DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The claims 1-17 are pending in this application. This is a non-final office action in response to Application Number 18/435,687 filed on 7 February 2024 and claiming foreign priority to Finnish Application 20235132 filed on 9 February 2023. The applicant of record is Nokia Technologies Oy and the inventors of record are Oana-Elena Barbu and Sajad Rezaie.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 29 July 2024 was filed after the filing date of the instant application on 7 February 2024 and before the mailing date of the first office action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The claims have been considered according to the latest Patent Eligibility Guidelines and are considered eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Barbu et al. (U.S. Patent Publication 2022/0232509) in view of Sundararajan et al. (U.S. Patent Publication 2022/0044091), hereinafter referred to as “Sun”.
Examiner notes that Barbu et al. (U.S. Patent Publication 2022/0232509) is published before the effective filing date of the instant application.
Regarding claim 1, Barbu disclosed an apparatus, comprising one or more processors and one or more memories storing instructions that, when executed by the one or more processors (see Barbu Fig. 14 apparatus, processor, memory), cause the apparatus at least to perform:
receive, by a node in a wireless communications system from another node in the wireless communications system, reference signals for determining positioning information corresponding to a user equipment in the wireless communication system (see Barbu [0003]: “…obtaining reference signals (e.g. uplink sounding reference signals) received at a plurality of nodes of a communication system (e.g. a mobile communication system) from a user device…and generating a first three-dimensional position estimate for the user device by applying signals based on the generated signal signature matrices to an input of a model. The nodes may be serving and neighbour nodes of the user device…The first three-dimensional position estimate may be a coarse position estimate.” | [0065]: “In an implementation of the algorithm 40, radio signals (e.g. reference signals, such as sounding reference signals) may be obtained from one of the user devices 52a to 52d at a plurality of nodes of the system 50 (e.g. serving and neighbour nodes of the user device concerned). Signal signature matrices based on real and imaginary components of obtained reference signals (e.g. wideband reference signals) may be obtained and provided to the model in the operation 44 for use in generating a three-dimensional position estimate for the user device, as discussed further below.”);
measure, by the node, the reference signals (see Barbu [0048]: “Examples of mechanisms for using radio signals transmitted between a user device and one or more nodes of a mobile communication system to estimate the location of that user device comprise:” | [0068]: “Thus, to train a model, a network collects (in the operation 62) uplink radio signals (e.g. reference signals (RS), such as UE-specific uplink sounding reference signals (SRS), as received by the serving and neighbour nodes). In this first “data association” phase, the network can build a mapping between the received reference signals (the input features) and a 3D position (the labels). These data can be used for training machine learning module(s). To generate labelled training data, the network can adopt various strategies, e.g.”; [0069]: “Using live network measurements. In this case, a network may select and assign a set of reference UEs whose 3D positions are known (e.g. communicated by the UEs themselves, as obtained from UE sensors/non-cellular receivers). The network may collect reference UE measurements over a predefined time window and/or until enough labelled training data has been obtained…” | [0073]: “At operation 63, signals from individual user devices as received at communication nodes (e.g. the nodes 54a to 54e) are isolated from one another. This may be achieved using cross-correlation between a known signal transmitted by a particular user device and the signals received at a particular node. For example, a TRP can cross-correlate the received signal with the locally generated copy of the UE-specific transmit signal and analyse whether the specific signature is present in the received signal.” | [0086]: “As discussed above, in operation 63, the node/TRP n computes the cross correlation between the known transmit signal from a UE a, i.e. s(a), and v(n). That cross-correlation is stored (in operation 64) into a row vector with G elements t(n,a)=xcorr{v(n,a), s(a)}. This is the signal signature of UE a at node/TRP n. In addition, the node/TRP may compute a signal-noise ratio (SNR) level of the received signal, i.e. SNR(n,a). The cross-correlation t(n,a) can be labelled with the position of the user device (UE) after discretization [xa, ya, fa].”);
apply, by the node, a function to the measured reference signals to form an output corresponding to a fixed set of input features (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes…generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0013]: “…using data augmentation to generate missing data points in said signal signature matrices. Said data augmentation may use GAN or other machine learning principles…” | [0019]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes…generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0115]: “The algorithm 120 starts at operation 122, where data augmentation is triggered. For example, the operation 112 may comprise a determination that a number of null data entries is above a threshold (which determination can be used to trigger the use of data augmentation). It should be noted that other triggers for data augmentation are possible. For example, data augmentation may be triggered if a position estimate (e.g. as generated in the operation 116 of the algorithm 110) has a high degree of uncertainty (e.g. a large variance).”);
determine, by the node, the positioning information of the user equipment using the output (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes…generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0117]: “At operation 126, an updated position estimate for the user device is obtained by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.”); and
send a measurement report, comprising indication of the positioning information, from the node toward a further node in the wireless communications system (see Sun combination below).
Barbu did not explicitly disclose “send a measurement report, comprising indication of the positioning information, from the node toward a further node in the wireless communications system”. However in a related art, Sun disclosed:
See Sun [0138]: “…Alternatively, the non-co-located physical TRPs may be the serving base station receiving the measurement report from the UE and a neighbor base station whose reference RF signals the UE is measuring. Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.” | [0177]: “Similar to the functionality described in connection with the DL transmission by the base station 304, the processing system 332 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting..”
See Sun [0210]: “For low latency positioning, a gNB may trigger a UL SRS-P via a DCI (e.g., transmitted SRS-P may include repetition or beam-sweeping to enable several gNBs to receive the SRS-P). Alternatively, the gNB may send information regarding aperiodic PRS transmission to the UE (e.g., this configuration may include information about PRS from multiple gNBs to enable the UE to perform timing computations for positioning (UE-based) or for reporting (UE-assisted). While various aspects of the present disclosure relate to DL PRS-based positioning procedures, some or all of such aspects may also apply to UL SRS-P-based positioning procedures.”; [0211]: “…the terms “sounding reference signal”, “SRS” and “SRS-P” refer to any type of reference signal that can be used for positioning…”; [0213]: “Layer-3 (L3) signaling (e.g., RRC or Location Positioning Protocol (LPP)) is typically used to transport reports that comprise location-based data in association with UE-assisted positioning techniques…”; [0219]: “More recently, L1 and L2 signaling has been contemplated for use in association with PRS-based reporting. For example, L1 and L2 signaling is currently used in some systems to transport CSI reports (e.g., reporting of Channel Quality Indications (CQIs), Precoding Matrix Indicators (PMIs), Layer Indicators (Lis), L1-RSRP, etc.)...Linkages (e.g., time offsets) are defined between instances of RSs being measured and corresponding reporting. In some designs, CSI-like reporting of PRS-based measurement data using L1 and L2 signaling may be implemented.”
See Sun Fig. 6, [0223]: “A location server (e.g., location server 230) may send assistance data to the UE 604 that includes an identification of one or more neighbor cells of base stations 602 and configuration information for reference RF signals transmitted by each neighbor cell. Alternatively, the assistance data can originate directly from the base stations 602 themselves (e.g., in periodically broadcasted overhead messages, etc.). Alternatively, the UE 604 can detect neighbor cells of base stations 602 itself without the use of assistance data. The UE 604 (e.g., based in part on the assistance data, if provided) can measure and (optionally) report the OTDOA from individual network nodes and/or RSTDs between reference RF signals received from pairs of network nodes. Using these measurements and the known locations of the measured network nodes (i.e., the base station(s) 602 or antenna(s) that transmitted the reference RF signals that the UE 604 measured), the UE 604 or the location server can determine the distance between the UE 604 and the measured network nodes and thereby calculate the location of the UE 604.”; [0224]: “The term “position estimate” is used herein to refer to an estimate of a position for a UE 604, which may be geographic (e.g., may comprise a latitude, longitude, and possibly altitude) or civic (e.g., may comprise a street address, building designation, or precise point or area within or nearby to a building or street address, such as a particular entrance to a building, a particular room or suite in a building, or a landmark such as a town square).”; [0225]: “…Alternatively, the non-co-located physical transmission points may be the serving base station receiving the measurement report from the UE (e.g., UE 604) and a neighbor base station whose reference RF signals the UE is measuring. Thus, FIG. 6 illustrates an aspect in which base stations 602a and 602b form a DAS/RRH 620. For example, the base station 602a may be the serving base station of the UE 604 and the base station 602b may be a neighbor base station of the UE 604. As such, the base station 602b may be the RRH of the base station 602a. The base stations 602a and 602b may communicate with each other over a wired or wireless link 622.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 2, Barbu-Sun. The apparatus according to claim 1, wherein the function produces the output corresponding to the fixed set of input features when fed with positioning signals that are one or both of: aggregated over a variable number of carriers; or of variable size in time and frequency domains. (see Sun [0153]: “In 5G, the frequency spectrum in which wireless nodes (e.g., base stations 102/180, UEs 104/182) operate is divided into multiple frequency ranges, FR1 (from 450 to 6000 MHz), FR2 (from 24250 to 52600 MHz), FR3 (above 52600 MHz), and FR4 (between FR1 and FR2)...The primary carrier carries all common and UE-specific control channels, and may be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2) that may be configured once the RRC connection is established between the UE 104 and the anchor carrier and that may be used to provide additional radio resources…; [0154]: “For example, still referring to FIG. 1, one of the frequencies utilized by the macro cell base stations 102 may be an anchor carrier (or “PCell”) and other frequencies utilized by the macro cell base stations 102 and/or the mmW base station 180 may be secondary carriers (“SCells”). The simultaneous transmission and/or reception of multiple carriers enables the UE 104/182 to significantly increase its data transmission and/or reception rates. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically lead to a two-fold increase in data rate (i.e., 40 MHz), compared to that attained by a single 20 MHz carrier.” | [0174]: “...The transmitter 354 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an orthogonal frequency division multiplexing (OFDM) subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and/or frequency domain, and then combined together using an Inverse Fast Fourier Transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream…”; [0175]: “…The receiver 312 then converts the OFDM symbol stream from the time-domain to the frequency domain using a fast Fourier transform (FFT). The frequency domain signal comprises a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 304...” | [0219]: “More recently, L1 and L2 signaling has been contemplated for use in association with PRS-based reporting...With 2-part CSI reporting, the part 1s of all reports are grouped together, and the part 2s are grouped separately, and each group is separately encoded (e.g., part 1 payload size is fixed based on configuration parameters, while part 2 size is variable and depends on configuration parameters and also on associated part 1 content). A number of coded bits/symbols to be output after encoding and rate-matching is computed based on a number of input bits and beta factors, per the relevant standard. Linkages (e.g., time offsets) are defined between instances of RSs being measured and corresponding reporting. In some designs, CSI-like reporting of PRS-based measurement data using L1 and L2 signaling may be implemented.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding details about the neural network and positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 3, Barbu-Sun disclosed the apparatus according to claim 1, wherein the applying comprises identifying that a signal sample at entry (fx, tx), having a frequency index fx and time index tx, is missing, applying the function on measured samples ym to reconstruct a missing signal ye(fx, tx): ye(fx,tx)=f(y,fx,tx), where the function f( ) is parameterized at least by the following: measured signal samples y; and the time and frequency indices of the missing entry (see Barbu [0009]: “using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes...generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0114]: “FIG. 12 is a flow chart showing an algorithm, indicated generally by the reference numeral 120, in accordance with an example embodiment. As discussed further below, the algorithm 120 uses data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices.” | [0115]: “The algorithm 120 starts at operation 122, where data augmentation is triggered. For example, the operation 112 may comprise a determination that a number of null data entries is above a threshold (which determination can be used to trigger the use of data augmentation). It should be noted that other triggers for data augmentation are possible. For example, data augmentation may be triggered if a position estimate (e.g. as generated in the operation 116 of the algorithm 110) has a high degree of uncertainty (e.g. a large variance).”).
Regarding claim 4, Barbu-Sun disclosed the apparatus according to claim 1, wherein the apparatus is further configured to perform: perform a check on the output corresponding to the fixed set of input features, and perform one or more modifications corresponding to the output in response to at least part of the output not being within a range (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes. Some example embodiments further comprising means for performing: triggering the use of said data augmentation in the event that a number of null data entries in the signal signature matrices is above a threshold and/or when the final estimate has a high degree of uncertainty. Some example embodiments further comprise means for performing: generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0115]: “The algorithm 120 starts at operation 122, where data augmentation is triggered. For example, the operation 112 may comprise a determination that a number of null data entries is above a threshold (which determination can be used to trigger the use of data augmentation). It should be noted that other triggers for data augmentation are possible. For example, data augmentation may be triggered if a position estimate (e.g. as generated in the operation 116 of the algorithm 110) has a high degree of uncertainty (e.g. a large variance).”).
Regarding claim 5, Barbu-Sun disclosed the apparatus according to claim 1, wherein the apparatus is further configured to perform, after applying the function but before determining the positioning information, apply a list of weights, individual weights applied to corresponding individual carriers in the output (see Sun [0271]: “Referring to FIGS. 9-10, in some designs, the UE-feature or BS-feature processing neural network function(s) may be specific to:”; [0273] “a carrier” | [0279]: “Referring to FIGS. 9-10, in some designs, a version of a neural network function may be obtained by the BS at 1010 and sent to the UE at 1020, whereby the neural network undergoes further refinement or modification at the UE. In this case, the neural network function obtained at 910 may correspond to an initial version received from the BS or a version of the neural network function that is further refined at the UE. For example, an initial version of the neural network function (e.g., comprising a set of default weights, offsets, etc. for processing of measurement data into features) may be sent by the BS to the UE in conjunction with training data that is applied by the UE in accordance with machine-learning. In some designs, the initial version of the neural network function may be configured conservatively so as not to override UE-specific parameters that may already be in effect. In this case, the training data may be used so as to accommodate these UE-specific parameters (e.g., the training data may be used to refine the UE-specific parameters rather than simply override such parameters with different values).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe details about the neural network and positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 6, Barbu-Sun disclosed the apparatus according to claim 4, wherein the one or more modifications are applied to part of the list of weights based on the check indicating the output was not in range and based on the weights that apply to the part of the output that was not in range (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes. Some example embodiments further comprising means for performing: triggering the use of said data augmentation in the event that a number of null data entries in the signal signature matrices is above a threshold and/or when the final estimate has a high degree of uncertainty. Some example embodiments further comprise means for performing: generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.”; examiner notes that comparing and updating weights are inherently used within a Generative Adversarial Network (GAN).).
Regarding claim 7, Barbu-Sun disclosed the apparatus according to claim 5, wherein indications of the function and the weight list are carried in one or more information elements as part of one of long-term evolution positioning protocol assistance data or new radio positioning protocol A assistance data (see Sun [0137]: “A base station may operate according to one of several RATs in communication with UEs depending on the network in which it is deployed…” | Fig. 1, [0140]: “According to various aspects, FIG. 1 illustrates an exemplary wireless communications system 100...” | [0142]: “The base stations 102 may wirelessly communicate with the UEs 104...In some cases, different cells may be configured according to different protocol types (e.g., machine-type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or others) that may provide access for different types of UEs.” | [0146]: “The small cell base station 102′ may operate in a licensed and/or an unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell base station 102′ may employ LTE or NR technology and use the same 5 GHz unlicensed frequency spectrum as used by the WLAN AP 150. The small cell base station 102′, employing LTE/5G in an unlicensed frequency spectrum, may boost coverage to and/or increase capacity of the access network. NR in unlicensed spectrum may be referred to as NR-U. LTE in an unlicensed spectrum may be referred to as LTE-U, licensed assisted access (LAA), or MulteFire.” | [0213]: “Layer-3 (L3) signaling (e.g., RRC or Location Positioning Protocol (LPP)) is typically used to transport reports that comprise location-based data in association with UE-assisted positioning techniques...” | [0220]: “FIG. 6 illustrates an exemplary wireless communications system 600 according to various aspects of the disclosure. In the example of FIG. 6, a UE 604, which may correspond to any of the UEs described above with respect to FIG. 1 (e.g., UEs 104, UE 182, UE 190, etc.), is attempting to calculate an estimate of its position, or assist another entity (e.g., a base station or core network component, another UE, a location server, a third party application, etc.) to calculate an estimate of its position...”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe details about the neural network and positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 8, Barbu-Sun disclosed the apparatus according to claim 5, wherein sending the measurement report comprises sending one or both of indication of a result of the check on the output or indication of the list of weights actually used (see Sun [0210]: “For low latency positioning, a gNB may trigger a UL SRS-P via a DCI (e.g., transmitted SRS-P may include repetition or beam-sweeping to enable several gNBs to receive the SRS-P). Alternatively, the gNB may send information regarding aperiodic PRS transmission to the UE (e.g., this configuration may include information about PRS from multiple gNBs to enable the UE to perform timing computations for positioning (UE-based) or for reporting (UE-assisted). While various aspects of the present disclosure relate to DL PRS-based positioning procedures, some or all of such aspects may also apply to UL SRS-P-based positioning procedures.”; [0211]: “…the terms “sounding reference signal”, “SRS” and “SRS-P” refer to any type of reference signal that can be used for positioning…”; [0213]: “Layer-3 (L3) signaling (e.g., RRC or Location Positioning Protocol (LPP)) is typically used to transport reports that comprise location-based data in association with UE-assisted positioning techniques…”; [0219]: “More recently, L1 and L2 signaling has been contemplated for use in association with PRS-based reporting. For example, L1 and L2 signaling is currently used in some systems to transport CSI reports (e.g., reporting of Channel Quality Indications (CQIs), Precoding Matrix Indicators (PMIs), Layer Indicators (Lis), L1-RSRP, etc.)...Linkages (e.g., time offsets) are defined between instances of RSs being measured and corresponding reporting. In some designs, CSI-like reporting of PRS-based measurement data using L1 and L2 signaling may be implemented.” | Fig. 9, [0248]: “At 930, UE 302 (e.g., processing system 332, measurement module 342, etc.) determines a positioning estimate (e.g., a WWAN position estimate, a WLAN position estimate, a GNSS position estimate, a sensor-based position estimate, etc.) for the UE based at least in part upon the positioning measurement data and the at least one neural network function. In some designs, at 930, UE 302 may directly feed a channel estimate into the neural network function(s) as an input. In other designs, UE 302 may first extract some features such as time-of-arrival, reference signal time-difference, angle of departure, timing and magnitude of a pre-defined number of peaks in the channel estimate, etc. and feeds such features to the neural network function(s). In some designs, the neural network function(s) may output a likelihood of particular feature(s) being present at particular candidate location(s), in which case a post-processing function of combining the likelihood(s) across all measurements (or features) can be computed into a combined likelihood function, as discussed below in more detail with respect to FIGS. 11-13. For example, if the neural network function(s) indicate that the positioning measurement data is 99.9% likely to be present at a given candidate location and less than 1% chance to be present at any other candidate location, then the given candidate location may be determined as the positioning estimate (e.g., or at least, the given candidate location may be weighted more favorably as the positioning estimate in a positioning algorithm).” | [0252]: “Referring to FIGS. 9-10, in some designs, the at least one neural network function may comprise at least one UE-feature processing neural network function. The UE-feature processing neural network function is used to process positioning measurement features that are based upon a set of positioning measurements measured at the UE (e.g., PRS measurements, etc.). In some designs, the UE-feature processing neural network function may receive one or more UE-side positioning measurement features, and the UE-feature processing neural network function may output likelihoods of the one or more UE-side positioning measurement features being present at one or more candidate positioning estimates for the UE. The outputted (or derived) likelihoods can then be factored into the positioning estimate for the UE (e.g., low-likelihood positioning estimates are excluded or weighted less heavily, high-likelihood positioning estimates are weighted more heavily, etc.)...” | [0261]: “Referring to FIGS. 9-10, in some designs, the at least one neural network function may comprise at least one BS-feature processing neural network function. The BS-feature processing neural network function is used to process positioning measurement features that are based upon a set of positioning measurements measured at the network-side, such as the serving BS or non-serving BS(s) of the UE (e.g., SRS-P measurements, etc.). In some designs, the BS-feature processing neural network function may receive one or more BS-side positioning measurement features, and the BS-feature processing neural network function may output likelihoods of the one or more BS-side positioning measurement features being present at one or more candidate positioning estimates for the UE. The outputted (or derived) likelihoods can then be factored into the positioning estimate for the UE (e.g., low-likelihood positioning estimates are excluded or weighted less heavily, high-likelihood positioning estimates are weighted more heavily, etc.).” | [0279]: “Referring to FIGS. 9-10, in some designs, a version of a neural network function may be obtained by the BS at 1010 and sent to the UE at 1020, whereby the neural network undergoes further refinement or modification at the UE. In this case, the neural network function obtained at 910 may correspond to an initial version received from the BS or a version of the neural network function that is further refined at the UE. For example, an initial version of the neural network function (e.g., comprising a set of default weights, offsets, etc. for processing of measurement data into features) may be sent by the BS to the UE in conjunction with training data that is applied by the UE in accordance with machine-learning. In some designs, the initial version of the neural network function may be configured conservatively so as not to override UE-specific parameters that may already be in effect. In this case, the training data may be used so as to accommodate these UE-specific parameters (e.g., the training data may be used to refine the UE-specific parameters rather than simply override such parameters with different values).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 9, Barbu-Sun disclosed the apparatus according to claim 1, wherein the determining the positioning information of the user equipment using the output uses a positioning module that was previously trained to produce positioning information based on input having the fixed set of input features (see Barbu [0010]: “In a second aspect, this specification describes an apparatus (e.g. a model generator) comprising means for performing: obtaining reference signals (e.g. uplink sounding reference signals) from a plurality of user devices at a plurality of nodes (e.g. serving and neighbour nodes) of a communication system, wherein each user device has an identified position (e.g. a known or estimated position) within a three-dimensional space; using cross-correlation to isolate reference signals received from individual user devices at each communication node; generating, for each user device, first and second signal signature matrices based on real and imaginary components of the isolated reference signals respectively; mapping each signal signature matrix to the identified position of the corresponding user device; and training a model (e.g. a machine-learning model (such as CNN. DNN, ResNet etc.)) based on the generated first and second signal matrices and the corresponding identified positions. The said cross-correlation may be between a known signal transmitter by a particular user device and signals received at a particular node of the communication system.” | Fig. 6, [0066]: “FIG. 6 is a flow chart showing an algorithm, indicated generally by the reference numeral 60, in accordance with an example embodiment. The algorithm 60 may be used for training a model, such as the model used in the operation 44 of the algorithm 40 discussed above.”; [0067]: “The algorithm 60 starts at operation 62, where radio signals from a plurality of user device (such as some or all of the user devices 52a to 52d) are received at a plurality of communication nodes (such as some or all the nodes 54a to 54e). Each user device has an identified position (e.g. a known or estimated position) within a three-dimensional space. Thus, the data obtained in the operation 62 is labelled data that can be used for training a model, as discussed further below. In one example embodiment, the radio signals are reference signals, such as uplink sounding reference signals (SRS). The nodes receiving the radio signals may be include a serving node and one or more neighbour nodes of particular user device.” | [0078]: “At operation 66, the generated first and second signal matrices and the corresponding identified positions are used to train a model. The model may be an ML model (such as CNN, DNN, ResNet etc.) that is trained using machine learning principles. For example, the feature matrices Tr and Ti discussed above may be labelled with the UE location and fed to a supervised machine learning model, e.g. CNN, DNN, ResNet, called FloorML that matches the input feature matrix set to an output consisting of a 3D discrete position [kx, ky, kz] in a 3D positioning grid of chosen resolution D.”).
Regarding claim 10, Barbu-Sun disclosed the apparatus according to claim 1, wherein the node sends a configuration defining at least a number of aggregated carriers, bands, and durations used for the fixed set of input features (see Sun Fig. 4A, [0188]: “As illustrated in FIG. 4A, some of the REs carry DL reference (pilot) signals (DL-RS) for channel estimation at the UE. The DL-RS may include demodulation reference signals (DMRS) and channel state information reference signals (CSI-RS), exemplary locations of which are labeled “R” in FIG. 4A.”; Fig. 4B, [0189]: “FIG. 4B illustrates an example of various channels within a DL subframe of a frame. The physical downlink control channel (PDCCH) carries DL control information (DCI) within one or more control channel elements (CCEs), each CCE including nine RE groups (REGs), each REG including four consecutive REs in an OFDM symbol. The DCI carries information about UL resource allocation (persistent and non-persistent) and descriptions about DL data transmitted to the UE. Multiple (e.g., up to 8) DCIs can be configured in the PDCCH, and these DCIs can have one of multiple formats. For example, there are different DCI formats for UL scheduling, for non-MIMO DL scheduling, for MIMO DL scheduling, and for UL power control.” | [0184]: “LTE, and in some cases NR, utilizes OFDM on the downlink and single-carrier frequency division multiplexing (SC-FDM) on the uplink. Unlike LTE, however, NR has an option to use OFDM on the uplink as well. OFDM and SC-FDM partition the system bandwidth into multiple (K) orthogonal subcarriers, which are also commonly referred to as tones, bins, etc. Each subcarrier may be modulated with data. In general, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers may be fixed, and the total number of subcarriers (K) may be dependent on the system bandwidth…”; [0185], Table 1: “LTE supports a single numerology (subcarrier spacing, symbol length, etc.). In contrast NR may support multiple numerologies” | [0190]: “ A primary synchronization signal (PSS) is used by a UE to determine subframe/symbol timing and a physical layer identity. A secondary synchronization signal (SSS) is used by a UE to determine a physical layer cell identity group number and radio frame timing. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a PCI. Based on the PCI, the UE can determine the locations of the aforementioned DL-RS. The physical broadcast channel (PBCH), which carries an MIB, may be logically grouped with the PSS and SSS to form an SSB (also referred to as an SS/PBCH). The MIB provides a number of RBs in the DL system bandwidth and a system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted through the PBCH such as system information blocks (SIBs), and paging messages. | [0192]: “A PRS configuration is defined with reference to the SFN of a cell that transmits PRS. PRS instances, for the first subframe of the N.sub.PRS downlink subframes comprising a first PRS positioning occasion…”; Fig. 5, [0194]: “In some aspects, when a UE receives a PRS configuration index I.sub.PRS in the OTDOA assistance data for a particular cell, the UE may determine the PRS periodicity T.sub.PRS 520 and PRS subframe offset Δ.sub.PRS using Table 2. The UE may then determine the radio frame, subframe, and slot when a PRS is scheduled in the cell (e.g., using equation (1)). The OTDOA assistance data may be determined by, for example, the location server (e.g., location server 230, LMF 270), and includes assistance data for a reference cell, and a number of neighbor cells supported by various base stations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe details about the neural network and positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 11, Barbu-Sun disclosed the apparatus according to claim 1, wherein the node and further node are as follows: for uplink, the node is a base station and the other node is the user equipment; and for downlink, the node is the user equipment and the other node is the base station (see Sun [0144]: “The communication links 120 between the base stations 102 and the UEs 104 may include UL (also referred to as reverse link) transmissions from a UE 104 to a base station 102 and/or downlink (DL) (also referred to as forward link) transmissions from a base station 102 to a UE 104...” | [0152]: “Note that a “downlink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the downlink beam to transmit a reference signal to a UE, the downlink beam is a transmit beam. If the UE is forming the downlink beam, however, it is a receive beam to receive the downlink reference signal. Similarly, an “uplink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the uplink beam, it is an uplink receive beam, and if a UE is forming the uplink beam, it is an uplink transmit beam.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 12, Barbu disclosed an apparatus, comprising one or more processors and one or more memories storing instructions that, when executed by the one or more processors (see Barbu Fig. 14 apparatus, processor, memory), cause the apparatus at least to perform:
for a wireless communications system where a node measures positioning reference signals sent from another node, send from a further node in the wireless communications system to the node indication of a function to be used by the node to apply to positioning reference signals to form an output of a fixed set of input features used to determine positioning information for a user equipment in the wireless communications system (see Barbu [0003]: “…obtaining reference signals (e.g. uplink sounding reference signals) received at a plurality of nodes of a communication system (e.g. a mobile communication system) from a user device…and generating a first three-dimensional position estimate for the user device by applying signals based on the generated signal signature matrices to an input of a model. The nodes may be serving and neighbour nodes of the user device…The first three-dimensional position estimate may be a coarse position estimate.” | [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes…generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0046]: “The algorithm 20 starts at operation 22, where radio signals transmitted between the user device 12 and one or more of the nodes 14a to 14c are obtained. The radio signals may be obtained at the user device 12, at the relevant nodes 14a to 14c and/or at another node (such as a server, e.g. a location management function (LMF)).” | [0065]: “In an implementation of the algorithm 40, radio signals (e.g. reference signals, such as sounding reference signals) may be obtained from one of the user devices 52a to 52d at a plurality of nodes of the system 50 (e.g. serving and neighbour nodes of the user device concerned). Signal signature matrices based on real and imaginary components of obtained reference signals (e.g. wideband reference signals) may be obtained and provided to the model in the operation 44 for use in generating a three-dimensional position estimate for the user device, as discussed further below.” | [0115]: “The algorithm 120 starts at operation 122, where data augmentation is triggered. For example, the operation 112 may comprise a determination that a number of null data entries is above a threshold (which determination can be used to trigger the use of data augmentation). It should be noted that other triggers for data augmentation are possible. For example, data augmentation may be triggered if a position estimate (e.g. as generated in the operation 116 of the algorithm 110) has a high degree of uncertainty (e.g. a large variance).” | [0127]: “The embodiments described above are generally trained and deployed at the network side. However, the methods can generally be implemented at the user device side, for example with model downloading and tuning post-training.”);
receive, by the further node, a measurement report comprising at least indication of the positioning information based at least on the positioning reference signals and the function (see Sun (see Sun combination below); and
update, by the further node, at least the function (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes…generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0079]: “As discussed above, in order to generate the model that delivers a mapping between UL RS and an accurate 3D discrete position, the network (e.g. LMF) may collect labelled data. This is accomplished by creating a mapping between the uplink RS sent from a location [a,b,c] and received by N TRPs. To do that, the network may:”; [0081]: “Use live data and designate reference UEs that transmit RS, and whose 3D position is known in advance, e.g. extracted from UE sensors (WiFi receiver, barometric pressure sensors, gyroscope, etc).” | [0116]: “At operation 124, machine-learning principles are used to estimate missing data points (i.e. at least some of the null entries are estimated). As discussed further below, the missing data points may be based on available reference signals and position estimates of the user device relative to said plurality of nodes.” | [0117]: “At operation 126, an updated position estimate for the user device is obtained by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.”) based at least on the measurement report (see explanation in Barbu-Sun combination below).
Barbu did not explicitly disclose “receive, by the further node, a measurement report comprising at least indication of the positioning information based at least on the positioning reference signals and the function” and that updating the function is “based at least on the measurement report”.
However in a related art, Sun disclosed:
See Sun [0138]: “…Alternatively, the non-co-located physical TRPs may be the serving base station receiving the measurement report from the UE and a neighbor base station whose reference RF signals the UE is measuring. Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.” | [0177]: “Similar to the functionality described in connection with the DL transmission by the base station 304, the processing system 332 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting..”
See Sun [0210]: “For low latency positioning, a gNB may trigger a UL SRS-P via a DCI (e.g., transmitted SRS-P may include repetition or beam-sweeping to enable several gNBs to receive the SRS-P). Alternatively, the gNB may send information regarding aperiodic PRS transmission to the UE (e.g., this configuration may include information about PRS from multiple gNBs to enable the UE to perform timing computations for positioning (UE-based) or for reporting (UE-assisted). While various aspects of the present disclosure relate to DL PRS-based positioning procedures, some or all of such aspects may also apply to UL SRS-P-based positioning procedures.”; [0211]: “…the terms “sounding reference signal”, “SRS” and “SRS-P” refer to any type of reference signal that can be used for positioning…”; [0213]: “Layer-3 (L3) signaling (e.g., RRC or Location Positioning Protocol (LPP)) is typically used to transport reports that comprise location-based data in association with UE-assisted positioning techniques…”; [0219]: “More recently, L1 and L2 signaling has been contemplated for use in association with PRS-based reporting. For example, L1 and L2 signaling is currently used in some systems to transport CSI reports (e.g., reporting of Channel Quality Indications (CQIs), Precoding Matrix Indicators (PMIs), Layer Indicators (Lis), L1-RSRP, etc.)...Linkages (e.g., time offsets) are defined between instances of RSs being measured and corresponding reporting. In some designs, CSI-like reporting of PRS-based measurement data using L1 and L2 signaling may be implemented.”
See Sun Fig. 6, [0223]: “A location server (e.g., location server 230) may send assistance data to the UE 604 that includes an identification of one or more neighbor cells of base stations 602 and configuration information for reference RF signals transmitted by each neighbor cell. Alternatively, the assistance data can originate directly from the base stations 602 themselves (e.g., in periodically broadcasted overhead messages, etc.). Alternatively, the UE 604 can detect neighbor cells of base stations 602 itself without the use of assistance data. The UE 604 (e.g., based in part on the assistance data, if provided) can measure and (optionally) report the OTDOA from individual network nodes and/or RSTDs between reference RF signals received from pairs of network nodes. Using these measurements and the known locations of the measured network nodes (i.e., the base station(s) 602 or antenna(s) that transmitted the reference RF signals that the UE 604 measured), the UE 604 or the location server can determine the distance between the UE 604 and the measured network nodes and thereby calculate the location of the UE 604.”; [0224]: “The term “position estimate” is used herein to refer to an estimate of a position for a UE 604, which may be geographic (e.g., may comprise a latitude, longitude, and possibly altitude) or civic (e.g., may comprise a street address, building designation, or precise point or area within or nearby to a building or street address, such as a particular entrance to a building, a particular room or suite in a building, or a landmark such as a town square).”; [0225]: “…Alternatively, the non-co-located physical transmission points may be the serving base station receiving the measurement report from the UE (e.g., UE 604) and a neighbor base station whose reference RF signals the UE is measuring. Thus, FIG. 6 illustrates an aspect in which base stations 602a and 602b form a DAS/RRH 620. For example, the base station 602a may be the serving base station of the UE 604 and the base station 602b may be a neighbor base station of the UE 604. As such, the base station 602b may be the RRH of the base station 602a. The base stations 602a and 602b may communicate with each other over a wired or wireless link 622.”
Examiner notes that in light of Sun’s teachings regarding receiving the measurement report including positioning information (see citations above) and Barbu’s teachings regarding updating the function based on positioning information (see citations above). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that the combination of Barbu and Sun would teach that the Barbu’s updating is based on Sun’s measurement report.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 13, Barbu-Sun disclosed the apparatus according to claim 12, wherein: the apparatus is further configured to perform:
receive indication of a configuration that defines at least a number of aggregated carriers, bands, and durations used for the fixed set of input features (see Sun Fig. 4A, [0188]: “As illustrated in FIG. 4A, some of the REs carry DL reference (pilot) signals (DL-RS) for channel estimation at the UE. The DL-RS may include demodulation reference signals (DMRS) and channel state information reference signals (CSI-RS), exemplary locations of which are labeled “R” in FIG. 4A.”; Fig. 4B, [0189]: “FIG. 4B illustrates an example of various channels within a DL subframe of a frame. The physical downlink control channel (PDCCH) carries DL control information (DCI) within one or more control channel elements (CCEs), each CCE including nine RE groups (REGs), each REG including four consecutive REs in an OFDM symbol. The DCI carries information about UL resource allocation (persistent and non-persistent) and descriptions about DL data transmitted to the UE. Multiple (e.g., up to 8) DCIs can be configured in the PDCCH, and these DCIs can have one of multiple formats. For example, there are different DCI formats for UL scheduling, for non-MIMO DL scheduling, for MIMO DL scheduling, and for UL power control.” | [0184]: “LTE, and in some cases NR, utilizes OFDM on the downlink and single-carrier frequency division multiplexing (SC-FDM) on the uplink. Unlike LTE, however, NR has an option to use OFDM on the uplink as well. OFDM and SC-FDM partition the system bandwidth into multiple (K) orthogonal subcarriers, which are also commonly referred to as tones, bins, etc. Each subcarrier may be modulated with data. In general, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers may be fixed, and the total number of subcarriers (K) may be dependent on the system bandwidth…”; [0185], Table 1: “LTE supports a single numerology (subcarrier spacing, symbol length, etc.). In contrast NR may support multiple numerologies” | [0190]: “ A primary synchronization signal (PSS) is used by a UE to determine subframe/symbol timing and a physical layer identity. A secondary synchronization signal (SSS) is used by a UE to determine a physical layer cell identity group number and radio frame timing. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a PCI. Based on the PCI, the UE can determine the locations of the aforementioned DL-RS. The physical broadcast channel (PBCH), which carries an MIB, may be logically grouped with the PSS and SSS to form an SSB (also referred to as an SS/PBCH). The MIB provides a number of RBs in the DL system bandwidth and a system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted through the PBCH such as system information blocks (SIBs), and paging messages. | [0192]: “A PRS configuration is defined with reference to the SFN of a cell that transmits PRS. PRS instances, for the first subframe of the N.sub.PRS downlink subframes comprising a first PRS positioning occasion…”; Fig. 5, [0194]: “In some aspects, when a UE receives a PRS configuration index I.sub.PRS in the OTDOA assistance data for a particular cell, the UE may determine the PRS periodicity T.sub.PRS 520 and PRS subframe offset Δ.sub.PRS using Table 2. The UE may then determine the radio frame, subframe, and slot when a PRS is scheduled in the cell (e.g., using equation (1)). The OTDOA assistance data may be determined by, for example, the location server (e.g., location server 230, LMF 270), and includes assistance data for a reference cell, and a number of neighbor cells supported by various base stations.”);
form the function based at least on the configuration; and sending the indication of the function comprises sending the indication of the formed function (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes. Some example embodiments further comprising means for performing: triggering the use of said data augmentation in the event that a number of null data entries in the signal signature matrices is above a threshold and/or when the final estimate has a high degree of uncertainty. Some example embodiments further comprise means for performing: generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0047]: “At operation 24, a location of the user device 12 is estimated on the basis of the radio signals obtained in the operation 22. The position estimate may be generated at the user device 12, at the relevant nodes 14a to 14c and/or at another node (such as a server, e.g. a location management function (LMF)).” | [0054]: “In 3GPP Rel-16, downlink positioning reference signal (PRS) and uplink sounding reference signal (SRS) arrangements were provided for positioning purposes. In 3GPP Rel-17, further developments are being made in New Radio (NR) positioning, including relating to increasing the accuracy of vertical position estimates.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 14, Barbu-Sun disclosed the apparatus according to claim 12, wherein the receiving the measurement report further comprises receiving a result of a check as to whether or not the output is within a range (see Sun [0210]: “For low latency positioning, a gNB may trigger a UL SRS-P via a DCI (e.g., transmitted SRS-P may include repetition or beam-sweeping to enable several gNBs to receive the SRS-P). Alternatively, the gNB may send information regarding aperiodic PRS transmission to the UE (e.g., this configuration may include information about PRS from multiple gNBs to enable the UE to perform timing computations for positioning (UE-based) or for reporting (UE-assisted). While various aspects of the present disclosure relate to DL PRS-based positioning procedures, some or all of such aspects may also apply to UL SRS-P-based positioning procedures.”; [0211]: “…the terms “sounding reference signal”, “SRS” and “SRS-P” refer to any type of reference signal that can be used for positioning…”; [0213]: “Layer-3 (L3) signaling (e.g., RRC or Location Positioning Protocol (LPP)) is typically used to transport reports that comprise location-based data in association with UE-assisted positioning techniques…”; [0219]: “More recently, L1 and L2 signaling has been contemplated for use in association with PRS-based reporting. For example, L1 and L2 signaling is currently used in some systems to transport CSI reports (e.g., reporting of Channel Quality Indications (CQIs), Precoding Matrix Indicators (PMIs), Layer Indicators (Lis), L1-RSRP, etc.)...Linkages (e.g., time offsets) are defined between instances of RSs being measured and corresponding reporting. In some designs, CSI-like reporting of PRS-based measurement data using L1 and L2 signaling may be implemented.”), and using the result to update the function (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes. Some example embodiments further comprising means for performing: triggering the use of said data augmentation in the event that a number of null data entries in the signal signature matrices is above a threshold and/or when the final estimate has a high degree of uncertainty. Some example embodiments further comprise means for performing: generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0117]: “At operation 126, an updated position estimate for the user device is obtained by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 15, Barbu-Sun disclosed the apparatus according to claim 12, wherein the apparatus is further configured to perform: determine a list of weights, wherein individual weights are to be applied to corresponding individual carriers in the output, and sending indication of the list of weights from the further node to the node (see Sun [0271]: “Referring to FIGS. 9-10, in some designs, the UE-feature or BS-feature processing neural network function(s) may be specific to:”; [0273] “a carrier” | [0279]: “Referring to FIGS. 9-10, in some designs, a version of a neural network function may be obtained by the BS at 1010 and sent to the UE at 1020, whereby the neural network undergoes further refinement or modification at the UE. In this case, the neural network function obtained at 910 may correspond to an initial version received from the BS or a version of the neural network function that is further refined at the UE. For example, an initial version of the neural network function (e.g., comprising a set of default weights, offsets, etc. for processing of measurement data into features) may be sent by the BS to the UE in conjunction with training data that is applied by the UE in accordance with machine-learning. In some designs, the initial version of the neural network function may be configured conservatively so as not to override UE-specific parameters that may already be in effect. In this case, the training data may be used so as to accommodate these UE-specific parameters (e.g., the training data may be used to refine the UE-specific parameters rather than simply override such parameters with different values).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe details about the neural network and positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 16, Barbu-Sun disclosed the apparatus according to claim 15, wherein the receiving the measurement report further comprises receiving indication of weights actually used for the positioning information, and wherein the apparatus is further configured to perform: update the list of weights based on the indication of weights actually used (see Sun [0210]: “For low latency positioning, a gNB may trigger a UL SRS-P via a DCI (e.g., transmitted SRS-P may include repetition or beam-sweeping to enable several gNBs to receive the SRS-P). Alternatively, the gNB may send information regarding aperiodic PRS transmission to the UE (e.g., this configuration may include information about PRS from multiple gNBs to enable the UE to perform timing computations for positioning (UE-based) or for reporting (UE-assisted). While various aspects of the present disclosure relate to DL PRS-based positioning procedures, some or all of such aspects may also apply to UL SRS-P-based positioning procedures.”; [0211]: “…the terms “sounding reference signal”, “SRS” and “SRS-P” refer to any type of reference signal that can be used for positioning…”; [0213]: “Layer-3 (L3) signaling (e.g., RRC or Location Positioning Protocol (LPP)) is typically used to transport reports that comprise location-based data in association with UE-assisted positioning techniques…”; [0219]: “More recently, L1 and L2 signaling has been contemplated for use in association with PRS-based reporting. For example, L1 and L2 signaling is currently used in some systems to transport CSI reports (e.g., reporting of Channel Quality Indications (CQIs), Precoding Matrix Indicators (PMIs), Layer Indicators (Lis), L1-RSRP, etc.)...Linkages (e.g., time offsets) are defined between instances of RSs being measured and corresponding reporting. In some designs, CSI-like reporting of PRS-based measurement data using L1 and L2 signaling may be implemented.” | Fig. 9, [0248]: “At 930, UE 302 (e.g., processing system 332, measurement module 342, etc.) determines a positioning estimate (e.g., a WWAN position estimate, a WLAN position estimate, a GNSS position estimate, a sensor-based position estimate, etc.) for the UE based at least in part upon the positioning measurement data and the at least one neural network function. In some designs, at 930, UE 302 may directly feed a channel estimate into the neural network function(s) as an input. In other designs, UE 302 may first extract some features such as time-of-arrival, reference signal time-difference, angle of departure, timing and magnitude of a pre-defined number of peaks in the channel estimate, etc. and feeds such features to the neural network function(s). In some designs, the neural network function(s) may output a likelihood of particular feature(s) being present at particular candidate location(s), in which case a post-processing function of combining the likelihood(s) across all measurements (or features) can be computed into a combined likelihood function, as discussed below in more detail with respect to FIGS. 11-13. For example, if the neural network function(s) indicate that the positioning measurement data is 99.9% likely to be present at a given candidate location and less than 1% chance to be present at any other candidate location, then the given candidate location may be determined as the positioning estimate (e.g., or at least, the given candidate location may be weighted more favorably as the positioning estimate in a positioning algorithm).” | [0252]: “Referring to FIGS. 9-10, in some designs, the at least one neural network function may comprise at least one UE-feature processing neural network function. The UE-feature processing neural network function is used to process positioning measurement features that are based upon a set of positioning measurements measured at the UE (e.g., PRS measurements, etc.). In some designs, the UE-feature processing neural network function may receive one or more UE-side positioning measurement features, and the UE-feature processing neural network function may output likelihoods of the one or more UE-side positioning measurement features being present at one or more candidate positioning estimates for the UE. The outputted (or derived) likelihoods can then be factored into the positioning estimate for the UE (e.g., low-likelihood positioning estimates are excluded or weighted less heavily, high-likelihood positioning estimates are weighted more heavily, etc.)...” | [0261]: “Referring to FIGS. 9-10, in some designs, the at least one neural network function may comprise at least one BS-feature processing neural network function. The BS-feature processing neural network function is used to process positioning measurement features that are based upon a set of positioning measurements measured at the network-side, such as the serving BS or non-serving BS(s) of the UE (e.g., SRS-P measurements, etc.). In some designs, the BS-feature processing neural network function may receive one or more BS-side positioning measurement features, and the BS-feature processing neural network function may output likelihoods of the one or more BS-side positioning measurement features being present at one or more candidate positioning estimates for the UE. The outputted (or derived) likelihoods can then be factored into the positioning estimate for the UE (e.g., low-likelihood positioning estimates are excluded or weighted less heavily, high-likelihood positioning estimates are weighted more heavily, etc.).” | [0279]: “Referring to FIGS. 9-10, in some designs, a version of a neural network function may be obtained by the BS at 1010 and sent to the UE at 1020, whereby the neural network undergoes further refinement or modification at the UE. In this case, the neural network function obtained at 910 may correspond to an initial version received from the BS or a version of the neural network function that is further refined at the UE. For example, an initial version of the neural network function (e.g., comprising a set of default weights, offsets, etc. for processing of measurement data into features) may be sent by the BS to the UE in conjunction with training data that is applied by the UE in accordance with machine-learning. In some designs, the initial version of the neural network function may be configured conservatively so as not to override UE-specific parameters that may already be in effect. In this case, the training data may be used so as to accommodate these UE-specific parameters (e.g., the training data may be used to refine the UE-specific parameters rather than simply override such parameters with different values).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Regarding claim 17, Barbu disclosed a method comprising:
receiving, by a node in a wireless communications system from another node in the wireless communications system, reference signals for determining positioning information corresponding to a user equipment in the wireless communication system (see Barbu [0003]: “…obtaining reference signals (e.g. uplink sounding reference signals) received at a plurality of nodes of a communication system (e.g. a mobile communication system) from a user device…and generating a first three-dimensional position estimate for the user device by applying signals based on the generated signal signature matrices to an input of a model. The nodes may be serving and neighbour nodes of the user device…The first three-dimensional position estimate may be a coarse position estimate.” | [0065]: “In an implementation of the algorithm 40, radio signals (e.g. reference signals, such as sounding reference signals) may be obtained from one of the user devices 52a to 52d at a plurality of nodes of the system 50 (e.g. serving and neighbour nodes of the user device concerned). Signal signature matrices based on real and imaginary components of obtained reference signals (e.g. wideband reference signals) may be obtained and provided to the model in the operation 44 for use in generating a three-dimensional position estimate for the user device, as discussed further below.”);
measuring, by the node, the reference signals (see Barbu [0048]: “Examples of mechanisms for using radio signals transmitted between a user device and one or more nodes of a mobile communication system to estimate the location of that user device comprise:” | [0068]: “Thus, to train a model, a network collects (in the operation 62) uplink radio signals (e.g. reference signals (RS), such as UE-specific uplink sounding reference signals (SRS), as received by the serving and neighbour nodes). In this first “data association” phase, the network can build a mapping between the received reference signals (the input features) and a 3D position (the labels). These data can be used for training machine learning module(s). To generate labelled training data, the network can adopt various strategies, e.g.”; [0069]: “Using live network measurements. In this case, a network may select and assign a set of reference UEs whose 3D positions are known (e.g. communicated by the UEs themselves, as obtained from UE sensors/non-cellular receivers). The network may collect reference UE measurements over a predefined time window and/or until enough labelled training data has been obtained…” | [0073]: “At operation 63, signals from individual user devices as received at communication nodes (e.g. the nodes 54a to 54e) are isolated from one another. This may be achieved using cross-correlation between a known signal transmitted by a particular user device and the signals received at a particular node. For example, a TRP can cross-correlate the received signal with the locally generated copy of the UE-specific transmit signal and analyse whether the specific signature is present in the received signal.” | [0086]: “As discussed above, in operation 63, the node/TRP n computes the cross correlation between the known transmit signal from a UE a, i.e. s(a), and v(n). That cross-correlation is stored (in operation 64) into a row vector with G elements t(n,a)=xcorr{v(n,a), s(a)}. This is the signal signature of UE a at node/TRP n. In addition, the node/TRP may compute a signal-noise ratio (SNR) level of the received signal, i.e. SNR(n,a). The cross-correlation t(n,a) can be labelled with the position of the user device (UE) after discretization [xa, ya, fa].”);
applying, by the node, a function to the measured reference signals to form an output corresponding to a fixed set of input features (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes…generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0115]: “The algorithm 120 starts at operation 122, where data augmentation is triggered. For example, the operation 112 may comprise a determination that a number of null data entries is above a threshold (which determination can be used to trigger the use of data augmentation). It should be noted that other triggers for data augmentation are possible. For example, data augmentation may be triggered if a position estimate (e.g. as generated in the operation 116 of the algorithm 110) has a high degree of uncertainty (e.g. a large variance).”);
determining, by the node, the positioning information of the user equipment using the output (see Barbu [0009]: “…using data augmentation (e.g. using GAN principles) to generate estimated missing data points in said signal signature matrices. The data augmentation may use machine-learning principles to estimate missing data points based on available reference signals and position estimates of the user device relative to said plurality of nodes…generating a second three-dimensional position estimate for the user device by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.” | [0117]: “At operation 126, an updated position estimate for the user device is obtained by applying the generated signal signature matrices, including the estimated missing data points, to the input of said model.”); and
sending a measurement report, comprising indication of the positioning information, from the node toward a further node in the wireless communications system (see Sun combination below).
Barbu did not explicitly disclose “sending a measurement report, comprising indication of the positioning information, from the node toward a further node in the wireless communications system”.
However in a related art, Sun disclosed:
See Sun [0138]: “…Alternatively, the non-co-located physical TRPs may be the serving base station receiving the measurement report from the UE and a neighbor base station whose reference RF signals the UE is measuring. Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.” | [0177]: “Similar to the functionality described in connection with the DL transmission by the base station 304, the processing system 332 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting..”
See Sun [0210]: “For low latency positioning, a gNB may trigger a UL SRS-P via a DCI (e.g., transmitted SRS-P may include repetition or beam-sweeping to enable several gNBs to receive the SRS-P). Alternatively, the gNB may send information regarding aperiodic PRS transmission to the UE (e.g., this configuration may include information about PRS from multiple gNBs to enable the UE to perform timing computations for positioning (UE-based) or for reporting (UE-assisted). While various aspects of the present disclosure relate to DL PRS-based positioning procedures, some or all of such aspects may also apply to UL SRS-P-based positioning procedures.”; [0211]: “…the terms “sounding reference signal”, “SRS” and “SRS-P” refer to any type of reference signal that can be used for positioning…”; [0213]: “Layer-3 (L3) signaling (e.g., RRC or Location Positioning Protocol (LPP)) is typically used to transport reports that comprise location-based data in association with UE-assisted positioning techniques…”; [0219]: “More recently, L1 and L2 signaling has been contemplated for use in association with PRS-based reporting. For example, L1 and L2 signaling is currently used in some systems to transport CSI reports (e.g., reporting of Channel Quality Indications (CQIs), Precoding Matrix Indicators (PMIs), Layer Indicators (Lis), L1-RSRP, etc.)...Linkages (e.g., time offsets) are defined between instances of RSs being measured and corresponding reporting. In some designs, CSI-like reporting of PRS-based measurement data using L1 and L2 signaling may be implemented.”
See Sun Fig. 6, [0223]: “A location server (e.g., location server 230) may send assistance data to the UE 604 that includes an identification of one or more neighbor cells of base stations 602 and configuration information for reference RF signals transmitted by each neighbor cell. Alternatively, the assistance data can originate directly from the base stations 602 themselves (e.g., in periodically broadcasted overhead messages, etc.). Alternatively, the UE 604 can detect neighbor cells of base stations 602 itself without the use of assistance data. The UE 604 (e.g., based in part on the assistance data, if provided) can measure and (optionally) report the OTDOA from individual network nodes and/or RSTDs between reference RF signals received from pairs of network nodes. Using these measurements and the known locations of the measured network nodes (i.e., the base station(s) 602 or antenna(s) that transmitted the reference RF signals that the UE 604 measured), the UE 604 or the location server can determine the distance between the UE 604 and the measured network nodes and thereby calculate the location of the UE 604.”; [0224]: “The term “position estimate” is used herein to refer to an estimate of a position for a UE 604, which may be geographic (e.g., may comprise a latitude, longitude, and possibly altitude) or civic (e.g., may comprise a street address, building designation, or precise point or area within or nearby to a building or street address, such as a particular entrance to a building, a particular room or suite in a building, or a landmark such as a town square).”; [0225]: “…Alternatively, the non-co-located physical transmission points may be the serving base station receiving the measurement report from the UE (e.g., UE 604) and a neighbor base station whose reference RF signals the UE is measuring. Thus, FIG. 6 illustrates an aspect in which base stations 602a and 602b form a DAS/RRH 620. For example, the base station 602a may be the serving base station of the UE 604 and the base station 602b may be a neighbor base station of the UE 604. As such, the base station 602b may be the RRH of the base station 602a. The base stations 602a and 602b may communicate with each other over a wired or wireless link 622.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Barbu and Sun to further describe how to share positioning information. Incorporating Sun’s teachings regarding sending the measurement report including positioning information would enhance signaling efficiencies and reduce latency (see Sun [0004]), improve accuracy of EU positioning estimates and provide quicker UE positioning estimates (see Sun [0243]), while also reducing the amount of positioning measurement data to be transported between the UE and gNB (see Sun [0244]) and overcoming some difficulties related to mathematical modeling for mapping position estimates (see Sun [0242]).
Conclusion
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/ANGELA WIDHALM DE RODRIGUEZ/Examiner, Art Unit 2443