公开(公告)号：US20230352847A1

公开(公告)日：2023-11-02

申请号：US18342181

申请日：2023-06-27

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

Inventor： Ahmed ALKHATEEB ,  Abdelrahman TAHA ,  Muhammed 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.

公开(公告)号：US20230123472A1

公开(公告)日：2023-04-20

申请号：US17906198

申请日：2021-03-15

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

Inventor： Ahmed ALKHATEEB ,  Muhammed ALRABEIAH ,  Andrew HREDZAK

IPC: H04B7/06 ,  H04W36/32 ,  H04W64/00 ,  G01S13/86

Abstract: Vision-aided wireless communications systems are provided. Embodiments disclosed herein leverage visual data sensors (such as red-blue-green (RGB)/depth cameras) to adapt communications (e.g., predict beamforming directions) in large-scale antenna arrays, such as used in millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) systems. These systems face two important challenges: (i) a large training overhead associated with selecting an optimal beam and (ii) a reliability challenge due to high sensitivity of mmWave and similar signals to link blockages. Interestingly, most devices that employ mmWave antenna arrays, such as 5G phones, self-driving vehicles, and virtual/augmented reality headsets, will likely also use cameras. Therefore, an efficient olution is presented which uses cameras at base stations and/or handsets to help overcome the beam selection and blockage prediction challenges.