loading page

An Outlook on the Interplay of Machine Learning and Reconfigurable Intelligent Surfaces: An Overview of Opportunities and Limitations
  • +4
  • Lina Mohjazi ,
  • Ahmed Zoha ,
  • Lina Bariah ,
  • Sami Muhaidat ,
  • Paschalis C. Sofotasios ,
  • Muhammad Ali Imran ,
  • Octavia A. Dobre
Lina Mohjazi
University of Glasgow

Corresponding Author:[email protected]

Author Profile
Ahmed Zoha
Author Profile
Lina Bariah
Author Profile
Sami Muhaidat
Author Profile
Paschalis C. Sofotasios
Author Profile
Muhammad Ali Imran
Author Profile
Octavia A. Dobre
Author Profile

Abstract

Recent advances in programmable metasurfaces, also dubbed as reconfigurable intelligent surfaces (RISs), are
envisioned to offer a paradigm shift from uncontrollable to fully tunable and customizable wireless propagation environments, enabling a plethora of new applications and technological trends. Therefore, in view of this cutting edge technological concept, we first review the architecture and electromagnetic waves manipulation functionalities of RISs. We then detail some of the recent advancements that have been made towards realizing these programmable functionalities in wireless communication applications. Furthermore, we elaborate on how machine learning (ML) can address various constraints introduced by real-time deployment of RISs, particularly in terms of latency, storage, energy efficiency, and computation. A review of the state-of-the-art research on the integration of ML with RISs is presented, highlighting their potentials as well as challenges. Finally, the paper concludes by offering a look ahead towards unexplored possibilities of ML mechanisms in the context of RISs.