Arizona State University

Muhammad Alrabeiah

Ahmed Alkhateeb

Abdelrahman Taha

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.

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Tempe, AZ, United States of America

Muhammad Alrabeiah