公开(公告)号：US20230031124A1

公开(公告)日：2023-02-02

申请号：US17863567

申请日：2022-07-13

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Ahmed Alkhateeb ,  Muhammad Alrabeiah ,  Gouranga Charan

IPC: H04N7/18 ,  H04W64/00 ,  G06V10/77 ,  G06N20/00

Abstract: Wireless transmitter identification in visual scenes is provided. This technology enables important wireless communications and sensing applications such as (i) fast beam/blockage prediction in fifth generation (5G)/sixth generation (6G) systems using camera data, (ii) identifying cars and people in a surveillance camera feed using joint visual and wireless data processing, and (iii) enabling efficient autonomous vehicle communication relying on both the camera and wireless data. This is done by developing multimodal machine learning based frameworks that use the sensory data obtained by visual and wireless sensors. More specifically, given some visual data, an algorithm needs to perform the following: (i) predict whether an object responsible for a received radio signal is present or not, (ii) if it is present, detect which object it is out of the candidate transmitters, and (iii) predict what type of signal the detected object is transmitting.

公开(公告)号：US11728571B2

公开(公告)日：2023-08-15

申请号：US16901899

申请日：2020-06-15

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Ahmed Alkhateeb ,  Abdelrahman Taha ,  Muhammad Alrabeiah

IPC: H01Q15/14

CPC classification number: H01Q15/148

Abstract: Large intelligent surfaces (LISs) with sparse channel sensors are provided. Embodiments described herein provide efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. Consequently, an LIS architecture based on sparse channel sensors is provided where all LIS elements are passive reconfigurable elements except for a few elements that are active (e.g., connected to baseband). Two solutions are developed that design LIS reflection matrices with negligible training overhead. First, compressive sensing tools are leveraged to construct channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. Second, a deep learning-based solution is deployed where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations.