Efficient subsampling for channel measurement reporting

Abstract

According to some embodiments, a method is performed by a network node. The method comprises: obtaining N t total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal, wherein N t comprises an integer value; determining a window of size W consecutive samples out of the N t total samples, wherein the W consecutive samples have a highest sum power among the N t total samples, wherein W comprises an integer value; selecting a subset Nt' of samples from the window of size W consecutive samples, where Nt' comprises an integer value less than or equal to W; and transmitting a measurement report to a network node, the measurement report comprising timing information of the selected subset Nt' of samples from the window of size W consecutive samples.

Description

EFFICIENT SUBSAMPLING FOR CHANNEL MEASUREMENT REPORTING TECHNICAL FIELD

[0001] Embodiments of the present disclosure are directed to wireless communications and,more particularly to efficient subsampling for channel measurement reporting. BACKGROUND

[0002] Artificial intelligence (AI) and machine learning (ML) are promising tools to optimizethe 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 multiple- input multiple-output (MIMO) precoding problems.

[0003] Third Generation Partnership Project (3GPP) New Radio (NR) standardization workincludes a release 18 study item on AI / ML 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 / overhead. 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] 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.

[0005] Direct AI / ML positioning is where the AI / ML model output is UE location. DirectAI / ML positioning typically refers to radio fingerprinting, where channel observation is used as the input of an AI / ML model.

[0006] AI / ML assisted positioning is where the AI / ML model output is new measurementand / 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.

[0007] When applying the direct and assisted AI / ML positioning to a NR wirelesscommunication network, the following cases are further identified for standardization in 3GPP for the NR system.P112190WO01 PCT APPLICATION 2 of 62 ^Case 1: UE-based positioning with UE-side model, direct AI / ML or AI / ML assistedpositioning ^Case 2a: UE-assisted / location management function (LMF)-based positioning withUE-side model, AI / ML assisted positioning ^Case 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / MLpositioning ^Case 3a: Next generation radio access network (NG-RAN) node assisted positioningwith gNB-side model, AI / ML assisted positioning ^Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / MLpositioning

[0008] Case 2b is further illustrated in FIGURE 1, which illustrates UE-assisted / LMF-basedpositioning 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.

[0009] Case 3b is further illustrated in FIGURE 2, which illustrates NG-RAN node assistedpositioning 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.

[0010] In addition to the above operation modes for using AI / ML models for accurate UEpositioning (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 3, where the ML measurement report contains channel measurements corresponding to model input.P112190WO01 PCT APPLICATION 3 of 62

[0011] FIGURE 3 illustrates AI / ML model positioning system setup which includes at least aML measurement data collection node.

[0012] As described above, the ML data measurement node can be a UE measuring downlinkreference 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.

[0013] The ML measurement data collection node may perform one or more functions for life-cycle 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.

[0014] The ML measurement data collection node can be a UE, a gNB, an LMF, another radionetwork node (e.g., network data analytics function (NWDAF)), or a node outside of the radio network.

[0015] For radio signal-based positioning methods, conventional methods rely on the existenceof enough LOS links in the radio environment for the trilateration algorithms to work correctly. An example is illustrated in FIGURE 4.

[0016] FIGURE 4 illustrates a multipath radio environment between a UE and two TRPs. ForTRP A, a LOS path exists between the UE’s transmitter and TRP A’s receiver. For TRP B, however, only NLOS paths exist between the UE’s transmitter and TRP B’s receiver because of the blockers in the environment.

[0017] Between the UE and the TRP A, there is an unblocked direct path (i.e., a LOS link) forthe radio signal to travel between the two radio nodes.

[0018] Between the UE and the TRP B, there is no unblocked direct path between the twonodes. The radio signals from one node will need to be reflected off other surfaces to reach the other node. The radio link between the UE and the TRP B is then a NLOS link.

[0019] In a cluttered environment, there is often a low probability of line-of-sight for a radiolink 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.P112190WO01 PCT APPLICATION 4 of 62

[0020] Thus, conventional positioning methods struggle to locate a target UE in a heavilycluttered environment. Evaluations show that the 90-percentile positioning accuracy of 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.

[0021] This motivates the application of AI / ML based positioning in such challengingdeployment environments.

[0022] To improve positioning accuracy, AI / ML deep learning models have been introducedto 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).

[0023] More specifically, assume ^^^^[^^] is the received reference symbol (primary referencesignal (PRS) or secondary reference signal (SRS)) at the sub-carrier ^^ of a receive antenna port ^^. The measured frequency domain channel response (FD CR) samples are obtained as^^^^[^^] ≜ ^^^∗^ ^^^^[^^], where ^^^∗^ is complex conjugate of the known reference symbol at sub- carrier ^^. 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: ℎ^^[^^] ≜ IFFT({^^^^[^^]}^^), where ^^ = 0, 1, … ,^^FFT − 1 and ^^^^^^^^ is the size of theIFFT.

[0024] The time domain or frequency domain channel measurement samples are directlyobservable at the receiver. Further processing on the measurement samples can be applied as follows.

[0025] A truncated TD CIR is obtained from the TD CIR by keeping only the first ^^^^ samplesand discarding the last ^^FFT − ^^^^ samples.

[0026] A TD power delay profile (TD PDP) is obtained from the (truncated) TD CIR bykeeping only the power information across the antenna ports at each sampling grid point, whilediscarding the phase information of each sample: ^^[^^] ≜ ∑ ^^ |ℎ^^[^^]|2.

[0027] A sub-sampled TD CIR / PDP is obtained from a (truncated) TD CIR / PDP by keepingthe values at the ^^^′^samples by selecting the ^^^′^samples which satisfy a certain criteria (for example, a typical criteria is to select the ^^^′^samples with the largest powers) and setting theother ^^^^ − ^^^′^samples to zeros.P112190WO01 PCT APPLICATION 5 of 62

[0028] A time domain delay profile (TD DP) is obtained from a sub-sampled TD PDP bysetting the ^^^′^samples with the largest powers to a specific value. The specific value could aconstant such as 1 or the reference signal received power (RSRP) of the link: RSRP = ∑^^ ^^[^^] .

[0029] The CIR samples are complex valued as illustrated by the example in FIGURE 5A withtwo receive antenna ports, where each CIR sample at an antenna port consists of apart andan imaginary part. In FIGURE 5A, the CIR is truncated to the first ^^^^ = 128 samples. Afterdown sampling to the ^^′^^ = 9 strongest samples, the sub-sampled CIR is illustrated in FIGURE5B, where nonzero values are present in only 9 of the sampling points with the rest set to zero.

[0030] FIGURE 5 illustrates an example of measured two-port CIR samples: (A) truncated to^^^^ = 128 samples, and (B) further sub-sampled to ^^ ′^^ = 9 strongest samples.

[0031] The PDP samples are real-valued because the samples are represented by the receivedpower at the sample points. A truncated PDP is illustrated in FIGURE 6A. After down sampling to the ^^′^^ = 9 strongest samples, the sub-sampled PDP is illustrated in FIGURE 6B, wherenonzero values are present in only 9 of the sampling points with the rest set to zero.

[0032] FIGURE 6 illustrates an example of power delay profile (PDP) samples computed fromthe example two-port CIR in Figure 5: (A) truncated to ^^^^ = 128 samples, and (B) further sub-sampled to ^^′^^ = 9 strongest samples. (The square root of PDP is plotted for easier inspection.)

[0033] The DP samples are illustrated in FIGURE 7. FIGURE 7 illustrates an example of delayprofile (DP) samples computed from the example PDP in FIGURE 6A.

[0034] From the above description and illustrations, the components of the three different typeof positioning related measurement reports can be decomposed as: ^Channel impulse response (CIR)o Timing information of the nonzero sampleso Power information of the nonzero sampleso Phase information of the nonzero samples^ Power delay profile (PDP)o Timing information of the nonzero sampleso Power information of the nonzero samples^ Delay profile (DP)o Timing information of the nonzero samplesP112190WO01 PCT APPLICATION 6 of 62

[0035] The NR system supports flexible time and frequency domain resource allocation to aNR signal. For example, in the time domain, a physical downlink shared channel (PDSCH) can be allocated to be present in orthogonal frequency division multiplexing (OFDM) symbol #2 to #12. As another example, a physical uplink shared channel (PUSCH) can be allocated to be present in a range of frequency domain resource blocks (RB). For example, the UE has a bandwidth part size of 100 RBs. The network can signal to the UE that the UE can transmit a PUSCH using RB#10 to RB#24 (in total of 15 RBs).

[0036] Such contiguous resource allocation in time or frequency domain can be represented bya bitmap, in which a certain range contains all ones with the rest containing all zeros. An efficient approach to signal such contiguous all-ones in NR is using the start and length indicator value (SLIV). Given the following variables: ^The starting point ^^ (with value between 0 and ^^ − 1), and^ The length of the contiguous resources ^^ (with value between 1 and ^^ − ^^)^ The overall available resource size ^^ (i.e., the scope of the indicator).The SLIV is calculated by the following procedure as described in the NR specifications. It can be presented by the following function. Function SLIV(^^,^^,^^) ^if ^^ − 1 ≤ ⌊^^ / 2 ⌋return ^^ × (^^ − 1) + ^^^ elsereturn ^^ × (^^ − ^^ + 1) + ^^ − 1 − ^^For an overall available resource size ^^, the number of bits needed to represent the SLIV isgiven by the following function: ^^(^^) = ⌈log^^(^^+1) 2 2 ⌉, where ⌈^^⌉ is the ceiling function returning an integer no smaller than ^^.

[0037] For example, the UE has a bandwidth part size of 100 RBs. The network can signal tothe UE that the UE can transmit a PUSCH using RB #10 to RB#24 (in total of 15 RBs) using SLIV=1410, which has a 13-bit binary representation of 0010110000010.

[0038] There currently exist certain challenges. For example, for AI / ML-based positioning, acrucial factor is determining the model input from channel measurements. The model inputs should capture the comprehensive information from channel observations while minimizing the signaling overhead.P112190WO01 PCT APPLICATION 7 of 62

[0039] As discussed previously, sub-sampling the positioning-related measurements isessential for reducing the signaling overhead associated with TD CIR / PDP / DP for enabling AI / ML-based positioning. A straightforward method involves selecting the strongest ^^^′ ^ samples. However, this method incurs significant signaling overhead because of the sparsity of the non-zero sub-samples, which result from numerous reflections and scatterers in highly NLOS environments. Joint design of the subsampling and efficient representation is a key to reduce signaling overhead.

[0040] The existing NR specifications (i.e., Rel-17 / Rel-18) supports signaling of the timing ofeach nonzero sub-sample individually.

[0041] For positioning in the downlink, the UE performs measurement on the PRS from oneor more TRP. The measurements include first path timing information, which can be downlink reference signal time difference (DL-RSTD) or UE-RxTxTimeDiff using 16 to 21 bits, and for the (n-1) additional paths, per-path timing information, which is relative path delay, thus using smaller 9 to 14 bits.

[0042] For positioning, in the uplink, NR-RAN performs measurement on the SRS, which istransmitted by the target UE. The measurements include: first path timing information, which is uplink relative time of arrival (UL RTOA) using 16 to 21 bits, and for the (n-1) additional paths: per-path timing information, which is a relative path delay, thus using smaller 9 to 14 bits.

[0043] In the above, n is the total number of paths signaled, and the value of n is set separatelyfor downlink and uplink.

[0044] Parameter n in the existing NR specification is analogous to the parameter ^^^′^in AI / ML model input. Assuming the ^^^′^sub-samples are signaled in the same manner as the n paths in Rel-17 / Rel-18 NR specifications, the timing signaling overhead scales with the number of nonzero sub-samples (^^^′^). Furthermore, some methods incur significant signaling overhead because of the sparsity of the non-zero sub-samples. The overhead is not directly affected by the number of available samples (^^^^) before down-sampling.

[0045] When applied to larger reference signal bandwidth (which requires more bits for eachmeasurement timing) and larger number of nonzero sub-samples (^^^′^) to enable high positioning accuracy in highly NLOS environments, the existing NR signaling approaches require large signaling sizes.P112190WO01 PCT APPLICATION 8 of 62

[0046] Considering a nonlimiting example of frequency range one (FR1) carrier of 100 MHzwith subcarrier spacing of 30 kHz. Using a regular FFT size of 4096, the sampling frequencyis given by ^^^^ = 4096 × 30^^3 = 122.88 MHz. The sampling granularity is thus 1 / ^^^^ =8.14 ns. Expressed in the NR fundamental time unit, this sampling granularity is 16 × ^^^^,where ^^^^ ≜ (4096 × 480^^3)−1second. For this sampling granularity, the existing NR specifications as described above use 17 bits for the first path and 10 bits for each of the additional paths. Thus, to signal ^^′^^ = 9 paths for each of six positioning related measurementreports, 6 × 17 = 102 bits are used for signaling the first paths and 6 × 8 × 10 = 480 bits areused for signaling the remaining ^^′^^ − 1 paths.

[0047] The NR specifications allow the configuration of using even finer granularity forreporting the path timings. For these cases, even more signaling bits will be used.

[0048] A bitmap-based timing report signaling approach has been discussed. That is, a bitmapof length ^^^^is used to represent whether each of the ^^^^measurement samples are retained and nonzero, which is represented by a value of 1 in the bitmap, or zero (i.e., discarded), which is represented by a value 0 in the bitmap.

[0049] FIGURE 8 illustrates example timing bitmaps of six positioning related measurementreports. The illustrated example includes six reports (one for each row) with ^^^^ = 128 samplesbefore down-sampling and ^^′^^ = 9 nonzero sub-samples. The bitmap-based timing signalingoverhead scales with the number of available samples (^^^^) before down-sampling. The overhead is not directly affected by the number of nonzero sub-samples (^^^′^). The total numberof bits to signal the six bitmaps is 6 × 128 = 768 bits.

[0050] Some other approaches have included using a single SLIV, multiple SLIVs and multipleadaptive SLIVs in both straight and time-reverse versions. The issue with these proposed methods is that the sparsity of nonzero sub-samples (^^^′^) across the available samples (^^^^) leads to increased overhead. This is particularly problematic in heavily NLoS environments where the signal power is less concentrated and more distributed over the available samples (^^^^).P112190WO01 PCT APPLICATION 9 of 62 SUMMARY

[0051] As described above, certain challenges currently exist with efficient subsampling forchannel measurement reporting. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges.

[0052] For example, particular embodiments include enhanced sub-sampling for positioningrelated measurements, designed to optimize the representation of timing information for high- accuracy machine learning (ML) positioning. Particular embodiments effectively preserve the comprehensive details of the positioning-related measurements while minimizing the signaling overhead, ensuring that the critical timing information is efficiently captured to support accurate and reliable artificial intelligence (AI) / ML-based positioning models.

[0053] Particular embodiments identify a time interval (referred to as a window) that containsimportant information in the positioning-related measurements and performs sub-sampling within the window. Particular embodiments leverage the joint design of the enhanced sub- sampling technique and the representation of the timing information.

[0054] By focusing on the window, particular embodiments capture the most relevant timingdata, ensuring that essential details are retained. This approach leverages a joint design that integrates the enhanced sub-sampling technique with the efficient representation of timing information, resulting in a more accurate and efficient extraction and signaling of the positioning-related measurements for AI / ML-based positioning applications.

[0055] According to some embodiments, a method is performed by a wireless device. Themethod comprises: obtaining ^^^^total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal, wherein ^^^^comprises an integer value; determining a window of size ^^ consecutive samples out of the ^^^^total samples, wherein the ^^ consecutive samples have a highest sum power among the ^^^^total samples, wherein ^^comprises an integer value; selecting a subset Nt’ of samples from the window of size ^^ consecutive samples, where Nt’ comprises an integer value less than or equal to ^^; and transmitting a measurement report to a network node, the measurement report comprising timing information of the selected subset Nt’ of samples from the window of size ^^ consecutive samples.

[0056] In particular embodiments, determining the window of size ^^ consecutive samplescomprises computing a sum power of samples within at least one window of size ^^ consecutive samples. The window of size ^^ consecutive samples may start from a first sampleP112190WO01 PCT APPLICATION 10 of 62 of the ^^^^total samples or may start from a sample of the ^^^^total samples other than a first sample of the ^^^^total samples. The window of size ^^ consecutive samples may end at a last sample of the ^^^^total samples.

[0057] In particular embodiments, selecting the subset Nt’ of samples from the window of size^^ consecutive samples comprises selecting ^^^′^samples with the largest powers within the window of size ^^ consecutive samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting the first ^^^′^samples within the window of size ^^ consecutive samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting samples with the largest powers within the window of size ^^ consecutive samples along with one sample before the selected sample and one sample after the selected sample for a total of ^^^′^selected samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting random samples from the window of size ^^ consecutive samples.

[0058] In particular embodiments, the measurement report comprises at least one start andlength indicator value (SLIV) representing the timing information.

[0059] According to some embodiments, a wireless device comprises processing circuitryoperable to perform any of the methods of the wireless receiver described above.

[0060] Also disclosed is a computer program product comprising a non-transitory computerreadable 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.

[0061] According to some embodiments, a method is performed by a network node. Themethod comprises: obtaining ^^^^total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal, wherein ^^^^comprises an integer value; determining a window of size ^^ consecutive samples out of the ^^^^total samples, wherein the ^^ consecutive samples have a highest sum power among the ^^^^total samples, wherein ^^comprises an integer value; selecting a subset Nt’ of samples from the window of size ^^ consecutive samples, where Nt’ comprises an integer value less than or equal to ^^; and transmitting a measurement report to a network node, the measurement report comprising timing information of the selected subset Nt’ of samples from the window of size ^^ consecutive samples.P112190WO01 PCT APPLICATION 11 of 62

[0062] In particular embodiments, determining the window of size ^^ consecutive samplescomprises computing a sum power of samples within at least one window of size ^^ consecutive samples. The window of size ^^ consecutive samples may start from a first sample of the ^^^^total samples or may start from a sample of the ^^^^total samples other than a first sample of the ^^^^total samples. The window of size ^^ consecutive samples may end at a last sample of the ^^^^total samples.

[0063] In particular embodiments, selecting the subset Nt’ of samples from the window of size^^ consecutive samples comprises selecting ^^^′^samples with the largest powers within the window of size ^^ consecutive samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting the first ^^^′^samples within the window of size ^^ consecutive samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting samples with the largest powers within the window of size ^^ consecutive samples along with one sample before the selected sample and one sample after the selected sample for a total of ^^^′^selected samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting random samples from the window of size ^^ consecutive samples.

[0064] In particular embodiments, the measurement report comprises at least one start andlength indicator value (SLIV) representing the timing information.

[0065] According to some embodiments, a network node comprises processing circuitryoperable to perform any of the methods of the network node described above.

[0066] Also disclosed is a computer program product comprising a non-transitory computerreadable 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.

[0067] Certain embodiments may provide one or more of the following technical advantages.For example, particular embodiments ensure that the subsampling technique preserves the important timing information while it can be signaled more efficiently to enable high accuracy ML positioning.

[0068] The Third Generation Partnership Project (3GPP) indoor factory (InF) model illustratedin FIGURE 9 is a nonlimiting example of the known deployment. In this scenario, 18 transmission / reception points (TRPs) are deployed in the factory with TRP locations known atP112190WO01 PCT APPLICATION 12 of 62 the network. With 60% clutter density and clutter height and width of 6 m and 2 m, respectively, this indoor factory scenario has less than 1% line of sight (LOS) probability from a user equipment (UE) to any TRPs.

[0069] Using the basic bitmap approach to signal the sample timing information related to^^^^^^^^ = 18 TRPs for the case of ^^^^ = 256 available samples, the bitmaps will need^^^^^^^^ × ^^^^ = 4608 bits. The sub-sampling factor, for example ^^ ′^^ = 9, does not affect thefeedback size.

[0070] Particular embodiments may identify a window of size ^^ = 32 consecutive samplesout of ^^^^ = 256 available samples that has the highest sum power. Then, pick the strongest^^′^^ = 9 sub-samples within that window.

[0071] Combining the sub-sampling technique with a start and length indicator value (SLIV)-based representation previously described, reduces the average number of bits for signaling the 18 sample timing reports to 541 bits.

[0072] Using a single SLIV and a copy of the indicated span, the average number of bits forsignaling the 18 sample timing reports is reduced to 569 bits.

[0073] Using multiple SLIV and copies of the indicated spans, the average number of bits forsignaling the 18 sample timing reports is reduced to 567 bits.

[0074] Using multiple SLIV and shortened copies of the indicated spans, the average numberof bits for signaling the 18 sample timing reports is reduced to 548 bits.

[0075] Using multiple adaptive SLIV and shortened copies of the indicated spans, the averagenumber of bits for signaling the 18 sample timing reports is reduced to 478 bits.

[0076] That is, the average size of signaling the 18 sample timing reports can be reduced by90%. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] For a more complete understanding of the disclosed embodiments and their featuresand advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which: FIGURE 1 illustrates user equipment (UE)-assisted / location management function (LMF)-based positioning with LMF-side model and direct artificial intelligence (AI) / machine learning (ML) positioning;P112190WO01 PCT APPLICATION 13 of 62 FIGURE 2 illustrates next generation radio access network (NG-RAN) node assisted positioning with LMF-side model and direct AI / ML positioning; FIGURE 3 illustrates AI / ML model positioning system setup which includes at least a ML measurement data collection node; FIGURE 4 illustrates a multipath radio environment between a UE and twotransmission / reception points (TRPs); FIGURE 5A illustrates an example of measured two-port channel impulse response(CIR) samples truncated to ^^^^ = 128 samples;FIGURE 5B illustrates the example of FIGURE 5A further sub-sampled to ^^′^^ = 9strongest samples; FIGURE 6A illustrates an example of power delay profile (PDP) samples computedfrom the example two-port CIR in FIGURE 5A truncated to ^^^^ = 128 samples;FIGURE 6B illustrates the example of FIGURE 6A further sub-sampled to ^^′^^ = 9strongest samples; FIGURE 7 illustrates an example of delay profile (DP) samples computed from the example PDP in FIGURE 6A; FIGURE 8 illustrates example timing bitmaps of six positioning related measurement reports; FIGURE 9 illustrates an example of an indoor factory (InF) model; FIGURE 10 illustrates the cumulative distribution function (CDF) of the two- dimensional (2D) positioning errors for various AI / ML models trained with the enhanced timing report information; FIGURE 11 illustrates an example communication system, according to certain embodiments; FIGURE 12 illustrates an example user equipment (UE), according to certain embodiments; FIGURE 13 illustrates an example network node, according to certain embodiments; FIGURE 14 illustrates a block diagram of a host, according to certain embodiments; FIGURE 15 illustrates a method performed by a wireless device, according to certain embodiments; and FIGURE 16 illustrates a method performed by a network node, according to certain embodiments.P112190WO01 PCT APPLICATION 14 of 62 DETAILED DESCRIPTION

[0078] As described above, certain challenges currently exist with efficient subsampling forchannel measurement reporting. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges.

[0079] For example, particular embodiments include enhanced sub-sampling for positioningrelated measurements, designed to optimize the representation of timing information for high- accuracy machine learning (ML) positioning. Particular embodiments effectively preserve the comprehensive details of the positioning-related measurements while minimizing the signaling overhead, ensuring that the critical timing information is efficiently captured to support accurate and reliable artificial intelligence (AI) / ML-based positioning models.

[0080] Particular embodiments identify a time interval (referred to as a window) that containsimportant information in the positioning-related measurements and performs sub-sampling within the window. By focusing on the window, particular embodiments capture the most relevant timing data, ensuring that essential details are retained.

[0081] Particular embodiments are described more fully with reference to the accompanyingdrawings. 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.

[0082] As described above, the ML data measurement node may be a user equipment (UE)measuring downlink reference signals transmitted from the radio network nodes (such as the transmission / reception points (TRPs)) or a radio network node (such as a TRP or a gNB) measuring uplink reference signals transmitted from a UE.

[0083] In some embodiments the non-limiting terms user equipment (UE) or a wireless deviceare 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-complexity UE, a sensor equipped with UE, tablet, mobile terminals, smart phone, laptop embedded equipment (LEE), laptop mounted equipment (LME), universal serial bus (USB) dongles,P112190WO01 PCT APPLICATION 15 of 62 customer premises equipment (CPE), an Internet of things (IoT) device, or a narrowband IoT (NB-IOT) device, etc.

[0084] Particular embodiments include methods and protocols to reduce signaling overhead ofthe timing bitmap reports.

[0085] Some embodiments are related to a positioning-related measurement, which is anessential part of enabling AI / ML-based positioning. To obtain sub-sampled time domain (TD) channel impulse response (CIR) / power delay profile (PDP) from a (truncated) TD CIR / PDP, particular embodiments include a sub-sampling technique to select the ^^^′^samples out of the ^^^^total samples.

[0086] The timing information of the selected ^^^′^samples is reported to a ML inference node or a ML data collection node. Additional power and / or phase information of the ^^^′^samples may also be reported to a ML inference node or a ML data collection node.

[0087] A first group of embodiments (Embodiment 1) includes enhanced sub-samplingmethods. In a first embodiment, the sub-sampling procedure comprises the following steps.

[0088] Step 1: Identify the window of size ^^, i.e., ^^ consecutive samples, out of ^^^^ totalsamples that has the highest sum power of the ^^ samples. The identifying a window of size ^^ comprises computing the sum power of samples within at least one window of ^^ samples.

[0089] The at least one window of ^^ samples may comprise a window of ^^ samples startingfrom the first sample in the ^^^^total samples. The at least one window of ^^ samples may comprise a window of ^^ samples starting from another sample in the ^^^^total samples. The at least one window of ^^ samples may comprise a window of ^^ samples ending at the last sample of the ^^^^total samples.

[0090] Step 2: Determine the positions of the ^^^′^samples within the window of size ^^ by selecting the ^^^′^samples according to at least one criterion. A nonlimiting example of the selection criterion is to select the ^^^′^samples with the largest powers within the window of size ^^. Another nonlimiting example criterion is to select first ^^^′^samples within the window of size ^^. Another nonlimiting example criterion is to select the ^^^′^′samples with the largest powers along with at least one sample before and one sample after each of the selected ^^^′ ^′ samples, resulting in a total of ^^^′^selected samples. Another nonlimiting example criterion is to select ^^^′^random samples within the window of size ^^.P112190WO01 PCT APPLICATION 16 of 62

[0091] Step 3: Report at least the timing information of the selected ^^^′^samples to an inference node or a data collection node. The timing information reporting may comprise at least timing information of the first selected sample. The timing information reporting may comprise at least length of bitmap of subsequent selected samples. The timing information reporting may comprise at least a bitmap of the length for the subsequent selected samples. The timing information reporting may comprise at least a start and length indicator value (SLIV) of additional span of timing information. The timing information reporting may comprise at least a bitmap of the additional span of timing information.

[0092] A second group of embodiments (Embodiment 2) also includes enhanced sub-samplingmethods. In a second embodiment, the sub-sampling procedure comprises the following steps.

[0093] Step 1: Determine ^^^′^samples according to at least one criterion from a window of size ^^, wherein the window of size ^^ comprises ^^ consecutive samples out of ^^^^total samples. The window of ^^ samples may comprise a window of ^^ samples starting from the first sample in the ^^^^total samples. The window of ^^ samples may comprise a window of ^^ samples starting from another sample in the ^^^^total samples. The window of ^^ samples may comprise a window of ^^ samples ending at the last sample of the ^^^^total samples.

[0094] The criterion for determining the ^^^′^samples within a window of size ^^ may be to select the ^^^′^samples with the largest powers within the window. The criterion for determining the ^^^′^samples within a window of size ^^ may be to select first ^^^′^samples within the window of size ^^. The criterion for determining the ^^^′^samples within a window of size ^^ may be to select the ^^^′^′samples with the largest powers along with at least one sample before and one sample after each of the selected ^^^′^′samples, resulting in a total of ^^^′^selected samples. The criterion for determining the ^^^′^samples within a window of size ^^ may be to select ^^^′^random samples within the window of size ^^.

[0095] Step 2: Report at least the timing information of the selected ^^^′^samples to an inference node or a data collection node. The timing information reporting may comprise at least timing information of the first selected sample. The timing information reporting may comprise at least a length of bitmap of subsequent selected samples. The timing information reporting may comprise at least a bitmap of the length of the subsequent selected samples. The timing information reporting may comprise at least a start and length indicator value (SLIV) ofP112190WO01 PCT APPLICATION 17 of 62 additional span of timing information. The timing information reporting may comprise at least a bitmap of an additional span of timing information.

[0096] The following is a nonlimiting example demonstration of particular embodiments.

[0097] FIGURE 10 illustrates the cumulative distribution function (CDF) of the two-dimensional (2D) positioning errors for various AI / ML models trained with the enhanced timing report information in an InF-DH environment, characterized by clutter parameters of {60%, 6m, 2m}. In this nonlimiting example, the AI / ML models are trained and tested usingPDP signals with a length of ^^^^ = 128, ^^ ′^^ = 9, while experimenting with different windowsizes (^^).

[0098] As detailed in Table 1, using a window size of 32 results in less than a 5% performancedegradation. However, the minor reduction in performance is offset by almost 29% decrease in the number of bits. Furthermore, using a window size of 24 achieves a 40% reduction in the number of bits with only a 10% decrease in performance. Table 1: CDF percentile of the 2D positioning errors and Single SLIV compression ratio of different AI / ML models trained with proposed enhanced timing report information in InF-DH with clutter parameter {60%, 6m, 2m}. Window CDF Percentile # of bits size 50 67 80 90 Benchmark 0.40 0.53 0.66 0.82 775 16 0.49 0.65 0.82 1.04 368 24 0.45 0.58 0.72 0.91 468 32 0.42 0.55 0.67 0.86 551 40 0.41 0.53 0.67 0.85 614 100 0.41 0.53 0.66 0.82 771

[0099] As detailed in Table 1, using a window size of 32 results in less than a 5% performancedegradation. However, the minor reduction in performance is offset by almost 29% decrease in the number of bits. Furthermore, using a window size of 24 achieves a 40% reduction in the number of bits with only a 10% decrease in performance.

[0100] The following are example embodiments for reducing signaling overhead.

[0101] A first group of embodiments (Embodiment 1) includes a single SLIV and a copy of aspan from the bitmap. According to the first embodiment, the span of the bitmap of length ^^^^starting from the location with the first value one, whose index location is denoted ^^, andP112190WO01 PCT APPLICATION 18 of 62 ending in the location with last value one, whose index location is denoted by ^^, is determined. A more efficient representation of the bitmap has two parts: [SLIV(^^,^^ − ^^ + 1,^^^^), copy of the bitmap from ^^ to ^^]Where the first part is the start and length indicator for a span starting from ^^ and with a lengthof ^^ − ^^ + 1 from the overall available scope of ^^^^.

[0102] The second part is a copy of the span from the original bitmap starting from location ^^and ending in location ^^.

[0103] Using the first timing report bitmap from FIGURE 8 as a nonlimiting illustrativeexample, the first one is at location ^^ = 4 and the last one is at location ^^ = 29. Thus, thedetermined span has a length of ^^ = ^^ − ^^ + 1 = 26. The first part of the efficientrepresentation is SLIV(^^ = 4, ^^ = 26,^^ = 128) = 3204Because ^^^^ = 128, the binary representation of SLIV will use a ^^(^^ = 128) = 14 bits. Thus,the SLIV of the identified span is represented by 00110010000100. Using both parts of the efficient representation for the first timing report bitmap from Figure 8, the bitmap can be signaled as 0011001000010011111110000000000000100001 SLIVcopy of the indicated spanIt can be observed that the second part is a copy of the original bitmap starting from location^^ = 4 and ending in location ^^ = 29.

[0104] Applying the present embodiment to all six timing report bitmaps from Figure 8 obtainsthe more efficient representation of the original six bitmaps with the following six-bit sequences: 0011001000010011111110000000000000100001 00010110001110111111110001 001001000011111111001100011000001 010101000010111100101000010001100000010000000000000000001 010010111100001001000000000000000000100000000000000000000000001100000000000 0010000000000000001100000000001 011111011001011100111000000000000000100000000000000000010000000000000000000 000011P112190WO01 PCT APPLICATION 19 of 62

[0105] The original all six timing report bitmaps in FIGURE 8 use 768 bits in total. The newmore efficient representation uses only 343 bits. That is, the signaling size has been reduced by 55.3%.

[0106] A second group of embodiments (Embodiment 2) uses multiple SLIV and copies ofspans from the bitmap. According to the second embodiment, multiple SLIVs and multiple copies of the indicated spans from the bitmap are used to represent the original bitmap more efficiently: [SLIV0, copy of span 0, SLIV1, copy of span 1, … ]Starting from the beginning of the original bitmap, a span is identified starting from a location with value one and terminating in a location with value one. The terminating location of a span is determined by the following rule: ^The termination location is the last location in the bitmap with value one; or^ The termination location is followed by a string of value zeros with a length of equal toor great than a threshold ^^.

[0107] The threshold of the zero run can be set to the length of the SLIV. For example, withthe original bitmap length of ^^^^ = 128, the SLIV length is ^^(^^^^) = 14. That is, if the valueones are separated by a string of at least 14 zeros, the starting span should be terminated, and a new span is created by skipping the string of at least 14 zeros.

[0108] Using the fourth timing report bitmap from FIGURE 8 as a nonlimiting illustrativeexample, embodiment 1 determines a span starting from ^^ = 11 and ending in ^^ = 53, whichis represented by a SLIV: SLIV(^^ = 11,^^ = 43,^^ = 128) = 5387 = [01010100001011]and creates a representation as follows: 17 zeros

[0109] 010101000010111100101000010001100000010000000000000000001 SLIVcopy of the indicated spanIt can be observed that there is a run of 17 zeros in the copied span.

[0110] Applying Embodiment 2, the first span should start from ^^0 = 11 and terminate in ^^0 =34 because it is followed by 17 zeros. A second span is determined as start from ^^1 = 53 andterminate in ^^1 = 53 (i.e., length of ^^ = 1). The first and the second SLIVs are given bySLIV0 =(^^0 = 11, ^^0 = 24,^^ = 128) = 2955 = [00101110001011]SLIV1 = SLIV(^^1 = 53, ^^1 = 1,^^ = 128) = 53 = [00000000110101]P112190WO01 PCT APPLICATION 20 of 62

[0111] Thus, according to Embodiment 2, the fourth timing report bitmap from FIGURE 8 canbe more efficiently represented by 0010111000101111001010000100011000000100000000110101 ^ 1 SLIV0 copy of span 0SLIV1 copy of span 1

[0112] Applying the present embodiment to all six timing report bitmaps from FIGURE 8obtains the more efficient representation of the original six bitmaps with the following six-bit sequences: 0011001000010011111110000000000000100001 00010110001110111111110001 001001000011111111001100011000001 00101110001011110010100001000110000001000000001101011 000001100011111001000000001001011000111101111111100000000000001000110010111 101100000000001 0000110001101011001110000000011000010000000100001110000001101101111

[0113] The original all six timing report bitmaps in FIGURE 8 use 768 bits in total. The newmore efficient representation uses only 309 bits. That is, the signaling size has been reduced by 59.8%.

[0114] A third group of embodiments (Embodiment 3) includes shortened copies of spans fromthe bitmap. According to the third embodiment, the single or multiple copies of the indicated spans from the bitmap are shortened in any of the above single or multiple SLIV embodiments.

[0115] Using multiple SLIVs as a nonlimiting example embodiment, the timing reportinformation of the bitmap may be efficiently represented as: [SLIV0, shortened copy of span 0, SLIV1, shortened copy of span 1, … ]where a shortened copy may be of length zero.

[0116] The multiple spans are determined according to the same methods disclosed inEmbodiment 2. For an indicated span starting from location ^^ and ending in location ^^, the first value and the last value of the copied span must be one because this is how the spans are determined. Therefore, there is no need to copy the first and the last value of an indicated span.There are two cases. In case 1, the indicated span length ^^ is greater than 2 (i.e., ^^ > ^^ + 1).In this case, the shortened copy starts from location ^^ + 1 and ends in location ^^ − 1.P112190WO01 PCT APPLICATION 21 of 62

[0117] In case 2, the indicated span length ^^ is equal to 1 or 2 (i.e., ^^ = ^^ or ^^ = ^^ + 1). Inthis case, there is no need to copy the span because the span is either [1] or

[0011] . Thus, the shortened copy of the span is of length zero.

[0118] Using the fourth timing report bitmap from FIGURE 8 as a nonlimiting illustrativeexample, embodiment 2 creates a representation as 0010111000101111001010000100011000000100000000110101 ^ 1 SLIV0 copy of span 0SLIV1 copy of span 1

[0119] Using the present Embodiment 3, the copies of the indicated spans are replaced by theshortened copies to yield the following more efficient representation: 00101110001011100101000010001100000000000000110101 SLIV0 shortened copy of span 0SLIV1

[0120] Applying the present embodiment to all six timing report bitmaps from FIGURE 8obtains the more efficient representation of the original six bitmaps with the following six-bit sequences: 0000110000010011111000010100110000000 000101100011101111111000 0010010000111111100110001100000 00101110001011100101000010001100000000000000110101 000001100011110000000000100101000000101111110000000100111000011001011110100 00000000 0000110001101010011000000001100000000000100001100000011011011

[0121] The original all six timing report bitmaps in FIGURE 8 use 768 bits in total. The newmore efficient representation uses only 286 bits. That is, the signaling size has been reduced by 62.8%.

[0122] A fourth group of embodiments (Embodiment 4) includes multiple adaptive SLIV andshortened copies of spans from the bitmap. According to the fourth embodiment, multiple SLIVs of adaptive lengths and multiple shortened copies of the indicated spans from the bitmap are used to represent the original bitmap more efficiently.

[0123] The multiple spans are determined according to the same methods disclosed inEmbodiment 2. However, the binary representation of a SLIV is according to the remaining scope of the bitmap.

[0124] Using the fifth timing report bitmap from FIGURE 8 as a nonlimiting illustrativeexample, embodiment 3 determines five spans with the following SLIVs:P112190WO01 PCT APPLICATION 22 of 62SLIV0 = SLIV(^^0 = 15, ^^0 = 4,^^ = 128) = 399 = [00000110001111]SLIV1 = SLIV(^^1 = 37, ^^1 = 1,^^ = 128) = 37 = [00000000100101]SLIV2 = SLIV(^^2 = 63, ^^2 = 2,^^ = 128) = 191 = [00000010111111]SLIV3 = SLIV(^^3 =SLIV4 = SLIV(^^4 = 94, ^^4 = 13,^^ = 128) = 1630 = [00011001011110]That is, the ending indices of the spans are ^^0 = 18, ^^1 = 37, ^^2 = 64, ^^3 = 78, ^^4 = 106.

[0125] The bitmap is represented as follows:00000110001111 0^0 00000000100101000000101111110000000100111000011001011110 SLIV0 shortened copySLIV1SLIV2SLIV3SLIV4of span 0

[0126] It can be observed in the above that all the SLIVs are computed and represented basedon the scope of ^^ = 128. This can result in unnecessarily long bit-lengths to represent theSLIV. For example, after the first four SLIVs are computed, the remaining length of the bitmap is much smaller than 128. Because SLIV3indicates a span starting from 78 with a length 1, the remaining bitmap is only for locations starting from 79 until 127, which is in fact a scope of^^ = 127 − 79 + 1 = 127 − 78 = 49. With a scope of ^^ = 49, the binary representation ofthe SLIV4 needs only ^^(^^ = 49) = 11 bits.

[0127] Therefore, according to the present embodiment, the SLIVs should be computed basedon the actual remaining scope sizes:SLIV0 = SLIV(^^0 = 15, ^^0 = 4,^^ = 128) = 399 = [00000110001111]SLIV1 = SLIV(^^1 = 37 − 18 − 1, ^^1 = 1,^^ = 127 − 18 = 109) = 18= [0000000010010]SLIV2 = SLIV(^^2 = 63 − 37 − 1,^^2 = 2,^^ = 127 − 37 = 90) = 115 = [000001110011]SLIV3 = SLIV(^^3 = 78 − 64 − 1,^^3 = 1,^^ = 127 − 64 = 63) = 13 = [00000001101]SLIV4 = SLIV(^^4 = 94 − 78 − 1,^^4 = 13,^^ = 127 − 78 = 49) = 603 = [01001011011]

[0128] It can be observed in the above, adapting SLIV computation and representation to theactual remain bitmap scopes can reduce the numbers of bits to represent them. Thus, according to the present embodiment, the fifth timing report bitmap from FIGURE 8 may be more efficiently represented as 00000110001111 0^0 0000000010010000001110011000000011010100101101110000000000 SLIV0 shortened copySLIV1SLIV2SLIV3SLIV4shortened of span 0 of span

[0129] In general, the first SLIV can be computed as according to previous embodiments:P112190WO01 PCT APPLICATION 23 of 62SLIV(^^ = ^^0, ^^ = ^^0 − ^^0 + 1,^^ = ^^^^)

[0130] For subsequent SLIVs, given the ending sample index of the previous span denoted by^^^^−1, and the starting and ending indices of the current span denoted by ^^^^and ^^^^, the SLIV of the current span is computed asSLIV(^^ = ^^^^ − ^^^^−1 − 1, ^^ = ^^^^ − ^^^^ + 1,^^ = ^^^^ − 1 − ^^^^−1)

[0131] Applying the present embodiment to all six timing report bitmaps from FIGURE 8obtains the more efficient representation of the original six bitmaps with the following six-bit sequences: 000011000001001111100010010101100000 000101100011101111111000 0010010000111111100110001100000 0010111000101110010100001000110000000000000010010 00000110001111000000000010010000001110011000000011010100101101100100000000 0000110001101010011000000000111100000001001000001010011

[0132] The original all six timing report bitmaps in FIGURE 8 use 768 bits in total. The newmore efficient representation uses only 269 bits. That is, the signaling size has been reduced by 65%.

[0133] A fifth group of embodiments (Embodiment 5) includes encoding in reverse order. Thetiming report bitmap is first put into a reverse order. The reversed bitmap is then encoded into a more efficient representation using any of the above embodiments.

[0134] Equivalently, any of the above embodiments may be applied to a bitmap starting fromthe end instead of starting from the beginning of the bitmap.

[0135] Applying the present embodiment to all six timing report bitmaps from FIGURE 8obtains the more efficient representation of the original six bitmaps with the following six-bit sequences: 00001011100010000001001110100000 000101111001100001111111 0010010101111000000110001100111 00000001001010100110101010000000000000000000000 000110000101010000000000100101100100010000000000000100000011001001000000100 0000001010001100000000101110000000100100010010111100000P112190WO01 PCT APPLICATION 24 of 62

[0136] The original all six timing report bitmaps in FIGURE 8 use 768 bits in total. The newmore efficient representation uses only 264 bits. That is, the signaling size has been reduced by 65.6%.

[0137] A sixth group of embodiments (Embodiment 6) includes bit packing and reportingformats. A nonlimiting example reporting format may be as follows, where a length filed indicates the number of bits for the timingInfo containing an efficient representation of a timing report bitmap according to any of the above embodiments. sampleTimingReport ::= SEQUENCE { length INTEGER (1..256) timingInfo BIT STRING (SIZE (length)) }

[0138] With any of the above embodiments, an efficient representation of a timing reportbitmap is pack into multiples of 8 bits (i.e., a byte) by appending zeros after the efficient representation if necessary.

[0139] A nonlimiting example reporting format can be as follows, where a length filedindicates the number of bytes for the timingInfo containing an efficient representation of a timing report bitmap according to any of the above embodiments. sampleTimingReport ::= SEQUENCE { length INTEGER (1..16) timingInfo BIT STRING (SIZE (8*length)) }

[0140] The above sampleTimingReport can be part of NR-DL-TDOA-MeasElement for a UEto report to the LMF. The above sampleTimingReport can be part of UL measurement reports from gNB to the LMF.

[0141] The following are example embodiments for determining a window size.

[0142] Particular embodiments include a procedure for determining Nt’ samples for thepurpose 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.

[0143] Step 1: Define the timing grid using (a) the reference time Tref (seconds) and (b) thetiming granularity of T (seconds), where T = 2k* Tc.

[0144] In this context, the reference time Tref only anchors the grid points, i.e., Tref is on thegrid where consecutive grid points are spaced by T (sec). Tref is not intended to set the boundaryP112190WO01 PCT APPLICATION 25 of 62 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.

[0145] For NG-RAN side measurements (i.e., Case 3a and 3b), the reference time is theexisting UL RTOA reference time T0+tSRSas defined in TS 38.215. Different TRP has its local UL RTOA reference time. Thus, different TRP may have its own grid timing for sampling the channel measurements.

[0146] For UE-side measurements (i.e., Case 1, 2a, 2b), the reference time may be an absoluteclock time, or a receive timing of PRS of a reference TRP.

[0147] Step 2: Determine the starting point T0,meas_window of the sequence of Nt consecutivesamples according to the timing grid laid down in Step 1. Thus, T0,meas_windowcorresponds to a grid point, and the neighboring samples are spaced by T (sec).

[0148] Together (a) the starting point T0,meas_window and (b) a sequence of Nt consecutivesamples spaced by T (sec) define the measurement report window. That is, the measurement report window is: [T0,meas_window, T0,meas_window + (Nt -1)*T] (sec).

[0149] The above only provides one format to define the measurement report window. Otherformats 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.

[0150] The measurement report window is typically quantized to the sample grid determinedin Step 1.

[0151] Step 3: Select Nt’ samples from the sequence of Nt consecutive samples based on aselection 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.

[0152] For NG-RAN side measurements (i.e., Case 3a and 3b), the selection rule may be oneof the following, or a combination thereof: ^In one example, selecting the Nt’ samples with the highest power in the measurementreport window, i.e., select the Nt’ samples according to the ranking of power, from high to low.P112190WO01 PCT APPLICATION 26 of 62 ^In another example, selecting the Nt’ samples with the highest power in themeasurement report window, with a minimum power threshold under which no further samples are reported. ^In another example, selecting Nt’ samples with the highest power in the measurementreport window as well as their adjacent samples. ^In another example, selecting the earliest Nt’ samples in the measurement reportwindow. ^In another example, selecting Nt’ samples that are closest to detected paths, where thepaths are those detected for the multi-path channel.

[0153] For UE-side measurements (i.e., Case 1, 2a, 2b), similar rule may be adopted as forNG-RAN side measurement, i.e., any of (1)-(5) above or a combination thereof.

[0154] In terms of parameter configuration, measurement report window size Nt (samples),number of selected samples Nt’, and granularity factor k may be configured by LMF.

[0155] For measurement by NG-RAN (Case 3a, 3b), at least for Case 3b, {Nt, Nt’, k} aresignaled from LMF to NG-RAN via NRPPa protocol. For Case 3a, if CU-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 F1-AP protocol. Otherwise (i.e., no split architecture in gNB), it can be up to the gNB implementation to select its own {Nt, Nt’, k}. For training data collection, the parameters {Nt, Nt’, k} need to be recorded as metadata, along with the SRS measurement data.

[0156] For measurement by UE (Case 1, 2a, 2b), at least for Case 2b, {Nt, Nt’, k} are signaledfrom LMF to UE via LPP protocol, where the channel measurement made by the UE needs to be transmitted to LMF. The UE capabilities need to be exchanged before {Nt, Nt’, k} are signaled from LMF, so that LMF can select those {Nt, Nt’, k} values that are within the range supported by UE.

[0157] For Case 1 and 2a, it can be up to the UE implementation to choose {Nt, Nt’, k}. Fortraining data collection, the parameters {Nt, Nt’, k} need to be recorded as metadata, along with the PRS measurement data.

[0158] For case 1 and 2a, the parameters {Nt, Nt’, k} recorded as metadata may be consideredas 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 will not be accurate. In essence, consistency between training and inference need to be maintained with regard to the {Nt, Nt’, k} parameter value range.P112190WO01 PCT APPLICATION 27 of 62

[0159] Some embodiments include determining end of measurement report window. Anadditional 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(T0,meas_window+ (Nt-1)*T, Tref+ (Rt-1)*T).

[0160] That is, using Case 2b and 3b as an example, Tref + (Rt -1)*T represents the sample timebeyond which the LMF is not interested in receiving a measurement.

[0161] When this parameter is not configured, the end of the measurement report window isdetermined as per previous embodiments: T0,meas_window+ (Nt-1)*T. That is, Rtis treated as infinity when not configured.

[0162] The parameter Rt may be configured by the various types of measurement nodes via thecorresponding 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.

[0163]

[0164] In a first group of embodiments, the start of N consecutive samples is based on the firsttdetected sample (or path). In these embodiments, T0,meas_windowis 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.

[0165] In one example, T is the timing (sec) with reference to ^^ as shown below:0,meas_window ^^^^^^T^̂^1−^^^^^^0,meas_window = ^^^ + T × ^^ ( ^^ ) (1)^^^^^^^ where ^̂^ is the For NG-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).

[0166] Function ^^(. ) may take one of several variants, including the following:^̂^ −^^ ^̂^ −^^1 ^^^^^^ 1 ^^^^^^ Option 1. ^^ = − ^^, if ^̂^ is always on the timing grid defined by T and T ( ) ref1^^ ^^ (i.e.,^̂^ is integer multiple of T). For example, the channel estimator takes thesample that is above a noise threshold as the first detected sample.P112190WO01 PCT APPLICATION 28 of 62 Option 2. ^^(. ) quantizes ^̂^1 towards the timing grid using a floor or round or ceil function,when ^̂^1 is not always the timing grid. That is, ^^ (^̂^1−^^^^^^^^^̂^1−^^ ^^ ) is equal to ⌊^^^^^^^^⌋ − ^^, or^^^^^^^^^^^̂^1−^^^^^^^^ − ^^, or^̂^1−^^^^^^^^^^) ⌈^^⌉ − ^^.which implies that a small shift of ^^ samples mayto give a small shift in time relative to the first detectedsample (or path). If a non-zero ^^ is applied, then the value of ^^ may be determined in various ways, including: Option (A). ^^ is a predetermined, fixed value. Preferably ^^ is a small positive integer. For example, ^^ = 3.Option (B). ^^ is a predetermined integer value (i.e., not configured), which may vary with system parameters and conditions. For example, ^^ may vary with frequency layer, and / or frequency range, and / or carrier frequency, and / or subcarrier spacing, and / or bandwidth of the reference signal, and / or deployment environment condition (e.g., line-of-sight or non-line-of- sight environment), and / or UE speed. Option (C). ^^ is a configurable value. For example, for Case 2b and 3b, LMF may configure ^^ 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), UE speed. For example, in Case 2b and 3b, the LMF may set the ^^ value for the UE and gNB based on the LoS probability of the operation site. In highly NLoS environments or NLoS links, δ may be a negative integer (i.e., delay the start of ^^^^consecutive 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, δ >=0 can be used so that δ samples before the LoS path (approximately equal to the first detected path) are included in the list of ^^^^consecutive samples.

[0168] In the simpler case, ^^ = 0 is chosen to simply ^^(. ), i.e. no shift is applied. In this case,there is no need to determine or configure ^^ value.

[0169] Sometimes time ^̂^1 may be defined relative to Tref and may take positive or negativevalues relative to Tref.relative time, (1) can be simplified to the following, because Tref is essentially taken as time 0.P112190WO01 PCT APPLICATION 29 of 62 T ^̂^ 0,meas_windo = T × ^^ ( 1w^^) (2)

[0170] In a second on the start of searchwindow of theis based on the start of search window for receiving the reference signal. In one example, T0,meas_window is the timing (sec) with reference to ^^^^^^^^as shown below: T ^^0,^^^^^^^^^^ℎ_^^^^^^^^^^^^−^^^^^^^^0,meas_window = ^^^^^^^^ + T × ^^ (^^) (3)where applied to^^

[0171] In one example, function g(. ) quantizes ^^0,^^^^^^^^^^ℎ_^^^^^^^^^^^^ towards the timing grid usinga floor or round or ceil function. That is, ^^ (^^0,^^^^^^^^^^ℎ_^^^^^^^^^^^^−^^^^^^^^^^ ) is equal tobe applied in ^^(. ), where ^^ is an integer value to give a small shift in time relative to the startof the search window.

[0174] Also, sometimes T0,meas_window may be defined relative to Tref, and it may take positiveor negative values. Using relative time, (3) can be simplified to the following, because Trefis essentially taken as time 0. T0,meas_window = T × ^^ (^^0,^^^^^^^^^^ℎ_^^^^^^^^^^^^^^) (4)

[0175] Foris defined bythe 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 LMF to the NG-RAN node for each TRP.

[0176] Thus, the measurement report window may be defined by the ‘Start’ and ‘End’ of theSRS search window: Tstart = ^^0 + ^^SRS + Expected_propagation_delay – Delay_uncertainty, (5)Tend = ^^0 + ^^SRS + Expected_propagation_delay - Delay_uncertainty, (6)

[0177] The Tstart and Tend may be further quantized onto the sample grid. One examplequantization is:P112190WO01 PCT APPLICATION 30 of 62 T ^^^^^^^^^^^^0,meas_window = T × ^^ (^^ ) , (7) ^^^^^^^^

[0178] AnotherT = ^^ (9) )0,meas_window ^^^^^^^^ thetime onto the timing grid with timing granularity T.

[0180] With T and T known, the number of samples in the0,meas_window end,meas_windowmeasurement report window (N) may be obtained, with the knowledge of timing granularityt T.

[0181] For measurements at the UE, similarly, the PRS search window, T and T , may bestart endused 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.

[0182] 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.

[0183] For a given TRP at a given frequency layer, T and T are:start endT = T ^ Tstart center,search_window uncertainty end center,search_window uncertaintyT = T + T

[0184] In a typical method, the center of the PRS search window T is:center,search_windowREF sT +1 millisecond^N+nr-DL-PRS-ExpectedRSTD^4^T And the uncertainty T of PRS search window is:uncertainty nr-DL-PRS-ExpectedRSTD-Uncertainty^R

[0185] Similar to uplink SRS measurements, the downlink PRS measurements may useEquation (7)-(8) for determining the quantized measurement report window. If the referenceP112190WO01 PCT APPLICATION 31 of 62 time of PRS measurement is taken into account, then quantization step similar to (9)-(10) may be used alternatively.

[0186] FIGURE 11 illustrates an example of a communication system 100 in accordance withsome 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.

[0187] Moreover, as will be appreciated by those of skill in the art, a network node is notnecessarily 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.

[0188] Examples of an ORAN network node include an open radio unit (O-RU), an opendistributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O- CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1, F1, W1, E1, 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 beP112190WO01 PCT APPLICATION 32 of 62 implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.

[0189] In some embodiments, the telecommunication network 102 includes a non-terrestrialnetwork, 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 a ground 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.

[0190] 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- 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)P112190WO01 PCT APPLICATION 33 of 62 or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification).

[0191] Example wireless communications over a wireless connection include transmittingand / 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.

[0192] The UEs 112 may be any of a wide variety of communication devices, includingwireless 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.

[0193] In the depicted example, the core network 106 connects the network nodes 110 to oneor 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 of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).P112190WO01 PCT APPLICATION 34 of 62

[0194] The host 116 may be under the ownership or control of a service provider other than anoperator 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.

[0195] As a whole, the communication system 100 of FIGURE 11 enables connectivitybetween 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.

[0196] In some examples, the telecommunication network 102 is a cellular network thatimplements 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) / Massive IoT services to yet further UEs.

[0197] In some examples, the UEs 112 are configured to transmit and / or receive informationwithout 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 or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, aP112190WO01 PCT APPLICATION 35 of 62 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).

[0198] In the example, the hub 114 communicates with the access network 104 to facilitateindirect 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 IoT devices.

[0199] The hub 114 may have a constant / persistent or intermittent connection to the networknode 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 other 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 isP112190WO01 PCT APPLICATION 36 of 62 additionally capable of operating as a communication start and / or end point for certain data channels.

[0200] FIGURE 12 shows a UE 200 in accordance with some embodiments. As used herein, aUE 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.

[0201] A UE may support device-to-device (D2D) communication, for example byimplementing 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).

[0202] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204to 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 12. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.P112190WO01 PCT APPLICATION 37 of 62

[0203] The processing circuitry 202 is configured to process instructions and data and may beconfigured 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).

[0204] In the example, the input / output interface 206 may be configured to provide an interfaceor 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.

[0205] In some embodiments, the power source 208 is structured as a battery or battery pack.Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.P112190WO01 PCT APPLICATION 38 of 62

[0206] The memory 210 may be or be configured to include memory such as random-accessmemory (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.

[0207] The memory 210 may be configured to include a number of physical drive units, suchas 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.

[0208] The processing circuitry 202 may be configured to communicate with an accessnetwork or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may beP112190WO01 PCT APPLICATION 39 of 62 coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0209] In the illustrated embodiment, communication functions of the communicationinterface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0210] Regardless of the type of sensor, a UE may provide an output of data captured by itssensors, 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).

[0211] As another example, a UE comprises an actuator, a motor, or a switch, related to acommunication 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.

[0212] A UE, when in the form of an Internet of Things (IoT) device, may be a device for usein one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an IoT device are a device which is or which is embedded in: a connected refrigerator or freezer,P112190WO01 PCT APPLICATION 40 of 62 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 IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 200 shown in FIGURE 12.

[0213] As yet another specific example, in an IoT scenario, a UE may represent a machine orother 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.

[0214] 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.

[0215] FIGURE 13 shows a network node 300 in accordance with some embodiments. As usedherein, 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,P112190WO01 PCT APPLICATION 41 of 62 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 11).

[0216] 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).

[0217] Other examples of network nodes include multiple transmission point (multi-TRP) 5Gaccess 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).

[0218] The network node 300 includes a processing circuitry 302, a memory 304, acommunication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In suchP112190WO01 PCT APPLICATION 42 of 62 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.

[0219] The processing circuitry 302 may comprise a combination of one or more of amicroprocessor, 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.

[0220] 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.

[0221] The memory 304 may comprise any form of volatile or non-volatile computer-readablememory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store anyP112190WO01 PCT APPLICATION 43 of 62 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.

[0222] The communication interface 306 is used in wired or wireless communication ofsignaling 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.

[0223] In certain alternative embodiments, the network node 300 does not include separateradio 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).

[0224] The antenna 310 may include one or more antennas, or antenna arrays, configured tosend 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 / orP112190WO01 PCT APPLICATION 44 of 62 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.

[0225] The antenna 310, communication interface 306, and / or the processing circuitry 302 maybe 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.

[0226] The power source 308 provides power to the various components of network node 300in 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.

[0227] Embodiments of the network node 300 may include additional components beyondthose shown in FIGURE 13 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 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.

[0228] FIGURE 14 is a block diagram of a host 400, which may be an embodiment of the host116 of FIGURE 11, 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 virtualP112190WO01 PCT APPLICATION 45 of 62 machine, container, or processing resources in a server farm. The host 400 may provide one or more services to one or more UEs.

[0229] The host 400 includes processing circuitry 402 that is operatively coupled via a bus 404to 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.

[0230] The memory 412 may include one or more computer programs including one or morehost 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.

[0231] FIGURE 15 is a flowchart illustrating an example method 1500 in a wireless device,according to certain embodiments. In particular embodiments, one or more steps of FIGURE 15 may be performed by UE 200 described with respect to FIGURE 12.

[0232] The method begins at step 1512, where the wireless device (e.g., UE 200) obtains ^^^^total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal. The value ^^^^comprises an integer value.P112190WO01 PCT APPLICATION 46 of 62

[0233] The wireless device may obtain the value ^^^^ from a network node, such as a base stationor a positioning network node, or may be preconfigured with the value ^^^^, or obtain the value ^^^^from a specification, or may dynamically determine the value ^^^^.

[0234] The wireless device may obtain the ^^^^ total samples of a plurality of positioningmeasurements estimating a time domain channel impulse response of a reference signal according to any of the embodiments and examples described herein.

[0235] At step 1514, the wireless device determines a window of size ^^ consecutive samplesout of the ^^^^total samples. The ^^ consecutive samples have a highest sum power among the ^^^^total samples. The value ^^comprises an integer value.

[0236] The wireless device may obtain the value ^^ from a network node, such as a base stationor a positioning network node, or may be preconfigured with the value ^^, or obtain the value ^^ from a specification, or may dynamically determine the value ^^.

[0237] In particular embodiments, determining the window of size ^^ consecutive samplescomprises computing a sum power of samples within at least one window of size ^^ consecutive samples. For example, determining the window of size ^^ consecutive samples comprises computing a sum power of samples within one or more windows of size ^^ consecutive samples and determining which of the one or more windows has a highest sum power among the ^^^^total samples.

[0238] The window of size ^^ consecutive samples may start from a first sample of the ^^^^ totalsamples or may start from a sample of the ^^^^total samples other than a first sample of the ^^^^total samples. The window of size ^^ consecutive samples may end at a last sample of the ^^^^total samples.

[0239] In particular embodiments, the wireless device determines the window of size ^^consecutive samples out of the ^^^^total samples according to any of the embodiments and examples described herein.

[0240] At step 1516, the wireless device selects a subset Nt’ of samples from the window ofsize ^^ consecutive samples. The value Nt’ comprises an integer value less than or equal to ^^.

[0241] The wireless device may obtain the value Nt’ from a network node, such as a basestation or a positioning network node, or may be preconfigured with the value Nt’, or obtain the value Nt’ from a specification, or may dynamically determine the value Nt’.P112190WO01 PCT APPLICATION 47 of 62

[0242] In particular embodiments, selecting the subset Nt’ of samples from the window of size^^ consecutive samples comprises selecting ^^^′^samples with the largest powers within the window of size ^^ consecutive samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting the first ^^^′^samples within the window of size ^^ consecutive samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting samples with the largest powers within the window of size ^^ consecutive samples along with one sample before the selected sample and one sample after the selected sample for a total of ^^^′^selected samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting random samples from the window of size ^^ consecutive samples.

[0243] In particular embodiments, the wireless device may select the subset Nt’ of samplesfrom the window of size ^^ consecutive samples according to any of the embodiments and examples described herein.

[0244] In some embodiments, when Nt’ equals ^^, the selecting step may be implicit. Forexample, selecting the subset Nt’ may comprise simply selecting the entire window of size ^^ consecutive samples.

[0245] At step 1518, the wireless device transmits a measurement report to a network node.The measurement report comprises timing information of the selected subset Nt’ of samples from the window of size ^^ consecutive samples. In particular embodiments, the network node may comprise an AI / ML inference node or a data collection network node.

[0246] 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.

[0247] FIGURE 16 is a flowchart illustrating an example method 1600 in a network node,according to certain embodiments. In particular embodiments, one or more steps of FIGURE 16 may be performed by network node 300 described with respect to FIGURE 13.

[0248] The method begins at step 1612, where the network node (e.g., network node 300)obtains ^^^^total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal. The value ^^^^comprises an integer value.P112190WO01 PCT APPLICATION 48 of 62

[0249] The network node may obtain the value ^^^^ from another network node, such as a basestation or a positioning network node, or may be preconfigured with the value ^^^^, or obtain the value ^^^^from a specification, or may dynamically determine the value ^^^^.

[0250] The network node may obtain the ^^^^ total samples of a plurality of positioningmeasurements estimating a time domain channel impulse response of a reference signal according to any of the embodiments and examples described herein.

[0251] At step 1614, the network node determines a window of size ^^ consecutive samplesout of the ^^^^total samples. The ^^ consecutive samples have a highest sum power among the ^^^^total samples. The value ^^comprises an integer value.

[0252] The network node may obtain the value ^^ from another network node, such as a basestation or a positioning network node, or may be preconfigured with the value ^^, or obtain the value ^^ from a specification, or may dynamically determine the value ^^.

[0253] In particular embodiments, determining the window of size ^^ consecutive samplescomprises computing a sum power of samples within at least one window of size ^^ consecutive samples. For example, determining the window of size ^^ consecutive samples comprises computing a sum power of samples within one or more windows of size ^^ consecutive samples and determining which of the one or more windows has a highest sum power among the ^^^^total samples.

[0254] The window of size ^^ consecutive samples may start from a first sample of the ^^^^ totalsamples or may start from a sample of the ^^^^total samples other than a first sample of the ^^^^total samples. The window of size ^^ consecutive samples may end at a last sample of the ^^^^total samples.

[0255] In particular embodiments, the network node determines the window of size ^^consecutive samples out of the ^^^^total samples according to any of the embodiments and examples described herein.

[0256] At step 1616, the network node selects a subset Nt’ of samples from the window of size^^ consecutive samples. The value Nt’ comprises an integer value less than or equal to ^^.

[0257] The network node may obtain the value Nt’ from another network node, such as a basestation or a positioning network node, or may be preconfigured with the value Nt’, or obtain the value Nt’ from a specification, or may dynamically determine the value Nt’.P112190WO01 PCT APPLICATION 49 of 62

[0258] In particular embodiments, selecting the subset Nt’ of samples from the window of size^^ consecutive samples comprises selecting ^^^′^samples with the largest powers within the window of size ^^ consecutive samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting the first ^^^′^samples within the window of size ^^ consecutive samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting samples with the largest powers within the window of size ^^ consecutive samples along with one sample before the selected sample and one sample after the selected sample for a total of ^^^′^selected samples. Selecting the subset Nt’ of samples from the window of size ^^ consecutive samples may comprise selecting random samples from the window of size ^^ consecutive samples.

[0259] In particular embodiments, the network node may select the subset Nt’ of samples fromthe window of size ^^ consecutive samples according to any of the embodiments and examples described herein.

[0260] In some embodiments, when Nt’ equals ^^, the selecting step may be implicit. Forexample, selecting the subset Nt’ may comprise simply selecting the entire window of size ^^ consecutive samples.

[0261] At step 1618, the network node transmits a measurement report to a network node. Themeasurement report comprises timing information of the selected subset Nt’ of samples from the window of size ^^ consecutive samples. In particular embodiments, the network node may comprise an AI / ML inference node or a data collection network node.

[0262] Modifications, additions, or omissions may be made to method 1600 of FIGURE 16.Additionally, one or more steps in the method of FIGURE 16 may be performed in parallel or in any suitable order.

[0263] Modifications, additions, or omissions may be made to the methods disclosed hereinwithout 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.

[0264] Some example embodiments follow.Group A Embodiments 1. A method performed by a wireless device, the method comprising:− determining a window of measurement samples according to any of the embodiments for enhanced sub-sampling described above;P112190WO01 PCT APPLICATION 50 of 62 − selecting a subset of a samples from the window for use as input to a machine learning model; − obtaining a bitmap representing the selected subset of samples; − representing the bitmap by any of the start and length indicator value (SLIV) values described with respect to Embodiments 1-6 above; and − transmitting the representation of the bitmap to a positioning node.2. The method of the previous embodiment, wherein the bitmap is represented using asingle SLIV to indicate the span of a first sample with value one and a last sample with value one and a copy of the bitmap values within the span is attached after the SLIV.3. The method of embodiment 1, wherein the bitmap is represented using multiple SLIVsto indicate multiple spans, each of which starts with value one and ends with a value one and a copy of the bitmap values within each of the spans is attached after the each of the SLIV.4. The method of embodiment 1, wherein the bitmap is represented using single ormultiple SLIVs to indicate multiple spans, each of which starts with value one and ends with a value one and a shortened copy of the bitmap values within each of the spans is attached after the each of the SLIV, where the starting value one and the ending value one for each span are not copied.5. The method of embodiment 1, wherein the bitmap is represented using multipleadaptive SLIVs to indicate multiple spans, each of which starts with value one and ends with a value one and a shortened copy of the bitmap values within each of the spans is attached after the each of the SLIV, where the starting value one and the ending value one for each span are not copied and the first SLIV is computed with a full scope but subsequence SLIV is computed with a scope of those after that have already been signaled.6. The method of any one of the previous embodiments, wherein the bitmap is representedby applying a time-reverse version of the positioning measurement timing bitmap.7. A method performed by a wireless device, the method comprising:P112190WO01 PCT APPLICATION 51 of 62 − any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above. 8. The method of the previous embodiment, further comprising one or more additionalwireless device steps, features or functions described above. 9. 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 Embodiments 10. A method performed by a base station, the method comprising:− determining a window of measurement samples according to any of the embodiments for enhanced sub-sampling described above; − selecting a subset of a samples from the window for use as input to a machine learning model; − obtaining a bitmap representing the selected subset of samples; − representing the bitmap by any of the start and length indicator value (SLIV) values described with respect to Embodiments 1-6 above; and − transmitting the representation of the bitmap to a positioning node. 11. The method of the previous embodiment, wherein the bitmap is represented using asingle SLIV to indicate the span of a first sample with value one and a last sample with value one and a copy of the bitmap values within the span is attached after the SLIV. 12. The method of embodiment 10, wherein the bitmap is represented using multiple SLIVsto indicate multiple spans, each of which starts with value one and ends with a value one and a copy of the bitmap values within each of the spans is attached after the each of the SLIV. 13. The method of embodiment 10, wherein the bitmap is represented using single ormultiple SLIVs to indicate multiple spans, each of which starts with value one and endsP112190WO01 PCT APPLICATION 52 of 62 with a value one and a shortened copy of the bitmap values within each of the spans is attached after the each of the SLIV, where the starting value one and the ending value one for each span are not copied. 14. The method of embodiment 10, wherein the bitmap is represented using multipleadaptive SLIVs to indicate multiple spans, each of which starts with value one and ends with a value one and a shortened copy of the bitmap values within each of the spans is attached after the each of the SLIV, where the starting value one and the ending value one for each span are not copied and the first SLIV is computed with a full scope but subsequence SLIV is computed with a scope of those after that have already been signaled. 15. The method of any one of the previous embodiments, wherein the bitmap is representedby applying a time-reverse version of the positioning measurement timing bitmap. 16. 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. 17. The method of the previous embodiment, further comprising one or more additionalbase station steps, features or functions described above. 18. The method of any of the previous embodiments, further comprising:− obtaining user data; and − forwarding the user data to a host computer or a wireless device. Group C Embodiments 19. 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. 20. A base station comprising:P112190WO01 PCT APPLICATION 53 of 62 − 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.21. 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.22. 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 interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments.23. The communication system of the pervious embodiment further including the basestation.24. The communication system of the previous 2 embodiments, further including the UE,wherein the UE is configured to communicate with the base station.P112190WO01 PCT APPLICATION 54 of 62

[0265] 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.

[0266] References in the specification to “one embodiment,” “an embodiment,” “an exampleembodiment,” 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.

[0267] 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

P112190WO01 PCT APPLICATION 55 of 62 CLAIMS:

1. A method performed by a wireless device, the method comprising: obtaining (1512) ^^^^total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal, wherein ^^^^comprises an integer value; determining (1514) a window of size ^^ consecutive samples out of the ^^^^total samples, wherein the ^^ consecutive samples have a highest sum power among the ^^^^total samples, wherein ^^comprises an integer value; selecting (1516) a subset Nt’ of samples from the window of size ^^ consecutive samples, where Nt’ comprises an integer value less than or equal to ^^; and transmitting (1518) a measurement report to a network node, the measurement report comprising timing information of the selected subset Nt’ of samples from the window of size ^^ consecutive samples.

2. The method of claim 1, wherein determining the window of size ^^ consecutive samples comprises computing a sum power of samples within at least one window of size ^^ consecutive samples.

3. The method of any one of claims 1-2, wherein the window of size ^^ consecutive samples starts from a first sample of the ^^^^total samples.

4. The method of any one of claims 1-2, wherein the window of size ^^ consecutive samples starts from a sample of the ^^^^total samples other than a first sample of the ^^^^total samples.

5. The method of any one of claims 1-4, wherein the window of size ^^ consecutive samples ends at a last sample of the ^^^^total samples.

6. The method of any one of claims 1-5, wherein selecting the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting ^^^′^samples withP112190WO01 PCT APPLICATION 56 of 62 the largest powers within the window of size ^^ consecutive samples.

7. The method of any one of claims 1-5, wherein selecting the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting the first ^^^′ ^ samples within the window of size ^^ consecutive samples.

8. The method of any one of claims 1-5, wherein selecting the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting samples with the largest powers within the window of size ^^ consecutive samples along with one sample before the selected sample and one sample after the selected sample for a total of ^^^′^selected samples.

9. The method of any one of claims 1-5, wherein selecting the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting random samples from the window of size ^^ consecutive samples.

10. The method of any one of claims 1-9, wherein the measurement report comprises at least one start and length indicator value (SLIV) representing the timing information.

11. A wireless device (200) comprising processing circuitry (202) operable to: obtain ^^^^total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal, wherein ^^^^comprises an integer value; determine a window of size ^^ consecutive samples out of the ^^^^total samples, wherein the ^^ consecutive samples have a highest sum power among the ^^^^total samples, wherein ^^comprises an integer value; select a subset Nt’ of samples from the window of size ^^ consecutive samples, where Nt’ comprises an integer value less than or equal to ^^; and transmit a measurement report to a network node, the measurement report comprising timing information of the selected subset Nt’ of samples from the window of size ^^ consecutive samples.P112190WO01 PCT APPLICATION 57 of 62 12. The wireless device of claim 11, wherein the processing circuitry is operable to determine the window of size ^^ consecutive samples by computing a sum power of samples within at least one window of size ^^ consecutive samples.

13. The wireless device of any one of claims 11-12, wherein the window of size ^^ consecutive samples starts from a first sample of the ^^^^total samples.

14. The wireless device of any one of claims 11-12, wherein the window of size ^^ consecutive samples starts from a sample of the ^^^^total samples other than a first sample of the ^^^^total samples.

15. The wireless device of any one of claims 11-14, wherein the window of size ^^ consecutive samples ends at a last sample of the ^^^^total samples.

16. The wireless device of any one of claims 11-15, wherein the processing circuitry is operable to select the subset Nt’ of samples from the window of size ^^ consecutive samples by selecting ^^^′^samples with the largest powers within the window of size ^^ consecutive samples.

17. The wireless device of any one of claims 11-15, wherein the processing circuitry is operable to select the subset Nt’ of samples from the window of size ^^ consecutive samples by selecting the first ^^^′^samples within the window of size ^^ consecutive samples.

18. The wireless device of any one of claims 11-15, wherein the processing circuitry is operable to select the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting samples with the largest powers within the window of size ^^ consecutive samples along with one sample before the selected sample and one sample after the selected sample for a total of ^^^′^selected samples.

19. The wireless device of any one of claims 11-15, wherein the processing circuitry is operable to select the subset Nt’ of samples from the window of size ^^ consecutive samplesP112190WO01 PCT APPLICATION 58 of 62 by selecting random samples from the window of size ^^ consecutive samples.

20. The wireless device of any one of claims 21-29, wherein the measurement report comprises at least one start and length indicator value (SLIV) representing the timing information.

21. A method performed by a network node, the method comprising: obtaining (1612) ^^^^total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal, wherein ^^^^comprises an integer value; determining (1614) a window of size ^^ consecutive samples out of the ^^^^total samples, wherein the ^^ consecutive samples have a highest sum power among the ^^^^total samples, wherein ^^comprises an integer value; selecting (1616) a subset Nt’ of samples from the window of size ^^ consecutive samples, where Nt’ comprises an integer value less than or equal to ^^; and transmitting (1618) a measurement report to a network node, the measurement report comprising timing information of the selected subset Nt’ of samples from the window of size ^^ consecutive samples.

22. The method of claim 21, wherein determining the window of size ^^ consecutive samples comprises computing a sum power of samples within at least one window of size ^^ consecutive samples.

23. The method of any one of claims 21-22, wherein the window of size ^^ consecutive samples starts from a first sample of the ^^^^total samples.

24. The method of any one of claims 21-22, wherein the window of size ^^ consecutive samples starts from a sample of the ^^^^total samples other than a first sample of the ^^^^total samples.

25. The method of any one of claims 21-24, wherein the window of size ^^P112190WO01 PCT APPLICATION 59 of 62 consecutive samples ends at a last sample of the ^^^^total samples.

26. The method of any one of claims 21-25, wherein selecting the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting ^^^′^samples with the largest powers within the window of size ^^ consecutive samples.

27. The method of any one of claims 21-25, wherein selecting the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting the first ^^^′ ^ samples within the window of size ^^ consecutive samples.

28. The method of any one of claims 21-25, wherein selecting the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting samples with the largest powers within the window of size ^^ consecutive samples along with one sample before the selected sample and one sample after the selected sample for a total of ^^^′^selected samples.

29. The method of any one of claims 21-25, wherein selecting the subset Nt’ of samples from the window of size ^^ consecutive samples comprises selecting random samples from the window of size ^^ consecutive samples.

30. The method of any one of claims 21-29, wherein the measurement report comprises at least one start and length indicator value (SLIV) representing the timing information.

31. A network node (300) comprising processing circuitry (302) operable to: obtain ^^^^total samples of a plurality of positioning measurements estimating a time domain channel impulse response of a reference signal, wherein ^^^^comprises an integer value; determine a window of size ^^ consecutive samples out of the ^^^^total samples, wherein the ^^ consecutive samples have a highest sum power among the ^^^^total samples, wherein ^^comprises an integer value; select a subset Nt’ of samples from the window of size ^^ consecutive samples, where Nt’ comprises an integer value less than or equal to ^^; andP112190WO01 PCT APPLICATION 60 of 62 transmit a measurement report to a network node, the measurement report comprising timing information of the selected subset Nt’ of samples from the window of size ^^ consecutive samples.

32. The network node of claim 21, wherein the processing circuitry is operable to determine the window of size ^^ consecutive samples by computing a sum power of samples within at least one window of size ^^ consecutive samples.

33. The network node of any one of claims 31-32, wherein the window of size ^^ consecutive samples starts from a first sample of the ^^^^total samples.

34. The network node of any one of claims 31-32, wherein the window of size ^^ consecutive samples starts from a sample of the ^^^^total samples other than a first sample of the ^^^^total samples.

35. The network node of any one of claims 31-34, wherein the window of size ^^ consecutive samples ends at a last sample of the ^^^^total samples.

36. The network node of any one of claims 31-35, wherein the processing circuitry is operable to select the subset Nt’ of samples from the window of size ^^ consecutive samples by selecting ^^^′^samples with the largest powers within the window of size ^^ consecutive samples.

37. The network node of any one of claims 31-35, wherein the processing circuitry is operable to select the subset Nt’ of samples from the window of size ^^ consecutive samples by selecting the first ^^^′^samples within the window of size ^^ consecutive samples.

38. The network node of any one of claims 31-35, wherein the processing circuitry is operable to select the subset Nt’ of samples from the window of size ^^ consecutive samples by selecting samples with the largest powers within the window of size ^^ consecutive samples along with one sample before the selected sample and one sample after the selected sample forP112190WO01 PCT APPLICATION 61 of 62 a total of ^^^′^selected samples.

39. The network node of any one of claims 21-25, wherein the processing circuitry is operable to select the subset Nt’ of samples from the window of size ^^ consecutive samples by selecting random samples from the window of size ^^ consecutive samples.

40. The network node of any one of claims 31-39, wherein the measurement report comprises at least one start and length indicator value (SLIV) representing the timing information.

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