Ai / ML positioning for location management function

Abstract

Methods and systems are described for positioning procedures. For example, particular embodiments address the message size issue between the different entities involved in NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning, by sending input data that have been fragmented by the NG-RAN in separate messages to the LMF. The fragmentation may be done based on the NG-RAN decision or by recommendation from LMF.

Description

AI / ML POSITIONING FOR LOCATION MANAGEMENT FUNCTIONCROSS REFERENCE TO RELATED INFORMATION

[0001] This application claims the benefit of United States of America priority application No. 63 / 613134 filed on December 21, 2023, titled “AI / ML Positioning for Location Management Function.”TECHNICAL FIELD

[0002] The present disclosure generally relates to systems and methods for AI / ML enhanced positioning procedures.BACKGROUND

[0003] Third Generation Partnership Project (3GPP) TR 38.843 describes potential enhancements necessary to enable artificial intelligence (AI) / machine learning (ML) for positioning accuracy enhancements with New Radio (NR) radio access technology (RAT)- dependent positioning methods. Evaluation scenarios and key performance indicators (KPIs) were identified for system level analysis of AI / ML enabled RAT-dependent positioning techniques as described in clause 6.4 of TR 38.843.

[0004] Direct AI / ML positioning and AI / ML assisted positioning were identified and selected as the representative sub-use cases. Evaluation results have shown that in considered evaluation scenarios (i.e., indoor factory dense high (InF-DH), and other InF scenarios), both direct AI / ML positioning and AI / ML assisted can significantly improve the positioning accuracy compared to existing RAT-dependent positioning methods. Various aspects of AI / ML for positioning accuracy enhancement were investigated and evaluated as described in clause 6.4 that provides summary of evaluation results from different sources.

[0005] The necessity, feasibility and potential enhancements to facilitate the support of AI / ML for positioning accuracy enhancements with NR RAT-dependent positioning methods were studied and the outcome are outlined in clause 7 of TS 38.843.

[0006] Measurements, signaling and procedures were studied to enable AI / ML for positioning accuracy enhancements with NR RAT-dependent positioning methods. A variety ofenhancements for measurements (e.g., based on extensions to current positioning measurements or with new measurements) were identified as potentially beneficial (e.g., trade-off positioning accuracy requirement and signaling overhead). It is recommended to specify necessary measurement, signaling and procedure to facilitate training, inference, monitoring and / or other lifecycle management (LCM) operations for both direct AI / ML positioning and AI / ML assisted positioning, including the following. One goal is to specify necessary signaling of data collection and other information for supporting data collection. Another goal is to investigate signaling details of measurement enhancements and monitoring method(s).

[0007] The following are selected as representative sub-use cases:• Direct AI / ML positioning, such as with AI / ML model output: user equipment (UE) location, e.g., fingerprinting based on channel observation as the input of AI / ML model• AI / ML assisted positioning, such as AI / ML model output: new measurement and / or enhancement of existing measurement, e.g., line of sight (LOS) / near line of sight (NLOS) identification, timing and / or angle of measurement, likelihood of measurement

[0008] More specifically, the following cases are considered for the study:• Case 1 : UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioning;• Case 2a: UE-assisted / location management function (LMF)-based positioning with UE-side model, AI / ML assisted positioning;• Case 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning;• Case 3a: Next-generation radio access network (NG-RAN) node assisted positioning with gNB-side model, AI / ML assisted positioning;• Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning.

[0009] One-sided model whose inference is performed entirely at the UE or at the network is prioritized in Rel-18 SI.

[0010] For the positioning enhancement use case, for model training, training data can be generated by UE / positioning reference unit (PRU) / gNB / LMF. For LMF-side modelinference (Case 2b, Case 3b), input data can be generated by UE / gNB and terminated at LMF. For gNB-side model inference (Case 3a), input data is internally available at gNB. For UE-side model inference (Case 1, Case 2a), input data is internally available at UE.

[0011] For performance monitoring at the LMF side, calculated performance metrics (if needed) or data needed for performance metric calculation (if needed) can be generated by UE / gNB and terminated at LMF. For performance monitoring at the gNB side, calculated performance metrics (if needed) or data needed for performance metric calculation (if needed) can be generated by at least gNB.NG-RAN Architecture

[0012] The NG-RAN consists of a set of gNBs connected to the fifth-generation core (5GC) through the NG interface. TS 38.401 describes the overall NG-RAN architecture. A disaggregated gNB may consist of a gNB central unit (CU) and one or more gNB distributed units (DUs). A gNB-CU and a gNB-DU are connected via Fl interface. As shown in Figure 1, this interface is responsible for possible information and control signaling from the Packet Data Convergence Protocol (PDCP) entity located in CU and Radio Link Control (RLC) entity located in DU. Figure 1 is a block network diagram illustrating the Fl interface between gNB-CU and gNB-DU.

[0013] NG-RAN positioning architecture is shown in Figure 2. Figure 2 is a block network diagram illustrating UE positioning overall architecture applicable to NG-RAN. The access and mobility management function (AMF) receives a request for some location service associated with a particular target UE from another entity (e.g., gateway mobile location center (GMLC) or UE), or the AMF itself decides to initiate a location service on behalf of a particular target UE (e.g., for an Internet Protocol (IP) multimedia subsystem (IMS) emergency call from the UE). The AMF then sends a location services request to an LMF. The LMF processes the location services request which may include transferring assistance data to the target UE to assist with UE- based and / or UE-assisted positioning and / or may include positioning of the target UE. The LMF then returns the result of the location service back to the AMF (e.g., a position estimate for the UE. For a location service requested by an entity other than the AMF (e.g., a GMLC or UE), the AMF returns the location service result to this entity. An NG-RAN node may control severaltransmission reception points (TRPs) / TPs, such as remote radio heads, or downlink positioning reference signal (DL-PRS)-only TPs for support of PRS-based terrestrial beacon systems (TBS).

[0014] There currently exist certain challenges. For example, the 3GPP positioning solutions and methods have been relying on measurement reports by the UE and gNB to the LMF. The measured quantities were typically the result of processing at the UE / gNB, e.g., the time of arrival or power from a reference signal. These reports were thus reasonably small in size. In Rel- 19, 3GPP will support “direct AI / ML based positioning” in network nodes (LMF, gNB CU, or gNB DU), where the network collects a significantly more raw set of measurements, which are channel impulse responses consisting of multipath complex-valued channel response. For model inference on the LMF side (case 3b from the 3 GPP TR), with input data generated by the NG- RAN and terminated at the LMF, the issue of signaling overhead may quickly arise on the NRPPa (New Radio Positioning Protocol A) interface between the NG-RAN node and the LMF. An NG- RAN node can host up to 65536 TRPs, and for each of them input data needs to be reported from the measuring TRPs to the LMF hosting the inference model for AI / ML direct positioning. The NG-RAN node needs to report all the input data to the LMF for the LMF to calculate the UE location. Due to this centralized model at the LMF side and the incurring large amount of information for input data that needs to be reported, the overhead will impact the transport network and make the NRPPa positioning messages significantly large in size. This creates a decoding problem at the receiver, because decoding capabilities are limited by a maximum message size. Another problem is that the current specifications do not allow for the transferring of input measurements needed at the LMF to enable the AI / ML model at the LMF to correctly infer a UE position. Additionally, the current specifications do not allow an LMF to request input measurements from the NG-RAN to understand what are the AI / ML positioning capabilities of the NG-RAN and whether the NG-RAN is capable of providing the requested measurements or not.SUMMARY

[0015] One embodiment under the present disclosure comprises a method performed by a LMF for performing positioning. The method comprises: transmitting a positioning request to a network node; receiving first one or more positioning responses from the network node, wherein the first one or more positioning responses include an indication that the first oneor more positioning responses comprise first one or more partial positioning information; and inputting the first one or more partial positioning information into an AI / ML positioning model.

[0016] Another embodiment under the present disclosure comprises a method performed by a LMF for performing positioning. The method comprises transmitting, to one or more network nodes, a request indicator via NRPPa for the one or more network nodes to provide information about whether they support the provisioning of input data for AI / ML direct positioning and their capabilities.

[0017] Another embodiment comprises a method performed by a network node for performing or assisting positioning. The method comprises receiving a positioning request from a LMF; obtaining positioning measurements from one or more UEs; and transmitting a first positioning response to the LMF, wherein the first positioning response includes an indication that the first positioning response comprises first partial positioning information that is a subset of the obtained positioning measurements.

[0018] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an indication of the scope of the claimed subject matter.BRIEF DESCRIPTION OF THE DRAWINGS

[0019] For a more complete understanding of the present disclosure, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

[0020] Fig. 1 illustrates an example of 5G Core network architecture and the Fl interface between gNB-CU and gNB-DU;

[0021] Fig. 2 illustrates an example of UE positioning and overall architecture applicable to NG-RAN;

[0022] Fig. 3 illustrates a process flow of NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning and partial input data signaling;

[0023] Fig. 4 illustrates a flow-chart of a method embodiment under the present disclosure;

[0024] Fig. 5 illustrates a flow-chart of a method embodiment under the present disclosure;

[0025] Fig. 6 illustrates a flow-chart of a method embodiment under the present disclosure;

[0026] Fig. 7 illustrates possible edits to the NRPPa specification under the present disclosure;

[0027] Fig. 8 illustrates possible edits to the NRPPa specification under the present disclosure;

[0028] Fig. 9 illustrates possible edits to the NRPPa specification under the present disclosure;

[0029] Fig. 10 illustrates possible edits to the NRPPa specification under the present disclosure;

[0030] Fig. 11 shows a schematic of a communication system embodiment under the present disclosure;

[0031] Fig. 12 shows a schematic of a user equipment embodiment under the present disclosure;

[0032] Fig. 13 shows a schematic of a network node embodiment under the present disclosure;

[0033] Fig. 14 shows a schematic of a host embodiment under the present disclosure;

[0034] Fig. 15 shows a schematic of a virtualization environment embodiment under the present disclosure; and

[0035] Fig. 16 shows a schematic representation of an embodiment of communication amongst nodes, hosts, and user equipment under the present disclosure.DETAILED DESCRIPTION

[0036] Before describing various embodiments of the present disclosure in detail, it is to be understood that this disclosure is not limited to the parameters of the particularly exemplified systems, methods, apparatus, products, processes, and / or kits, which may, of course, vary. Thus, while certain embodiments of the present disclosure will be described in detail, with reference to specific configurations, parameters, components, elements, etc., the descriptions areillustrative and are not to be construed as limiting the scope of the claimed embodiments. In addition, the terminology used herein is for the purpose of describing the embodiments and is not necessarily intended to limit the scope of the claimed embodiments.

[0037] As described above, there are currently certain challenges in the prior art. Amongst other issues, 3 GPP positioning solutions and methods have been relying on measurement reports by the UE and gNB to the LMF. The measured quantities were typically the result of processing at the UE / gNB, e.g., the time of arrival or power from a reference signal. These reports were thus reasonably small in size. Going forward however, 3 GPP will support “direct AI / ML based positioning” in network nodes (LMF, gNB CU, or gNB DU), where the network collects a significantly more raw set of measurements, which are channel impulse responses consisting of multipath complex-valued channel response. With more data, the issue of signaling overhead may quickly arise on the NRPPa interface between the NG-RAN node and the LMF. With embodiments with centralized models at the LMF side and the incurring large amount of information for input data that needs to be reported, the overhead will impact the transport network and make the NRPPa positioning messages significantly large in size. This creates a decoding problem at the receiver, because decoding capabilities are limited by a maximum message size. In addition, current specifications do not allow for the transferring of input measurements needed at the LMF to enable the AI / ML model at the LMF to correctly infer a UE position. Current specifications also do not allow an LMF to request input measurements from the NG-RAN to understand what are the AI / ML positioning capabilities of the NG-RAN and whether the NG-RAN is capable of providing the requested measurements or not.

[0038] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments address the message size issue between the different entities involved in NG-RAN node assisted positioning with LMF- side model, direct AI / ML positioning, by sending input data that have been fragmented by the NG- RAN in separate messages to the LMF. The fragmentation may be done based on the NG-RAN decision or by recommendation from LMF.

[0039] For example, in certain embodiments, a first positioning message (e.g., NRPPa Positioning message from NG-RAN to LMF) provides a first fragmented input data, then a second message can follow up providing additional fragmented input data, with status indication whether it is still partial reporting or represents the final input data fragment from the NG-RAN.Each fragment can be characterized by an age of location and to which group of UEs it is associated to. In some embodiments, a partial reporting indication of AI / ML positioning information may be provided from the NG-RAN to the LMF. Such partial indication may be signaled over NRPPa interface either in existing positioning procedures, or new ones. The fragmentation may also be done over NG protocol between the NG-RAN and core network (CN), fragmenting each NRPPa Packet data unit based on the contained input data. Some embodiments include AI / ML positioning inference model capability declaration embodiments. In some embodiments, the LMF declares that it supports positioning model for AI / ML direct positioning by sending an indication in a message to other network nodes, e.g., in the NRPPa positioning message to NG-RAN.

[0040] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments reduce NRPPa positioning and NGAP protocol message size over the transport network between NG-RAN and LMF. Particular embodiments reduce overhead for AI / ML side model at LMF for direct positioning where the output is the UE location. Particular embodiments enable transferring of new input data information from the NG- RAN to the LMF for informing the LMF of the supported capabilities at the NG-RAN for the provisioning of inputs needed for AI / ML direct positioning.

[0041] Certain embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. It should be noted that particular embodiments described herein may be applicable to, but are not limited to, 3 GPP NR. Thus, a base station (BS) may be a NR gNB, or a sixth generation (6G)-RAT base station, or any other device with similar function.

[0042] Figure 3 illustrates one method embodiment under the present disclosure. Figure 3 is a flow diagram illustrating a method 200 for performing NG-RAN node assisted positioning. This example includes an LMF-side model, direct AI / ML positioning, and partial input data signaling. Components shown are an LMF 201, NG-RAN (or any other type of network node) 202, and UE 203. Step 210 is a TRP information capability exchange, with LMF 201 indicating (NG-RAN 202 receiving) AI / ML inference model support for direct AI / ML positioning. Step 215 comprises a NRPPa positioning information request with indication of UE 203 priority for input data reporting. Step 220 is the NG-RAN 202 providing / configuring (UE 203 receiving / being configured) an SRS configuration. Step 225 is the NG-RAN 202 transmitting(LMF 201 receiving) the NRPPa positioning information response. Step 230 is the LMF 201 transmitting (NG-RAN 202 receiving) an NRPPa measurement request with indication of input data time window. Step 235 is the NG-RAN 202 receiving (UE 203 sending) input data and the NG-RAN 202 applying fragmentation if needed. Step 240 is the NG-RAN 202 transmitting (LMF 201 receiving) an NRPPa measurement response. The NRPPa measurement response can include, e.g.: a TRP measurement result, input data, partial indicator if fragmented, and an age of information of the input data. Step 245 is LMF 201 applying the inference model training. Step 250 is the NG-RAN 202 transmitting (LMF 201 receiving) another NRPPa measurement response. The additional NRPPa measurement response can include, e.g.: a TRP measurement result, further input data, partial indicator if fragmented, and an age of information of the further input data. Step 255 is the LMF 201 applying the inference model training and generating one or more predicted UE locations. Step 260 is LMF 201 transmitting (NG-RAN 202 receiving) an NRPPa measurement update or measurement abort message (which can include a stop indication of reporting fragmented input data). Method 200 can include various alternative, optional, and / or additional steps and other variations.

[0043] Although Figure 3 sets forth one possible embodiment, other embodiments and variations are possible under the present disclosure. Various other embodiments and variation are further described herein.AI / ML Positioning Inference Model Capability Declarations

[0044] Certain embodiments under the present disclosure can comprise AI / ML positioning inference model capability declarations. For example, in some embodiments, the LMF(s) can declare their capability of supporting the model inference for AI / ML direct positioning via operations and management (0AM) pre- configuration. In some embodiments, the LMF sends a request indicator via NRPPa to enquire the network nodes such as NG-RAN nodes (gNB-CU, gNB-DU) to provide information about whether they support the provisioning of input data for AI / ML direct positioning and what are their capabilities, e.g., via a new codepoint in the NRPPa TRP INFORMATION REQUEST message to NG-RAN node. In some embodiments, the NG-RAN node signals its capability for supporting the provisioning of input data needed for AI / ML direct positioning to the LMF, e.g., via a new information element (IE) in the NRPPa INFORMATION RESPONSE message to LMF. Such capability may consist of details about theinput data supported and optionally the characteristics of such, input data. Examples of such information may consist of e.g., fingerprinting based on channel observation. In some embodiments, the LMF knows the capabilities of a gNB providing input data for AI / ML direct positioning via pre-configuration from an external node / function / system such as the 0AM.NG-RAN Node Assisted Positioning

[0045] Certain embodiments comprise NG-RAN node assisted positioning. For example, this can include e.g., LMF-side models, and / or direct AI / ML positioning. In some embodiments, the LMF signals a request to the connected NG-RAN nodes for the input data needed for AI / ML direct positioning. The NG-RAN node receives the request to provide inputs data for AI / ML direct positioning.

[0046] As a consequence of receiving such request, the NG-RAN node may positively reply confirming that the input data can be provided. Alternatively, if the NG-RAN node is not able to produce some or all of the input data, the NG-RAN node may reply with a message stating which input data types (beam type, channel impulse type, etc.) failed to be provided and cannot be reported, and which input data types were successfully collected and can be reported. In some embodiments, if the NG-RAN node fails to admit all the input data, the NG-RAN node signals back a failure message including information about why the requested input data types were not admitted.

[0047] In some embodiments, any of the messages above describing that some or all of the requested input data types could not be provided may contain information concerning whether the input data could not be collected due to temporary issues, such as shortage of processing power, or whether the input data are not supported, namely the NG-RAN node is not capable of collecting such information. In some embodiments, any of the messages above describing that some or all of the requested input data could not be provided may contain information concerning the capabilities of the NG-RAN node, where such capabilities may follow the same description provided above.

[0048] In some embodiments, the LMF that hosts the model inference for AI / ML direct positioning indicates to the NG-RAN that it supports receiving fragmented (i.e., partial) input data over NRPPa message. Such information may be provided to the NG-RAN in the samemessage where the LMF requests for input data, positioning measurements, or in a separate message.

[0049] In some embodiments, after having received a request for providing input data for AI / ML direct positioning, the NG-RAN sends input data to the LMF over NRPPa message, the LMF uses this input data together with the received positioning measurements to calculate the predicted UE location.

[0050] In some embodiments, the NG-RAN adds an indication in the NRPPa message containing the input data to the LMF indicating the status of this input data report, whether it is full or partial report. If the indicator is set to “partial”, the LMF understands that this represents a fragment of the input data, and that more fragmented input data is to be provided in the next message by NG-RAN.

[0051] In some embodiments, a fragment of the input data consists of data concerning one or more UEs. Namely, the data will be grouped with a per UE granularity and the set of data signaled in a fragment consists of all the input data collected for one UE or for a group of UEs.

[0052] In some embodiments, the LMF may request the NG-RAN node to signal input data as soon as they are collected or no later than a given time window starting at input data collection. In these embodiments, the gNB-CU may include in the NRPPa message requesting input data at the NG-RAN an indication of such maximum time.

[0053] In some embodiments, the NG-RAN replies with NRPPa messages including input data collected within the time limits indicated by the LMF or within the time limits configured at the NG-RAN (if such time limits are not explicitly signaled to the NG-RAN node by the LMF). The NRPPa message carrying such input data therefore only contains a subset of the overall set of input data collected for all the UEs from which input data are collected. Such subset of input data can be calculated by:• Taking the maximum message size into account, namely signaling only a number of input data that can fit into the maximum NRPPa message size;• Taking the time limits indicated by the LMF into account, namely every reported input data “fragment” shall not be “older” than the maximum time limit indicated by the LMF;• Taking into account that the set of input data fragments provided by the NG-RAN node shall consist of all the input data collected for one UE or for a group of UEs.

[0054] In some embodiments, a maximum “threshold value” of the input data “volume” (e.g. number of paths, complexity, etc.) is either declared by the gNB to the LMF, or configured between the two nodes above (e.g., via dedicated signaling or 0AM), which the gNB is not expected to signal input data within the indicated time limit. For input data above the threshold value in volume, a fallback behavior may be to set a new separate time limit, or instead do best-effort reporting, or even discard.

[0055] As a consequence of this embodiment, the NG-RAN node may need to discard some of the input data collected and not send them to the LMF because, e.g., such data do not fit into the maximum message size requested by the LMF or pre-configured at the NG-RAN node or because the input data are “older” than the maximum time limit declared by the LMF node or preconfigured at the NG-RAN node.

[0056] In some embodiments, the message from NG-RAN to LMF with the partial (i.e., fragmented) input data contains the age of information of the fragment. In some embodiments, the NG-RAN node may not have received a time limit, measured from the input data collection and within which the information have to be reported. In this case, the NG-RAN node is still constrained in sending the input data according to the maximum message size requested. Therefore, some of the input data might have to be signaled in consecutive messages to the LMF. For this reason, the NG-RAN node includes in the message the “age” of the reported input data. Such information may be measured in, e.g. ms, and it may consist of an average of the age of the reported input data or in the age of the oldest information reported. In some embodiments, the message from NG-RAN node to LMF with the partial (i.e., fragmented) input data contains information concerning the one or more UEs to which the input data is associated. In some embodiments, input data may be grouped on a per UE basis and each of such data group may be associated to an identifier for the UE, e.g. LMF RAN UE measurement ID.

[0057] In some embodiments, the NG-RAN node may include information about all the UEs for which input data are reported. As an example, this can be achieved by listing in the message UE identifiers for the UEs of concern, e.g. their LMF RAN UE measurement IDs. In some embodiments, the partial input data may be sent per TRP ID, or per positioning measurementin the NRPPa message. In some embodiments, the NG-RAN may send a second message over NRPPa message containing other input data reports. An indication is added to indicate whether this is the last fragmented input data (i.e., “full”). In that case the LMF considers that the partial reporting of input data session has ended by the sender. In some embodiments, the LMF may decide to use only the first fragment or a limited number of fragments of input data to generate the predicted UE location with the received positioning measurements. In some embodiments, the LMF once obtaining a first or second fragmented input data, and upon deciding to use only the first received fragment(s) for UE location prediction, may decide to abort the reporting by the NG- RAN by sending an Abort message over NRPPa, or a stop indication to the measuring TRP in NG- RAN. In some embodiments, when the LMF provides the predicted position of the UE to the client, it also provides a quality metric indicating the accuracy of the prediction.

[0058] In some embodiments, the LMF includes in the location results of AI / ML direct positioning to the client additional information concerning the predicted location. Such additional information enables the client to better understand how the predictions were derived. Such additional information may comprise one or more of the following:• Information concerning the accuracy or the uncertainty of the predicted location;• Information concerning the training data used to derive the one or more AI / ML model used at the LMF to derive the inferred UE location that was requested. Examples of such information may be ranges of data used for training, provided for each data type, e.g. for each measurement used as a training data;• Information concerning the time horizon for which the inferred location were calculated. Namely, information about how far into the future and for which points in time into the future the one or more models in use at the LMF has calculated the location predictions;• Information about the whether the predicted location was subject to epistemic uncertainty, namely whether the predicted location was derived from inputs for which the model in use at the LMF was not trained or it was not sufficiently trained;• Information concerning the quality of the predicted location, compared to the non-predicted location. Such quality score may be provided as a percentage of error, an accuracy or an uncertainty.

[0059] In some embodiments, the LMF, once it receives input data from the one or more NG-RAN node, provides predicted location to the client that follow specific requests from the client. Such specific requests may be signaled by the LMF to GMLC / UE. The requirements to follow at the LMF may consist of one or more of the following:• The requested predicted location shall have an uncertainty or accuracy better or equal than a specific threshold;• The requested predicted location should be calculated for a specific point in time in the future. This point in time may be specified as a time window, e.g. in seconds or ms, starting at the time of the reception of the predicted location request at the LMF;• The requested predicted location shall be inferred with quality levels equal or better than requested thresholds. Quality levels may be calculated with respect to the non-predicted location. Such quality thresholds / requirements may be provided as a percentage of error, an accuracy or an uncertainty.Higher-Layer Controlled Input Data Fragmentation

[0060] Certain embodiments can comprise higher-layers control of input data fragmentation. For example, in some embodiments, the LMF provides assistance information to assist the NG-RAN performing fragmentation of the input data. For example, the LMF provides a time window for receiving the input data, before which the input data is considered obsolete by the LMF inference model to calculate the UE position. In some embodiments, during the sounding reference signal (SRS) configuration phase, the LMF signals an indication in the NRPPa POSITIONING INFORMATION REQUEST an indication that for this UE, input data shall be prioritized by the NG-RAN. In some embodiments, the LMF signals prioritization for different UEs during the SRS configuration process, indicating, whether the input data for some UEs can be grouped by the NG-RAN when providing the partial (i.e., fragmented) reporting.NGAP NRPPa PDU Fragmentation

[0061] Certain embodiments under the present disclosure comprise NGAP NRPPa PDU fragmentation. For example, in some embodiments, the CN sends an indication over NG interface to NG-RAN that it supports receiving fragmented NRPPa PDUs. Such indication and the behavior of the receiving node is described by the embodiments above detailed for the same indication signaled over NRPPa. In some embodiments, when the NG-RAN provides over NGAP the uplink NRPPa transport message carrying the NRPPa protocol data units (PDUs) (e.g., UPLINK UE ASSOCIATED NRPPA TRANSPORT or UPLINK NON UE ASSOCIATED NRPPA TRANSPORT messages) it adds an indication whether the contained NRPPa PDU is partial (i. e. , fragmented) or full. Such indication and the behavior of the receiving node is described by the embodiments above detailed for the same indication signaled over NRPPa. In some embodiments, the CN once obtaining a first or second fragmented NRPPa PDU over NGAP, and decides to use only the first received fragment(s), can decide to abort the reporting by the NG- RAN by sending an Abort message over NGAP.Additional Embodiments

[0062] Another possible method embodiment under the present disclosure is shown in Figure 4. Method 400 comprises a method performed by a LMF for performing positioning. Step 410 is transmitting a positioning request to a network node. Step 420 is receiving first one or more positioning responses from the network node, wherein the first one or more positioning responses include an indication that the first one or more positioning responses comprise first one or more partial positioning information. Step 430 is inputting the first one or more partial positioning information into an AI / ML positioning model. Method 400 can comprise multiple variations and embodiments and / or additional and / or alternative steps.

[0063] Another embodiment possible method embodiment under the present disclosure is shown in Figure 5. Method 600 comprises a method performed by a LMF for performing positioning. Step 610 is transmitting, to one or more network nodes, a request indicator via NRPPa for the one or more network nodes to provide information about whether they support the provisioning of input data for AI / ML direct positioning and their capabilities. Method 600 can comprise multiple alternative embodiments with additional or alternative steps.

[0064] Another embodiment possible method embodiment under the present disclosure is shown in Figure 6. Method 800 comprises a method performed by a network nodefor performing positioning or assisting an LMF with positioning. Step 810 is receiving a positioning request from a LMF. Step 820 is obtaining positioning measurements from one or more UEs. Step 830 is transmitting a first positioning response to the LMF, wherein the first positioning response includes an indication that the first positioning response comprises first partial positioning information that is a subset of the obtained positioning measurements. Method 800 can comprise multiple alternative embodiments with additional or alternative steps.

[0065] Figure 7-10 illustrate some possible changes to the NRPPa specification in accordance with certain embodiments of the present disclosure. Changes to the specification are shaded. Figure 7 shows changes to the TRP Measurement Result information element, from TS 38.455. Changes to the specification include Input Data List, Fingerprint information and age of information. Figures 8 and 9 show changes to the Measurement Response and Measurement Report information elements, from TS 38.455. Changes include the Input Data List, UE ID, Fingerprint information, and Partial information indicator. Figure 10 shows changes to the Measurement Characteristics Request Indicator, from TS 38.455. Changes include the XY and XX bit descriptions. Changes to the NRPPa specification could take other embodiments under the present disclosure.

[0066] Figure 11 shows an example of a communication system 2100 in accordance with some embodiments. In the example, the communication system 2100 includes a telecommunication network 2102 that includes an access network 2104, such as a RAN, and a core network 2106, which includes one or more core network nodes 2108. The access network 2104 includes one or more access network nodes, such as network nodes 2110a and 2110b (one or more of which may be generally referred to as network nodes 2110), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 2110 facilitate direct or indirect connection of UE, such as by connecting UEs 2112a, 2112b, 2112c, and 2112d (one or more of which may be generally referred to as UEs 2112) to the core network 2106 over one or more wireless connections.

[0067] 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 1100 may include any number of wired or wireless networks, network nodes, UEs, and / orany 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 2100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0068] The UEs 2112 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 2110 and other communication devices. Similarly, the network nodes 2110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 2112 and / or with other network nodes or equipment in the telecommunication network 2102 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 2102.

[0069] In the depicted example, the core network 2106 connects the network nodes 2110 to one or more hosts, such as host 2116. 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 2106 includes one more core network nodes (e.g., core network node 2108) 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 2108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0070] The host 2116 may be under the ownership or control of a service provider other than an operator or provider of the access network 2104 and / or the telecommunication network 2102, and may be operated by the service provider or on behalf of the service provider. The host 2116 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 remotedevices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0071] As a whole, the communication system 2100 of Figure 11 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.

[0072] In some examples, the telecommunication network 2102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 2102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 2102. For example, the telecommunications network 2102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0073] In some examples, the UEs 2112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 2104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 2104. 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).

[0074] In the example, the hub 2114 communicates with the access network 2104 to facilitate indirect communication between one or more UEs (e.g., UE 2112c and / or 2112d) andnetwork nodes (e.g., network node 2110b). In some examples, the hub 2114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 2114 may be a broadband router enabling access to the core network 2106 for the UEs. As another example, the hub 2114 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 2110, or by executable code, script, process, or other instructions in the hub 2114. As another example, the hub 2114 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 2114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 2114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 2114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 2114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0075] The hub 2114 may have a constant / persistent or intermittent connection to the network node 2110b. The hub 2114 may also allow for a different communication scheme and / or schedule between the hub 2114 and UEs (e.g., UE 2112c and / or 2112d), and between the hub 2114 and the core network 2106. In other examples, the hub 2114 is connected to the core network 2106 and / or one or more UEs via a wired connection. Moreover, the hub 2114 may be configured to connect to an M2M service provider over the access network 1104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 2110 while still connected via the hub 2114 via a wired or wireless connection. In some embodiments, the hub 2114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 2110b. In other embodiments, the hub 2114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 2110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0076] Figure 12 shows a UE 2200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicatewirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0077] 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), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0078] The UE 2200 includes processing circuitry 2202 that is operatively coupled via a bus 2204 to an input / output interface 2206, a power source 2208, a memory 2210, a communication interface 2212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 10. 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.

[0079] The processing circuitry 2202 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 2210. The processing circuitry 2202 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 2202 may include multiple central processing units (CPUs).

[0080] In the example, the input / output interface 2206 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 2200. 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 presencesensitive 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.

[0081] In some embodiments, the power source 2208 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 2208 may further include power circuitry for delivering power from the power source 2208 itself, and / or an external power source, to the various parts of the UE 2200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 2208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 2208 to make the power suitable for the respective components of the UE 2200 to which power is supplied.

[0082] The memory 2210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 2210 includes one or more application programs 2214, such as an operating system, web browser application, awidget, gadget engine, or other application, and corresponding data 2216. The memory 2210 may store, for use by the UE 2200, any of a variety of various operating systems or combinations of operating systems.

[0083] The memory 2210 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 2210 may allow the UE 2200 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 2210, which may be or comprise a device-readable storage medium.

[0084] The processing circuitry 2202 may be configured to communicate with an access network or other network using the communication interface 2212. The communication interface 2212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 2222. The communication interface 2212 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 2218 and / or a receiver 2220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 2218 and receiver 2220 may be coupled to one or more antennas (e.g., antenna 2222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0085] In the illustrated embodiment, communication functions of the communication interface 2212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia 1communication, 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, LIE, 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.

[0086] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 2212, 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).

[0087] 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.

[0088] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehiclecharging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 2200 shown in Figure 10.

[0089] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3 GPP context be referred to as an MTC device. As one particular example, the UE may implement the 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.

[0090] 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.

[0091] Figure 13 shows a network node 3300 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)).

[0092] 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 providedamount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0093] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSRBSs, 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).

[0094] The network node 3300 includes a processing circuitry 3302, a memory 3304, a communication interface 3306, and a power source 3308. The network node 3300 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 3300 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 1300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 3304 for different RATs) and some components may be reused (e.g., a same antenna 3310 may be shared by different RATs). The network node 3300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1300, 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 1300.

[0095] The processing circuitry 3302 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 3300 components, such as the memory 3304, to provide network node 3300 functionality.

[0096] In some embodiments, the processing circuitry 3302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 3302 includes one or more of radio frequency (RF) transceiver circuitry 3312 and baseband processing circuitry 3314. In some embodiments, the radio frequency (RF) transceiver circuitry 3312 and the baseband processing circuitry 3314 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 3312 and baseband processing circuitry 3314 may be on the same chip or set of chips, boards, or units.

[0097] The memory 3304 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), readonly 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 3302. The memory 3304 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 3302 and utilized by the network node 3300. The memory 3304 may be used to store any calculations made by the processing circuitry 3302 and / or any data received via the communication interface 3306. In some embodiments, the processing circuitry 3302 and memory 3304 is integrated.

[0098] The communication interface 3306 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 3306 comprises port(s) / terminal(s) 3316 to send and receive data, for example to and from a network over a wired connection. The communication interface 3306 also includes radio front-end circuitry 3318 that may be coupled to, or in certainembodiments a part of, the antenna 3310. Radio front-end circuitry 3318 comprises filters 3320 and amplifiers 3322. The radio front-end circuitry 3318 may be connected to an antenna 3310 and processing circuitry 3302. The radio front-end circuitry may be configured to condition signals communicated between antenna 3310 and processing circuitry 3302. The radio front-end circuitry 3318 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 3318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 3320 and / or amplifiers 3322. The radio signal may then be transmitted via the antenna 3310. Similarly, when receiving data, the antenna 3310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 3318. The digital data may be passed to the processing circuitry 3302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0099] In certain alternative embodiments, the network node 3300 does not include separate radio front-end circuitry 3318, instead, the processing circuitry 3302 includes radio frontend circuitry and is connected to the antenna 3310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 3312 is part of the communication interface 3306. In still other embodiments, the communication interface 3306 includes one or more ports or terminals 3316, the radio front-end circuitry 3318, and the RF transceiver circuitry 3312, as part of a radio unit (not shown), and the communication interface 3306 communicates with the baseband processing circuitry 3314, which is part of a digital unit (not shown).[000100] The antenna 3310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 3310 may be coupled to the radio front-end circuitry 3318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 3310 is separate from the network node 3300 and connectable to the network node 3300 through an interface or port.[000101] The antenna 3310, communication interface 3306, and / or the processing circuitry 3302 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 3310, the communication interface 3306, and / or the processing circuitry 3302 may be configured to perform any transmitting operations described herein as beingperformed 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.[000102] The power source 3308 provides power to the various components of network node 3300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 3308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 3300 with power for performing the functionality described herein. For example, the network node 3300 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 3308. As a further example, the power source 3308 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.[000103] Embodiments of the network node 3300 may include additional components beyond those shown in Figure 13 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 3300 may include user interface equipment to allow input of information into the network node 3300 and to allow output of information from the network node 3300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 3300.[000104] Figure 14 is a block diagram of a host 4400, which may be an embodiment of the host 2116 of Figure 11, in accordance with various aspects described herein. As used herein, the host 4400 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 4400 may provide one or more services to one or more UEs.[000105] The host 4400 includes processing circuitry 4402 that is operatively coupled via a bus 4404 to an input / output interface 4406, a network interface 4408, a power source 4410, and a memory 4412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previousfigures, such as Figures 12 and 13, such that the descriptions thereof are generally applicable to the corresponding components of host 4400.[000106] The memory 4412 may include one or more computer programs including one or more host application programs 4414 and data 4416, which may include user data, e.g., data generated by a UE for the host 4400 or data generated by the host 4400 for a UE. Embodiments of the host 4400 may utilize only a subset or all of the components shown. The host application programs 4414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (WC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 4414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 4400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 4414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.[000107] Figure 15 is a block diagram illustrating a virtualization environment 5500 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 5500 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.[000108] Applications 5502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 5500 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.[000109] Hardware 5504 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 5506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 5508a and 5508b (one or more of which may be generally referred to as VMs 5508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 5506 may present a virtual operating platform that appears like networking hardware to the VMs 5508.[000110] The VMs 5508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 5506. Different embodiments of the instance of a virtual appliance 5502 may be implemented on one or more of VMs 5508, 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.[000111] In the context of NFV, a VM 5508 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 5508, and that part of hardware 5504 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 5508 on top of the hardware 5504 and corresponds to the application 5502.[000112] Hardware 5504 may be implemented in a standalone network node with generic or specific components. Hardware 5504 may implement some functions via virtualization. Alternatively, hardware 5504 may be part of a larger cluster of hardware (e.g., such as in a datacenter or CPE) where many hardware nodes work together and are managed via management and orchestration 5510, which, among others, oversees lifecycle management of applications 5502. In some embodiments, hardware 5504 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 5512 which may alternatively be used for communication between hardware nodes and radio units.[000113] Figure 16 shows a communication diagram of a host 6602 communicating via a network node 6604 with a UE 6606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 2112a of Figure 11 and / or UE 2200 of Figure 12), network node (such as network node 2110a of Figure 11 and / or network node 3300 of Figure 13), and host (such as host 2116 of Figure 11 and / or host 4400 of Figure 14) discussed in the preceding paragraphs will now be described with reference to Figure 16.[000114] Like host 4400, embodiments of host 6602 include hardware, such as a communication interface, processing circuitry, and memory. The host 6602 also includes software, which is stored in or accessible by the host 6602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 6606 connecting via an over-the-top (OTT) connection 6650 extending between the UE 6606 and host 6602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 6650.[000115] The network node 6604 includes hardware enabling it to communicate with the host 6602 and UE 6606. The connection 6660 may be direct or pass through a core network (like core network 2106 of Figure 11) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.[000116] The UE 6606 includes hardware and software, which is stored in or accessible by UE 6606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable toprovide a service to a human or non-human user via UE 6606 with the support of the host 6602. In the host 6602, an executing host application may communicate with the executing client application via the OTT connection 6650 terminating at the UE 6606 and host 6602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 6650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 6650.[000117] The OTT connection 6650 may extend via a connection 6660 between the host 6602 and the network node 6604 and via a wireless connection 6670 between the network node 6604 and the UE 6606 to provide the connection between the host 6602 and the UE 6606. The connection 6660 and wireless connection 6670, over which the OTT connection 6650 may be provided, have been drawn abstractly to illustrate the communication between the host 6602 and the UE 1606 via the network node 6604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.[000118] As an example of transmitting data via the OTT connection 6650, in step 6608, the host 6602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 6606. In other embodiments, the user data is associated with a UE 6606 that shares data with the host 6602 without explicit human interaction. In step 6610, the host 6602 initiates a transmission carrying the user data towards the UE 6606. The host 6602 may initiate the transmission responsive to a request transmitted by the UE 6606. The request may be caused by human interaction with the UE 6606 or by operation of the client application executing on the UE 6606. The transmission may pass via the network node 6604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 6612, the network node 6604 transmits to the UE 6606 the user data that was carried in the transmission that the host 6602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 6614, the UE 6606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 6606 associated with the host application executed by the host 6602.[000119] In some examples, the UE 6606 executes a client application which provides user data to the host 6602. The user data may be provided in reaction or response to the data received from the host 6602. Accordingly, in step 6616, the UE 6606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 6606. Regardless of the specific manner in which the user data was provided, the UE 6606 initiates, in step 6618, transmission of the user data towards the host 6602 via the network node 6604. In step 6620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 6604 receives user data from the UE 6606 and initiates transmission of the received user data towards the host 6602. In step 6622, the host 6602 receives the user data carried in the transmission initiated by the UE 6606.[000120] One or more of the various embodiments improve the performance of OTT services provided to the UE 6606 using the OTT connection 6650, in which the wireless connection 6670 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate, latency, and / or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, improved content resolution, better responsiveness, and / or extended battery lifetime.[000121] In an example scenario, factory status information may be collected and analyzed by the host 6602. As another example, the host 6602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 6602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 6602 may store surveillance video uploaded by a UE. As another example, the host 6602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 6602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.[000122] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTTconnection 6650 between the host 6602 and UE 6606, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 6602 and / or UE 6606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 6650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 6650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 6604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 6602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 6650 while monitoring propagation times, errors, etc.[000123] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface.In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.[000124] 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.[000125] It will be appreciated that computer systems are increasingly taking a wide variety of forms. In this description and in the claims, the terms “controller,” “computer system,” or “computing system” are defined broadly as including any device or system — or combination thereof — that includes at least one physical and tangible processor and a physical and tangible memory capable of having thereon computer-executable instructions that may be executed by a processor. By way of example, not limitation, the term “computer system” or “computing system,” as used herein is intended to include personal computers, desktop computers, laptop computers, tablets, hand-held devices (e.g., mobile telephones, PDAs, pagers), microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, multi-processor systems, network PCs, distributed computing systems, datacenters, message processors, routers, switches, and even devices that conventionally have not been considered a computing system, such as wearables (e.g., glasses).[000126] The computing system also has thereon multiple structures often referred to as an “executable component.” For instance, the memory of a computing system can include an executable component. The term “executable component” is the name for a structure that is well understood to one of ordinary skill in the art in the field of computing as being a structure that can be software, hardware, or a combination thereof. For instance, when implemented in software, oneof ordinary skill in the art would understand that the structure of an executable component may include software objects, routines, methods, and so forth, that may be executed by one or more processors on the computing system, whether such an executable component exists in the heap of a computing system, or whether the executable component exists on computer-readable storage media. The structure of the executable component exists on a computer-readable medium in such a form that it is operable, when executed by one or more processors of the computing system, to cause the computing system to perform one or more functions, such as the functions and methods described herein. Such a structure may be computer-readable directly by a processor — as is the case if the executable component were binary. Alternatively, the structure may be structured to be interpretable and / or compiled — whether in a single stage or in multiple stages — so as to generate such binary that is directly interpretable by a processor.[000127] The terms “component,” “service,” “engine,” “module,” “control,” “generator,” or the like may also be used in this description. As used in this description and in this case, these terms — whether expressed with or without a modifying clause — are also intended to be synonymous with the term “executable component” and thus also have a structure that is well understood by those of ordinary skill in the art of computing.[000128] In terms of computer implementation, a computer is generally understood to comprise one or more processors or one or more controllers, and the terms computer, processor, and controller may be employed interchangeably. When provided by a computer, processor, or controller, the functions may be provided by a single dedicated computer or processor or controller, by a single shared computer or processor or controller, or by a plurality of individual computers or processors or controllers, some of which may be shared or distributed. Moreover, the term “processor” or “controller” also refers to other hardware capable of performing such functions and / or executing software, such as the example hardware recited above.[000129] In general, the various exemplary embodiments may be implemented in hardware or special purpose chips, circuits, software, logic, or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor, or other computing device, although the disclosure is not limited thereto. While various aspects of the exemplary embodiments of this disclosure may be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks,apparatus, systems, techniques, or methods described herein may be implemented in, as nonlimiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.[000130] While not all computing systems require a user interface, in some embodiments a computing system includes a user interface for use in communicating information from / to a user. The user interface may include output mechanisms as well as input mechanisms. The principles described herein are not limited to the precise output mechanisms or input mechanisms as such will depend on the nature of the device. However, output mechanisms might include, for instance, speakers, displays, tactile output, projections, holograms, and so forth. Examples of input mechanisms might include, for instance, microphones, touchscreens, projections, holograms, cameras, keyboards, stylus, mouse, or other pointer input, sensors of any type, and so forth.Abbreviations and Defined Terms[000131] To assist in understanding the scope and content of this written description and the appended claims, a select few terms are defined directly below. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure pertains.[000132] The terms “approximately,” “about,” and “substantially,” as used herein, represent an amount or condition close to the specific stated amount or condition that still performs a desired function or achieves a desired result. For example, the terms “approximately,” “about,” and “substantially” may refer to an amount or condition that deviates by less than 10%, or by less than 5%, or by less than 1%, or by less than 0.1%, or by less than 0.01% from a specifically stated amount or condition.[000133] Various aspects of the present disclosure, including devices, systems, and methods may be illustrated with reference to one or more embodiments or implementations, which are exemplary in nature. As used herein, the term “exemplary” means “serving as an example, instance, or illustration,” and should not necessarily be construed as preferred or advantageous over other embodiments disclosed herein. In addition, reference to an “implementation” of the present disclosure or embodiments includes a specific reference to one or more embodiments thereof, and vice versa, and is intended to provide illustrative examples without limiting the scopeof the present disclosure, which is indicated by the appended claims rather than by the present description.[000134] As used in the specification, a word appearing in the singular encompasses its plural counterpart, and a word appearing in the plural encompasses its singular counterpart, unless implicitly or explicitly understood or stated otherwise. Thus, it will be noted that, as used in this specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. For example, reference to a singular referent (e.g., “a widget”) includes one, two, or more referents unless implicitly or explicitly understood or stated otherwise. Similarly, reference to a plurality of referents should be interpreted as comprising a single referent and / or a plurality of referents unless the content and / or context clearly dictate otherwise. For example, reference to referents in the plural form (e.g., “widgets”) does not necessarily require a plurality of such referents. Instead, it will be appreciated that independent of the inferred number of referents, one or more referents are contemplated herein unless stated otherwise.[000135] References in the specification to "one embodiment," "an embodiment," "an example embodiment," and the like indicate that the embodiment described may include a particular feature, structure, or characteristic, but it is not necessary that every embodiment includes 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 affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.[000136] It shall be understood that although the terms "first" and "second" etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed terms.[000137] It will be further understood that the terms "comprises", "comprising", "has", "having", "includes" and / or "including", when used herein, specify the presence of statedfeatures, elements, and / or components etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof.Conclusion[000138] The present disclosure includes any novel feature or combination of features disclosed herein either explicitly or any generalization thereof. Various modifications and adaptations to the foregoing exemplary embodiments of this disclosure may become apparent to those skilled in the relevant arts in view of the foregoing description, when read in conjunction with the accompanying drawings. However, any and all modifications will still fall within the scope of the non-limiting and exemplary embodiments of this disclosure.[000139] It is understood that for any given component or embodiment described herein, any of the possible candidates or alternatives listed for that component may generally be used individually or in combination with one another, unless implicitly or explicitly understood or stated otherwise. Additionally, it will be understood that any list of such candidates or alternatives is merely illustrative, not limiting, unless implicitly or explicitly understood or stated otherwise.[000140] In addition, unless otherwise indicated, numbers expressing quantities, constituents, distances, or other measurements used in the specification and claims are to be understood as being modified by the term “about,” as that term is defined herein. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the subject matter presented herein. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the subject matter presented herein are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical values, however, inherently contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.[000141] Any headings and subheadings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. The terms and expressions which have been employed herein are used as terms of description and not oflimitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the present disclosure. Thus, it should be understood that although the present disclosure has been specifically disclosed in part by certain embodiments, and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and such modifications and variations are considered to be within the scope of this present description.[000142] It will also be appreciated that systems, devices, products, kits, methods, and / or processes, according to certain embodiments of the present disclosure may include, incorporate, or otherwise comprise properties or features (e.g., components, members, elements, parts, and / or portions) described in other embodiments disclosed and / or described herein. Accordingly, the various features of certain embodiments can be compatible with, combined with, included in, and / or incorporated into other embodiments of the present disclosure. Thus, disclosure of certain features relative to a specific embodiment of the present disclosure should not be construed as limiting application or inclusion of said features to the specific embodiment. Rather, it will be appreciated that other embodiments can also include said features, members, elements, parts, and / or portions without necessarily departing from the scope of the present disclosure.[000143] Moreover, unless a feature is described as requiring another feature in combination therewith, any feature herein may be combined with any other feature of a same or different embodiment disclosed herein. Furthermore, various well-known aspects of illustrative systems, methods, apparatus, and the like are not described herein in particular detail in order to avoid obscuring aspects of the example embodiments. Such aspects are, however, also contemplated herein.[000144] It will be apparent to one of ordinary skill in the art that methods, devices, device elements, materials, procedures, and techniques other than those specifically described herein can be applied to the practice of the described embodiments as broadly disclosed herein without resort to undue experimentation. All art-known functional equivalents of methods, devices, device elements, materials, procedures, and techniques specifically described herein are intended to be encompassed by this present disclosure.[000145] When a group of materials, compositions, components, or compounds is disclosed herein, it is understood that all individual members of those groups and all subgroupsthereof are disclosed separately. When a Markush group or other grouping is used herein, all individual members of the group and all combinations and sub-combinations possible of the group are intended to be individually included in the disclosure.[000146] The above-described embodiments are 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, which is defined solely by the appended claims.

Claims

CLAIMSWhat is claimed is:

1. A method performed by a location management function, LMF (201), for performing positioning, the method comprising: transmitting (410) a positioning request to a network node (202); receiving (420) first one or more positioning responses from the network node (202), wherein the first one or more positioning responses include an indication that the first one or more positioning responses comprise first one or more partial positioning information; and inputting (430) the first one or more partial positioning information into an artificial intelligence / machine learning, AI / ML, positioning model.

2. The method of claim 1, wherein the network node (202) comprises at least one of: a base station; a Next Generation Radio Access Node, NG-RAN; a user equipment, UE; a gNB.

3. The method of claim 1 or 2, wherein the positioning comprises LMF-side model direct AI / ML positioning.

4. The method of any of claims 1 to 3, wherein the first one or more partial positioning information comprises a first fragment of a first positioning information.

5. The method of claim 4, wherein the first positioning information was fragmented based on at least one of: a network node decision; a recommendation from the LMF.

6. The method of any of claims 1 to 5, further comprising receiving a second one or more positioning responses from the network node, wherein the second one or more positioning responses include an indication that the second one or more positioning responses comprise second one or more partial positioning information.

7. The method of claim 6, wherein the second partial positioning information comprises a second fragment of the first positioning information.

8. The method of claim 6 or 7, further comprising receiving a status indication from the network node, the status indication configured to indicate whether there are additional fragments of the first positioning information.

9. The method of any of claims 1 to 8, wherein the first partial positioning information or the second partial positioning information comprise at least one of: an age of location of a UE; which group of UEs it is associated to.

10. The method of any of claims 1 to 9, further comprising inputting the first one or more partial positioning information or the second one or more partial positioning information into the AI / ML positioning model to estimate a position.

11. The method of any of claims 1 to 10, further comprising receiving, from the network node, a partial reporting indication of AI / ML positioning information.

12. The method of claim 11, wherein the partial reporting indication is signaled over New Radio Positioning Protocol A, NRPPa.

13. The method of any of claims 1 to 12, wherein the fragmentation is done over Next Generation protocol between the NG-RAN and core network, CN, fragmenting each NRPPa Packet data unit based on the contained input data.

14. The method of any of claims 1 to 13, further comprising transmitting to any network node, by the LMF, that it supports an AI / ML positioning model.

15. A method performed by a location management function, LMF (201), for performing positioning, the method comprising: transmitting (610), to one or more network nodes (202), a request indicator via New Radio Positioning Protocol A, NRPPa, for the one or more network nodes (202) to provide informationabout whether they support the provisioning of input data for artificial intelligence / machine learning, AI / ML, direct positioning and their capabilities.

16. The method of claim 15, further comprising receiving, from the one or more network nodes, their capability for supporting the provisioning of input data needed for AI / ML direct positioning.

17. The method of claim 16, wherein the capability is sent via an NRPPa information element in an NRPPa INFORMATION RESPONSE.

18. The method of any of claims 15 to 17, wherein the capabilities comprise at least one of: one or more details about the input data supported; one or more characteristics of the input data; fingerprinting based on channel observation.

19. The method of any of claims 15 to 18, wherein the LMF knows the capabilities of at least one network node providing input data for AI / ML direct positioning via pre-configuration from at least one of: an external node; an external function; an external system; the OAM.

20. The method of any of claims 1 to 19, further comprising transmitting an indication to the network node to abort positioning reporting.

21. The method of any of claims 1 to 20, further comprising: based on a input to the AI / ML positioning model, determining a position of a UE (203); and transmitting an indication of the determined position of the UE (203) to a client.

22. A method performed by a network node (202) for performing positioning, the method comprising: receiving (810) a positioning request from a location management function, LMF (201); obtaining (820) positioning measurements from one or more user equipment, UEs (203); andtransmitting (830) a first positioning response to the LMF (201), wherein the first positioning response includes an indication that the first positioning response comprises first partial positioning information that is a subset of the obtained positioning measurements.

23. The method of claim 22, further comprising transmitting a capability information message to the LMF, wherein the capability information comprises information regarding an ability to fragment a positioning response for artificial intelligence / machine learning, AI / ML, positioning.

24. The method of claim 22 or 23, further comprising transmitting a second positioning response to the LMF, wherein the second positioning response includes an indication that the second positioning response comprises second partial positioning information that is a subset of the obtained positioning measurements.

25. The method of any of claims 22 to 24, further comprising transmitting a third positioning response to the LMF, wherein the third positioning response includes an indication that the third positioning response comprises final partial positioning information that is a subset of the obtained positioning measurements.

26. The method of any of claims 22 to 25, wherein the first positioning response further comprises a time period associated with the first partial positioning information.

27. The method of any of claims 22 to 26, wherein the first positioning response further comprises an identifier of one or more UEs associated with the first partial positioning information.

28. The method of any of claims 22 to 27, further comprising receiving an indication to abort positioning reporting.

29. A mobile terminal (203, 202) comprising: processing circuitry configured to perform any of the steps of any of claims 1 to 28; and power supply circuitry configured to supply power to the wireless device.

30. A network node (202) comprising: processing circuitry configured to perform any of the steps of any of claims 1 to 28; power supply circuitry configured to supply power to the wireless device.

31. A user equipment, UE (203), comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of claims 1 to 21; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

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