AIRIS: Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications

Abstract

Reconfigurable intelligent surface (RIS) is an emerging meta-surface that can provide additional communications links through reflecting the signals, and has been recognized as a strong candidate of 6G mobile communications systems. Meanwhile, it has been recently admitted that implementing artificial intelligence (AI) into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments. Besides the conventional model-driven approaches, AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data. Hence, AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching, insufficient resource, hardware impairment, as well as dynamical transmissions. As one of the earliest survey papers, we will introduce the merging of AI and RIS, called AIRIS, over various signal processing topics, including environmental sensing, channel acquisition, beamforming design, and resource scheduling, etc. We will also discuss the challenges of AIRIS and present some interesting future directions.

Machine Learning Approaches for Reconfigurable Intelligent Surfaces: A Survey

“…In [67], the authors discussed the relationship between RL and an RIS-enabled software-controlled environment, as well as the notion of Wireless 2.0, which proposes significant modifications to the existing wireless network. Moreover, the importance of DL-focused RIS technology in communication systems and certain future research directions that are aimed at providing diverse technical discussions are presented [68]- [70].…”

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

“…In this survey paper, the focus is specifically on RL approaches. We invite the readers to consult [143], [144], [145] for tutorials and overviews on deep learning schemes for communication systems based on RISs, which mainly focus on supervised learning methodologies. Herein, we do not attempt to introduce ANNs nor the concept of deep learning; we assume, though, a broad level of familiarity.…”