Qualcomm Incorporated

Himaja Kesavareddigari

Kapil Gulati

Kyle Chi Guan

Hong Cheng

Qing Li

Anantharaman Balasubramanian

Mahmoud Ashour

Junyi Li

Chang-Sik Choi

Preeti Kumari

Navid Abedini

Sourjya Dutta

Methods, systems, and devices for wireless communications are described. A network entity may partition one or more user equipments (UEs) within a target area into groups and assign one UE per set to be a lead UE to aggregate shared sensor data without large sidelink overhead from multicasting all sensor data or uplink signaling overhead from aggregating data at the network entity. For example, the network entity may transmit, and a UE may receive, a partition request message corresponding to a hierarchical partitioning scheme. The UE may transmit a partition request feedback message in response to the partition request message, which may include an indication of the availability of the UE to act as the lead UE. The UE may receive a partition assignment message from the network entity, which may include a partition between sets of UEs and an indication of the lead UE for the set of UEs.

Hierarchical partitioning and sensor data aggregation in perceptive wireless communications systems

The apparatus may be a first wireless device configured to receive a first indication that a second wireless device associated with the first wireless device and a network device is classified as providing data of a first type. The apparatus may further be configured to receive a first set of data elements from the second wireless device. The apparatus may be configured to identify, based on the first set of data elements and a second set of data elements available at the first wireless device, that the classification of the second wireless device may be inaccurate and transmit, based on the identification that the classification of the second wireless device may be inaccurate, at least one of a second indication for the second wireless device to transmit a re-evaluation request or a third indication to the second wireless device to terminate a communication session with the first wireless device.

Mitigating the effects of rogue actors in vehicle-to-vehicle perceptive wireless communications

Methods, systems, and devices for wireless communications are described. The described techniques provide for using situational information to aid in beam-based communications between a first user equipment (UE) and a network entity. The first UE may collect data at one or more sensors and may generate the situational information using the collected data. The first UE may then use the situational information in a beam selection procedure to identify suitable candidate beams for communicating with the network entity. The first UE may also transmit the situational information to the network entity in a beam management report, and the network entity may use the situational information to identify suitable candidate beams for communicating with the first UE. In some cases, the network entity may also use the situational information to identify suitable candidate beams for communicating with a second UE associated with the first UE.

Perception-aided beam-based communications

Aspects presented herein may enable wireless communications to be adaptive to a dynamic environment, where wireless devices may manage wireless communications, such as performing beam managements, based at least in part on environmental conditions. In one aspect, a network entity receives, from a sensor device or a UE, a request for an ML data service. The network entity establishes, with the sensor device or the UE, the ML data service based on the request. The network entity receives, from the sensor device, ML data including a set of features extracted from at least one sensor of the sensor device or information indicative of at least one beam for the ML data service. The network entity transmits, to the UE, a beam indication to modify the at least one beam based at least in part on the ML data received from the sensor device during the ML data service.

In-vehicle machine learning service

Aspects presented herein may enable a first UE to utilize inference results provided by at least a second UE to improve its inference accuracy. In one aspect, a first UE establishes an ML inference result sharing session with one or more second UEs for at least one ML inference task. The first UE receives a first set of ML inference results associated with the at least one ML inference task, where the first set of ML inference results is received from the one or more second UEs during the ML inference result sharing session. The first UE estimates an aggregated ML inference result based on at least one of the first set of ML inference results or a second set of ML inference results, where the second set of ML inference results is configured by the first UE for the at least one ML inference task.

User equipment pairing and cooperative machine learning inference result sharing

A first UE and a second UE may exchange information relating to one or more machine learning data services. The one or more machine learning data services may be provided by the second UE. The first UE and the second UE may pair with each other for the one or more machine learning data services. The second UE may perform machine learning inference using at least one machine learning inference model, and may transmit the inference result (e.g., a link quality prediction) associated with a link between a network node and the first UE to the network node. The network node may switch (the direction of) the network node beam if the inference result predicts that the degree of link quality degradation associated with a current network node beam is going to be greater than a prespecified threshold.

In-vehicle machine learning service discovery in perceptive wireless communications

An apparatus may be a UE configured to receive, from a network entity associated with a machine learning procedure, a first indication that a first set of data elements transmitted by the wireless device at a first time is categorized as misinformation and that, based on the categorization of the first set of data elements as misinformation, the network entity will temporarily exclude data from the wireless device from propagation as input for a subsequent machine learning procedure. The apparatus may further be configured to receive a second indication of a set of criteria for requesting a reevaluation of the categorization and transmit, based on meeting one or more criteria in the set of criteria, a second set of data elements to the network entity at a second time.

Mitigating misinforming rogue actors in perceptive wireless communications

Aspects of dynamic and interactive machine learning and feature extraction techniques for performing beam interference management are disclosed. In one aspect, upon entering a coverage area, a UE may transmit a discovery message including one or more machine learning (ML) services for ML service discovery. Based on the ML service discovery, the UE may transmit a session request to establish a data service session between the UE and a network node based on the ML service discovery and other criteria such as extracted features. The network node may receive the ML discovery data and extracted features and aggregate this information with other intelligent network devices to enable the network node to predict a beam blockage during the ML inference data service session. The network node can adapt beam blockage predictions by changing the timing and direction of communications between network entities.

Service discovery and session establishment for machine-learning-based beam prediction in wireless communications

Methods, systems, and devices for wireless communications are described. The described techniques support communication between one or more network entities and user equipment (UEs) via configurable intelligent surfaces. A first UE may transmit, to a second UE associated with a set of configurable intelligent surfaces, first information that indicates a tracking period and a data period that the second UE is to use to manage the configurable intelligent surfaces. The second UE may adjust the configurable intelligent surfaces for the communications between the first UE and network entities during the tracking period and the data period. The second UE may adjust the configurable intelligent surfaces based on measurement reports generated by the first UE based on communication during the tracking and data periods.

Intelligent surface enabled techniques for interference management

Methods, systems, and devices for wireless communications are described. A base station may transmit a request for a first user equipment (UE) to provide sensing capability information associated with UEs other than the first UE (e.g., an indication of one or more sensors associated with the corresponding UE, an orientation of the corresponding UE, a field of view associated with the corresponding UE). Additionally, the first UE may receive, from each of a set of second UEs, indications of the sensing capability information associated with the corresponding second UEs. Based on the sensing capability information indicated for each of the set of second UEs, the first UE select a subset of second UEs and transmit an indication of the sensing capability information associated with the subset of second UEs to the base station.

Measurement reports based on sensing capabilities

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first wireless communication device may determine that a second wireless communication device is to communicate with a third wireless communication device using one or more beams that pass through a target area. The first wireless communication device may obtain information indicating a first object that is approaching the target area and that has a beam blocking size above a threshold. The first wireless communication device may cause the one or more of the second wireless communication device or third wireless communication device to adjust usage of the one or more beams during an expected blockage time in the target area. Numerous other aspects are described.

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Bridgewater, NJ, United States of America

Himaja Kesavareddigari