may be implemented. The UE described above (e.g., UE 500) corresponds to any of the UEs shown in Figure 6 (or subsequent figure), the network node (e.g., gNB) described above (e.g., network node 502) corresponds to any of the network nodes shown in Figure 6 (or subsequent figure), a TRP described above may correspond to a TRP of any of the network noes shown in Figure 6 (or subsequent figure), etc.

[0087] 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.

[0088] Examples of an ORAN network node include an Open Radio Unit (O-RU), an Open Distributed Unit (O-DU), an Open Central Unit (O-CU), including an O-CU Control Plane (O- CU-CP) or an O-CU User Plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an 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.

[0089] 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.

[0090] 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.

[0091] In the depicted example, the core network 606 connects the network nodes 610 to one or more hosts, such as host 616. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 606 includes one more core network nodes (e.g., core network node 608)that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 608. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-Concealing Function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0092] The host 616 may be under the ownership or control of a service provider other than an operator or provider of the access network 604 and / or the telecommunication network 602, and may be operated by the service provider or on behalf of the service provider. The host 616 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0093] 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.

[0094] In some examples, the telecommunication network 602 is a cellular network that implements 3GPP 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.

[0095] 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).

[0096] 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.

[0097] 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 another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with thenetwork nodes 610 while still connected via the hub 614 via a wired or wireless connection. In some embodiments, the hub 614 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 610B. In other embodiments, the hub 614 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and the network node 61 OB, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0098] 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.

[0099] A UE may support Device-to-Device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), Vehi cl e-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).

[0100] 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 multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0101] 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).

[0102] 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.

[0103] In some embodiments, the power source 708 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 708 may further include power circuitry for delivering power from the power source 708 itself, and / or an external power source, to the various parts of the UE 700 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 708. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 708 to make the power suitable for the respective components of the UE 700 to which power is supplied.

[0104] 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, removablecartridges, 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.

[0105] 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.

[0106] 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.

[0107] 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 ofthe 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 / Internet 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.

[0108] 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).

[0109] 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.

[0110] 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 charging station, 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 intendedapplication of the loT device in addition to other components as described in relation to the UE 700 shown in Figure 7.[oni] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0112] 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.

[0113] 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 0-RAN nodes or components of an 0-RAN node (e.g., 0-RU, 0-DU, O-CU).

[0114] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an 0-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).

[0115] 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).

[0116] 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.

[0117] 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 logic operable to provide, either alone or in conjunction with other network node 800 components, such as the memory 804, to provide network node 800 functionality.

[0118] 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. Inalternative 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.

[0119] 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.

[0120] 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 the filters 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.

[0121] 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-endcircuitry 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).

[0122] 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.

[0123] 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.

[0124] 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 808 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0125] 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 interfaceequipment 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.

[0126] Figure 9 is a block diagram of a host 900, which may be an embodiment of the host 616 of Figure 6, in accordance with various aspects described herein. As used herein, the host 900 may be or comprise various combinations of hardware and / or software including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 900 may provide one or more services to one or more UEs.

[0127] The host 900 includes processing circuitry 902 that is operatively coupled via a bus 904 to an input / output interface 906, a network interface 908, a power source 910, and memory 912. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 7 and 8, such that the descriptions thereof are generally applicable to the corresponding components of the host 900.

[0128] The memory 912 may include one or more computer programs including one or more host application programs 914 and data 916, which may include user data, e.g. data generated by a UE for the host 900 or data generated by the host 900 for a UE. Embodiments of the host 900 may utilize only a subset or all of the components shown. The host application programs 914 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), Moving Picture Experts Group (MPEG), VP9) and audio codecs (e.g., Free Lossless Audio Codec (FLAC), Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of LEs (e.g., handsets, desktop computers, wearable display systems, and heads-up display systems). The host application programs 914 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 900 may select and / or indicate a different host for Over-The-Top (OTT) services for a UE. The host application programs 914 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (DASH or MPEG-DASH), etc.

[0129] Figure 10 is a block diagram illustrating a virtualization environment 1000 in which functions implemented by some embodiments may be virtualized. In the present context,virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices, and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more Virtual Machines (VMs) implemented in one or more virtual environments 1000 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1000 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.

[0130] Applications 1002 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1000 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0131] Hardware 1004 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1006 (also referred to as hypervisors or VM Monitors (VMMs)), provide VMs 1008 A and 1008B (one or more of which may be generally referred to as VMs 1008), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 1006 may present a virtual operating platform that appears like networking hardware to the VMs 1008.

[0132] The VMs 1008 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 1006. Different embodiments of the instance of a virtual appliance 1002 may be implemented on one or more of the VMs 1008, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as Network Function Virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers and customer premise equipment.

[0133] In the context of NFV, a VM 1008 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1008, and that part of the hardware 1004 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs 1008, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1008 on top of the hardware 1004 and corresponds to the application 1002.

[0134] The hardware 1004 may be implemented in a standalone network node with generic or specific components. The hardware 1004 may implement some functions via virtualization. Alternatively, the hardware 1004 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1010, which, among others, oversees lifecycle management of the applications 1002. In some embodiments, the hardware 1004 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a RAN or a base station. In some embodiments, some signaling can be provided with the use of a control system 1012 which may alternatively be used for communication between hardware nodes and radio units.

[0135] Figure 11 shows a communication diagram of a host 1102 communicating via a network node 1104 with a UE 1106 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as the UE 612A of Figure 6 and / or the UE 700 of Figure 7), the network node (such as the network node 610A of Figure 6 and / or the network node 800 of Figure 8), and the host (such as the host 616 of Figure 6 and / or the host 900 of Figure 9) discussed in the preceding paragraphs will now be described with reference to Figure 11.

[0136] Like the host 900, embodiments of the host 1102 include hardware, such as a communication interface, processing circuitry, and memory. The host 1102 also includes software, which is stored in or is accessible by the host 1102 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 1106 connecting via an OTT connection 1150 extending between the UE 1106 and the host 1102. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1150.

[0137] The network node 1104 includes hardware enabling it to communicate with the host 1102 and the UE 1106. The connection 1160 may be direct or pass through a core network (like the core network 606 of Figure 6) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0138] The UE 1106 includes hardware and software, which is stored in or accessible by the UE 1106 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via the UE 1106 with the support of the host 1102. In the host 1102, an executing host application may communicate with the executing client application via the OTT connection 1150 terminating at the UE 1106 and the host 1102. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 1150 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 1150.

[0139] The OTT connection 1150 may extend via the connection 1160 between the host 1102 and the network node 1104 and via a wireless connection 1170 between the network node 1104 and the UE 1106 to provide the connection between the host 1102 and the UE 1106. The connection 1160 and the wireless connection 1170, over which the OTT connection 1150 may be provided, have been drawn abstractly to illustrate the communication between the host 1102 and the UE 1106 via the network node 1104, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0140] As an example of transmitting data via the OTT connection 1150, in step 1108, the host 1102 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 1106. In other embodiments, the user data is associated with a UE 1106 that shares data with the host 1102 without explicit human interaction. In step 1110, the host 1102 initiates a transmission carrying the user data towards the UE 1106. The host 1102 may initiate the transmission responsive to a request transmitted by the UE 1106. The request may be caused by human interaction with the UE 1106 or by operation of the client application executing on the UE 1106. The transmission may pass via the network node 1104 in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1112, the network node 1104 transmits to the UE 1106 the user data that was carried in the transmission that the host 1102initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1114, the UE 1106 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1106 associated with the host application executed by the host 1102.

[0141] In some examples, the UE 1106 executes a client application which provides user data to the host 1102. The user data may be provided in reaction or response to the data received from the host 1102. Accordingly, in step 1116, the UE 1106 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 1106. Regardless of the specific manner in which the user data was provided, the UE 1106 initiates, in step 1118, transmission of the user data towards the host 1102 via the network node 1104. In step 1120, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1104 receives user data from the UE 1106 and initiates transmission of the received user data towards the host 1102. In step 1122, the host 1102 receives the user data carried in the transmission initiated by the UE 1106.

[0142] One or more of the various embodiments improve the performance of OTT services provided to the UE 1106 using the OTT connection 1150, in which the wireless connection 1170 forms the last segment.

[0143] In an example scenario, factory status information may be collected and analyzed by the host 1102. As another example, the host 1102 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1102 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1102 may store surveillance video uploaded by a UE. As another example, the host 1102 may store or control access to media content such as video, audio, VR, or AR which it can broadcast, multicast, or unicast to UEs. As other examples, the host 1102 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing, and / or transmitting data.

[0144] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency, and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 1150 between the host 1102 and the UE 1106 in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection1150 may be implemented in software and hardware of the host 1102 and / or the UE 1106. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1150 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or by supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 1150 may include message format, retransmission settings, preferred routing, etc.; the reconfiguring need not directly alter the operation of the network node 1104. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency, and the like by the host 1102. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1150 while monitoring propagation times, errors, etc.

[0145] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Determining, calculating, obtaining, or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box or nested within multiple boxes, in practice computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0146] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored in memory, which in certainembodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hardwired 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.

[0147] 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.EMBODIMENTSGroup A Embodiments

[0148] Embodiment 1 : A method performed by a User Equipment, UE, the method comprising: obtaining (504) measurement related to a link between the UE and another node (e.g., an anchor node, a network node, a TRP, another UE, or the like), wherein the measurement is for either a physical Line of Sight, LOS, path between the UE and the other node or a virtual LOS path between the UE and the other node; and sending (506), to a network node, the measurement and information that indicates whether the measurement is for a physical LOS path or a virtual LOS path.

[0149] Embodiment 2: The method of embodiment 1, wherein the measurement is a positioning related measurement.

[0150] Embodiment 3: The method of embodiment 1, wherein the measurement comprises timing information (e.g., Time of Arrival, ToA, or Reference Signal Timing Difference, RSTD, or Relative Time of Arrival, RTOA, or UE RxTxTimeDiff, or gNB RxTxTimeDiff) and / or angle information (e.g., Angle of Arrival, AoA, or Angle of Departure, AoD).

[0151] Embodiment 4: The method of any of embodiments 1 to 3, wherein: sending (506) the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises sending (506) a measurement report comprising the measurement and sending (506) an indication of whetherArtificial Intelligence, Al, or non-AI based technique is used to generate the measurement; and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises the indication of whether an Al or non-AI based technique is used to generate the measurement.

[0152] Embodiment 5: The method of embodiment 4, wherein the indication of whether an Al or non-AI based technique is used to generate the measurement is separate from the measurement report.

[0153] Embodiment 6: The method of embodiment 4, wherein the indication of whether an Al or non-AI based technique is used to generate the measurement is comprised in the measurement report.

[0154] Embodiment 7: The method of any of embodiments 4 to 6, wherein: if the indication of whether an Al or non-AI based technique is used to generate the measurement indicates that an Al-based technique is used to generate the measurement, then the indication indicates that the measurement is for a virtual LOS path; and if the indication of whether an Al or non-AI based technique is used to generate the measurement indicates that a non-ALbased technique is used to generate the measurement, then the indication indicates that the measurement is for a physical LOS path.

[0155] Embodiment 8: The method of any of embodiments 1 to 3, wherein: sending (506) the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises sending (506) a measurement report comprising the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path.

[0156] Embodiment 9: The method of embodiment 8, wherein the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises a type indicator for the LOS / NLOS indicator that indicates whether the LOS / NLOS indicator and the measurement are for a physical LOS path or a virtual LOS path.

[0157] Embodiment 10: The method of embodiment 8, wherein the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises a virtual LOS indicator that indicates whether the measurement is for a physical LOS path or a virtual LOS path.

[0158] Embodiment 11 : The method of embodiment 8, wherein the measurement report comprises a measurement information element that comprises the measurement and theinformation that indicates whether the measurement is for a physical LOS path or a virtual LOS path.

[0159] Embodiment 12: The method of embodiment 8, wherein the measurement report comprises a first information element that contains the measurement if the measurement is for a physical LOS path and a second information element that contains the measurement if the for a virtual LOS path, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path is implicit information about whether the measurement is comprised in the first information element or the second information element.

[0160] Embodiment 13 : The method of any of embodiments 4 to 12, wherein the measurement report further comprises a LOS / NLOS indicator.

[0161] Embodiment 14: The method of any of embodiments 1 to 13, wherein the other node is an anchor node, a TRP, a network node, or another UE.

[0162] Embodiment 15: The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.Group B Embodiments

[0163] Embodiment 16: A method performed by a network node, the method comprising: receiving (506), from a UE, a measurement related to a link between the UE and another node (e.g., an anchor node, a network node, a TRP, another UE, or the like) and information that indicates whether the measurement is for a physical LOS path or a virtual LOS path; and performing (508) one or more actions using the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path,

[0164] Embodiment 17: The method of embodiment 16, wherein performing (508) the one or more actions comprise using the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path for generating a positioning estimate for the UE (e.g., using an AI / ML model).

[0165] Embodiment 18: The method of embodiment 16 or 17, wherein the measurement is a positioning related measurement.

[0166] Embodiment 19: The method of embodiment 16 or 17, wherein the measurement comprises timing information (e.g., Time of Arrival, ToA, or Reference Signal Timing Difference, RSTD, or Relative Time of Arrival, RTOA, or UE RxTxTimeDiff, or gNB RxTxTimeDiff) and / or angle information (e.g., Angle of Arrival, AoA, or Angle of Departure, AoD).

[0167] Embodiment 20: The method of any of embodiments 16 to 19, wherein:receiving (506) the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises receiving (506) a measurement report comprising the measurement and receiving (506) an indication of whether Artificial Intelligence, Al, or non-AI based technique is used to generate the measurement; and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises the indication of whether an Al or non-AI based technique is used to generate the measurement.

[0168] Embodiment 21 : The method of embodiment 20, wherein the indication of whether an Al or non-AI based technique is used to generate the measurement is separate from the measurement report.

[0169] Embodiment 22: The method of embodiment 20, wherein the indication of whether an Al or non-AI based technique is used to generate the measurement is comprised in the measurement report.

[0170] Embodiment 23: The method of any of embodiments 20 to 22, wherein: if the indication of whether an Al or non-AI based technique is used to generate the measurement indicates that an Al-based technique is used to generate the measurement, then the indication indicates that the measurement is for a virtual LOS path; and if the indication of whether an Al or non-AI based technique is used to generate the measurement indicates that a non-ALbased technique is used to generate the measurement, then the indication indicates that the measurement is for a physical LOS path.

[0171] Embodiment 24: The method of any of embodiments 16 to 19, wherein: receiving (506) the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises receiving (506) a measurement report comprising the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path.

[0172] Embodiment 25: The method of embodiment 24, wherein the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises a type indicator for the LOS / NLOS indicator that indicates whether the LOS / NLOS indicator and the measurement are for a physical LOS path or a virtual LOS path.

[0173] Embodiment 26: The method of embodiment 24, wherein the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path comprises a virtual LOS indicator that indicates whether the measurement is for a physical LOS path or a virtual LOS path.

[0174] Embodiment 27: The method of embodiment 24, wherein the measurement report comprises a measurement information element that comprises the measurement and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path.

[0175] Embodiment 28: The method of embodiment 24, wherein the measurement report comprises a first information element that contains the measurement if the measurement is for a physical LOS path and a second information element that contains the measurement if the for a virtual LOS path, and the information that indicates whether the measurement is for a physical LOS path or a virtual LOS path is implicit information about whether the measurement is comprised in the first information element or the second information element.

[0176] Embodiment 29: The method of any of embodiments 20 to 28, wherein the measurement report further comprises a LOS / NLOS indicator.

[0177] Embodiment 30: The method of any of embodiments 16 to 29, wherein the other node is an anchor node, a TRP, a network node, or another UE.

[0178] Embodiment 31 : The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.Group C Embodiments

[0179] Embodiment 32: A user equipment comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0180] Embodiment 33: A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0181] Embodiment 34: A user equipment (UE) comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input ofinformation into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

[0182] Embodiment 35: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0183] Embodiment 36: The host of the previous embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

[0184] Embodiment 37: A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0185] Embodiment 38: The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.

[0186] Embodiment 39: The method of any of the previous 2 embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application.

[0187] Embodiment 40: A communication system configured to provide an over-the-top (OTT) service, the communication system comprising: a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; anda network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0188] Embodiment 41 : The communication system of the previous embodiment, further comprising: the network node; and / or the UE.

[0189] Embodiment 42: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to receive the user data from a user equipment (UE) for the host.

[0190] Embodiment 43: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application that receives the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0191] Embodiment 44: The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data.

[0192] Embodiment 45: A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B embodiments to receive the user data from the UE for the host.

[0193] Embodiment 46: The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host.

[0194] Embodiment 47: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising:processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the operations of any of the Group A embodiments to receive the user data from the host.

[0195] Embodiment 48: The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host.

[0196] Embodiment 49: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0197] Embodiment 50: A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host.

[0198] Embodiment 51 : The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the host application.

[0199] Embodiment 52: The method of the previous embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

[0200] Embodiment 53: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE beingconfigured to perform any of the steps of any of the Group A embodiments to transmit the user data to the host.

[0201] Embodiment 54: The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host.

[0202] Embodiment 55: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0203] Embodiment 56: A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, receiving user data transmitted to the host via the network node by the UE, wherein the UE performs any of the steps of any of the Group A embodiments to transmit the user data to the host.

[0204] Embodiment 57: The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

[0205] Embodiment 58: The method of the previous 2 embodiments, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

Claims

CLAIMS1. A method performed by a User Equipment, UE, the method comprising: obtaining (504) a positioning-related measurement for to a link between the UE and an anchor node; and sending (506), to a network node, the positioning-related measurement and information that indicates whether the positioning-related measurement was obtained using an Artificial Intelligence, Al, / Machine Learning, ML, based technique or a non-AI / ML technique .

2. The method of claim 1, wherein the positioning-related measurement comprises timing information.

3. The method of claim 2, wherein the timing information comprises a Time of Arrival, ToA, or Reference Signal Timing Difference, RSTD, or Relative Time of Arrival, RTOA, or UE RxTxTimeDiff, or gNB RxTxTimeDiff.

4. The method of any of claims 1 to 3, wherein the positioning-related measurement comprises angle information.

5. The method of claim 4, wherein the angle information comprises Angle of Arrival, AoA, or Angle of Departure, AoD.

6. The method of any of claims 1 to 5, wherein: sending (506) the positioning-related measurement and the information that indicates whether the positioning-related measurement was obtained using an AI / ML based technique or a non- AI / ML technique comprises sending (506) a measurement report comprising the measurement and sending (506) an indication of whether the positioning-related measurement was obtained using an AI / ML based technique or a non-AI / ML technique.

7. The method of claim 6, wherein the indication of whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement is separate from the measurement report.

8. The method of claim 6, wherein the indication of whether an AI / ML based technique or non-AI / ML based technique was used to generate the positioning-related measurement iscomprised in the measurement report.

9. The method of any of claims 1 to 5, wherein: sending (506) the measurement and the information that indicates whether an AI / ML- based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises sending (506) a measurement report comprising the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement.

10. The method of claim 9, wherein the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises a type indicator for the LOS / NLOS indicator that indicates whether the LOS / NLOS indicator and the measurement are for a physical Line-of-Sight, LOS, path or a virtual LOS path.

11. The method of claim 9, wherein the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises a virtual LOS indicator that indicates whether or not the measurement is for a virtual LOS path.

12. The method of claim 9, wherein the measurement report comprises a measurement information element that comprises the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement.

13. The method of claim 9, wherein the measurement report comprises a first information element that contains the measurement if the measurement was obtained using a non-AI / ML based technique and a second information element that contains the measurement if the measurement was obtained using an AI / ML-based technique, and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement is implicit information about whether the measurement is comprised in the first information element or the second information element.

14. The method of any of claims 6 to 9, 12, and 13, wherein the measurement report furthercomprises a LOS / NLOS indicator.

15. The method of any of claims 1 to 14, wherein the anchor node is a radio network node or another UE.

16. A User Equipment, UE, (500; 700), comprising: a communication interface (712) comprising a transmitter (718) and a receiver (720); and processing circuitry (702) associated with the communication interface (712), the processing circuitry (702) configured to cause the UE (500; 700) to: obtain (504) a positioning-related measurement for to a link between the UE and an anchor node; and send (506), to a network node, the measurement and information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement.

17. The UE (500; 700) of claim 16, wherein the processing circuitry (702) is further configured to cause the UE (500; 700) to perform the method of any of claims 2 to 15.

18. A method performed by a network node (502), the method comprising: receiving (506), from a User Equipment, UE, (500), a positioning-related measurement for to a link between the UE and an anchor node and information that indicates whether an Artificial Intelligence, Al, / Machine Learning, ML, -based technique or a non-AI / ML based technique was used to generate the positioning-related measurement; and performing (508) one or more actions using the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement,19. The method of claim 18, wherein performing (508) the one or more actions comprise using the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement for generating a positioning estimate for the UE.

20. The method of claim 18 or 19, wherein the measurement comprises timing information.

21. The method of claim 20, wherein the timing information comprises a Time of Arrival, ToA, or Reference Signal Timing Difference, RSTD, or Relative Time of Arrival, RTOA, or UE RxTxTimeDiff, or gNB RxTxTimeDiff22. The method of any of claims 18 to 21, wherein the measurement comprises angle information.

23. The method of claim 22, wherein the angle information comprises Angle of Arrival, AoA, or Angle of Departure, AoD.

24. The method of any of claims 18 to 23, wherein: receiving (506) the measurement and the information that indicates whether an AI / ML- based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises receiving (506) a measurement report comprising the measurement and receiving (506) the indication of whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement.

25. The method of claim 24, wherein the indication of whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement is separate from the measurement report.

26. The method of claim 24, wherein the indication of whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement is comprised in the measurement report.

27. The method of any of claims 18 to 20, wherein: receiving (506) the measurement and the information that indicates whether an AI / ML- based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises receiving (506) a measurement report comprising the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement.

28. The method of claim 27, wherein the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether an AI / ML-based technique or anon-AI / ML based technique was used to generate the positioning-related measurement comprises a type indicator for the LOS / NLOS indicator that indicates whether the LOS / NLOS indicator and the measurement are for a physical LOS path or a virtual LOS path.

29. The method of claim 28, wherein the measurement report further comprises a LOS / NLOS indicator, and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement comprises a virtual LOS indicator that indicates whether or not the measurement is for a virtual LOS path.

30. The method of claim 28, wherein the measurement report comprises a measurement information element that comprises the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement.

31. The method of claim 28, wherein the measurement report comprises a first information element that contains the measurement if the measurement was obtained using a non-AI / ML based technique and a second information element that contains the measurement if the measurement was obtained using an AI / ML based technique, and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement is implicit information about whether the measurement is comprised in the first information element or the second information element.

32. The method of any of claims 24 to 27, 30, and 31, wherein the measurement report further comprises a LOS / NLOS indicator.

33. The method of any of claims 18 to 32, wherein the anchor node is a radio network node or another UE.

34. A network node (502; 800), comprising processing circuitry (802) configured to cause the network node (502; 800) to: receive (506), from a User Equipment, UE, (500), a positioning-related measurement for to a link between the UE and an anchor node and information that indicates whether an ArtificialIntelligence, Al, / Machine Learning, ML, -based technique or a non-AI / ML based technique was used to generate the positioning-related measurement; and perform (508) one or more actions using the measurement and the information that indicates whether an AI / ML-based technique or a non-AI / ML based technique was used to generate the positioning-related measurement,35. The network node (502; 800) of claim 34, wherein the processing circuitry (802) is further configured to cause the network node (502; 800) to perform the method of any of claims 19 to 33.

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