Reporting channel measurements for machine learning positioning

Abstract

According to some embodiments, a method is performed by a network node. The method comprises: obtaining a measurement timing grid comprising a reference time and a timing granularity; determining a starting point of a measurement window based on the measurement timing grid; determining a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid; and performing a plurality of positioning measurements during the measurement window, the plurality of positioning measurements performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response. The method further comprises transmitting a measurement report to a positioning node, the measurement report comprising results of one or more of the plurality of positioning measurements.

Description

REPORTING CHANNEL MEASUREMENTS FOR MACHINE LEARNING POSITIONINGTECHNICAL FIELD

[0001] Embodiments of the present disclosure are directed to wireless communications and, more particularly to reporting channel measurements for machine learning positioning.BACKGROUND

[0002] Artificial intelligence (Al) and machine learning (ML) are promising tools to optimize the design of air-interface in wireless communication networks. Example use cases include using autoencoders for channel state information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to enhance the positioning accuracy; and using reinforcement learning for beam selection at the network side and / or the user equipment (UE) side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex multipleinput multiple-output (MIMO) precoding problems.

[0003] Third Generation Partnership Project (3GPP) New Radio (NR) standardization work includes a release 18 study item on AEML for NR air interface. The study item explores the benefits of augmenting the air-interface with features enabling improved support of AI / ML- based algorithms for enhanced performance and / or reduced complexity / o verhead. Through studying a few selected use cases (CSI feedback, beam management and positioning), this study item lays the foundation for future air-interface use cases leveraging AI / ML techniques.

[0004] Building an AI / ML model includes several development steps where the training of the Al model is one step in a training pipeline. An important part in AI / ML development is AEML model lifecycle management. This is illustrated in FIGURE 1.

[0005] FIGURE 1 is a flow diagram illustrating training and inference pipelines, and their interactions within a model lifecycle management procedure.

[0006] The Al model lifecycle management typically consists of a training (re-training) pipeline, a deployment stage, an inference pipeline, and drift detection stage.

[0007] The training (re-training) pipeline includes data ingestion, referring to gathering raw (training) data from a data storage. After data ingestion, there may also be a step that controls the validity of the gathered data. Data pre-processing refers to feature engineering applied toP112181WO01 PCT APPLICATION2 of 53 the gathered data, e.g., it may include data normalization and possibly a data transformation required for the input data to the AI / ML model.

[0008] The model training steps are where a model is obtained using the training dataset. Model evaluation refers to benchmarking the performance to a baseline. The iterative steps of model training and model evaluation continue until achieving an acceptable level of performance.

[0009] Model registration refers to registering the AI / ML model, including any corresponding AI / ML-meta data that provides information on how the AI / ML model was developed, and possibly AI / ML model evaluations performance outcomes.

[0010] The deployment stage makes the trained (or re-trained) AI / ML model part of the inference pipeline.

[0011] An inference pipeline includes data ingestion referring to gathering raw (inference) data from a data storage. A data pre-processing stage is typically identical to corresponding processing that occurs in the training pipeline.

[0012] Model operational refers to using the trained and deployed model in an operational mode.

[0013] Data and model monitoring refers to validating that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts.

[0014] The drift detection stage informs about any drifts in the model operations.

[0015] One important AI / ML physical layer (PHY) use case is the positioning of a target UE. Both positioning approaches below are effective in obtaining target UE location.

[0016] Direct AI / ML positioning is where the AI / ML model output is UE location. Direct AI / ML positioning typically refers to radio fingerprinting, where channel observation is used as the input of an AI / ML model.

[0017] AI / ML assisted positioning is where the AI / ML model output is new measurement and / or enhancement of existing measurement. The model output can be, for example, LOS / NLOS identification, timing and / or angle measurement, and / or likelihood or reliability of the measurement. The model input is also channel observations.

[0018] When applying the direct and assisted AI / ML positioning to a NR wireless communication network, the following cases are further identified for standardization in 3GPP for the NR system.P112181WO01 PCT APPLICATION3 of 53• 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

[0019] Case 2b is further illustrated in FIGURE 2, which illustrates UE-assisted / LMF-based positioning with LMF-side model and direct AI / ML positioning. In this positioning use case, the UE performs measurements on reference signals (for example, the downlink positioning reference signals) from multiple transmission and reception points (TRP). The UE then prepares and signals positioning related reports over the radio network to the LMF. AI / ML models are employed at the LMF to process the positioning related reports to generate an estimate of the UE position. The dashed lines illustrate a downlink reference signal (e.g., PRS), and the solid lines illustrate a measurement report from UE to LMF.

[0020] Case 3b is further illustrated in FIGURE 3, which illustrates NG-RAN node assisted positioning with LMF-side model and direct AI / ML positioning. In this positioning use case, multiple TRPs perform measurements on reference signals (for example, the uplink sounding reference signals) from a UE. The TRPs or the gNB controlling the TRPs then prepare and signal positioning related reports to the LMF. AI / ML models are employed at the LMF to process the positioning related reports to generate an estimate of the UE position. The dashed lines illustrate a reference signal, and the solid lines illustrate a measurement report.

[0021] In addition to the above operation modes for using AI / ML models for accurate UE positioning (i.e., for model inference), the positioning related measurement reports may also be collected by an ML measurement data collection node to compile suitable training datasets for training high performance ML positioning models (i.e., for model training). Therefore, a general system setup may be represented in FIGURE 4, where the ML measurement report contains channel measurements corresponding to model input.P112181WO01 PCT APPLICATION4 of 53

[0022] FIGURE 4 illustrates AI / ML model positioning system setup which includes at least a ML measurement data collection node.

[0023] As described above, the ML data measurement node can be a UE measuring downlink reference signals transmitted from the radio network nodes (such as the TRPs) or a radio network node (such as a TRP or a gNB) measuring uplink reference signals transmitted from a UE.

[0024] The ML measurement data collection node may perform one or more functions for lifecycle management (LCM) of the AI / ML model, including the following. The ML measurement data collection node may collect the measurements and other related information to compile an AI / ML model training data set, which can be used by AI / ML training node to train or finetune AI / ML models. The ML measurement data collection node may perform model monitoring functions to ensure the AI / ML models are operating within prescribed performance targets.

[0025] The ML measurement data collection node can be a UE, a gNB, an LMF, another radio network node (e.g., network data analytics function (NWDAF)), or a node outside of the radio network.

[0026] In legacy positioning methods, to assist with the generation of channel measurements, the LMF provides a search window to a gNB (for NG-RAN measurements) or UE (for UE measurements). Therefore, the gNB or UE receiver can perform detection of the reference signal within the search window, thus reducing the detection complexity and latency. For the uplink, NG-RAN measurements are typically based on a sounding reference signal (SRS), more specifically, positioning SRS. For the downlink, UE measurements are typically based on positioning reference signal (PRS).

[0027] In the following, the information elements (IEs) to assist with SRS measurements at NG-RAN are shown, which are extracted from TS 38.455. These IEs are sent over the NR Positioning Protocol A (NRPPa), from LMF to NG-RAN, where the NG-RAN node is gNB for 5G NR system.

[0028] The Search Window Information IE contains search window information for the TRP.P112181WO01 PCT APPLICATION5 of 53

[0029] In the following, the IES to assist with the PRS measurements at the UE are shown, which are extracted from TS 37.355. These IEs are sent over the Long Term Evolution (LTE) Positioning Protocol (LPP), from LMF to UE, in the format of assistance data for PRS per TRP.] ]P112181WO01 PCT APPLICATION6 of 53§ nr-DL-PRS-ExpectedRSTD§ This field indicates the RSTD value that the target device is expected to measure§ between this TRP and the assistance data reference TRP. The nr-DL-PRS- i ExpectedRSTD field takes into account the expected propagation time difference as§ well as transmit time difference of DL-PRS positioning occasions between the two TRPs. The resolution is 4xTs, with Ts=l / (15000*2048) seconds.§ nr-DL-PRS-ExpectedRSTD-Uncertainty i This field indicates the uncertainty in nr-DL-PRS-ExpectedRSTD value. The§ uncertainty is related to the location server's a-priori estimate of the target device i location. The nr-DL-PRS-ExpectedRSTD and nr-DL-PRS-ExpectedRSTD-§ Uncertainty together define the search window for the target device.§ The resolution R is§ - Tsif all DL-PRS Resources are in frequency range 2,§ - 4x Tsotherwise,§ with Ts=l / (15000*2048) seconds.§ The target device may assume that the beginning of the subframe for the DL-PRS of i§ this TRP is received within the search window of size§ - \-nr- L-PRS-ExpectedRSTD-Uncertainty T ; nr-DL-PRS-ExpectedRSTD-§ Uncertainly centred at TREF+ 1 millisecondxN+wr-DL-T’RS-§ ExpeciedRSTD Ts,§ where TREF is the reception time of the beginning of the subframe for the DL-PRS of§ the assistance data reference TRP at the target device antenna connector, and N can i be calculated based on

[0030] For radio signal-based positioning methods, conventional methods rely on a sufficient number of line-of-sight (LoS) links, typically at least three to five LOS links depending on the positioning method, and whether vertical position is estimated in addition to horizontal position.

[0031] In a cluttered environment, there is often a low probability of line-of-sight for a radio link between a UE and a TRP. For example, for InF-DH (Indoor Factory with Dense clutter and High base station height (Tx or Rx elevated above the clutter)) environment, the LOS probability ranges from 44.9% in a mildly cluttered environment to only 0.8% in a heavily cluttered environment.

[0032] Thus, conventional positioning methods struggle to locate a target UE in a heavily cluttered environment. Evaluations show that the 90-percentile positioning accuracy ofP112181WO01 PCT APPLICATION7 of 53 conventional positioning methods is more than 15 meters in an InF-DH environment with clutter parameter {60%, 6m, 2m}, due to the unavailability of sufficient LOS links.

[0033] This motivates the application of AI / ML based positioning in such challenging deployment environments. Evaluations show that an AI / ML model can be trained to deliver 90 percentile positioning accuracy below 1 meter.

[0034] To improve positioning accuracy, AI / ML deep learning models have been introduced to use richer radio channel conditions than current positioning related reports in 3GPP as part of the positioning related measurement reports. The following three measurements have been considered for standardization for the NR system: channel impulse response (CIR), power delay profile (PDP), and delay profile (DP).

[0035] More specifically, assume Ra[Zc] is the received reference symbol (primary reference signal (PRS) or secondary reference signal (SRS)) at the sub-carrier k of a receive antenna port a. The measured frequency domain channel response (FD CR) samples are obtained as Wa[ / c] = S Ra[ / c], where sFis complex conjugate of the known reference symbol at subcarrier k. Taking the inverse fast Fourier transform (IFFT) of the frequency domain channel response samples gives the measured time domain channel impulse response (TD CIR) samples: ha[d] = IFFT({Ha[ / c]}fe), where d = 0, 1, ... , 1VFFT— 1 and NFFTis the size of the IFFT.

[0036] The time domain or frequency domain channel measurement samples are directly observable at the receiver. Further processing on the measurement samples can be applied as follows.

[0037] A truncated TD CIR is obtained from the TD CIR by keeping only the first Ntsamples and discarding the last / VFFT— Ntsamples.

[0038] A TD power delay profile (TD PDP) is obtained from the (truncated) TD CIR by keeping only the power information across the antenna ports at each sampling grid point, while discarding the phase information of each sample: p [d] = £a|a[d] |2.

[0039] A sub-sampled TD CIR / PDP is obtained from a (truncated) TD CIR / PDP by keeping the values at the V samples by selecting the lVt' samples which satisfy a certain criteria (for example, a typical criteria is to select the lVt' samples with the largest powers) and setting the other samples to zeros.P112181WO01 PCT APPLICATION8 of 53

[0040] A time domain delay profile (TD DP) is obtained from a sub-sampled TD PDP by setting the / Vt' samples with the largest powers to a specific value. The specific value could a constant such as 1 or the reference signal received power (RSRP) of the link: RSRP =p [d] .

[0041] The CIR samples are complex valued as illustrated by the example in FIGURE 5A with two receive antenna ports, where each CIR sample at an antenna port consists of a real part and an imaginary part. In FIGURE 5 A, the CIR is truncated to the first Nt= 128 samples. After down sampling to the / Vt' = 9 strongest samples, the sub-sampled CIR is illustrated in FIGURE 5B, where nonzero values are present in only 9 of the sampling points with the rest set to zero.

[0042] FIGURE 5 illustrates an example of measured two-port CIR samples: (A) truncated to Nt= 128 samples, and (B) further sub-sampled to / Vt' = 9 strongest samples.

[0043] The PDP samples are real-valued because the samples are represented by the received power at the sample points . A truncated PDP is illustrated in FIGURE 6A. After down sampling to the Nf = 9 strongest samples, the sub-sampled PDP is illustrated in FIGURE 6B, where nonzero values are present in only 9 of the sampling points with the rest set to zero.

[0044] FIGURE 6 illustrates an example of power delay profile (PDP) samples computed from the example two-port CIR in Figure 5 : (A) truncated to Nt= 128 samples, and (B) further subsampled to Nf = 9 strongest samples. (The square root of PDP is plotted for easier inspection.)

[0045] The DP samples are illustrated in FIGURE 7. FIGURE 7 illustrates an example of delay profile (DP) samples computed from the example PDP in FIGURE 6A.

[0046] From the above description and illustrations, the components of the three different type of positioning related measurement reports can be decomposed as:• Channel impulse response (CIR) o Timing information of the nonzero samples o Power information of the nonzero samples o Phase information of the nonzero samples• Power delay profile (PDP) o Timing information of the nonzero samples o Power information of the nonzero samples• Delay profile (DP) o Timing information of the nonzero samplesP112181WO01 PCT APPLICATION9 of 53

[0047] There currently exist certain challenges. For example, for AI / ML based positioning, an important aspect is to generate the channel measurements for determining the model input. Ideally, the channel measurements capture the rich information of the channel observation as much as possible, while keeping the measurement size as low as possible.

[0048] For sample-based channel measurements, the generation of channel measurements is significantly affected by the definition of the measurement report window (also referred to interchangeably as the measurement window). Previously, it was unclear how to determine the measurement report window so that the channel measurements for model input can be generated in the most efficient way. Moreover, the measurement report window definition is needed so that consistent measurements are generated by different receiver implementations. Consistent measurements are also necessary between the training data collection stage and model inference stage.SUMMARY

[0049] As described above, certain challenges currently exist with reporting channel measurements for machine learning positioning. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges.

[0050] For example, particular embodiments derive a measurement report window for generating sample-based channel measurements. The sample-based channel measurements are used for determining the artificial intelligence (AI) / machine learning (ML) model input for positioning. Particular embodiments may be applied to any procedure where such channel measurements need to be generated, including training data collection, model inference, and model performance monitoring.

[0051] The AI / ML model may be located on the user equipment (UE) side (Case 1, 2a above), on the next generation radio access network (NG-RAN) side (Case 3a above), or on the location management function (LMF) side (Case 2b, 3b above).

[0052] The AI / ML based positioning may be direct AI / ML positioning or AI / ML assisted positioning.

[0053] In general, for AI / ML based positioning, particular embodiments derive the measurement report window for generating sample-based channel measurements. First, the sample grid for generating channel measurement samples is provided using a reference timeP112181WO01 PCT APPLICATION10 of 53 and a timing granularity. Second, the measurement report window is determined which gives the scope for generating channel measurement samples.

[0054] In some embodiments, the measurement report window is determined by (a) a starting point on the grid, and (b) a length of the measurement report window in terms of number of samples on the grid. Alternatively, the measurement report window may be determined by (a) a starting point on the grid and (b) an end point on the grid.

[0055] In some embodiments, the measurement report window is determined by (a) the center of the measurement report window and (b) the span of the measurement report window (e.g., defined by + / - the distance to the center). The measurement report window is typically quantized to the sample grid provided in first step.

[0056] Lastly, a list of channel measurement samples is selected within the measurement report window, on the sample grid.

[0057] The samples may be selected according to a predefined criterion. A variety of criteria may be used, including: (a) the samples that have the highest power levels within the measurement report window; or (b) the earliest samples within the measurement report window; or (c) the samples that are stronger than a power threshold and the earliest; or (d) the samples that are closest to detected paths, where the paths are those detected for the multi-path channel.

[0058] According to some embodiments, a method is performed by a wireless device. The method comprises: obtaining a measurement timing grid comprising a reference time and a timing granularity; determining a starting point of a measurement window based on the measurement timing grid; determining a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid; and performing a plurality of positioning measurements during the measurement window, the plurality of positioning measurements performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response. The method further comprises transmitting a measurement report to a positioning node, the measurement report comprising results of one or more of the plurality of positioning measurements.

[0059] In particular embodiments, the method further comprises selecting a subset Nt’ of the plurality of positioning measurements, wherein the measurement report comprises the results of the selected subset Nt’ of the plurality of positioning measurements.P112181WO01 PCT APPLICATION11 of 53

[0060] In particular embodiments, the timing granularity corresponds to T seconds, where T = 2k* Tc, where k represents a timing reporting granularity factor and Tcis a basic time unit for New Radio (NR).

[0061] In particular embodiments, the reference time comprises an anchor point on the measurement timing grid and each point of the measurement timing grid is spaced by the timing granularity. The reference time may be based on an absolute clock time or a receive timing of a positioning reference signal.

[0062] In particular embodiments, the starting point is determined based on a first detected sample or path. The starting point may be aligned to the measurement timing grid using a floor, round, or ceiling function.

[0063] In particular embodiments, selecting the subset Nt’ comprises selecting Nt’ samples with a highest power in the measurement window. Selecting the subset Nt’ may comprise selecting Nt’ samples above a minimum power threshold. Selecting the subset Nt’ may comprise selecting Nt’ samples with a highest power in the measurement window and their adjacent samples.

[0064] In particular embodiments, selecting the subset Nt’ comprises selecting the earliest Nt’ samples in the measurement window.

[0065] In particular embodiments, the method further comprises receiving, from the positioning node, configuration for: a number Nt for the Nt consecutive samples; a number Nt’ for a subset of samples to be selected from the Nt consecutive samples; and a granularity factor k associated with the timing granularity.

[0066] According to some embodiments, a wireless device comprises processing circuitry operable to perform any of the methods of the wireless receiver described above.

[0067] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the wireless device described above.

[0068] According to some embodiments, a method is performed by a network node. The method comprises: obtaining a measurement timing grid comprising a reference time and a timing granularity; determining a starting point of a measurement window based on the measurement timing grid; determining a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid; and performing a plurality ofP112181WO01 PCT APPLICATION12 of 53 positioning measurements during the measurement window, the plurality of positioning measurements performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response. The method further comprises transmitting a measurement report to a positioning node, the measurement report comprising results of one or more of the plurality of positioning measurements.

[0069] In particular embodiments, the method further comprises selecting a subset Nt’ of the plurality of positioning measurements, wherein the measurement report comprises the results of the selected subset Nt’ of the plurality of positioning measurements.

[0070] In particular embodiments, the method further comprises receiving, from the positioning node, configuration for: a number Nt for the Nt consecutive samples; a number Nt’ for a subset of samples to be selected from the Nt consecutive samples; and a granularity factor k associated with the timing granularity.

[0071] In particular embodiments, the reference time is based on an uplink relative time of arrival reference time for a sounding reference signal.

[0072] According to some embodiments, a network node comprises processing circuitry operable to perform any of the methods of the network node described above.

[0073] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network node described above.

[0074] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments derive a measurement report window for generating sample-based channel measurements. The embodiments may be used by various AI / ML based positioning methods, including direct and assisted AI / ML positioning, UE-side or networkside AI / ML models.

[0075] Particular embodiments facilitate alignment of model input generation among different vendors. Particular embodiments support the alignment of model input generation between different life cycle management stages of the AI / ML model, including model training and model inference. Without the proposed methods, the model input cannot be generated properly, and the AI / ML based positioning is not able to function.P112181WO01 PCT APPLICATION13 of 53BRIEF DESCRIPTION OF THE DRAWINGS

[0076] For a more complete understanding of the disclosed embodiments and their features and advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:FIGURE 1 is a flow diagram illustrating training and inference pipelines, and their interactions within a model lifecycle management procedure;FIGURE 2 illustrates user equipment (UEj-assisted / location management function (LMF)-based positioning with LMF-side model and direct artificial intelligence (AI) / machine learning (ML) positioning;FIGURE 3 illustrates next generation radio access network (NG-RAN) node assisted positioning with LMF-side model and direct A I / ML positioning;FIGURE 4 illustrates AI / ML model positioning system setup which includes at least a ML measurement data collection node;FIGURE 5A illustrates an example of measured two-port channel impulse response (CIR) samples truncated to Nt= 128 samples;FIGURE 5B illustrates the example of FIGURE 5A further sub-sampled to lVt' = 9 strongest samples;FIGURE 6A illustrates an example of power delay profile (PDP) samples computed from the example two-port CIR in FIGURE 5 A truncated to Nt= 128 samples;FIGURE 6B illustrates the example of FIGURE 6A further sub-sampled to lVt' = 9 strongest samples;FIGURE 7 illustrates an example of delay profile (DP) samples computed from the example PDP in FIGURE 6A;FIGURE 8 illustrates an example of generating channel measurement samples, where the start time of measurement report window is later than the reference time;FIGURE 9 illustrates an example of generating channel measurement samples, where the start time of measurement report window is earlier than the reference time;FIGURE 10 illustrates an example communication system, according to certain embodiments;FIGURE 11 illustrates an example user equipment (UE), according to certain embodiments;FIGURE 12 illustrates an example network node, according to certain embodiments;P112181WO01 PCT APPLICATION14 of 53FIGURE 13 illustrates a block diagram of a host, according to certain embodiments;FIGURE 14 illustrates a method performed by a wireless device, according to certain embodiments; andFIGURE 15 illustrates a method performed by a network node, according to certain embodiments.DETAILED DESCRIPTION

[0077] As described above, certain challenges currently exist with reporting channel measurements for machine learning positioning. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges.

[0078] For example, particular embodiments derive a measurement report window for generating sample-based channel measurements. The sample-based channel measurements are used for determining the artificial intelligence (AI) / machine learning (ML) model input for positioning. Particular embodiments may be applied to any procedure where such channel measurements need to be generated, including training data collection, model inference, and model performance monitoring.

[0079] In general, for AI / ML based positioning, particular embodiments derive the measurement report window for generating sample-based channel measurements. First, the sample grid for generating channel measurement samples is provided using a reference time and a timing granularity. Second, the measurement report window is determined which gives the scope for generating channel measurement samples. Lastly, a list of channel measurement samples is selected within the measurement report window, on the sample grid.

[0080] Particular embodiments are described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0081] In some embodiments the non-limiting terms user equipment (UE) or a wireless device are used interchangeably. The UE herein may be any type of wireless device capable of communicating with a network node or another UE over radio signals. The UE may also be a radio communication device, target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine communication (M2M), low-cost and / or low-complexityP112181WO01 PCT APPLICATION15 of 53UE, a sensor equipped with UE, tablet, mobile terminals, smart phone, laptop embedded equipment (LEE), laptop mounted equipment (LME), universal serial bus (USB) dongles, customer premises equipment (CPE), an Internet of things (loT) device, or a narrowband loT (NB-IOT) device, etc.

[0082] In some embodiments the generic term “anchor node” is used, which are used as reference points for determining the location of a target UE. In general, the anchor nodes for positioning may be a variety of nodes in the wireless network. For positioning using the radio link between the target UE and a radio network node, the anchor node may be any kind of a radio network node, which may comprise any of: base station, radio base station, base transceiver station, Node B, evolved Node B (eNB), next-generation Node B (gNodeB or gNB), NG-RAN node, transmission point (TP), transmission-reception point (TRP), multi- cell / multicast coordination entity (MCE), relay node, access point (AP), antenna reference point (ARP), radio access point, remote radio unit (RRU), remote radio head (RRH). For positioning using sidelink between two UEs, the anchor node is a UE or a wireless device.

[0083] For ease of discussion, the methods are described using the radio links between a UE and an anchor node. TRP is used as a representative example of the anchor node, where the TRP is connected to a gNB. Those skilled in the art understand that the same methodology may be applied to many other wireless communication scenarios, e.g., sidelink-based positioning where the radio link is between two peer UEs.

[0084] Particular embodiments include a procedure for determining Nt’ samples for the purpose of providing model input. In the following, the basic procedure is outlined for determining Nt’ samples for the purpose of providing model input for AI / ML based positioning.

[0085] Step 1: Define a timing grid using (a) a reference time Tref (seconds) and (b) a timing granularity of T (seconds), where T = 2k* Tc, where k represents the timing reporting granularity factor and Tcis the basic time unit for New Radio (NR) (as defined in 3GPP TS38.2I I).

[0086] In this context, the reference time Tref only anchors the grid points, i.e., Tref is on the grid where consecutive grid points are spaced by T (sec). Tref is not intended to set the boundary of the measurement report window, e.g., Tref is not intended to be the first, or last, or middle grid point. In general, numerous grid points are expected before and after Tref.

[0087] For NG-RAN side measurements (i.e., Case 3a and 3b above), the reference time is the existing uplink (UL) relative time of arrival (RTOA) reference time To+tsRS as defined in TSP112181WO01 PCT APPLICATION16 of 5338.215. Different TRPs have their local UL RTOA reference time. Thus, different TRPs may have their own grid timing for sampling the channel measurements.

[0088] For UE-side measurements (i.e., Case 1, 2a, 2b above), the reference time may be an absolute clock time, or a receive timing of a positioning reference signal (PRS) of a reference TRP.

[0089] Step 2: Determine the starting point To. ,meas_window of the sequence of Nt consecutive samples according to the timing grid defined in Step 1. Thus, To,meas_window corresponds to a grid point, and the neighboring samples are spaced by T (sec).

[0090] Together (a) the starting point To ,meas_window and (b) a sequence of Nt consecutive samples spaced by T (sec) define the measurement report window. That is, the measurement report window is: [To.meas_window, To ,meas_window + (Nt-1)*T] (sec).

[0091] The above only provides one format to define the measurement report window. Other formats may be used to define the measurement report window in an equivalent manner, including define (a) a starting point on the grid and (b) an end point on the grid; or define (a) the center of the measurement report window and (b) the span of the measurement report window. The span may be given by + / - d, where d is the distance to the center.

[0092] The measurement report window is typically quantized to the sample grid defined in Step 1.

[0093] Step 3: Select Nt’ samples from the sequence of Nt consecutive samples based on a selection rule. In the extreme case where Nt’= Nt, the rule is essentially not needed, i.e., all samples in the measurement report window are selected. In general, Nt’= Nt is allowed, which may simplify the receiver process for generating the measurement samples at the cost of higher signaling overhead for sending the measurement report.

[0094] For NG-RAN side measurements (i.e., Case 3a and 3b above), the selection rule may be one of the following, or a combination thereof:• In one example, selecting the Nt’ samples with the highest power in the measurement report window, i.e., select the Nt’ samples according to the ranking of power, from high to low.• In another example, selecting the Nt’ samples with the highest power in the measurement report window, with a minimum power threshold under which no further samples are reported.P112181WO01 PCT APPLICATION17 of 53• In another example, selecting Nt’ samples with the highest power in the measurement report window as well as their adjacent samples.• In another example, selecting the earliest Nt’ samples in the measurement report window.• In another example, selecting Nt’ samples that are closest to detected paths, where the paths are those detected for the multi-path channel.

[0095] For UE-side measurements (i.e., Case 1, 2a, 2b above), similar rules may be adopted as for NG-RAN side measurement, i.e., any of the above or a combination thereof.

[0096] To illustrate the procedures above, two examples are shown in FIGURE 8 and FIGURE 9.

[0097] FIGURE 8 illustrates an example of generating channel measurement samples, where the start time of measurement report window (To,meas_window) is later than the reference time (Tref). From the measurement report window, Nt’=9 samples are selected from Nt=16 consecutive samples for determining model input.

[0098] FIGURE 9 illustrates an example of generating channel measurement samples, where the start time of measurement report window (To,meas_window) is earlier than the reference time (Tref). From the measurement report window, Nt’=9 samples are selected from Nt=16 consecutive samples for determining model input.

[0099] In terms of parameter configuration, measurement report window size Nt (samples), number of selected samples Nt’, and granularity factor k may be configured by the LMF.

[0100] For measurement by NG-RAN (Case 3a, 3b above), at least for Case 3b, {Nt, Nt’, k} are signaled from LMF to NG-RAN via New Radio (NR) Positioning Protocol A (NRPPa). For Case 3a, if central unit (CU)-distributed unit (DU) split is applied at gNB and the AI / ML model is located at CU, then parameters {Nt, Nt’, k} are signaled from CU to DU via FLAP protocol. Otherwise (i.e., no split architecture in gNB), the gNB implementation may select its own {Nt, Nt’, k}. For training data collection, the parameters {Nt, Nt’, k} may be recorded as metadata, along with the sounding reference signal (SRS) measurement data.

[0101] For measurement by UE (Case 1, 2a, 2b above), at least for Case 2b, {Nt, Nt’, k} are signaled from LMF to UE via Long Term Evolution (LTE) Positioning Protocol (LPP), where the channel measurement made by the UE is transmitted to the LMF. The UE capabilities may be exchanged before {Nt, Nt’, k} are signaled from the LMF, so that the LMF may select those {Nt, Nt’, k} values that are within the range supported by the UE.P112181WO01 PCT APPLICATION18 of 53

[0102] For Case 1 and 2a, the UE implementation may choose {Nt, Nt’, k}. For training data collection, the parameters {Nt, Nt’, k} may be recorded as metadata, along with the PRS measurement data.

[0103] For case 1 and 2a, the parameters {Nt, Nt’, k} recorded as metadata may be considered as a side condition under which a data point was acquired during training. If the UE cannot use the same parameters during inference when measuring model input, the inference result may not be accurate. In essence, consistency between training and inference should be maintained with regard to the {Nt, Nt’, k} parameter value range.

[0104] Some embodiments include determining an end of the measurement report window. An additional parameter Rt(in samples) may be configured to a measurement node by the LMF. When this parameter is configured to a measurement node, the end of the measurement report window is determined as min(To,meas_wmdow + (Nt -I)*T, Tref + (Rt -1)*T).

[0105] That is, using Case 2b and 3b as an example, Tref + (Rt -1)*T represents the sample time beyond which the LMF is not interested in receiving a measurement.

[0106] When this parameter is not configured, the end of the measurement report window is determined as per previous embodiments: To,meas_window + (Nt -I)*T. That is, Rt is treated as infinity when not configured.

[0107] The parameter Rt may be configured by the various types of measurement nodes via the corresponding protocols disclosed above. For example, the list of configurable parameters is expanded from {Nt, Nt’, k} to {Nt, Nt’, k, Rt} for signaling over the relevant protocols.

[0108] In a first group of embodiments, the start of Nt consecutive samples is based on the first detected sample (or path). In these embodiments, To,meas_window is based on the timing of the first detected sample (or path). Note that this sample (or path) is the first one detected in time and may or may not have high enough power to be selected as part of the Nt’ samples.

[0109] In one example, To,meas_window is the timing (sec) with reference to Trej- as shown below:where1is the timing of first detected sample (or path) by a channel estimator. For NG-RAN side measurements, the channel estimator performs channel measurements based on uplink reference signal such as SRS (e.g., the positioning SRS). For UE-side measurements, the channel estimator performs channel measurements based on downlink reference signal such as positioning reference signal (PRS).P112181WO01 PCT APPLICATION19 of 53

[0110] Function (. ) may take one of several variants, including the following:Option 1. =T1^ref— 8. if f1is always on the timing grid defined by Tref and T(i.e., t is naturally an integer multiple of T). For example, the channel estimator takes the first sample that is above a noise threshold as the first detected sample.Option 2. (. ) quantizes towards the timing grid using a floor, round or ceil function, when is not always on the timing grid. That is, is equal to — 5, or[oni] In the above, an integer 8 is included, which implies that a small shift of 8 samples may be applied. That is, 8 is an integer value to give a small shift in time relative to the first detected sample (or path). If a non-zero 8 is applied, then the value of 8 may be determined in various ways, including:Option (A). 8 is a predetermined, fixed value. Preferably 8 is a small positive integer. For example, 5 = 3.Option (B). 5 is a predetermined integer value (i.e., not configured), which may vary with system parameters and conditions. For example, 5 may vary with frequency layer, frequency range, carrier frequency, subcarrier spacing, bandwidth of the reference signal, deployment environment condition (e.g., line-of-sight or non-line-of-sight environment), and / or UE speed. Option (C). 5 is a configurable value. For example, for Case 2b and 3b, the LMF may configure 5 value for UE and gNB, respectively. The configuration may take into account one or more of the system parameters and conditions, including: frequency layer, frequency range, carrier frequency, subcarrier spacing, bandwidth of the reference signal, deployment environment condition (e.g., line-of-sight or non-line-of-sight environment), and / or UE speed. For example, in Case 2b and 3b, the LMF may set the 5 value for the UE and gNB based on the LoS probability of the operation site. In highly NLoS environments or NLoS links, 5 may be a negative integer (i.e., delay the start of Ntconsecutive samples), so that the channel measurements cover the region where most of the power is concentrated. In contrast, for largely LoS environments or LoS links, 5 >=0 may be used so that 5 samples before the LoS path (approximately equal to the first detected path) are included in the list of Ntconsecutive samples.P112181WO01 PCT APPLICATION20 of 53

[0112] In the simpler case, 8 = 0 is chosen to simply (. ), i.e. no shift is applied. In this case, there is no need to determine or configure the 8 value.

[0113] Sometimes timemay be defined relative to Tref and may take positive or negative values relative to Tref. Using relative time, (1) can be simplified to the following, because Tref is essentially taken as time 0.

[0114] In a second group of embodiments, the start of Nt samples is based on the start of the search window of the reference signal. In these embodiments, To,meas_window is based on the start of the search window for receiving the reference signal. In one example, To,meas_window is the timing (sec) with reference to Trej- as shown below:where To Searchwindow is the start time of the RS search window, g(. ) is a function applied toTo, search window to quantize it onto the timing grid.

[0115] In one example, function g(. ) quantizes T0 searcfl windowtowards the timing grid using

[0116] Additionally, similar to the first group of embodiments, a small shift of 8 samples may be applied in g(. ), where 8 is an integer value to give a small shift in time relative to the start of the search window.

[0117] Also, sometimes To,meas_window may be defined relative to Tref, and it may take positive or negative values. Using relative time, (3) can be simplified to the following, because Tref is essentially taken as time 0.

[0118] For measurements at NG-RAN, for each TRP, the SRS search window is defined by the IE “Search Window Information”, which is in turn defined by sub-fields “Expected Propagation Delay” and “Delay Uncertainty”. The IE “Search Window Information” is included in a message from the LMF to the NG-RAN node for each TRP.

[0119] Thus, the measurement report window may be defined by the ‘Start’ and ‘End’ of the SRS search window:P112181WO01 PCT APPLICATION21 of 53Tstart = To+ tSRS+ Expected_propagation_delay - Delay_uncertainty, (5)Tend = To+ tSRS+ Expected_propagation_delay - Delay_uncertainty, (6)

[0120] The Tstart and Tena may be further quantized onto the sample grid. One example quantization is:

[0121] Another example quantization is:

[0122] The above takes into account that for NG-RAN measurement, To+ tSRSis used as the reference time Tref. Function g(. ) is applied to quantize the start and the end of measurement time onto the timing grid with timing granularity T.

[0123] With To meas-window and Tend meas windowknown, the number of samples in the measurement report window (Nt) may be obtained, with the knowledge of timing granularity T.

[0124] For measurements at the UE, similarly, the PRS search window, Tstart and Tend, may be used to derive the measurement report window for channel measurement samples. That is, the start and end time of PRS search window based on signaled IES are used as start and end time of the measurement report window.

[0125] Following the legacy signaling, the PRS search window is defined by IEs “nr-DL-PRS- ExpectedRSTD” and ^nr-DL-PRS-ExpectedRSTD-Uncertainty’ These IEs are part of NR-DL- PRS-AssistanceData, which is sent from the location server (e.g., LMF) to the target UE to provide DL-PRS assistance data.

[0126] For a given TRP at a given frequency layer, Tstart and Tend are:Tstart—Tcenter,search_window TuncertaintyTend=Tcenter,search_window + Tuncertainty

[0127] In atypical method, the center of the PRS search window Tcenter,search_window is:TREF+ 1 nu\hsecondx +nr-DL-PRS-ExpectedRSTDx4xTsAnd the uncertainty Tuncertainty of PRS search window is:P112181WO01 PCT APPLICATION22 of 53 nr-OL-PRS-ExpectedRSTD-Uncertainty R.

[0128] Similar to uplink SRS measurements, the downlink PRS measurements may use Equations (7)-(8) for determining the quantized measurement report window. If the reference time of PRS measurement is taken into account, then quantization step similar to (9)-(10) may be used alternatively.

[0129] FIGURE 10 illustrates an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3GPP access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0130] Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 102 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 102, including one or more network nodes 110 and / or core network nodes 108.

[0131] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O- CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by theP112181WO01 PCT APPLICATION23 of 53ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies.

[0132] In some embodiments, the telecommunication network 102 includes a non-terrestrial network, NTN. Unless otherwise described herein, embodiments applicable for NTN may be implanted according to the following clauses. An NTN is telecommunication network where the radio access payload is conveyed via satellite to aground station. E-UTRAN supports radio access over non-terrestrial networks for BL UEs, UEs in enhanced coverage and NB-IoT UEs. Support for non-terrestrial networks encompasses platforms that provide radio access through Geosynchronous orbits (GSO), Non-Geosynchronous Orbit (NGSO), which includes Low- Earth Orbit (LEO) and Medium Earth Orbit (MEO) or High-Altitude Platform Systems (HAPS). Another example of a Non-Terrestrial Network (NTN) provides non-terrestrial NR access to the UE by means of an NTN payload and an NTN Gateway, a service link between the NTN payload and a UE, and a feeder link between the NTN Gateway and the NTN payload exists. An access network 104 may include an NTN access network such as the 3GPP Satellite Access Node (SAN) which comprises non-NTN infrastructure base station functions (e.g. eNB / gNB) a terrestrial Gateway which provides the interface to the feeder link to an NTN payload RF node. In some embodiments a network node 110 comprises a SAN, wherein the location of base station functions for a network node 110 (described above for the general terrestrial access) vary between residing in the terrestrial access network node part of the SAN and the NTN Payload RF node functions depending on the supported architecture. One example of NTN architecture is called bent pipe or transparent architecture where the radio frequency processing function (transceiver) on a satellite platform is interconnected with a terrestrial base station, also known as transparent architecture, and the NTN payload is passed transparently, no unpacking. Another example of NTN architecture is called regenerative architecture, where part or all of the eNB / gNB can be in the satellite.

[0133] In some examples a SAN includes an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-P112181WO01 PCT APPLICATION24 of 53CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification).

[0134] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0135] The UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 102.

[0136] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features ofthese components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription IdentifierP112181WO01 PCT APPLICATION25 of 53De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0137] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102 and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more services. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0138] As a whole, the communication system 100 of FIGURE 10 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.

[0139] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0140] In some examples, the UEs 112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal orP112181WO01 PCT APPLICATION26 of 53 external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0141] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0142] The hub 114 may have a constant / persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 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 110b. In otherP112181WO01 PCT APPLICATION27 of 53 embodiments, the hub 114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0143] FIGURE 11 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over 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.

[0144] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle- to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0145] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIGURE 11. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multipleP112181WO01 PCT APPLICATION28 of 53 instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0146] The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine -readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs).

[0147] In the example, the input / output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0148] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the powerP112181WO01 PCT APPLICATION29 of 53 from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0149] The memory 210 may be or be configured to include memory such as random -access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0150] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium.

[0151] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / orP112181WO01 PCT APPLICATION30 of 53 a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0152] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0153] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0154] 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.

[0155] 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 wearableP112181WO01 PCT APPLICATION31 of 53 technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in FIGURE 11.

[0156] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship 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.

[0157] 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.P112181WO01 PCT APPLICATION32 of 53

[0158] FIGURE 12 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU), components of a satellite access network (SAN) (e.g., terrestrial base station, gateway, NTN payload RF function) (the NTN and components of the satellite network are described in more detail with respect to FIGURE 10).

[0159] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0160] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0161] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC mayP112181WO01 PCT APPLICATION33 of 53 control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0162] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0163] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0164] The memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information,P112181WO01 PCT APPLICATION34 of 53 including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0165] The communication interface 306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0166] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).P112181WO01 PCT APPLICATION35 of 53

[0167] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0168] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0169] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0170] Embodiments of the network node 300 may include additional components beyond those shown in FIGURE 12 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.P112181WO01 PCT APPLICATION36 of 53

[0171] FIGURE 13 is a block diagram of a host 400, which may be an embodiment of the host 116 of FIGURE 10, in accordance with various aspects described herein. As used herein, the host 400 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 400 may provide one or more services to one or more UEs.

[0172] The host 400 includes processing circuitry 402 that is operatively coupled via a bus 404 to an input / output interface 406, a network interface 408, a power source 410, and a memory 412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 3 and 4, such that the descriptions thereof are generally applicable to the corresponding components of host 400.

[0173] The memory 412 may include one or more computer programs including one or more host application programs 414 and data 416, which may include user data, e.g., data generated by a UE for the host 400 or data generated by the host 400 for a UE. Embodiments of the host 400 may utilize only a subset or all of the components shown. The host application programs 414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), 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 414 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 400 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 414 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.

[0174] FIGURE 14 is a flowchart illustrating an example method 1400 in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 14 may be performed by UE 200 described with respect to FIGURE 11.P112181WO01 PCT APPLICATION37 of 53

[0175] The method may begin at step 1410, where the wireless device (e.g., UE 200) receives, from a positioning node (e.g., location management function (LMF)), configuration for: a number Nt, a number Nt’, and a granularity factor k. The number Nt defines a number of consecutive samples to collect when performing positioning measurements and thus may facilitate determining a duration of a measurement window, as further described below (see, e.g., steps 1416 and 1418). The number Nt’ indicates a subset of samples to be selected from the Nt consecutive samples. The granularity factor k is associated with timing granularity (e.g., k may be used as a factor in an equation for determining the timing granularity, such as T = 2k * Tc). Parameters Nt, Nt’ and k are described in more detail in the following steps and in the embodiments and examples described above.

[0176] In particular embodiments, the configuration of step 1410 may be received via LPP.

[0177] In some embodiments, step 1410 is optional, and one or more parameters of the configuration may be determined by the wireless device and / or based on a specification.

[0178] At step 1412, the wireless device obtains a measurement timing grid comprising a reference time and a timing granularity. The measurement timing grid may be obtained in various ways. In some embodiments, the wireless device may obtain the measurement timing grid from another network node, or the wireless device may define its own measurement timing grid. Similarly, a portion of the measurement timing grid (e.g., the reference time and / or the timing granularity) may be obtained in various ways, such as from another network node or defined by the wireless device.

[0179] In particular embodiments, the timing granularity corresponds to T seconds, where T = 2k * Tc, where k represents a timing reporting granularity factor and Tc is a basic time unit for New Radio (NR) (as defined in 3GPP TS38.211).

[0180] In particular embodiments, the reference time comprises an anchor point on the measurement timing grid and each point of the measurement timing grid is spaced by the timing granularity. For example, a reference time Tref anchors the grid points, i.e., Tref is on the grid where consecutive grid points are spaced by T (sec). Tref may, but is not required to, set the boundary of the measurement window, e.g., Tref is not required to be the first, or last, or middle grid point. In general, numerous grid points may occur before and after Tref.

[0181] The reference time may be based on an absolute clock time or a receive timing of a positioning reference signal (e.g., downlink PRS).P112181WO01 PCT APPLICATION38 of 53

[0182] In particular embodiments, the measurement timing grid may be obtained according to any of the embodiments and examples described above.

[0183] At step 1414, the wireless device determines a starting point of a measurement window based on the measurement timing grid. In particular embodiments, the starting point is determined based on a first detected sample or path. The starting point may be aligned to the measurement timing grid using a floor, round, or ceiling function.

[0184] In particular embodiments, the starting point is determined according to any of the embodiments and examples described above.

[0185] At step 1416, the wireless device determines a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid. In particular embodiments, the wireless device may determine the duration in terms of a number of samples on the measurement timing grid (e.g., Nt), or a starting point and an end point on the measurement timing grid (where there are Nt consecutive samples between the starting point and the end point).

[0186] There are various ways to define the measurement window relative to the measurement timing grid. As an example, the measurement window may be defined by a starting point on the measurement timing grid and an end point on the measurement timing grid. As another example, the measurement window may be defined by a center point on the measurement timing grid and the span of the measurement window. The span may be given by + / - d, where d is the distance to the center.

[0187] In particular embodiments, the wireless device determines the duration of the measurement window according to any of the embodiments and examples described above.

[0188] At step 1418, the wireless device performs a plurality of positioning measurements during the measurement window. The plurality of positioning measurements are performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response. For example, see Figures 8-9, each of which illustrates an example of performing positioning measurements of Nt consecutive samples during the measurement window.

[0189] In particular embodiments, the wireless device performs the plurality of positioning measurements according to any of the embodiments and examples described herein.P112181WO01 PCT APPLICATION39 of 53

[0190] At step 1420, the wireless device may select a subset Nt’ of the plurality of positioning measurements. Where Nt’ = Nt, the wireless device may skip this step and continue to step 1422.

[0191] In particular embodiments, selecting the subset Nt’ comprises selecting Nt’ samples with a highest power in the measurement window. Selecting the subset Nt’ may comprise selecting Nt’ samples above a minimum power threshold. Selecting the subset Nt’ may comprise selecting Nt’ samples with a highest power in the measurement window and their adjacent samples. In particular embodiments, selecting the subset Nt’ comprises selecting the earliest Nt’ samples in the measurement window.

[0192] In particular embodiments, the wireless device may select the subset Nt’ according to any of the embodiments and examples described herein.

[0193] At step 1422, the wireless device transmits a measurement report to a positioning node. The measurement report comprises results of one or more of the plurality of positioning measurements. As an example, measurement report may comprise all Nt positioning measurements (e.g., when Nt’ = Nt), or the measurement report may comprise the subset Nt’ of the plurality of positioning measurements determined in step 1420.

[0194] Modifications, additions, or omissions may be made to method 1400 of FIGURE 14. Additionally, one or more steps in the method of FIGURE 14 may be performed in parallel or in any suitable order.

[0195] FIGURE 15 is a flowchart illustrating an example method 1500 in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 15 may be performed by network node 300 described with respect to FIGURE 12.

[0196] The method may begin at step 1510, where the network node (e.g., network node 300) receives, from a positioning node (e.g., location management function (LMF)), configuration for: a number Nt for the Nt consecutive samples; a number Nt’ for a subset of samples to be selected from the Nt consecutive samples; and a granularity factor k associated with the timing granularity. In particular embodiments, the configuration may be received viaNRPPa.

[0197] Step 1510 is similar to step 1410 described above and is described in more detail in the embodiments and examples described above.

[0198] At step 1512, the network node obtains a measurement timing grid comprising a reference time and a timing granularity. In some embodiments, the network node may obtainP112181WO01 PCT APPLICATION40 of 53 the measurement timing grid from another network node, or the network node may define its own measurement timing grid.

[0199] In particular embodiments, the reference time is based on an uplink relative time of arrival reference time for a sounding reference signal (e.g., uplink SRS).

[0200] Step 1512 is similar to step 1412 described above and is described in more detail in the embodiments and examples described above.

[0201] At step 1514, the network node determines a starting point of a measurement window based on the measurement timing grid. Step 1514 is similar to step 1414 described above and is described in more detail in the embodiments and examples described above.

[0202] At step 1516, the network node determines a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid. Step 1516 is similar to step 1416 described above and is described in more detail in the embodiments and examples described above.

[0203] At step 1518, the network node performs a plurality of positioning measurements during the measurement window. The plurality of positioning measurements are performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response.

[0204] Step 1518 is similar to step 1418 described above. In particular embodiments, the network node performs the plurality of positioning measurements according to any of the embodiments and examples described herein.

[0205] At step 1520, the network node may select a subset Nt’ of the plurality of positioning measurements. Where Nt’ = Nt, the network node may skip this step and continue to step 1522.

[0206] Step 1520 is similar to step 1420 described above and is described in more detail in the embodiments and examples described above.

[0207] At step 1522, the network node transmits a measurement report to a positioning node. The measurement report comprises results of one or more of the plurality of positioning measurements. Step 1522 is similar to step 1422 described above and is described in more detail in the embodiments and examples described above

[0208] Modifications, additions, or omissions may be made to method 1500 of FIGURE 15. Additionally, one or more steps in the method of FIGURE 15 may be performed in parallel or in any suitable order.P112181WO01 PCT APPLICATION41 of 53

[0209] In an embodiment, a method is performed by a channel estimator, where the channel estimator may be implemented by or as a component of UE 200 or network node 300, for example. The method comprises performing measurements of a reference signal (e.g., uplink for network node 300, downlink for UE 200) to obtain Nt consecutive samples. Examples of the reference signal include an SRS, such as an uplink positioning SRS, or a PRS, such as a downlink PRS.

[0210] The Nt consecutive samples estimate channel impulse response in the time domain. The measurements are performed according to a measurement timing grid that comprises a timing granularity defined relative to a reference time. An example timing granularity corresponds to T seconds, where T = 2k * Tc, and where k represents a timing reporting granularity factor and Tcis a basic time unit. The Nt consecutive samples start at a starting point based on a first detected sample or path associated with the reference signal, with the starting point aligned to the measurement timing grid by a floor, round, or ceiling function. The Nt consecutive samples continue according to the timing granularity until the number of consecutive samples obtained equals Nt.

[0211] The method further comprises selecting a subset Nt’ of measurements from the Nt consecutive samples. As one example, measurements having the highest power among the Nt consecutive samples may be selected for inclusion in the subset Nt’ of measurements.

[0212] The method further comprises transmitting, to a positioning node (e.g., LMF), a measurement report comprising results of the selected subset Nt’ measurements. In certain embodiments, prior to performing the above-described steps, the channel estimator obtains values for Nt, Nt’, and / or k from the positioning node, for example, via LPP or NRPPa signaling. Further variations are possible according to any of the embodiments and examples described herein.

[0213] Modifications, additions, or omissions may be made to the methods disclosed herein without departing from the scope of the invention. The methods may include more, fewer, or other steps. Additionally, steps may be performed in any suitable order.

[0214] Some example embodiments follow.Group A Embodiments1. A method performed by a wireless device, the method comprising:- obtaining a measurement timing grid comprising a reference time and timeP112181WO01 PCT APPLICATION42 of 53 granularity;- determining a starting point of a measurement window based on the timing grid;- determining a duration of the measurement window based on the timing grid;- performing a plurality of positioning measurements during the measurement window;- selecting a subset of the plurality of positioning measurements for use as input to a machine learning model; and- transmitting a measurement report to a positioning node, the measurement report comprising the selected subset of the plurality of positioning measurements.2. The method of the previous embodiment, wherein the starting point is determined according to any of the embodiments and examples described above.3. The method of any one of the previous embodiments, wherein the duration is determined according to any of the embodiments and examples described above.4. The method of any one of the previous embodiments, wherein the subset of the plurality of positioning measurements is selected according to any of the embodiments and examples described above.5. A method performed by a wireless device, the method comprising:- any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.6. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.7. The method of any of the previous two embodiments, further comprising:- providing user data; and- forwarding the user data to a host computer via the transmission to the base station.Group B EmbodimentsP112181WO01 PCT APPLICATION43 of 538. A method performed by a base station, the method comprising:- obtaining a measurement timing grid comprising a reference time and time granularity;- determining a starting point of a measurement window based on the timing grid;- determining a duration of the measurement window based on the timing grid;- performing a plurality of positioning measurements during the measurement window;- selecting a subset of the plurality of positioning measurements for use as input to a machine learning model; and- transmitting a measurement report to a positioning node, the measurement report comprising the selected subset of the plurality of positioning measurements.9. The method of the previous embodiment, wherein the starting point is determined according to any of the embodiments and examples described above.10. The method of any one of the previous embodiments, wherein the duration is determined according to any of the embodiments and examples described above.11. The method of any one of the previous embodiments, wherein the subset of the plurality of positioning measurements is selected according to any of the embodiments and examples described above.12. A method performed by a base station, the method comprising:- any of the steps, features, or functions described above with respect to base stations, either alone or in combination with other steps, features, or functions described above.13. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.14. The method of any of the previous embodiments, further comprising:- obtaining user data; andP112181WO01 PCT APPLICATION44 of 53- forwarding the user data to a host computer or a wireless device.C Embodiments15. A mobile terminal comprising:- processing circuitry configured to perform any of the steps of any of the Group A embodiments; and- power supply circuitry configured to supply power to the wireless device.16. A base station comprising:- processing circuitry configured to perform any of the steps of any of the Group B embodiments;- power supply circuitry configured to supply power to the wireless device.17. A user equipment (UE) comprising:- an antenna configured to send and receive wireless signals;- radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry;- the processing circuitry being configured to perform any of the steps of any of the Group A embodiments;- an input interface connected to the processing circuitry and configured to allow input 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.18. A communication system including a host computer comprising:- processing circuitry configured to provide user data; and- a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE),- wherein the cellular network comprises a base station having a radio interfaceP112181WO01 PCT APPLICATION45 of 53 and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments.19. The communication system of the pervious embodiment further including the base station.20. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station.

[0215] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0216] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0217] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

Claims

P112181WO01 PCT APPLICATION46 of 53CLAIMS:

1. A method performed by a wireless device, the method comprising: obtaining (1412) a measurement timing grid comprising a reference time and a timing granularity; determining (1414) a starting point of a measurement window based on the measurement timing grid; determining (1416) a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid; performing (1418) a plurality of positioning measurements during the measurement window, the plurality of positioning measurements performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response; and transmitting (1422) a measurement report to a positioning node, the measurement report comprising results of one or more of the plurality of positioning measurements.

2. The method of claim 1, further comprising selecting (1420) a subset Nt’ of the plurality of positioning measurements, wherein the measurement report comprises the results of the selected subset Nt’ of the plurality of positioning measurements.

3. The method of any one of claims 1-2, wherein the timing granularity corresponds to T seconds, where T = 2k* Tc, where k represents a timing reporting granularity factor and Tcis a basic time unit for New Radio (NR).

4. The method of any one of claims 1-3, wherein the reference time comprises an anchor point on the measurement timing grid and each point of the measurement timing grid is spaced by the timing granularity.

5. The method of any one of claims 1-4, wherein the reference time is based on an absolute clock time or a receive timing of a positioning reference signal.

6. The method of any one of claims 1-5, wherein the starting point is determinedP112181WO01 PCT APPLICATION47 of 53 based on a first detected sample or path.

7. The method of any one of claims 1-6, wherein the starting point is aligned to the measurement timing grid using a floor, round, or ceiling function.

8. The method of any one of claims 2-7, wherein selecting the subset Nt’ comprises selecting Nt’ samples with a highest power in the measurement window.

9. The method of any one of claims 2-8, wherein selecting the subset Nt’ comprises selecting Nt’ samples above a minimum power threshold.

10. The method of any one of claims 2-9, wherein selecting the subset Nt’ comprises selecting Nt’ samples with a highest power in the measurement window and their adjacent samples.

11. The method of any one of claims 2-7, wherein selecting the subset Nt’ comprises selecting the earliest Nt’ samples in the measurement window.

12. The method of any one of claims 1-11, further comprising receiving (1410), from the positioning node, configuration for: a number Nt for the Nt consecutive samples; a number Nt’ for a subset of samples to be selected from the Nt consecutive samples; and a granularity factor k associated with the timing granularity.

13. A wireless device (200) comprising processing circuitry (202) operable to: obtain a measurement timing grid comprising a reference time and timing granularity; determine a starting point of a measurement window based on the measurement timing grid; determine a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid; perform a plurality of positioning measurements during the measurement window, theP112181WO01 PCT APPLICATION48 of 53 plurality of positioning measurements performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response; and transmit a measurement report to a positioning node, the measurement report comprising results of one or more of the plurality of positioning measurements.

14. The wireless device of claim 13, the processing circuitry further operable to select a subset Nt’ of the plurality of positioning measurements, wherein the measurement report comprises the results of the selected subset Nt’ of the plurality of positioning measurements.

15. The wireless device of any one of claims 13-14, wherein the timing granularity corresponds to T seconds, where T = 2k* Tc, where k represents a timing reporting granularity factor and Tcis a basic time unit for New Radio (NR).

16. The wireless device of any one of claims 13-15, wherein the reference time comprises an anchor point on the measurement timing grid and each point of the measurement timing grid is spaced by the timing granularity.

17. The wireless device of any one of claims 13-16, wherein the reference time is based on an absolute clock time or a receive timing of a positioning reference signal.

18. The wireless device of any one of claims 13-17, wherein the starting point is determined based on a first detected sample or path.

19. The wireless device of any one of claims 13-18, wherein the starting point is aligned to the measurement timing grid using a floor, round, or ceiling function.

20. The wireless device of any one of claims 14-19, wherein the processing circuitry is operable to select the subset Nt’ by selecting Nt’ samples with a highest power in the measurement window.P112181WO01 PCT APPLICATION49 of 5321. The wireless device of any one of claims 14-20, wherein the processing circuitry is operable to select the subset Nt’ by selecting Nt’ samples above a minimum power threshold.

22. The wireless device of any one of claims 14-21, wherein the processing circuitry is operable to select the subset Nt’ by selecting Nt’ samples with a highest power in the measurement window and their adjacent samples.

23. The wireless device of any one of claims 14-19, wherein the processing circuitry is operable to select the subset Nt’ by selecting the earliest Nt’ samples in the measurement window.

24. The wireless device of any one of claims 13-23, wherein the processing circuitry is further operable to receive, from the positioning node, configuration for: a number Nt for the Nt consecutive samples; a number Nt’ for a subset of samples to be selected from the Nt consecutive samples; and a granularity factor k associated with the timing granularity.

25. A method performed by a network node, the method comprising: obtaining (1512) a measurement timing grid comprising a reference time and a timing granularity; determining (1514) a starting point of a measurement window based on the measurement timing grid; determining (1516) a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid; performing (1518) a plurality of positioning measurements during the measurement window, the plurality of positioning measurements performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response; and transmitting ( 1522) a measurement report to a positioning node, the measurement report comprising results of one or more of the plurality of positioning measurements.P112181WO01 PCT APPLICATION50 of 5326. The method of claim 25, further comprising selecting (1520) a subset Nt’ of the plurality of positioning measurements, wherein the measurement report comprises the results of the selected subset Nt’ of the plurality of positioning measurements.

27. The method of any one of claims 25-26, wherein the timing granularity corresponds to T seconds, where T = 2k* Tc, where k represents a timing reporting granularity factor and Tcis a basic time unit for New Radio (NR).

28. The method of any one of claims 25-27, wherein the reference time is based on an uplink relative time of arrival reference time for a sounding reference signal.

29. The method of any one of claims 25-28, wherein the reference time comprises an anchor point on the measurement timing grid and each point of the measurement timing grid is spaced by the timing granularity.

30. The method of any one of claims 25-29, wherein the starting point is determined based on a first detected sample or path.

31. The method of any one of claims 25-30, wherein the starting point is aligned to the measurement timing grid using a floor, round, or ceiling function.

32. The method of any one of claims 26-31, wherein selecting the subset Nt’ comprises selecting Nt’ samples with a highest power in the measurement window.

33. The method of any one of claims 26-32, wherein selecting the subset Nt’ comprises selecting Nt’ samples above a minimum power threshold.

34. The method of any one of claims 26-33, wherein selecting the subset Nt’ comprises selecting Nt’ samples with a highest power in the measurement window and their adjacent samples.

35. The method of any one of claims 26-31, wherein selecting the subset Nt’P112181WO01 PCT APPLICATION51 of 53 comprises selecting the earliest Nt’ samples in the measurement window.

36. The method of any one of claims 25-35, further comprising receiving (1510), from the positioning node, configuration for: a number Nt for the Nt consecutive samples; a number Nt’ for a subset of samples to be selected from the Nt consecutive samples; and a granularity factor k associated with the timing granularity.

37. A network node (300) comprising processing circuitry (302) operable to: obtain a measurement timing grid comprising a reference time and timing granularity; determine a starting point of a measurement window based on the measurement timing grid; determine a duration of the measurement window comprising Nt consecutive samples based on the measurement timing grid; perform a plurality of positioning measurements during the measurement window, the plurality of positioning measurements performed according to the measurement timing grid, wherein the plurality of positioning measurements estimate a time domain channel impulse response; and transmit a measurement report to a positioning node, the measurement report comprising results of one or more of the plurality of positioning measurements.

38. The network node of claim 37, the processing circuitry further operable to select a subset Nt’ of the plurality of positioning measurements, wherein the measurement report comprises the results of the selected subset Nt’ of the plurality of positioning measurements.

39. The network node of any one of claims 37-38, wherein the timing granularity corresponds to T seconds, where T = 2k* Tc, where k represents a timing reporting granularity factor and Tcis a basic time unit for New Radio (NR).

40. The network node of any one of claims 37-39, wherein the reference time is based on an uplink relative time of arrival reference time for a sounding reference signal.P112181WO01 PCT APPLICATION52 of 5341. The network node of any one of claims 37-40, wherein the reference time comprises an anchor point on the measurement timing grid and each point of the measurement timing grid is spaced by the timing granularity.

42. The network node of any one of claims 37-41, wherein the starting point is determined based on a first detected sample or path.

43. The network node of any one of claims 37-42, wherein the starting point is aligned to the measurement timing grid using a floor, round, or ceiling function.

44. The network node of any one of claims 38-43, wherein the processing circuitry is operable to select the subset Nt’ by selecting Nt’ samples with a highest power in the measurement window.

45. The network node of any one of claims 38-44, wherein the processing circuitry is operable to select the subset Nt’ by selecting Nt’ samples above a minimum power threshold.

46. The network node of any one of claims 38-45, wherein the processing circuitry is operable to select the subset Nt’ by selecting Nt’ samples with a highest power in the measurement window and their adjacent samples.

47. The network node of any one of claims 38-43 wherein selecting the subset Nt’ comprises selecting the earliest Nt’ samples in the measurement window.

48. The network node of any one of claims 37-47, wherein the processing circuitry is further operable to receive, from the positioning node, configuration for: a number Nt for the Nt consecutive samples; a number Nt’ for a subset of samples to be selected from the Nt consecutive samples; and a granularity factor k associated with the timing granularity.

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