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WO2025150009A1 - Mesure de réponse impulsionnelle de canal de liaison descendante pour positionnement d'apprentissage automatique - Google Patents

Mesure de réponse impulsionnelle de canal de liaison descendante pour positionnement d'apprentissage automatique

Info

Publication number
WO2025150009A1
WO2025150009A1 PCT/IB2025/051492 IB2025051492W WO2025150009A1 WO 2025150009 A1 WO2025150009 A1 WO 2025150009A1 IB 2025051492 W IB2025051492 W IB 2025051492W WO 2025150009 A1 WO2025150009 A1 WO 2025150009A1
Authority
WO
WIPO (PCT)
Prior art keywords
positioning
cir
measurements
reference signal
measurement
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/IB2025/051492
Other languages
English (en)
Inventor
Robin Rajan THOMAS
Vahid POURAHMADI
Ahmed HINDY
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Lenovo Singapore Pte Ltd
Original Assignee
Lenovo Singapore Pte Ltd
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Filing date
Publication date
Application filed by Lenovo Singapore Pte Ltd filed Critical Lenovo Singapore Pte Ltd
Publication of WO2025150009A1 publication Critical patent/WO2025150009A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009—Transmission of position information to remote stations
    • G01S5/0018—Transmission from mobile station to base station
    • G01S5/0036—Transmission from mobile station to base station of measured values, i.e. measurement on mobile and position calculation on base station
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0205—Details
    • G01S5/0236—Assistance data, e.g. base station almanac
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0252—Radio frequency fingerprinting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • H04L5/005—Allocation of pilot signals, i.e. of signals known to the receiver of common pilots, i.e. pilots destined for multiple users or terminals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0053—Allocation of signalling, i.e. of overhead other than pilot signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0091—Signalling for the administration of the divided path, e.g. signalling of configuration information
    • H04L5/0094—Indication of how sub-channels of the path are allocated
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
    • H04W88/18—Service support devices; Network management devices

Definitions

  • a transmitted signal in the wireless communications system may include a received signal strength, a delay profile, a power delay profile (e.g., reference signal received power (RSRP), reference signal received path power (RSRPP), a received signal strength indicator (RSSI) of a particular reference signal), and/or a CIR of a received signal.
  • RSRP reference signal received power
  • RSSI received signal strength indicator
  • a CIR of a received signal is a measure of how a wireless channel affects a transmitted signal over time, and can be used to characterize the multipath propagation, delay spread, and frequency selectivity of the channel.
  • aspects of the present disclosure support using artificial intelligence (AI) and/or machine learning (ML), and are directed to ascertaining the measurements, signaling, and features that support location management component (LCM) operations for direct and/or assisted AI and/or ML positioning.
  • a location estimation of a wireless device in the wireless communications system may be performed using AI and/or ML for NR air interface based at least in part on the identifying signature or fingerprint of a received signal that has been transmitted by the wireless device, such as a UE.
  • the location of the UE may be determined based on the radio frequency signature and other features associated with the signal at the particular location.
  • the location may be characterized and represented by the unique channel observations that are measured and/or determined as the signature or fingerprint of a transmitted signal from a wireless device at the particular location. This also supports positioning estimations of wireless device locations in indoor environments.
  • a ML model or algorithm e.g., a neural network, artificial intelligence (AI) algorithms.
  • a positioning equipment e.g., a location management function (LMF)
  • LMF location management function
  • a machine learning model may include AI, a ML model or algorithm, a neural network, and/or any other type of machine learning model to implement the described techniques.
  • a machine learning model refers to a computer representation that is trainable based on inputs to approximate unknown functions.
  • a machine learning model can utilize algorithms to learn from, and make predictions on, inputs of known data (e.g., training and/or reference data) by analyzing the known data to learn to generate outputs.
  • a machine learning model may receive input data as one or more DL CIR measurements on a reference signal and determine positioning estimations, such as a location of a UE in a wireless communications system.
  • Additional aspects of the present disclosure are directed to establishing a downlink signal measurement based on the signature or fingerprint of a transmitted signal, which may depend Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No.
  • the input data (e.g., as one or more DL CIR measurements) are configurable, such as depending on the application or scenario in which an AI and/or ML process (e.g., for training or inference) manages processing and resources overhead for training and/or performing positioning estimation, while maintaining accuracy of the measurements and estimations.
  • the DL CIR measurements support the AI and/or ML positioning techniques, such as based on a signal signature or fingerprinting of the signal.
  • FIG. 1 illustrates an example of a wireless communications system 100 in accordance with aspects of the present disclosure.
  • the wireless communications system 100 may include one or more NE 102, one or more UE 104, and a core network (CN) 106.
  • the wireless communications system 100 may support various radio access technologies.
  • the wireless communications system 100 may support Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 10 technologies, such as time division multiple access (TDMA), frequency division multiple access (FDMA), or code division multiple access (CDMA), etc.
  • TDMA time division multiple access
  • FDMA frequency division multiple access
  • CDMA code division multiple access
  • the one or more NE 102 may be dispersed throughout a geographic region to form the wireless communications system 100.
  • One or more of the NE 102 described herein may be or include or may be referred to as a network node, a base station, a network element, a network function, a network entity, network infrastructure (or infrastructure), a radio access network (RAN), a NodeB, an eNodeB (eNB), a next-generation NodeB (gNB), or other suitable terminology.
  • An NE 102 and a UE 104 may communicate via a communication link, which may be a wireless or wired connection.
  • an NE 102 and a UE 104 may perform wireless communication (e.g., receive signaling, transmit signaling) over a Uu interface.
  • An NE 102 may provide a geographic coverage area for which the NE 102 may support services for one or more UEs 104 within the geographic coverage area.
  • an NE 102 and a UE 104 may support wireless communication of signals related to services (e.g., voice, video, packet data, messaging, broadcast, etc.) according to one or multiple radio access technologies.
  • an NE 102 may be moveable, for example, a satellite associated with a non-terrestrial network (NTN).
  • NTN non-terrestrial network
  • different geographic coverage areas associated with the same or different radio access technologies may overlap, but the different geographic coverage areas may be associated with different NE 102.
  • An ANC may communicate with the one or more UEs 104 through one or more other access network transmission entities, which may be referred to as a radio heads, smart radio heads, or TRPs.
  • the CN 106 may support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions.
  • the CN 106 may be an evolved packet core (EPC), or a 5G core (5GC), which may include a control plane entity that manages access and mobility (e.g., a mobility management entity (MME), an access and mobility management functions (AMF)) and a user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW), a packet data network (PDN) gateway (P-GW), or a user plane function (UPF)).
  • EPC evolved packet core
  • 5GC 5G core
  • MME mobility management entity
  • AMF access and mobility management functions
  • S-GW serving gateway
  • PDN gateway packet data network gateway
  • UPF user plane function
  • the NEs 102 and the UEs 104 may support various frame structures (i.e., multiple frame structures).
  • the NEs 102 and the UEs 104 may support various frame structures based on one or more numerologies.
  • One or more numerologies may be supported in the wireless communications system 100, and a numerology may include a subcarrier spacing and a cyclic prefix.
  • a time interval of a resource (e.g., a communication resource) may be organized according to frames (also referred to as radio frames). Each frame may have a duration, for example, a 10 millisecond (ms) duration. In some implementations, each frame may include multiple subframes.
  • a slot may include 12 symbols.
  • EM electromagnetic
  • FR1 may be used by the NEs 102 and the UEs 104, among other equipment or devices for cellular communications traffic (e.g., control information, data).
  • FR2 may be used by the NEs 102 and the UEs 104, among other equipment or devices for short-range, high data rate capabilities.
  • FR1 may be associated with one or multiple numerologies (e.g., at least three numerologies).
  • FR2 may be associated with one or multiple numerologies (e.g., at least 2 numerologies).
  • a UE 104 receives, from a positioning equipment (e.g., a NE 102 implemented as a positioning equipment), a measurement configuration to conduct one or more DL CIR measurements on a reference signal.
  • the UE 104 transmits, to the positioning equipment for a positioning estimation, the one or more DL CIR measurements performed on the reference signal based on the measurement configuration.
  • a UE 104 is configured as a PRU UE that has a known location, and the positioning equipment is at least one of a location server, a LMF, an additional UE, or a PRU UE.
  • the one or more DL CIR measurements are input data to a machine learning model that determines a location of the UE 104 based on the positioning estimation. The input data is also usable to train the machine learning model to determine the positioning estimation.
  • FIG. 2 illustrates an example of system 200 for NR beam-based positioning in accordance with aspects of the present disclosure.
  • the system 200 illustrates a UE 104 and NEs 102 (e.g., gNBs).
  • the PRS can be transmitted by different base stations (serving and neighboring) using narrow beams over FR1 and FR2 as illustrated in the example system 200, which is relatively different when compared to LTE where the PRS was transmitted across the whole cell.
  • the PRS can be locally associated with a PRS resource identifier (ID) and resource set ID for a base station (e.g., a TRP).
  • UE positioning measurements such as reference signal time difference (RSTD) and PRS reference signal received power (RSRP) measurements are performed on a per beam basis (e.g., based on DL PRS resources, or DL PRS resource sets) as opposed to different cells, as was the case in LTE.
  • RSTD reference signal time difference
  • RSRP PRS reference signal received power
  • RS reference signal
  • Table (2) A reference signal (RS) to measurements mapping is shown below in Table (2) and in Table (3), which indicate the RS to measurements mapping for each of the supported RAT- Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 16 dependent positioning techniques at a UE and at a gNB, respectively.
  • RAT-dependent positioning techniques involve the 3GPP RAT and core network entities to perform a position estimation of a UE, which are differentiated from RAT-independent positioning techniques that rely on the global navigation satellite system (GNSS), inertial measurement unit (IMU) sensor, wireless local area network (WLAN), and Bluetooth technologies for performing target device (UE) positioning.
  • GNSS global navigation satellite system
  • IMU inertial measurement unit
  • WLAN wireless local area network
  • Bluetooth Bluetooth
  • DL/UL Reference UE Measurements To facilitate support of Signals the positioning techniques Rel.16 DL PRS DL RSTD DL-TDOA Rel.16 DL PRS DL PRS RSRP DL-TDOA, DL-AoD, Multi-RTT Rel.16 DL PRS / Rel.16 UE Rx-Tx time difference Multi-RTT SRS for positioning Rel.15 SSB / CSI-RS SS-RSRP(RSRP for RRM), NR E-CID for radio resource SS-RSRQ(for RRM), management (RRM) CSI-RSRP (for RRM), CSI-RSRQ (for RRM), SS-RSRPB (for RRM) Table (2): UE Measurements for RAT-dependent Positioning Techniques DL/UL Reference gNB Measureme To facilitate support of the Signals nts positioning techniques Rel.16 SRS for positioning UL RTOA UL-TDOA Rel.16 SRS for UL SRS-reference signal
  • DL-TDoA downlink time difference of arrival
  • AoD DL-angle of departure
  • E-CID enhanced cell-ID
  • UL-AoA uplink
  • the DL-TDoA positioning method makes use of the measured DL PRS RSRP of downlink signals received from multiple transmission points (TPs), at the UE.
  • the UE measures the DL PRS RSRP of the received signals using assistance data received from the positioning server, and the resulting measurements are used along with other configuration information to locate the UE in relation to the neighboring TPs.
  • the DL AoD positioning method makes use of the measured DL PRS RSRP of downlink signals received from multiple TPs, at the UE.
  • the UE measures the DL PRS RSRP of the received signals using assistance data received from the positioning server, and the resulting measurements are used along with other configuration information to locate the UE in relation to the neighboring TPs.
  • Figure 3 illustrates an example 300 of a multi-cell RTT signaling procedure in accordance with aspects of the present disclosure.
  • the multi-RTT positioning technique makes use of the UE Rx-Tx measurements and DL PRS RSRP of downlink signals received from multiple TRPs, as measured by the UE, as well as the measured gNB Rx-Tx measurements and uplink sounding reference signal (SRS) RSRP (UL SRS-RSRP) at multiple TRPs of uplink signals transmitted from a UE.
  • the UE measures the UE Rx-Tx measurements (and optionally DL PRS RSRP of the received signals) using assistance data received from the positioning server (also referred to herein as a location server, or positioning equipment), and the TRPs the gNB Rx-Tx measurements (and optionally UL SRS-RSRP of the received signals) using assistance data received from the positioning server.
  • the positioning server also referred to herein as a location server, or positioning equipment
  • the measurements are used to determine the RTT at the positioning server, which are used to estimate the location of the UE.
  • the multi-RTT is only supported for UE-assisted and NG-RAN assisted positioning techniques as noted in Table (1).
  • the E-CID positioning technique the position of a UE is estimated with the knowledge of its serving ng-eNB, gNB, and cell, and is based on LTE signals.
  • the information about the serving ng-eNB, gNB, and cell may be obtained by paging, registration, or other methods.
  • DL PRS-RSRP DL PRS reference signal received power
  • DL PRS-RSRP is the linear average over the power contributions (in 8) of the resource elements that carry DL PRS reference signals configured for RSRP measurements within the considered measurement frequency bandwidth.
  • the reference point for the DL PRS-RSRP shall be the antenna connector of the UE.
  • DL PRS-RSRP shall be measured based on the combined signal from antenna elements corresponding to a given receiver branch.
  • the reported DL PRS-RSRP value shall not be lower than the corresponding DL PRS-RSRP of any of the individual receiver branches.
  • DL PRS-RSRPP Reference Signal Received Path Power
  • DL PRS-RSRPP is the power of the linear average of the channel response at the i-th path delay of the resource elements that carry a DL PRS signal configured for the measurement, where DL PRS-RSRPP for the 1st path delay is the power contribution corresponding to the first detected path in time.
  • the reference point for the DL PRS-RSRPP shall be the antenna connector of the UE.
  • DL PRS-RSRPP shall be measured based on the combined signal from antenna elements corresponding to a given receiver branch.
  • TgNB-RX is the transmission-reception point (TRP) received timing of uplink subframe #i containing SRS associated with UE, defined by the first detected path in time.
  • TgNB-TX is the TRP transmit timing of downlink subframe #j that is closest in time to the subframe #i received from the UE.
  • Multiple SRS resources can be used to determine the start of one subframe containing SRS.
  • the reference point for T gNB-RX shall be: - for type 1-C base station: the Rx antenna connector, - for type 1-O or 2-O base station: the Rx antenna (i.e. the center location of the radiating region of the Rx antenna), - for type 1-H base station: the Rx transceiver array boundary connector.
  • the reference point for T gNB-TX shall be: Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 23 - for type 1-C base station TS 38.104]: the Tx antenna connector, - for type 1-O or 2-O base station TS 38.104]: the Tx antenna (i.e.
  • Figure 4 illustrates an example system 400 of a functional framework for a machine learning model and NR air interface, in accordance with aspects of the present disclosure.
  • the functional framework includes multiple processes that enable AI/ML functionality over the air interface.
  • Data collection 402 is a function that provides input data to model training 404, management 406, and inference 408 functions.
  • the DL-PRS fingerprint may be derived based on multiple transmitted DL-PRS resources that include one or more of positioning frequency layers (PFLs), TRPs, DL-PRS resource sets, DL-PRS resources, or a combination thereof.
  • This RF fingerprint represents a unique RF signature of the received DL-PRS at a given location.
  • Figure 6 illustrates an example procedure diagram 600 for using DL-based CIR measurements by a machine learning model to determine and output a location of a UE, in accordance with aspects of the present disclosure.
  • This example procedure diagram 600 represents an overview of the process from the perspective of receiver 602 (e.g., a UE, or a PRU UE that has a Attorney Ref. No.
  • a data collection entity 610 which may include a UE, a PRU UE, or a network entity (e.g., a LMF, a network data analytics function (NWDAF)) may pre-process the DL CIR measurement inputs based on the techniques described for overhead reduction, or remove outliers or add labels (e.g., reference location, timestamp information, CIR quality metrics, etc.) to filter the measurement data.
  • a network entity e.g., a LMF, a network data analytics function (NWDAF)
  • NWDAF network data analytics function
  • the raw measurements e.g., the unfiltered measurements from receiver processing at 606), or the processed input data (e.g., the filtered measurements for overhead reduction at 608)
  • an AI/ML model 614 is provided as input data to an AI/ML model 614.
  • classification or regression techniques, or unsupervised learning techniques are performed with a trained AI/ML model (also referred to herein as a machine learning model) with the goal of determining an output 618 as the location of a UE based on the DL-based CIR measurements.
  • the configured AI/ML model 614 outputs 2D and/or 3D location information of a target UE based on the trained, multi-dimensional CIR signatures or vectors.
  • a CIR measurement is generated (e.g., based on a received DL-PRS signal at a target UE or a PRU UE, which has been transmitted from a gNB or TRP).
  • This raw CIR of a received positioning reference signal may be utilized or the features of this CIR may be extracted, since the CIR is defined as a channel’s response to an impulse signal, which characterizes the wireless communication channel behavior of a pre-defined period of time.
  • the received signal ⁇ at a receiver is given by equation(1): Attorney Ref. No.
  • the CIR fingerprint measurement is a time domain representation given by equation(2), while in other implementations, the discretized CIR can be modelled as a tapped delay line (FIR filter), comprising of time varying coefficients h , characterized by equation(3).
  • the DL-PRS CIR may include the raw DL- PRS, which is generated using a correlation function between the received signal and a reference transmitted DL-PRS signal. This resulting DL-PRS provides an insight on the channel observations over a certain period of time.
  • a full wideband CIR has significant data overhead for transferring such information (e.g., in the case of UE-assisted or LMF-based positioning with a LMF-side model, or direct AI/ML positioning, where a UE or a PRU UE may need to transfer such Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 30 information to the LMF via LTE positioning protocol (LPP) control plane or user plane signaling (e.g., LCS-UPP).
  • LTP LTE positioning protocol
  • user plane signaling e.g., LCS-UPP
  • DL-PRS aggregation may also be applied to receive a wideband CIR.
  • G 41, ... , H8.
  • a 2D location (x 1 ,y 1 ) is given a label 1
  • a 2D location (x 2 ,y 2 ) is given a label 2D location (x q ,y q ) is given by label q, and so on.
  • a 3D location may also be utilized within a given environment.
  • a combination of the TRP location and UE or PRU UE reference location may give rise to a unique location pair that may be associated with a DL-based fingerprint.
  • I J G KL represents the multi-dimensional CIR vector signature, where the columns represent the number of locations over which a channel is viewed, and the rows are the different transmitters, for iterations of channel observations for the different transmitters.
  • a machine learning model may be trained on this input data of channel observations in equation(5): 2 ′?B ⁇ ' ⁇ ?B ⁇ ' ⁇ ?B ⁇ ' ⁇ ' 2′Q 2′R [0090]
  • This I J G KL multi-dimensional CIR vector signature or fingerprint may be collected during a so-called offline phase, where a series of CIR measurements are performed by a UE or PRU UE Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 31 per location point per received signal from a different TP before the deployment of an AI/ML model.
  • the I JKL may also be considered as the raw measurements CIR data, which can be utilized for performing direct AI/ML positioning, such as for a first case of UE-based positioning with a UE-side model, direct AI/ML.
  • RRC_CONNECTED following RRC states RRC_INACTIVE, RRC_IDLE DL PRS CIR (Raw CIR) based on I_ J G KL Definition
  • a DL post-processing window is defined, where a CIR processing window is defined for CIR samples falling within the window duration are reported.
  • Prioritization criteria of the reference locations, ground truth locations, and/or location pairs may be applied, where the DL-based CIR between two or more locations are relatively similar or are not sufficiently unique. Prioritization may be applied to the reference locations or to the ground truth locations which are deemed sufficiently unique. Additionally, or alternatively, a qualitative sorting may be applied, where relative good quality or better quality CIRs are considered, while relative bad quality CIRs are discarded (e.g., a bad quality CIR may be due to noise interference). Additionally, or alternatively, for a number of UEs reporting in a given area, the CIR overhead may be managed by understanding the amount of UEs required to report CIR fingerprint or signatures to avoid overload of the time-frequency resources.
  • An explicit priority may be assigned along with the TRP ID or related identifying information, and signaled to the UE via network signaling (e.g., LPP signaling, such as LPP ProvideAssistanceData or RequestLocationInformation) in order to determine which CIRs originate from which TRP should be measured.
  • LPP signaling such as LPP ProvideAssistanceData or RequestLocationInformation
  • An implicit priority may also be signaled to the UE, where the order of appearance may indicate which TRPs or TRP ID are to be Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No.
  • Locations and/or location pairs which do not provide sufficiently unique DL-based CIR measurements may be explicitly assigned a lower priority or implicitly prioritized in the order of appearance within a list of locations to be measured, or transmit SRS for positioning from, such as in a descending or ascending order appearance.
  • the granularity or spacing between each reference location, ground truth locations, and/or location pair may be adjusted from a network point of view. For example, an area of 100 m 2 (e.g., a factory warehouse) is divided into square grids of 25 grids corresponding to 25 reference locations or location pairs, with a spacing of 2m between each adjacent location, which implies 25 DL-based CIR measurements for each RP.
  • the quality of CIR samples may be determined based on a signal-noise-ratio (SNR), signal-to-interference-plus-noise ratio (SINR), or other received signal quality metrics.
  • SNR signal-noise-ratio
  • SINR signal-to-interference-plus-noise ratio
  • the processor 702 may be configured to operate the memory 704. In some other implementations, the memory 704 may be integrated into the processor 702. The processor 702 may be configured to execute computer-readable instructions stored in the memory 704 to cause the UE 700 to perform various functions of the present disclosure. [0115]
  • the memory 704 may include volatile or non-volatile memory.
  • the memory 704 may store computer-readable, computer-executable code including instructions when executed by the processor 702 cause the UE 700 to perform various functions described herein.
  • the code may be stored in a non-transitory computer-readable medium such as the memory 704 or another type of memory.
  • FIG. 8 illustrates an example of a processor 800 in accordance with aspects of the present disclosure.
  • the processor 800 may be an example of a processor configured to perform various operations in accordance with examples as described herein.
  • the processor 800 may include Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 44 a controller 802 configured to perform various operations in accordance with examples as described herein.
  • the processor 800 may optionally include at least one memory 804, which may be, for example, an L1/L2/L3 cache.
  • the processor 800 may optionally include one or more arithmetic-logic units (ALUs) 806.
  • ALUs arithmetic-logic units
  • One or more of these components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses).
  • the processor 800 may be a processor chipset and include a protocol stack (e.g., a software stack) executed by the processor chipset to perform various operations (e.g., receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) in accordance with examples as described herein.
  • the controller 802 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 804 and determine subsequent instruction(s) to be executed to cause the processor 800 to support various operations in accordance with examples as described herein.
  • the controller 802 may be configured to track memory addresses of instructions associated with the memory 804.
  • the controller 802 may be configured to decode instructions to determine the operation to be performed and the operands involved.
  • the controller 802 may be configured to interpret the instruction and determine control signals to be output to other components of the Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 45 processor 800 to cause the processor 800 to support various operations in accordance with examples as described herein.
  • the memory 804 may store computer-readable, computer-executable code including instructions that, when executed by the processor 800, cause the processor 800 to perform various functions described herein.
  • the code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory.
  • the controller 802 and/or the processor 800 may be configured to execute computer-readable instructions stored in the memory 804 to cause the processor 800 to perform various functions.
  • the processor 800 and/or the controller 802 may be coupled with or to the memory 804, the processor 800, and the controller 802, and may be configured to perform various functions described herein.
  • the processor 800 may include multiple processors and the memory 804 may include multiple memories.
  • One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions herein.
  • the one or more ALUs 806 may be configured to support various operations in accordance with examples as described herein.
  • the one or more ALUs 806 may reside within or on a processor chipset (e.g., the processor 800).
  • the one or more ALUs 806 may reside external to the processor chipset (e.g., the processor 800).
  • One or more ALUs 806 may perform one or more computations such as addition, subtraction, multiplication, and division on data.
  • one or more ALUs 806 may receive input operands and an operation code, which determines an operation to be executed.
  • One or more ALUs 806 may be configured with a variety of logical and arithmetic circuits, including adders, subtractors, shifters, and logic gates, to process and manipulate the data according to the operation. Additionally, or alternatively, the one or more ALUs 806 may support logical operations such as Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 46 AND, OR, exclusive-OR (XOR), not-OR (NOR), and not-AND (NAND), enabling the one or more ALUs 806 to handle conditional operations, comparisons, and bitwise operations.
  • logical operations such as Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 46 AND, OR, exclusive-OR (XOR), not-OR (NOR), and not-AND (N
  • the processor 800 may support wireless communication in accordance with examples as disclosed herein.
  • the processor 800 may be configured to or operable to support at least one controller (e.g., the controller 802) coupled with at least one memory (e.g., the memory 804) and configured to cause the processor to receive, from a positioning equipment, a measurement configuration to conduct one or more DL CIR measurements on a reference signal; and transmit, to the positioning equipment for a positioning estimation, the one or more DL CIR measurements performed on the reference signal based at least in part on the measurement configuration.
  • the processor 800 may be configured to or operable to support any one or combination of the measurement configuration indicates one or more types of DL CIRs to be measured.
  • the one or more DL CIR measurements are sampled with respect to a transmission reference point depending on maximum excess delay spread of a channel and an overall bandwidth of a transmitted DL PRS.
  • the at least one controller is configured to cause the processor to report the set of measurement samples as a configuration of at least one of a power threshold or a power interval.
  • the at least one Attorney Ref. No. SMM920230209-WO-PCT Lenovo Ref. No. SMM920230209-WO-PCT 47 controller is configured to cause the processor to report the set of measurement samples as part of a configured logarithmic transformation that removes measurement samples which are not usable as input data to a machine learning model that determines a location of a UE based on the positioning estimation.
  • a number of the one or more DL CIR measurements to be conducted is reduced based at least in part on one or more of a prioritization of TRPs to be measured, one or more quality metrics of the CIR, or a number of additional UEs reporting the one or more DL CIR measurements.
  • the at least one controller is configured to cause the processor to transmit at least one of the one or more DL CIR measurements and associated one or more measurement sample quality metrics.
  • the at least one controller is configured to cause the processor to perform the one or more DL CIR measurements on the reference signal based at least in part on a capability to perform the one or more DL CIR measurements.
  • Figure 9 illustrates an example of a positioning equipment 900 in accordance with aspects of the present disclosure.
  • the reference signal is at least one of a DL-PRS, a SSB, a CSI-RS, a DM-RS, a TRS, or a PT-RS.
  • the configuration parameters of the measurement configuration to conduct the one or more DL CIR measurements comprises one or more of complex values, time domain samples, a sampling frequency, an amplitude, gains, or a number of paths.
  • the one or more DL CIR measurements include CIR measurement information determined over a configured bandwidth of the reference signal.
  • the one or more DL CIR measurements are sampled to reduce a number of the one or more DL CIR measurements to a set of measurement samples.
  • the one or more DL CIR measurements are performed within a defined Attorney Ref. No.
  • the positioning equipment 900 may have more than one transceiver 908.
  • the transceiver 908 may represent a wireless transceiver.
  • the transceiver 908 may include one or more receiver chains 910, one or more transmitter chains 912, or a combination thereof.
  • a receiver chain 910 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium.
  • the receiver chain 910 may include one or more antennas to receive a signal over the air or wireless medium.
  • the receiver chain 910 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal.
  • LNA low-noise amplifier
  • the method may include transmitting, to the positioning equipment for a positioning estimation, the one or more DL CIR measurements performed on the reference signal based at least in part on the measurement configuration.
  • the operations of 1004 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1004 may be performed by a UE as described with reference to Figure 7.
  • Figure 11 illustrates a flowchart of a method 1100 in accordance with aspects of the present disclosure. The operations of the method may be implemented by a positioning equipment as described herein. In some implementations, the positioning equipment may execute a set of instructions to control the function elements of the positioning equipment to perform the described functions.
  • the method may include receiving, from the UE, the one or more DL CIR measurements performed on the reference signal at the UE based at least in part on the measurement configuration.
  • the operations of 1104 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1104 may be performed by a positioning equipment as described with reference to Figure 9.
  • the method may include performing a positioning estimation based at least in part on the one or more DL CIR measurements.
  • the operations of 1106 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1106 may be performed a positioning equipment as described with reference to Figure 9.

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Abstract

Divers aspects de la présente divulgation concernent une mesure de réponse impulsionnelle de canal de liaison descendante (CIR DL) pour un positionnement d'apprentissage automatique. Un appareil, tel qu'un UE, reçoit en provenance d'un équipement de positionnement, une configuration de mesure pour effectuer des mesures de CIR DL sur un signal de référence. L'UE transmet à l'équipement de positionnement, pour qu'il effectue une estimation de positionnement, les mesures de CIR DL effectuées sur le signal de référence sur la base de la configuration de mesure. L'équipement de positionnement transmet à l'UE la configuration de mesure pour effectuer les mesures de CIR DL sur un signal de référence. L'équipement de positionnement reçoit en provenance de l'UE les mesures de CIR DL effectuées sur le signal de référence au niveau de l'UE sur la base de la configuration de mesure. L'équipement de positionnement effectue une estimation de positionnement sur la base des mesures de CIR DL et détermine un emplacement de l'UE sur la base de l'estimation de positionnement.
PCT/IB2025/051492 2024-02-16 2025-02-13 Mesure de réponse impulsionnelle de canal de liaison descendante pour positionnement d'apprentissage automatique Pending WO2025150009A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200333424A1 (en) * 2018-01-05 2020-10-22 Huawei Technologies Co., Ltd. Method, apparatus, and system for positioning terminal device
US20230179953A1 (en) * 2021-12-06 2023-06-08 Qualcomm Incorporated Beam-based machine learning-enabled rffp positioning
US20230350002A1 (en) * 2022-04-29 2023-11-02 Qualcomm Incorporated User equipment (ue)-based radio frequency fingerprint (rffp) positioning with downlink positioning reference signals
WO2024027939A1 (fr) * 2022-08-05 2024-02-08 Lenovo (Singapore) Pte. Ltd Entraînement de modèles de positionnement à apprentissage automatique dans un réseau de communication sans fil

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200333424A1 (en) * 2018-01-05 2020-10-22 Huawei Technologies Co., Ltd. Method, apparatus, and system for positioning terminal device
US20230179953A1 (en) * 2021-12-06 2023-06-08 Qualcomm Incorporated Beam-based machine learning-enabled rffp positioning
US20230350002A1 (en) * 2022-04-29 2023-11-02 Qualcomm Incorporated User equipment (ue)-based radio frequency fingerprint (rffp) positioning with downlink positioning reference signals
WO2024027939A1 (fr) * 2022-08-05 2024-02-08 Lenovo (Singapore) Pte. Ltd Entraînement de modèles de positionnement à apprentissage automatique dans un réseau de communication sans fil

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