Signaling for uncertainty in ai / ML model-based positioning

Abstract

Systems and methods for indicating the use of an artificial intelligence and / or machine learning (AI / ML) model for determining positioning and / or uncertainty are provided. A wireless device receives, from a network node, a positioning request and performs positioning measurements on at least one reference signal. The wireless device processes the positioning measurements using an AI / ML method to determine a positioning result and transmits a positioning report that includes an indication that the positioning result was determined using the AI / ML method.

Description

SIGNAI TNG FOR UNCERTAINTY IN AI / ML MODEL-BASED POSITIONINGCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of US Provisional Application No. 63 / 702,950 filed on October 3, 2024, the entire contents of which are hereby incorporated by reference.TECHNICAL FIELD

[0002] The present disclosure generally relates to wireless communications and wireless communication networks.INTRODUCTION

[0003] Standardization bodies such as Third Generation Partnership Project (3GPP) are studying potential solutions for efficient operation of wireless communication in new radio (NR) networks. The next generation mobile wireless communication system 5G / NR will support a diverse set of use cases and a diverse set of deployment scenarios. The later includes deployment at both low frequencies (e.g. 100s of MHz), similar to LTE today, and very high frequencies (e.g. mm waves in the tens of GHz). Besides the typical mobile broadband use case, NR is being developed to also support machine type communication (MTC), ultra-low latency critical communications (URLCC), side-link device-to-device (D2D) and other use cases.

[0004] Positioning and location services have been topics in LTE standardization since 3GPP Release 9. An objective was to fulfill regulatory requirements for emergency call positioning but other use case like positioning for Industrial Internet of Things (I-IoT) are also considered.

[0005] NR positioning since Release 16, based on the 3 GPP NR radio-technology, has provided added value in terms of enhanced location capabilities. The operation in low and high frequency bands (i.e. below and above 6GHz) and utilization of massive antenna arrays provide additional degrees of freedom to substantially improve the positioning accuracy. The possibility to use wide signal bandwidth in low and especially in high bands brings new performance bounds for user location for well-known positioning techniques based on OTDOA and UTDOA, Cell-ID or E- Cell-ID etc., utilizing timing measurements to locate a UE.

[0006] Artificial intelligence (Al) and / or machine learning (ML) techniques comprise one or more algorithms which use a set of data as input for training one or more AI / ML models. The output of the AI / ML model can be used by the device (e.g. user equipment (UE), base station (BS) or another node) for performing certain operations or taking certain decisions (e.g. handover, etc.) fully or partially based on the prediction, which in turn depends on the trained model. The AI / ML model can be trained in the device online (or on-the-fly while processing the data) or offline in the background. More specifically:

[0007] - Online training is an AI / ML training process where the model being used for inference is (typically continuously) trained in (near) real-time with the arrival of new training samples or data.

[0008] - Offline training is an AI / ML training process where the model is trained based on collected samples or data, and where the trained model is later used or delivered for inference.

[0009] AI / ML model inference refers to a process of using a trained AI / ML model to produce a set of outputs based on a set of inputs.

[0010] The AL / ML model(s) can be trained in a device, which can be a UE, a network node, or another node. In this respect the AI / ML modes can be broadly classified as:

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

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

[0013] Case III: One-sided (AI / ML) model. It is a UE-side (AI / ML) model or a Network-side (AI / ML) model.

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

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

[0016] The term lifecycle management (LCM) of an AI / ML model refers to the process of developing, deploying and maintaining the AI / ML model. An example of an AI / ML model training pipeline illustrating the different steps / stages would include data ingestion, data pre-processing, model training, model evaluation and model registration.

[0017] For example, the pipeline can include several processing stages as gathering unprocessed input data from data repositories (data ingestion), finding high-quality input features (data pre-processing), finding the optimal mapping of the model input features to a desired model output target in a sense determined by a loss function (model training), evaluate model performance on unseen data from a functional level as well as from a system level when relevant (model evaluation). The training pipeline typically ends with a model registration stage, which may comprise of operations to make the ML model runnable via compilation to a specific hardware and of steps like versioning and packaging of the model so that it can be executed.

[0018] AI / ML modeling can be used for UE positioning. A UE or a gNB, depending on capability, can have a trained model stored inside the device, or have an untrained AI / ML that can be trained on-the-fly to either produce measurements that are required to localize a UE within a radio access network (RAN) coverage area or directly predict / determine the UE location by exploiting the measurements performed by the UE or gNB on reference signals such as positioning reference signal (PRS), sounding reference signal (SRS) etc. within a RAN coverage area.

[0019] Measurements predicted / determined by the UE by exploiting an AI / ML model can be defined as, but not limited to:

[0020] RSTD: It is defined as the Reference Signal Time Difference between the positioning node j and the reference positioning node i. It is measured on the DL PRS signals and always involve two cells (cell is interchangeably called a TRP).

[0021] UE Rx-Tx time difference: It is defined as TUE-RX - TUE-TX.

[0022] Where:

[0023] - TUE-RX is the UE received timing of downlink subframe #i from a positioning node, defined by the first detected path in time. It is measured on PRS signals received from the gNB.

[0024] - TUE-TX is the UE transmit timing of uplink subframe #j that is closest in time to the subframe #i received from the positioning node.

[0025] There currently exist certain challenges.SUMMARY

[0026] It is an object of the present disclosure to obviate or mitigate at least one disadvantage of the prior art.

[0027] There are provided systems and methods for indicating use of an AI / ML model for positioning and / or uncertainty determination.

[0028] In a first aspect there is provided a method performed by a wireless device, comprising a memory and processing circuitry, in a wireless communication system. The wireless device, receives, from a network node, a positioning request and performs positioning measurements on at least one reference signal. The wireless device processes the positioning measurements using an artificial intelligence and / or machine learning (AI / ML) method to determine a positioning result; and transmits, to the network node, a positioning report including the positioning result and an indication that the positioning result was determined using the AI / ML method.

[0029] In some embodiments, the positioning request can include an indication that the AL / ML method is to be used for determining the positioning result. In some embodiments, the positioning request can further include an uncertainty reporting configuration parameter specifying at least one uncertainty format type. The uncertainty format type can include at least one of: raw measurement uncertainty, AI / ML-based uncertainty, and non-AI / ML-based uncertainty.

[0030] In some embodiments, the wireless device can further calculate uncertainty information associated with the positioning result based on the specified uncertainty format type. Calculating the uncertainty information can comprise determining at least one of a mean value and a standard deviation of the positioning result determined by the AI / ML method.

[0031] In some embodiments, the positioning result can include at least one of: reference signal time difference measurements, user equipment receive-transmit time difference measurements, and location coordinates. In some embodiments, the positioning report can include a geographical area description shape that defines an uncertainty region around location coordinates.

[0032] In another aspect there is provided a method performed by a network node, comprising a memory and processing circuitry, in a wireless communication system. The network node transmits, to a wireless device, a positioning request; and receives, from the wireless device, a positioning report including a positioning result and an indication that the positioning result was determined using an artificial intelligence and / or machine learning (AI / ML) method.

[0033] In some embodiments, the network node can further transmit, to a Location Services (LCS) client, a positioning response indicating that the positioning result was determined using an AI / ML method.

[0034] In some embodiments, the positioning request can include an indication that the AL / ML method is to be used for determining the positioning result. In some embodiments, the positioning request can further include an uncertainty reporting configuration parameter specifying at least one uncertainty format type. The uncertainty format type can include at least one of: raw measurement uncertainty, AI / ML-based uncertainty, and non-AI / ML-based uncertainty.

[0035] In some embodiments, the positioning result can include at least one of: reference signal time difference measurements, user equipment receive-transmit time difference measurements, and location coordinates. In some embodiments, the positioning report includes a geographical area description shape that defines an uncertainty region around location coordinates.

[0036] The various aspects and embodiments described herein can be combined alternatively, optionally and / or in addition to one another.

[0037] Other aspects and features of the present disclosure will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying figures.BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Embodiments of the present disclosure will now be described, by way of example only, with reference to the attached Figures, wherein:

[0039] Figure 1 is an example communication system;

[0040] Figure 2 illustrates an example ellipsoid point with altitude and uncertainty ellipsoid;

[0041] Figure 3 illustrates and example AI / ML model;

[0042] Figure 4 illustrates an example rectangle with uncertainty box;

[0043] Figure 5A illustrates an example mean location and uncertainty box;

[0044] Figure 5B illustrates an example shape description of an ellipsoid point with altitude and uncertainty ellipsoid;

[0045] Figure 6 is a signaling diagram illustrating a first embodiment;

[0046] Figure 7 is a signaling diagram illustrating a second embodiment;

[0047] Figure 8 is a flow chart illustrating a method performed by a network node when a UE operates in UE-based mode;

[0048] Figure 9 is a flow chart illustrating a method performed by a network node when a UE operates in UE-assisted mode;

[0049] Figure 10 is a flow chart illustrating a method performed by a wireless device;

[0050] Figure 11 is a block diagram of an example wireless device;

[0051] Figure 12 is a block diagram of an example network node;

[0052] Figure 13 is a block diagram illustrating an example virtualization environment.DETAILED DESCRIPTION

[0053] The embodiments set forth below represent information to enable those skilled in the art to practice 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 description and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the description.

[0054] In the following description, numerous specific details are set forth. However, it is understood that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures, and techniques have not been shown in detail in order not to obscure the understanding of the description. Those of ordinary skill in the art, with the included description, will be able to implement appropriate functionality without undue experimentation.

[0055] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.

[0056] Figure 1 illustrates an example of a communication system 100 in accordance with some embodiments.

[0057] In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points. 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 102 includes one or more Open- RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 102 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 102, including one or more network nodes 110 and / or core network nodes 108.

[0058] 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 O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112A, 112B, 112C, and112D (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0059] 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 100 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 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0060] The UEs 112 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 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 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 102.

[0061] In the depicted example, the core network 106 connects the network nodes 110 to one or more host computing systems, such as host 116. 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 106 includes one more core network nodes (e.g., core network node 108) 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 108. 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 IdentifierDe-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0062] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102. The host 116 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.

[0063] As a whole, the communication system 100 of Figure 1 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0064] In some examples, the telecommunication network 102 is a cellular network that implements 3 GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 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 loT services to yet further UEs.

[0065] In some examples, the UEs 112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information tothe access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi -RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0066] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112C and / or 112D) and network nodes (e.g., network node HOB). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 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 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 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 114 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0067] The hub 114 may have a constant / persistent or intermittent connection to the network node HOB. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112C and / or 112D), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node HOB. In other embodiments, the hub 114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node HOB, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0068] Note that some embodiments given herein refer to a 3 GPP cellular communications system and, as such, 3GPP terminology or terminology similar to 3GPP terminology is oftentimes used. However, the concepts disclosed herein are not limited to a 3GPP system.

[0069] Note that, in the description herein, reference may be made to the term “cell”. However, particularly with respect to 5G / NR concepts, beams may be used instead of cells and, as such, it is important to note that the concepts described herein are equally applicable to both cells and beams.

[0070] Returning to the discussion of AI / ML model-based positioning, the following nonlimiting terminology will be used herein.

[0071] ML model: a manageable representation of an ML model algorithm.

[0072] NOTE 1 : an ML model algorithm is a mathematical algorithm through which running a set of input data can generate a set of inference output.

[0073] NOTE 2: ML model algorithm is proprietary and not in scope for standardization and therefore not treated in this specification.

[0074] NOTE 3: ML model may include metadata. Metadata may include e.g. information related to the trained model, and applicable runtime context.

[0075] ML model training: a process performed by an ML training function to take training data, run it through an ML model algorithm, derive the associated loss and adjust the parameterization of that ML model iteratively based on the computed loss and generate the trained ML model.

[0076] ML model initial training: a process of an initial version of an ML model.

[0077] ML model re-training: a process of training a previous version of an ML model and generate a new version.

[0078] NOTE 4: a new version of a trained ML model supports the same type of inference as the previous version of the ML model, i.e., the data type of inference input and data type ofinference output remain unchanged between the two versions of the ML model, but parameter values might be different for the re-trained model.

[0079] ML model joint training: a process of training a group of ML models.

[0080] ML training function: a logical function with ML model training capabilities.

[0081] ML model testing: a process of testing an ML model using testing data.

[0082] ML testing function: a logical function with ML model testing capabilities.

[0083] AI / ML inference: a process of running a set of input data through a trained ML model to produce set of output data, such as predictions.

[0084] NOTE 5: the inference represents the process to realize the Al capabilities by utilizing a trained ML model and other Al enablers if needed, hence the AI / ML prefix is used when referring to inference as compared to training and testing.

[0085] AI / ML inference function: a logical function that employs trained ML model(s) to conduct inference.

[0086] AI / ML inference emulation: running the inference process to evaluate the performance of an ML model in an emulation environment before deploying it into the target environment.

[0087] ML model deployment: a process of making a trained ML model available for use in the target environment.

[0088] Geographical Area Description (GAD) shapes

[0089] 3GPP TS 23.032 vl 8.0.0 incorporates a number of different shapes that can be chosen according to need.

[0090] - Ellipsoid Point;

[0091] - Ellipsoid point with uncertainty circle;

[0092] - Ellipsoid point with uncertainty ellipse;

[0093] - Polygon;

[0094] - Ellipsoid point with altitude;

[0095] - Ellipsoid point with altitude and uncertainty ellipsoid;

[0096] - Ellipsoid Arc;

[0097] - High Accuracy Ellipsoid point with uncertainty ellipse;

[0098] - High Accuracy Ellipsoid point with scalable uncertainty ellipse;

[0099] - High Accuracy Ellipsoid point with altitude and uncertainty ellipsoid;

[0100] - High Accuracy Ellipsoid point with altitude and scalable uncertainty ellipsoid.

[0101] Shapes relevant to Local Co-ordinates:

[0102] Local 2D point with uncertainty ellipse (only in 5GS);

[0103] Local 3D point with uncertainty ellipsoid (only in 5GS).

[0104] Shapes relevant to a pair of devices:

[0105] - Range and Direction (only in 5GS);

[0106] - Relative Location (only in 5GS).

[0107] 3GPP TS 37.355 v 18.0.0 includes the information element (IE) NR-TimingQuality defining the quality of a timing value (e.g. of a TOA measurement).— ASN1STARTNR-TimingQuality- rl 6 : : = SEQUENCE { timingQualityValue- rl6 INTEGER ( 0 . . 31 ) , timingQualityResolution- rl6 ENUMERATED { mdotl , ml , ml O , m30 , . . . } ,}— ASN1STOP

[0108] Ellipsoid point with altitude and uncertainty ellipsoid (TS 23,032)

[0109] The "ellipsoid point with altitude and uncertainty ellipsoid" is characterised by the coordinates of an ellipsoid point with altitude, distances rl (the "semi-major uncertainty"), r2 (the "semi-minor uncertainty") and r3 (the "vertical uncertainty") and an angle of orientation A (the "angle of the major axis"). It describes formally the set of points which fall within or on the surface of a general (three dimensional) ellipsoid centred on an ellipsoid point with altitude whose real semi-major, semi-mean and semi-minor axis are some permutation of rl, r2, r3 with rl > r2. The r3 axis is aligned vertically, while the rl axis, which is the semi-major axis of the ellipse in ahorizontal plane that bisects the ellipsoid, is oriented at an angle A (0 to 180 degrees) measured clockwise from north, as illustrated in Figure 2.

[0110] The typical use of this shape is to indicate a point when its horizontal position and altitude are known only with a limited accuracy, but the geometrical contributions to uncertainty can be quantified. The confidence level with which the position of a target entity is included within the shape is also included.[oni] With AI / ML for positioning, it is currently unclear how to signal the uncertainty in measurements and location. Without uncertainty information, the network will not be able to assess whether the positioning QoS was met or not, what is the positioning error, if any fallback option(s) have to be employed such as aborting the current method and selecting a new method. Further, a location client which has requested for a location should be aware of the accuracy of the location and what are the major / minor axis and / or the radius within which the UE can be found.

[0112] In some embodiments described herein, a mechanism is provided where a network node informs the location client that the results are generated using an AI / ML model. The information can be relayed using either a new GAP shape designed for AI / ML based output or by using an explicit indication (e.g. flag). Further network signaling configuration choices are provided on how to obtain the measurement uncertainly from the UE, especially when AI / ML based procedure can be applied. Accordingly, the uncertainty can be associated with the AI / ML output, where the output can either be measurement or location.

[0113] Several example methods are possible for generating the uncertainty of AI / ML model output.

[0114] Method 1 : Ensemble methods

[0115] In Method 1, multiples models are trained to generate the same output and fulfill the same functionality. The multiple models are trained to provide diversity and robustness for the AI / ML enabled functionality.

[0116] For model inference, each of the N individual models are executed in parallel. Each individual model takes the same model input data X, and the i-th model generates a model output Yi, i = 0,1, ... , N — 1. The predictions FQ< YI> ■■■ , !»_-£ of multiple models are combined into one final prediction Y* . One typical way to combine is to find the average value:

[0117] Method 1-A:

[0118] Since the AI / ML model employs methods which let the model produce a number of outputs as an intermediate output (below final; before producing a mean or optimum value), thus this method allows the UE / LMF to report the variance from the intermediate output which would then allow the client to gauge the variance and compute uncertainty. The client can produce different location points based upon the obtained variance / standard deviation and thus would be able to create an uncertainty area.

[0119] An example is illustrated in Figure 3 whereare intermediate outputs.

[0120] The predicted output is the mean of predictions from all ensemble models, given by

[0121] The predicted variance of the output is the variance of predictions from all ensemble models, given by

[0122] While operating as UE-assisted mode, the UE can provide the mean value as main nr- RSTD report and the variance / standard deviation as uncertainty. The LMF can take this report to compute the location and uncertainty in terms of variance and standard deviation.

[0123] While operating as UE-based mode, the UE can provide the mean value of longitude / latitude as main location and the variance / standard deviation as uncertainty.

[0124] Method 1-B:

[0125] For method 1-B, the individual model is trained with the same (or almost the same) training dataset, but the model parameters values are initialized with different random seeds when training the model. Random initialization leads to N different models which have the same model structure, but somewhat different model parameters.

[0126] Method 2: Single deterministic networks

[0127] In this method, the prediction is generated by running a single forward pass using a deterministic AI / ML model. The estimation of prediction uncertainty is generated as a part of the model output.

[0128] For instance, instead of simply generating a prediction value Y as model input, the final layer of the model is replaced by a probabilistic layer, which estimates the mean and variance <J of the of model output Y. The model output error is typically modelled as an independent and identically distributed (iid) Normal distribution<J) .

[0129] Once the parameters of the normal distribution are known, then uncertainty or confidence level of model output can be calculated.

[0130] Method 3 : Estimation of uncertainty based on test-time data augmentation

[0131] In this method, based on an original model input Xt, J versions of model input Xt, {X . Xi , x-1}, can be generated artificially via data augmentation.

[0132] Then for a given model input Xt, J versions of model output YtJ, {Y£°, Y*, ... . , K1], can be generated by running the model J times, each time with a different model input X- . The final output can be the average value of the J versions:

[0133] The standard deviation or confidence level of the output can be derived from the J versions of output {Y£°, Y*, ... . , Y^1}.

[0134] Method 4: Bayesian methods

[0135] In this method, model parameters are explicitly modeled as random variables. For a single forward pass, the parameter values of the model are sampled from its random distribution. Therefore, the prediction is stochastic and each prediction is based on different model weights. It can be viewed that J versions of the model are generated, each version with a (slightly) different model parameter value (i.e., weights and biases) via sampling the random distribution of the model parameter.

[0136] Then for a given model input Xt, the J versions of the model are executed, each generating a model output / .

[0137] Then the final output can be the average value of the J versions:

[0138] The standard deviation or confidence level of the output can be derived from the J versions of output {Y- Y*, ... . , Y^-1}.

[0139] Method 5 : Dropout based methods

[0140] In this method, multiple versions of the model are derived also. The different versions of a given model is generated by random dropout of connections in the model.

[0141] Types of messages for reporting uncertainty of AI / ML model output

[0142] Various types of messages for reporting uncertainty of AI / ML model output can be defined, including the following variants.

[0143] For regression models: mean and standard deviation of the model output can be generated. The standard deviation <JYrepresents the uncertainty of the model output. If needed, <JYcan be reported.

[0144] For classification models: estimated probability can be produced which indicates the likelihood the model output is correct.

[0145] The uncertainty of the model output can be reported via its confidence range.

[0146] - In one example, the confidence is defined using the range of model output Y,associated with a predefined percentage value p%. For example, 95% confidence range [F[Ow, Yhigh], i e., 95% confidence that the correct model output is in the range of Yiow< K < Yhigh. Other percentage values, e.g., 98%, 90%, 80% can be used to define the confidence range of the model output as well.

[0147] - In another example, the confidence is defined using the percentage value p% for a predefined range of model output values [F[Ow, Yhigh]- For instance, for estimating the horizontal location of a device, p% is estimated that the true UE location is within + / - 1.0 meter of the model output.

[0148] - In yet another example, the uncertainty report includes both (a) the range of model output Y, [Ffow, Yhigh] and (b) the associated percentage value p%. That is, neither is predefined. The report indicates that the correct model output is in the range of Yiow< Y < Yhighwith probability of p%.

[0149] In the above, p% may take values of 0% - 100%. The reporting can have granularity of 1%, so that integer (0, 1, ... 100) represents percentages (0%, 1%, . . . , 100%). 0% can be understood as “no information available”.

[0150] [Kiow, Yhigh] can be indicated by error e of model output Y. That is, Yiow= Y — e, ^low = Y + e . Thus, only the error e need to be reported.

[0151] For the use case of positioning, error e may be called “accuracy” or “uncertainty”, and p% may be called “confidence”. For example, in the IES below, the quality of horizonal and vertical location estimation are indicated by “accuracy” and “confidence”. A mapping table is defined for “accuracy” so that each integer value of “accuracy” maps to an error in meters. Field “confidence” have integer values 0,1, ... 100, to represent l%-100%, with ‘0’ indicates that the confidence info couldn’t be generated.Hori zontalAccuracy : : = SEQUENCE { accuracy INTEGER ( 0 . . 127 ) , confidence INTEGER ( 0 . . 100 ) ,}VerticalAccuracy : : = SEQUENCE { accuracy INTEGER ( 0 . . 127 ) , confidence INTEGER ( 0 . . 100 ) ,}

[0152] Use uncertainty of AI / ML model output for model LCM

[0153] The uncertainty information Ikof the k-th AI / ML model can be used to indicate how confident or reliable the model output Ykis. Here k can be an index related to timing of Yk.

[0154] By observing a sequence of uncertainty information Ikover time, this provides information on the model quality and robustness.

[0155] Thus, when entity#A reports uncertainty information Ikto entity#B, entity#B may keep track of the reported Ikvalues and make management decisions accordingly.

[0156] - For example, if the reported Ikvalues over a sliding time window indicates poor accuracy and / or low confidence of model output Yk, then entity#B may make a decision to deactivate the model in entity#A.

[0157] LMF-UE Signaling Embodiments

[0158] In some embodiments, the LMF is configured to provide the uncertainty using either non-AI / ML (e.g. conventional positioning method such as DL-TDOA without using AI / ML models) or using AI / ML models or both. If the LMF has to evaluate the performance of an AI / ML model, it may obtain both and compare the performance. Another aspect is that, for evaluating if positioning QoS (in terms of accuracy) is met or not, it may decide to base it upon the input raw measurements rather than the AI / ML model output. In such case, it may request the measurement uncertainty without using the AI / ML model.

[0159] NR-DL-TDOA-RequestLocationlnformation

[0160] The IE NR-DL-TDOA-RequestLocationlnformation is used by the location server to request NR DL-TDOA location measurements from a target device.— ASN1STARTNR-DL-TDOA-RequestLocationInformation-rl 6 : : = SEQUENCE { nr-DL-PRS-RstdMeasurementInfoRequest-rl 6 ENUMERATED { true } OPTIONAL, — Need ON nr-RequestedMeasurements-rl6 BIT STRING { prs rs rpReq ( 0 ) , firstPathRs rpReq-rl7 ( 1 ) , dl-PRS-RSCPD-Request-rl 8 ( 2 ) } ( SI ZE ( 1 . . 8 ) ) , nr-As sistanceAvailability- rl 6 BOOLEAN, nr- DL-TDOA- Report Conf ig-rl 6 NR- DL-TDOA- Report Conf ig-rl 6OPTIONAL, — Need ON additionalPaths-rl 6 ENUMERATED { requested }OPTIONAL, — Need ON nr-UE-RxTEG-Request-rl7 ENUMERATED { requested }OPTIONAL, — Need ON nr-los-nlos-IndicatorRequest-rl7 SEQUENCE { type-rl7 LOS -NLOS- Indi cat orTypel- rl7 , granularity-rl7 LOS-NLOS- IndicatorGranularityl- rl7 ,} OPTIONAL, —Need ON additionalPathsExt- rl7 ENUMERATED { requested }OPTIONAL, — Need ON additionalPathsDL-PRS-RSRP-Request- rl7 ENUMERATED { requested } OPTIONAL, — Need ON multiMeas InSameReport- rl7 ENUMERATED { requested }OPTIONAL — Need ON ] ] , [ [ nr-DL-PRS- JointMeasurementRequest- rl 8 SEQUENCE {nr-DL-PRS- JointMeasurementRequestedPFL-List-rl8 SEQUENCE (SIZE(2. .3) ) OF INTEGER (0. . nrMaxFreqLayers-l-rl6) OPTIONAL — Need ON } OPTIONAL, —Need ON nr-DL-PRS-RxHoppingRequest-rl8 SEQUENCE { nr-DL-PRS-RxHoppingTotalBandwidth-rl8 CHOICE { frl ENUMERATED {mhz40, mhz50, mhz80, mh z 100 } , fr2 ENUMERATED {mhzlOO, mhz200, mh z 400 } } OPTIONAL —Need ON} OPTIONAL —Need ON] ] ,[ [ requestUncertaintyFormat-rl9 BIT STRING { rawMeasurement (0) , aiml-ModelBased (1) , nonAIML-Based (2) } (SIZE(1. .8) ) , ] ] }

[0161] In some embodiments, the external client or Network function, which is the consumer of the positioning, can be informed (exposure) that the location output is performed using an AI / ML model. An example is shown below with reference to 3GPP TS 29.572.

[0162] The Location Data message can include parameters indicating location estimates using AI / ML techniques and / or positioning methods using AI / ML techniques.Table 6.1.4.2.2-2: Data structures supported by the POST Response Body on this resource

[0163] In TS 37.355, this can be reflected by the UE by including an AI / ML parameter in theLocation Source of the result.Locationsource- r 13 BIT STRING { a-gnss (0) , wlan ( 1 ) , bt (2) , tbs ( 3 ) , sensor ( 4 ) , ha-gnss-v!510 (5) , motion-sensor-v!550 (6) , dl-tdoa-rl6 (7) , dl-aod-rl6 (8) , aiml-rl9 (9) } ( SIZE ( 1. .16) )

[0164] Type of Shape

[0165] The Type of Shape information field identifies the type which is being coded in the Shape Description. The Type of Shape is coded as shown below with reference to 3GPP TS 23.032.Table 2a: Coding of Type of Shape

[0166] Figure 4 illustrates an example of the message format for a Rectangle with uncertainty box.

[0167] Shape Description:

[0168] The location can be described by a mean location (mean longitude and latitude) and there can at least be four quadrants based upon the standard deviation of longitude and latitude which can form an uncertainty box. It is possible to also deduce major and minor axis from the mean location and standard deviation and to be able to deduce uncertainty in terms of ellipsoid. It is also possible that the UE / LMF produces the mean and variance / standard deviation of the altitude.

[0169] Figure 5A illustrates an example of a mean location and uncertainty box.

[0170] It is also possible to reuse the existing shape where the spare bit can be used so that an additional octet is added to include the mean, standard deviation and / or variance.

[0171] An example is that existing Ellipsoid point with uncertainty Circle is extended with uncertainty / variance. Figure 5B illustrates an example of a shape description of an ellipsoid point with altitude and uncertainty ellipsoid.

[0172] 3GPP TS 37.355 includes the IE A7?-RSTDQuality. The IE A7?-TimingQuality can define the quality of a timing value of RSTD measurement assocaited with an AI / ML model output.— ASN1STARTNR-TimingQuality-rl9 : := SEQUENCE { timingQualityVariance-rl9 INTEGER (-10000..10000) , timingQualityStdDev-rl9 INTEGER (1..10000) ,}— ASN1STOP

[0173] For location info uncertainty, the below signaling can be used for reporting AI / ML model-based location output uncertainty.NR-LocationUncertainty-rl9 : := SEQUENCE { units-rl9 ENUMERATED {mm, cm, m, ... }, meanLocation-rl9 SEQUENCE { longitude-rl9 INTEGER (0..255) , latitude-rl9 INTEGER (0..255) }, stdDevLocation-rl9 SEQUENCE { longitude-rl9 INTEGER (0..255) , latitude-rl9 INTEGER (0..255) }, }

[0174] Figure 6 is a signaling diagram illustrating a first example embodiment. In step 150, the UE 112 transmits a request message requesting positioning uncertainty in either the conventionalmethod or an AI / ML method. In step 152, the LMF 108 transmits a response messaging providing the uncertainty format (e.g. conventional method or an AI / ML method) to be used for at least one positioning session.

[0175] Figure 7 is a signaling diagram illustrating a second example embodiment. In step 160, the LCS Client 168 transmits a positioning request to the LMF 108. The LMF transmits a request for UE capabilities to report measurement and / or location using uncertainty in the format of variance and / or standard deviation applicable for AI / ML (step 161). UE 112 responds by providing capabilities (step 162). The LMF transmits configuration information including configuration of AI / ML for positioning. The configuration information can further indicate a reporting format for uncertainty associated with the positioning (e.g. AI / ML or conventional format). UE 112 performs positioning measurements and transmits a measurement report, optionally including the uncertainty in the configured format (step 164). The LMF transmits a positioning report to the LCS Client (step 165). The positioning report can include an indication indicating if an AI / ML method / format was used for determining position and / or uncertainty.

[0176] The various example messages, IES, fields, parameters described herein can be used to communicate information in the non-limiting embodiments of Figures 6 and 7.

[0177] Figure 8 is a flow chart illustrating an example method performed by a network node when a UE operates in UE-based mode. The network node can be a core network node 108 such as the LMF as described herein.

[0178] Step 170: The network node optionally obtains UE capability associated with AI / ML for positioning.

[0179] Step 172: The network node obtains a measurement / location report. The location report can include a GAD shape with uncertainty information. The uncertainty information can be encoded as a variance, a standard deviation, and / or an indication that a AI / ML method was used for positioning / location determination.

[0180] Step 174: The network node transmits a report to the LCS Client including an indication that an AI / ML method was used for positioning / location determination.

[0181] It will be appreciated that one or more of the above steps can be performed simultaneously and / or in a different order. Also, steps illustrated in dashed lines are optional and can be omitted in some embodiments.

[0182] It will be appreciated that in some embodiments, a wireless device 112 can communicate (e.g. transmit / receive messages) directly with a network node such as core network node 108. In other embodiments, messages and signals between the entities may be communicated via other nodes, such as radio access node (e.g. gNB, eNB) 110.

[0183] Figure 9 is a flow chart illustrating an example method performed by a network node when a UE operates in UE-assisted mode. The network node can be a core network node 108 such as the LMF as described herein.

[0184] Step 180: The network node optionally obtains UE capability associated with AI / ML for positioning.

[0185] Step 182: The network node transmits a positioning request and / or configuration information message to the UE to configure the UE measurement reporting using an AI / ML method. The configuration information can further indicate to the UE to report uncertainty using an AI / ML format or a conventional uncertainty reporting format.

[0186] Step 184: The network node obtains a measurement / location report. The measurement report can include an indication that an AI / ML method was used to determine position. The report can further include uncertainty information including mean, variance and / or standard deviation generated by an AI / ML model.

[0187] It will be appreciated that one or more of the above steps can be performed simultaneously and / or in a different order. Also, steps illustrated in dashed lines are optional and can be omitted in some embodiments.

[0188] It will be appreciated that in some embodiments, a wireless device 112 can communicate (e.g. transmit / receive messages) directly with a network node such as core network node 108. In other embodiments, messages and signals between the entities may be communicated via other nodes, such as radio access node (e.g. gNB, eNB) 110.

[0189] Figure 10 is a flow chart illustrating an example method performed by a wireless device, such as UE 112 as described herein.

[0190] Step 190: The wireless device receives a positioning request and / or configuration information message. The configuration information can include an indication that the wireless device is to perform positioning measurement reporting using an AI / ML method. Theconfiguration information can further indicate to report uncertainty using an AI / ML format or a conventional uncertainty reporting format.

[0191] Step 192. The wireless device performs positioning measurements in accordance with the AI / ML method and transmits a measurement report indicating that the AI / ML method was used and, optionally, including the uncertainty in accordance with the configuration information.

[0192] It will be appreciated that one or more of the above steps can be performed simultaneously and / or in a different order. Also, steps illustrated in dashed lines are optional and can be omitted in some embodiments.

[0193] It will be appreciated that in some embodiments, a wireless device 112 can communicate (e.g. transmit / receive messages) directly with a network node such as core network node 108. In other embodiments, messages and signals between the entities may be communicated via other nodes, such as radio access node (e.g. gNB, eNB) 110.

[0194] Example embodiments

[0195] AL A method performed by a network node, the method comprising: obtaining device capability information associated with AI / ML positioning; and receiving a measurement report including an indication of AI / ML positioning.

[0196] A2. The method of Al, wherein the network node is a location management function.

[0197] A3. The method of Al to A2, wherein the measurement report includes an indication that at least one AI / ML method was used for positioning.

[0198] A4. The method of Al to A3, wherein the measurement report includes uncertainty information.

[0199] A5. The method of A4, wherein the uncertainty information includes at least one of mean, variance and / or standard deviation.

[0200] A6. The method of A4, wherein the uncertainty information includes an indication that the uncertainty was determined by an AI / ML model.

[0201] A7. The method of Al to A6, further comprising, transmitting a capability request associated with AI / ML positioning.

[0202] A8. The method of Al to A7, further comprising, transmitting configuration information to a wireless device.

[0203] A9. The method of A8, wherein the configuration information includes at least one of a configuration of a AI / ML model for positioning and / or a AI / ML reporting format for uncertainty.

[0204] A10. The method of Al to A9, further comprising, transmitting a positioning report indicating that a AI / ML method was used for positioning.

[0205] Al l. A network node comprising a radio interface and processing circuitry configured to perform the methods of any of embodiments A1-A10.

[0206] Bl. A method performed by a wireless device, the method comprising: obtaining configuration information associated with AI / ML positioning; and transmitting a measurement report including an indication of AI / ML positioning.

[0207] B2. The method of Bl, wherein the measurement report includes an indication that at least one AI / ML method was used for positioning.

[0208] B3. The method of Bl to B2, wherein the measurement report includes uncertainty information.

[0209] B4. The method of B3, wherein the uncertainty information includes at least one of mean, variance and / or standard deviation.

[0210] B5. The method of B3, wherein the uncertainty information includes an indication that the uncertainty was determined by an AI / ML model.

[0211] B6. The method of Bl to B5, further comprising, receiving a capability request associated with AI / ML positioning.

[0212] B7. The method of Bl to B6, further comprising, transmitting a capability response associated with AI / ML positioning.

[0213] B8. The method of Bl to B7, wherein the configuration information includes at least one of a configuration of an AI / ML model for positioning and / or an AI / ML reporting format for uncertainty.

[0214] B9. The method of B 1 to B8, further comprising, performing positioning measurements in accordance with the configuration information.

[0215] B10. A wireless device comprising a radio interface and processing circuitry configured to perform the methods of any of embodiments B1-B9.

[0216] Figure 11 shows a wireless device UE 200 in accordance with some embodiments. The UE 200 presents additional details of some embodiments of the UE 112 of Figure 1. As usedherein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0217] 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).

[0218] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in this figure. 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.

[0219] The processing circuitry 202 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 210. The processing circuitry 202 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 202 may include multiple central processing units (CPUs).

[0220] In the example, the input / output interface 206 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 200. 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.

[0221] In some embodiments, the power source 208 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 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0222] The memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0223] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 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 210, which may be or comprise a device-readable storage medium.

[0224] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 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 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0225] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0226] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, 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).

[0227] 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.

[0228] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / windowsensor, 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 wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200.

[0229] 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 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0230] 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.

[0231] Figure 12 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs,evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0232] 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 O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0233] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0234] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 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 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may alsoinclude multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0235] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0236] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0237] The memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 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 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0238] The communication interface 306 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 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 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 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0239] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0240] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / orsignals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0241] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0242] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 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.

[0243] Embodiments of the network node 300 may include additional components beyond those shown in this figure 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 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300. In some embodiments providing a core network node, such as core network node 108 of Figure 1, some components, such as the radio front-end circuitry 318 and the RF transceiver circuitry 312 may be omitted.

[0244] Figure 13 is a block diagram illustrating a virtualization environment 400 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 400 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 400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.

[0245] Applications 402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0246] Hardware 404 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 406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 408A and 408B (one or more of which may be generally referred to as VMs 408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 406 may present a virtual operating platform that appears like networking hardware to the VMs 408.

[0247] The VMs 408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 406. Different embodiments of the instance of a virtual appliance 402 may be implemented on one or more of VMs 408, 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.

[0248] In the context of NFV, a VM 408 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 408, and that part of hardware 404 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 408 on top of the hardware 404 and corresponds to the application 402.

[0249] Hardware 404 may be implemented in a standalone network node with generic or specific components. Hardware 404 may implement some functions via virtualization. Alternatively, hardware 404 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 410, which, among others, oversees lifecycle management of applications 402. In some embodiments, hardware 404 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 412 which may alternatively be used for communication between hardware nodes and radio units.

[0250] Although the computing devices described herein (e.g., UEs, network nodes) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that thesecomputing 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.

[0251] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0252] The above-described embodiments are intended to be examples only. Alterations, modifications and variations may be effected to the particular embodiments by those of skill in the art without departing from the scope of the description.ABBREVIATIONSAt least some of the following abbreviations may be used in this disclosure. If there is an inconsistency between abbreviations, preference should be given to how it is used above. If listed multiple times below, the first listing should be preferred over any subsequent listing(s).3 GPP 3rd Generation Partnership Project 5G 5th Generation 6G 6thGeneration ABS Almost Blank Subframe ARQ Automatic Repeat Request AWGN Additive White Gaussian Noise BCCH Broadcast Control Channel BCH Broadcast Channel CA Carrier Aggregation CC Carrier Component CCCH SDU Common Control Channel SDU CDMA Code Division Multiplex Access CGI Cell Global Identity CIR Channel Impulse Response CP Cyclic Prefix CPICH Common Pilot Channel CQI Channel Quality Information C-RNTI Cell RNTI CSI Channel State Information DCCH Dedicated Control Channel DL Downlink DM Demodulation DMRS Demodulation Reference Signal DRX Discontinuous Reception DTX Discontinuous Transmission DTCH Dedicated Traffic Channel DUT Device Under Test E-CID Enhanced Cell-ID (positioning method) Ec / No Received energy per chip divided by the power density in the band eMBMS Evolved Multimedia Broadcast Multicast Services ECGI Evolved CGI eNB E-UTRAN NodeB ePDCCH Enhanced Physical Downlink Control Channel E-SMLC Evolved Serving Mobile Location Center E-UTRAN Evolved Universal Terrestrial Radio Access Network FDD Frequency Division Duplex FFS For Further StudygNB Base station in NR GNSS Global Navigation Satellite System HARQ Hybrid Automatic Repeat Request HO Handover HSPA High Speed Packet Access HRPD High Rate Packet Data LOS Line of Sight LPP LTE Positioning Protocol LTE Long-Term Evolution MAC Medium Access Control MAC Message Authentication Code MBSFN Multimedia Broadcast Multicast Service Single Frequency Network MBSFN ABS MBSFN Almost Blank Subframe MDT Minimization of Drive Tests MIB Master Information Block MME Mobility Management Entity MSC Mobile Switching Center NPDCCH Narrowband Physical Downlink Control Channel NR New Radio OCNG OFDMA Channel Noise Generator OFDM Orthogonal Frequency Division Multiplexing OFDMA Orthogonal Frequency Division Multiple Access OSS Operations Support System OTDOA Observed Time Difference of Arrival O&M Operation and Maintenance PBCH Physical Broadcast Channel P-CCPCH Primary Common Control Physical Channel PCell Primary Cell PCFICH Physical Control Format Indicator Channel PDCCH Physical Downlink Control Channel PDCP Packet Data Convergence Protocol PDP Power Delay Profile PDSCH Physical Downlink Shared Channel PGW Packet Gateway PHICH Physical Hybrid-ARQ Indicator Channel PLMN Public Land Mobile Network PMI Precoding Matrix Indicator PRACH Physical Random Access Channel PRS Positioning Reference Signal PSS Primary Synchronization Signal PUCCH Physical Uplink Control Channel PUSCH Physical Uplink Shared Channel RACH Random Access Channel QAM Quadrature Amplitude Modulation RAN Radio Access NetworkRAT Radio Access Technology REC Radio Link Control RLM Radio Link Monitoring RNC Radio Network Controller RNTI Radio Network Temporary Identifier RRC Radio Resource Control RRM Radio Resource Management RS Reference Signal RSCP Received Signal Code Power RSRP Reference Symbol Received Power ORReference Signal Received PowerRSRQ Reference Signal Received Quality ORReference Symbol Received QualityRS SI Received Signal Strength Indicator RSTD Reference Signal Time Difference SCH Synchronization Channel SCell Secondary Cell SDAP Service Data Adaptation Protocol SDU Service Data Unit SFN System Frame Number SGW Serving Gateway SI System Information SIB System Information Block SNR Signal to Noise Ratio SON Self-Organizing Network ss Synchronization Signal sss Secondary Synchronization Signal TDD Time Division Duplex TDOA Time Difference of Arrival TOA Time of Arrival TSS Tertiary Synchronization Signal TTI Transmission Time Interval UE User Equipment UL Uplink UMTS Universal Mobile Telecommunications System USIM Universal Subscriber Identity Module UTDOA Uplink Time Difference of Arrival WCDMA Wideband CDMA WLAN Wireless Local Area Network

Claims

CLAIMS1. A method performed by a wireless device in a wireless communication system, the method comprising: receiving, from a network node, a positioning request; performing positioning measurements on at least one reference signal; processing the positioning measurements using an artificial intelligence and / or machine learning (AI / ML) method to determine a positioning result; and transmitting, to the network node, a positioning report including the positioning result and an indication that the positioning result was determined using the AI / ML method.

2. The method of claim 1, wherein the positioning request includes an indication that the AL / ML method is to be used for determining the positioning result.

3. The method of any one of claims 1 to 2, wherein the positioning request further includes an uncertainty reporting configuration parameter specifying at least one uncertainty format type.

4. The method of claim 3, wherein the uncertainty format type includes at least one of raw measurement uncertainty, AI / ML-based uncertainty, and non-AI / ML-based uncertainty.

5. The method of claim 3, further comprising, calculating uncertainty information associated with the positioning result based on the specified uncertainty format type.

6. The method of claim 5, wherein calculating the uncertainty information comprises determining at least one of a mean value and a standard deviation of the positioning result determined by the AI / ML method.

7. The method of any one of claims 1 to 6, wherein the positioning result includes at least one of reference signal time difference measurements, user equipment receive-transmit time difference measurements, and location coordinates.

8. The method of any one of claims 1 to 7, wherein the positioning report includes a geographical area description shape that defines an uncertainty region around location coordinates.

9. A wireless device comprising a memory and processing circuitry configured to:receive, from a network node, a positioning request; perform positioning measurements on at least one reference signal; process the positioning measurements using an artificial intelligence and / or machine learning (AI / ML) method to determine a positioning result; and transmit, to the network node, a positioning report including the positioning result and an indication that the positioning result was determined using the AI / ML method.

10. The wireless device of claim 9, wherein the positioning request includes an indication that the AL / ML method is to be used for determining the positioning result.

11. The wireless device of any one of claims 9 to 10, wherein the positioning request further includes an uncertainty reporting configuration parameter specifying at least one uncertainty format type.

12. The wireless device of claim 11, wherein the uncertainty format type includes at least one of: raw measurement uncertainty, AI / ML-based uncertainty, and non-AI / ML-based uncertainty.

13. The wireless device of claim 11, further configured to calculate uncertainty information associated with the positioning result based on the specified uncertainty format type.

14. The wireless device of claim 13, wherein calculating the uncertainty information comprises determining at least one of a mean value and a standard deviation of the positioning result determined by the AI / ML method.

15. The wireless device of any one of claims 9 to 14, wherein the positioning result includes at least one of: reference signal time difference measurements, user equipment receive-transmit time difference measurements, and location coordinates.

16. The wireless device of any one of claims 9 to 15, wherein the positioning report includes a geographical area description shape that defines an uncertainty region around location coordinates.

17. A method performed by a network node in a wireless communication system, the method comprising: transmitting, to a wireless device, a positioning request; andreceiving, from the wireless device, a positioning report including a positioning result and an indication that the positioning result was determined using an artificial intelligence and / or machine learning (AI / ML) method.

18. The method of claim 17, further comprising, transmitting, to a Location Services (LCS) client, a positioning response indicating that the positioning result was determined using an AI / ML method.

19. The method of any one of claims 17 to 18, wherein the positioning request includes an indication that the AL / ML method is to be used for determining the positioning result.

20. The method of any one of claims 17 to 19, wherein the positioning request further includes an uncertainty reporting configuration parameter specifying at least one uncertainty format type.

21. The method of claim 20, wherein the uncertainty format type includes at least one of: raw measurement uncertainty, AI / ML-based uncertainty, and non-AI / ML-based uncertainty.

22. The method of any one of claims 17 to 21, wherein the positioning result includes at least one of: reference signal time difference measurements, user equipment receive-transmit time difference measurements, and location coordinates.

23. The method of any one of claims 17 to 22, wherein the positioning report includes a geographical area description shape that defines an uncertainty region around location coordinates.

24. A network node comprising a memory and processing circuitry configured to: transmit, to a wireless device, a positioning request; and receive, from the wireless device, a positioning report including a positioning result and an indication that the positioning result was determined using an artificial intelligence and / or machine learning (AI / ML) method.

25. The network node of claim 24, further configured to transmit, to a Location Services (LCS) client, a positioning response indicating that the positioning result was determined using an AI / ML method.

26. The network node of any one of claims 24 to 25, wherein the positioning request includes an indication that the AL / ML method is to be used for determining the positioning result.

27. The network node of any one of claims 24 to 26, wherein the positioning request further includes an uncertainty reporting configuration parameter specifying at least one uncertainty format type.

28. The network node of claim 27, wherein the uncertainty format type includes at least one of: raw measurement uncertainty, AI / ML-based uncertainty, and non-AI / ML-based uncertainty.

29. The network node of any one of claims 24 to 28, wherein the positioning result includes at least one of: reference signal time difference measurements, user equipment receive-transmit time difference measurements, and location coordinates.

30. The network node of any one of claims 24 to 29, wherein the positioning report includes a geographical area description shape that defines an uncertainty region around location coordinates.

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