Qualcomm Incorporated

Mahmoud Ashour

Kapil Gulati

Kyle Chi Guan

Himaja Kesavareddigari

Anantharaman Balasubramanian

Hong Cheng

Qing Li

Preeti Kumari

Navid Abedini

Sourjya Dutta

Libin Liu

Aspects are provided which allow a UE to achieve sidelink parameter coordination through various signaling in clustered FL or peer-to-peer FL. The UE provides a first ML model information update and a measurement associated with the update. The UE obtains an aggregated ML model information update aggregating the first update with a second ML model information update of either a first network node in a FL cluster including the UE or a second network node in a second FL cluster. The UE provides a sidelink communication parameter in SCI to a network node, which parameter is an output of an ML model associated with the aggregated ML model information update. As a result, UEs may derive common SCI parameters from their updated ML models to apply in sidelink communications, thereby leading to maximized packet reception rate, maximized throughput, or minimized latency in communication.

Methods for enhanced sidelink communications with clustered or peer-to-peer federated learning

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 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

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

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.

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San Diego, CA, United States of America

Mahmoud Ashour