Qualcomm Incorporated

Roohollah Amiri

Rajat Prakash

Srinivas Yerramalli

Marwen Zorgui

Xiaoxia Zhang

Taesang Yoo

Mohammed Ali Mohammed Hirzallah

Tao Luo

Junyi Li

Peter S Gaal

Sony Akkarakaran

Wooseok Nam

Yucheng Dai

Mohammad Tarek Fahim

Mohammed Hirzallah

Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) transmits one or more provide capabilities messages to a location server, the one or more provide capabilities messages indicating at least a first set of capabilities of the UE to engage in a downlink radio frequency fingerprint (DL-RFFP) positioning procedure, and receives one or more positioning assistance data messages for the DL-RFFP positioning procedure from the location server, the one or more positioning assistance data messages based at least on the first set of capabilities.

User equipment (UE)-based radio frequency fingerprint (RFFP) positioning with downlink positioning reference signals

Multipath angle estimation and reporting for positioning is described. An apparatus is configured to receive a set of multipath signals for a set of VAs. Each VA in the set of VAs is associated with a corresponding reflecting surface in a UE communication environment. Reflection of the set of multipath signals occurs via the corresponding reflecting surface. The apparatus is configured to estimate at least one AoA associated with the set of multipath signals based on at least one of a set of ToF values associated with the set of multipath signals, a UE location, or a VA location. The apparatus is configured to provide, to a network entity, an indication of multipath information associated with the set of multipath signals for the set of VAs. The multipath information includes the at least one AoA associated with at least one multipath signal in the set of multipath signals.

Multipath angle estimation and reporting for NR positioning

Disclosed are techniques for identifying virtual anchors. In an aspect, a network entity obtains a set of data samples associated with a user equipment (UE), each data sample comprising a location of the UE and a set of multipath components (MPCs) obtained by the UE, and determines whether any MPCs in a subset of non-line-of-sight (NLOS) MPCs selected from at least one set of MPCs are associated with any previously identified virtual anchors in a database of found virtual anchors, wherein one or more first NLOS MPCs are removed from the set of data samples based on the one or more first NLOS MPCs being associated with a previously identified virtual anchor, and wherein one or more second NLOS MPCs are added to the database of found virtual anchors based on the one or more second NLOS MPCs not being associated with any previously identified virtual anchor.

Virtual anchor detection based on channel tap removal

Methods, systems, and devices for wireless communications are described. The method may include receiving, from a network node, a configuration for radar waveform reporting, the radar waveform reporting providing object detection for one or more objects within a detectable range, receiving a radar waveform, and transmitting, to the network node according to the received configuration, a radar reporting message including an indication of one or more parameter values associated with the received radar waveform.

Waveform reporting for cooperative sensing

In an aspect, a network component (e.g., BS, server, etc.) obtains measurement information associated with uplink signal(s) from UE(s), with the uplink signal(s) having reciprocity with one or more downlink beams of wireless node(s) (e.g., TRP, reference UE, etc.). The network component determines (e.g., generates or refines) a measurement (e.g., RFFP-P) model based on the measurement information. The network component provides the measurement (e.g., RFFP-P) model to a target UE. The target UE receives at least one signal (e.g., PRS) on the one or more downlink beams from the wireless node(s). The target UE processes the at least one signal (e.g., predicts target UE location) based at least in part on the measurement (e.g., RFFP-P) model.

Measurement model based on uplink signals with reciprocity to downlink beam

Methods and apparatus for predicting wireless measurements based on virtual access points is described. In some embodiments, a location of a user equipment (UE) may be obtained, and given the location of the UE, an output may be generated using a machine learning model, the output including one or more predicted wireless measurements. The output may be indicative of a wireless channel in a multipath environment. In some variants, the machine learning model may have been trained by obtaining a training dataset including multipath components data and ground truth locations of a wireless device, and performing an optimization with respect to the multipath components data and the ground truth locations. In some implementations, a training output including a predicted multipath component may be produced during the training, and the optimization may include an iterative minimization of an error between at least the predicted multipath component and a labeled multipath component.

Predicting wireless measurements based on virtual access points

Disclosed are techniques for positioning. In an aspect, a network entity receives, from at least one network node, a measurement report including one or more radio frequency fingerprint (RFFP) measurements, wherein the one or more RFFP measurements include at least one RFFP measurement of at least one sidelink channel between a first user equipment (UE) and a second UE, determines one or more locations of a target UE based on the one or more RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

Network-based sidelink-aware radio frequency fingerprinting (RFFP) positioning

Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) determines a positioning measurement of a first multipath component of a radio frequency (RF) signal transmitted by a transmission-reception point (TRP), determines a first additional positioning measurement of a second multipath component of the RF signal, determines a second additional positioning measurement of a third multipath component of the RF signal, and transmits a measurement report to a location server, the measurement report including at least the positioning measurement, the first additional positioning measurement, the second additional positioning measurement, and one or more parameters associated with the first additional positioning measurement and the second additional positioning measurement.

Reporting potential virtual anchor locations for improved positioning

Disclosed are techniques for wireless positioning. In an aspect, a network entity, such as a location server, may determine information characterizing a channel associated with a wireless device within a region of interest. The network entity may determine a location of the wireless device within the region of interest based on that information and a set of radio frequency fingerprints (RFFPs), where each RFFP identifies a geographic location within a region of interest and a channel condition at that geographic location within the region of interest, and where each geographic location within the region of interest is represented by a set of RFFPs having different channel conditions due to different states of at least one reconfigurable intelligent surface (RIS) within the region of interest.

Reconfigurable intelligent surface assisted radio frequency fingerprinting for positioning

In an aspect, a first user equipment (UE) obtains one or more first radio frequency fingerprint (RFFP) measurements of one or more first downlink channels received at the first UE, one or more first sidelink channels received at the first UE, or both, and determines one or more locations of a target UE based on the one or more first RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

User equipment (UE)-based sidelink-aware radio frequency fingerprinting (RFFP) positioning

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit a first sensing signal of a radio frequency (RF) sensing procedure at a first transmit power. The UE may receive, from a base station at least in part in response to the first sensing signal, a power control message for sensing signals of the RF sensing procedure. The power control message may indicate a power control strategy for transmitting sensing signals of the RF sensing procedure. The UE may then transmit, at a second transmit power based on the received power control message, a second sensing signal of the RF sensing procedure.

Power control techniques for cooperative sensing

Certain aspects of the present disclosure provide techniques for adaptive node activation and configuration in cooperative sensing. A method that may be performed by a sensing management function (SnMF) entity includes receiving a first radio frequency (RF) sensing request, from a first entity, for scanning an environment to detect at least a first object, initiating a first RF sensing session in the environment in response to the first RF sensing request, receiving a second RF sensing request, from a second entity, for scanning the environment to detect at least a second object during the first RF sensing session, accommodating the second RF sensing request using output from the first RF sensing session, wherein the first RF sensing session is ongoing, detecting the first and second objects in the environment, and transmitting information about the detected first and second object to the first and second entity, respectively.

Adaptive node activation and configuration in cooperative sensing

Example aspects include a method, apparatus, and computer-readable medium for sidelink-aided radio frequency (RF) sensing at user equipment (UE) of a wireless communication network, comprising receiving a sensing establishment message associated with a sensing session. The aspects further include receiving one or more sensing measurement information obtained by the one or more sidelink UEs corresponding to the sensing session, or a first sensing outcome information corresponding to the sensing session obtained by one of the one or more sidelink UEs based on the one or more sensing measurement information. Additionally, the aspects include transmitting the one or more sensing measurement information obtained by the one or more sidelink UEs corresponding to the sensing session, or a second sensing outcome information corresponding to the sensing session.

Sidelink-aided radio frequency sensing

Disclosed are techniques for positioning. In an aspect, a positioning entity determines a first time of flight (ToF) of a first non-line-of-sight (NLOS) multipath component of a first radio frequency (RF) signal transmitted by a physical transmission-reception point (TRP) to at least a first user equipment (UE), determines a location of a virtual TRP associated with the physical TRP based at least on the first ToF, and determines a location of at least a second UE based, at least in part, on the location of the virtual TRP.

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San Diego, CA, United States of America

Roohollah Amiri