Qualcomm Incorporated

Vamsi Vegunta

Simone Merlin

Bibhu P Mohanty

Brian Michael Buesker

Harshit Joshi

Raamkumar Balamurthi

Hemanth Sampath

Yanjun Sun

Daniel Hendricus Franciscus Dijkman

Farhad Ghazvinian Zanjani

Danyang Cong

Nitin Ravinder

Ilia Karmanov

Yuan Zhou

Danny Jen

Eric Vanbuhler

Ishaque Ashar Kadampot

Joseph Binamira Soriaga

Jamie Menjay Lin

Fatih Murat Porikli

Rui Liang

This disclosure describes systems, devices, apparatus, and methods, including computer programs encoded on storage media, for controlling a network with one or more access points (APs). A controller may be configured to schedule at least some resources of the one or more APs to improve network efficiency. The APs may measure and report a variety of information and statistics to the controller. The controller may determine one or more transmission parameters and one or more operations a given AP is permitted to perform during a respective time interval. The controller may communicate the one or more transmission parameters using an indication, such as a message. At least one AP associated with the controller may communicate with stations (STAs) using the one or more transmission parameters.

Techniques for controlling a network

Disclosed are systems, methods, and non-transitory media for performing passive radio frequency (RF) location detection operations. In some aspects, RF data, such as RF signals including channel state information (CSI), can be received from a wireless device. The RF data can be provided to a self-supervised machine-learning architecture that is configured to perform object location estimation.

Self-supervised passive positioning using wireless data

Disclosed are systems, methods, and non-transitory media for performing radio frequency sensing detection operations. For instance, radio frequency data can be received that is associated with at least one wireless device. The radio frequency data can be based on radio frequency signals reflected from a first object and received by the at least one wireless device. Training label data can also be obtained (e.g., from a labeling device, from the at least one wireless device, etc.). The training label data can be based at least in part on the first object and input data (e.g., received by the labeling device, the at least one wireless device, etc.). A sensing model can be generated based on the radio frequency data and the training label data.

Passive positioning with radio frequency sensing labels

Disclosed are systems, methods, and non-transitory media for sensing radio frequency signals. For instance, radio frequency data can be received by an apparatus and from at least one wireless device in an environment. Based on the radio frequency data received from the at least one wireless device, the apparatus can determine sensing coverage of the at least one wireless device. The apparatus can further provide the determined sensing coverage and a position of at least one device to a user device.

Passive positioning with radio frequency sensing

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San Diego, CA, United States of America

Vamsi Vegunta