Qualcomm Incorporated

Diego Calzolari

David Loweth Winslow

Peyman Hesami

John Boro

Guilherme Hoefel

Mustafa Saglam

Tomas Galvez Santaella

Chintan Pravin Turakhia

Certain aspects of the present disclosure provide techniques for adaptively tuning a wireless data transmission system in an electronic device, including receiving one or more operating characteristics of a wireless data transmission system of a device; determining, using a wireless data transmission system configuration model, a target wireless data transmission system configuration based on the one or more operating characteristics; and implementing the target wireless data transmission system configuration in the wireless data transmission system.

Machine learning-assisted adaptive antenna tuning

Techniques and apparatus for tuning a wireless data transmission system in an electronic device. One example method includes receiving a measured impedance value corresponding to a current user interaction state with respect to one or more antennas of a mobile device; determining a user interaction stability state of the mobile device based on the measured impedance value and a set of historical impedance values; classifying the current user interaction state based at least on the measured impedance value and a plurality of impedance values from the set of historical impedance values; and tuning the one or more antennas based on the classified current user interaction state and the determined user interaction stability state of the mobile device.

Avoiding ping-pong between different antenna tuning configurations

Certain aspects of the present disclosure provide techniques for adaptively tuning a wireless data transmission system in an electronic device, including: generating a plurality of measurements of an element of the wireless data transmission system in the electronic device; generating a determined use case for the electronic device based on a use case determination model and the plurality of measurements; determining one or more antenna settings associated with the determined use case; tuning the wireless data transmission system based on the one or more antenna settings; and transmitting data via the wireless data transmission system using the one or more antenna settings.

Machine learning-assisted use case classification and adaptive antenna tuning

Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may implement a dynamic threshold for antenna switching. The dynamic threshold may improve the percentage of time that a UE operates using an 'optimal' antenna (e.g., an antenna with the highest reference signal received power (RSRP)) as compared to a static threshold. For example, a UE may communicate with another device using a first antenna. The UE may track a current communication measurement for the first antenna (e.g., an RSRP value) and may perform an antenna switching test based on the measurement. For the test, the UE may update the dynamic threshold for antenna switching based on a machine learning agent, where the agent may include a neural network that determines the updated dynamic threshold value. The UE may perform a comparison with the dynamic threshold to determine whether to switch operating antennas.

Dynamic thresholds for antenna switching diversity

Aspects of the present disclosure generally relate to wireless communications. In some aspects, a device may receive information identifying a plurality of measurements of a communication metric related to a network at a plurality of time intervals. The device may determine a plurality of colors corresponding to a plurality of values of the communication metric. The plurality of values may correspond to the plurality of measurements of the communication metric at the plurality of time intervals. The device may combine the plurality of colors to generate a composite color for the communication metric.

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San Diego, CA, United States of America

Diego Calzolari