Qualcomm Incorporated

Mohammed Ali Mohammed Hirzallah

Srinivas Yerramalli

Xiaoxia Zhang

Marwen Zorgui

Rajat Prakash

Taesang Yoo

Arumugam Chendamarai Kannan

Roohollah Amiri

Tao Luo

Ahmed Attia Abotabl

Aleksandar Damnjanovic

Vinay Chande

Marco Papaleo

Giovanni Chisci

Jing X Sun

Vasanthan Raghavan

Ahmed Elshafie

Mohammad Tarek Fahim

Aspects described herein relate to adapting one of a parameter for transmitting a signal as a narrow beam in a wireless network or a corresponding threshold based on an operating bandwidth. A node can determine whether a condition is met for narrow beam channel access based on the bandwidth-adjusted (or bandwidth-specific) parameter or threshold.

Techniques for normalizing narrow beam channel access parameters based on bandwidth in wireless communications

In some implementations, a base device may determine a quali-colocation (QCL) relation between a first wireless reference signal transmitted by a first sidelink (SL) anchor and a second wireless reference signal transmitted by a second SL anchor, wherein: the first SL anchor and the second SL anchor comprise UEs separate from the receiving UE, and the first wireless reference signal and the second wireless reference signal are transmitted for positioning of the receiving UE, performing radio frequency (RF) sensing, or both. The base device may wirelessly send, from the base device to the receiving UE, the mSL-QCL information, wherein the mSL-QCL information is indicative of the QCL relation between the first wireless reference signal and the second wireless reference signal.

Multi-anchor sidelink quasi-colocation indication and configuration for enhanced positioning and sensing

Wireless communications systems and methods related to accessing a channel based on an interference condition in a wireless communication network are provided. For example, a method of wireless communication performed by a wireless communications device may include receiving, from a second wireless communication device, one or more signals associated with a beam parameter, determining, at each of a plurality of locations, a signal measurement for at least one received signal of the one or more received signals, and determining, based at least in part on a cumulative distribution of the signal measurements at the plurality of locations and a reference probability distribution, whether the second wireless communication device satisfies an interference condition. Other features are also claimed and described.

Divergence statistical approach for narrow beam-based channel access

Methods, systems, and devices for wireless communications are described. The method may include the user equipment (UE) receiving control signaling indicating a set of schemes for reporting an environmental state associated with the UE and measuring one or more channel characteristics according to a scheme of the set of schemes. Moreover, the UE may transmit a report indicating the environmental state associated with the UE based on measuring the one or more channel characteristics. A machine learning model implemented by the UE, a network entity, or both is based on the indicated environmental state associated with the UE.

Reporting environmental states of a user equipment

In an aspect, a method of wireless communication performed by a user equipment (UE) includes receiving first assistance data indicating a first set of network devices, wherein the first assistance data indicates that the first set of network device are to be used for obtaining radio frequency fingerprint positioning (RFFP) measurements during a model monitoring occasion in which performance of a positioning model is to be monitored; receiving second assistance data indicating a second set of network devices for obtaining a second set of RFFP measurements during a positioning occasion in which the performance of the positioning model is not monitored; and measuring position reference signals (PRS) of the first set of network devices during the model monitoring occasion.

Node configuration for positioning model monitoring

Methods performed by a network node includes obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with known positioning parameters associated with a known location of a user equipment (UE) are described. Each RFFP measurement of the plurality of RFFP measurements is further associated with a transmission point configuration of a plurality of transmission point configurations; and training a positioning model to provide an estimate of one or more positioning parameters associated with a location of the UE. The training of the positioning model is based on the plurality of RFFP measurements, known positioning parameters associated with the known location, and the plurality of transmission point configurations.

Positioning model based on multiple transmission point and/or multiple bandwidth configurations

Aspects presented herein may enable an LMF to configure AI/ML-based techniques/measurements for a set of base stations/TRPs to improve the performance of UL-based positioning. In one aspect, a network entity transmits a configuration to at least one network node to configure the at least one network node to perform an UL-based positioning measurement with at least one ML model, where each of the at least one ML model is associated with a corresponding ML model ID in which the at least one network node uses for the UL-based positioning measurement, where the UL-based positioning measurement is for a set of SRSs from a UE. The network entity receives the UL-based positioning measurement for the set of SRSs from the at least one network node. The network entity estimates a position of the UE based on the UL-based positioning measurement for the set of SRSs.

ML-based measurements for uplink 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 communication are described. Aspects of the disclosure describe narrow beam-based channel access that enables a device to communicate in a shared radio frequency spectrum band without performing channel access procedures. Specifically, aspects of the disclosure describe techniques for defining one or more directional beams as a narrow beam, where the relative narrowness of the beam may be determined in the context of interference (e.g., as opposed to being defined from a geometric perspective). For example, a particular beam may be determined to be a narrow beam, and therefore associated with communications in shared radio frequency spectrum bands without channel access procedures, based on one or more metrics and a number of spatial streams associated with the beam. A device may use such narrow beams for communications without channel access procedures in shared radio frequency spectrum bands.

Directional channel access using a narrow beam with multiple spatial streams

In some implementations, an example method of wireless communication at a base station for wireless positioning a user equipment (UE) may comprise: transmitting, to a different device, assistance indicating a location of antenna reference points (ARPs) of the base station for a first set of TRPs configured for receiving uplink (UL) sounding reference signals (SRSs) different from a location of antenna reference points (ARPs) of the base station for a second set of TRPs configured for transmitting downlink (DL) position reference signals (PRSs). The method also comprises performing a measurement of a UL reference signal received by the first set of TRPs. And the method further comprises transmitting the measurement to the different device.

Location indication for uplink resources for positioning

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, systems, and devices for wireless communications are described. In some wireless communications systems, a base station may transmit a set of reference signals spanning a channel to a user equipment (UE), where each of the reference signals is associated with one of a set of transmit beams. The UE may measure a signal quality of each of the set of reference signals. In some cases, each measured signal quality may correspond to one of a set of subbands of the channel and one of the set of transmit beams. The UE may then transmit an indication of at least one signal quality associated with one of the set of subbands and one of the set of transmit beams. In some cases, the UE may indicate the at least one signal quality according to a configuration that may be indicated by the base station or selected by the UE.

Subband reference signal measurements

In an aspect, a method of wireless communication performed by a user equipment (UE) includes receiving a positioning model codepoint (PMC) index of a plurality of PMC indexes, wherein each PMC index of the plurality of PMC indexes is mapped to a corresponding positioning model of at least a subset of a plurality of positioning models configured at the UE; and applying a positioning model, mapped to the PMC index, to one or more RFFP positioning measurements to obtain one or more position parameters corresponding to a location of the UE during a positioning session.

Positioning model codepoint configuration

Disclosed are techniques for training a position estimation module. In an aspect, a first network entity obtains a plurality of positioning measurements, obtains a plurality of positions of one or more user equipments (UEs), the plurality of positions determined based on the plurality of positioning measurements, stores the plurality of positioning measurements as a plurality of features and the plurality of positions as a plurality of labels corresponding to the plurality of features, and trains the position estimation module with the plurality of features and the plurality of labels to determine a position of a UE from positioning measurements taken by the UE.

Data gathering and data selection to train a machine learning algorithm

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.

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San Diego, CA, United States of America

Mohammed Ali Mohammed Hirzallah