Qualcomm Incorporated

Tom Edward Botterill

Peerapol Tinnakornsrisuphap

Ravi Agarwal

Hussein Metwaly Saad

Jonathan Kies

Ramesh Chandrasekhar

Sebastien Mounier

Yann Lebrun

Simone Merlin

Olufunmilola Omolade Awoniyi-Oteri

Danlu Zhang

Ajit Deepak Gupte

Gerhard Reitmayr

Pushkar Gorur Sheshagiri

Chiranjib Choudhuri

Juan Carlos Bucheli Garcia

A first device may receive, from a second device, perception information for an environment of the first device based at least in part on a perception capability of the second device. The first device may generate a perception associated with a communication by the first device based at least in part on the perception information from the second device, where the perception indicates characteristics of the environment. The first device may adjust a parameter associated with the communication based at least in part on the perception. Numerous other aspects are described.

Perception-assisted wireless communication

Methods, systems, and devices for wireless communications are described. A user equipment (UE), such as a UE associated with an extended reality (XR) device, may obtain frame data from one or more cameras associated with the UE. The UE may determine a path of a beam providing communication between the UE and a base station, determine a location of a base station in communication with the UE, or both, where the base station location may be relative to a location of the UE. The UE may identify, based on the frame data, the path of the beam, the base station location, the UE location, or a combination thereof, a physical blockage to the communication. The UE may perform beam management for the communication based on identifying the physical blockage, so as to mitigate the impact the physical blockage may have on the communications between the UE and the base station.

Techniques for identifying blockages between devices

This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for beam selection and codebook learning techniques based on XR perception information. A UE may receive sensor information indicative of a direction of a current serving beam relative to the UE. The UE may be configured to communicate with a base station based on a BPL associated with the direction of the current serving beam. The UE may perform a measurement for the base station and the UE to communicate over the BPL based on the sensor information indicating that the direction of the current serving beam relative to the UE is different from a previous serving beam direction relative to the UE.

Beam selection and codebook learning based on XR perception

Systems, methods, and non-transitory media are provided for using a wearable ring device for extended reality (XR) functionalities. An example wearable device can include a structure defining a receiving space configured to receive a finger associated with a user, the structure including a first surface configured to contact the finger received via the receiving space; one or more sensors integrated into the structure, the one or more sensors being configured to detect a rotation of at least a portion of the structure about a longitudinal axis of the receiving space; and a wireless transmitter configured to send, to an electronic device, data based on the rotation.

Wearable ring device and user interface processing

Systems, methods, and non-transitory media are provided for tracking operations using data received from a wearable device. An example method can include determining a first position of a wearable device in a physical space; receiving, from the wearable device, position information associated with the wearable device; determining a second position of the wearable device based on the received position information; and tracking, based on the first position and the second position, a movement of the wearable device relative to an electronic device.

Electronic device for tracking objects

Methods, systems, and devices for deep learning based head motion prediction for extended reality are described. The head pose prediction may involve training one or more layers of a machine learning network based on application data and an estimated head motion range associated with the extended reality system. The network may receive one or more bias corrected inertial measurement unit (IMU) measurements based on a sensor. The network may model a relative head pose of the user as a polynomial of time over a prediction interval based on the bias corrected IMU measurements and the trained one or more layers of the machine learning network. The network may determine a future relative head pose of the user based on the polynomial (e.g., which may be used for virtual object generation, display, etc. within an extended reality system).

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San Diego, CA, United States of America

Tom Edward Botterill