Full metadata
Title
Reconfigurable Intelligent Surfaces for Next-Generation Communication and Sensing Systems
Description
With the rapid development of reflect-arrays and software-defined meta-surfaces, reconfigurable intelligent surfaces (RISs) have been envisioned as promising technologies for next-generation wireless communication and sensing systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals in a smart way to improve the performance of such systems. In RIS-aided communication systems, designing this smart interaction, however, requires acquiring large-dimensional channel knowledge between the RIS and the transmitter/receiver. Acquiring this knowledge is one of the most crucial challenges in RISs as it is associated with large computational and hardware complexity. For RIS-aided sensing systems, it is interesting to first investigate scene depth perception based on millimeter wave (mmWave) multiple-input multiple-output (MIMO) sensing. While mmWave MIMO sensing systems address some critical limitations suffered by optical sensors, realizing these systems possess several key challenges: communication-constrained sensing framework design, beam codebook design, and scene depth estimation challenges. Given the high spatial resolution provided by the RISs, RIS-aided mmWave sensing systems have the potential to improve the scene depth perception, while imposing some key challenges too. In this dissertation, for RIS-aided communication systems, efficient RIS interaction design solutions are proposed by leveraging tools from compressive sensing and deep learning. The achievable rates of these solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead. For RIS-aided sensing systems, a mmWave MIMO based sensing framework is first developed for building accurate depth maps under the constraints imposed by the communication transceivers. Then, a scene depth estimation framework based on RIS-aided sensing is developed for building high-resolution accurate depth maps. Numerical simulations illustrate the promising performance of the proposed solutions, highlighting their potential for next-generation communication and sensing systems.
Date Created
2023
Contributors
- Taha, Abdelrahman (Author)
- Alkhateeb, Ahmed (Thesis advisor)
- Bliss, Daniel (Committee member)
- Tepedelenlioğlu, Cihan (Committee member)
- Michelusi, Nicolò (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
179 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.187375
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2023
Field of study: Electrical Engineering
System Created
- 2023-06-06 07:26:50
System Modified
- 2023-06-06 07:26:56
- 1 year 5 months ago
Additional Formats