End-to-End Learning for RIS Profile Design and Channel Parameter Estimation under Pixel Fa...

Reconfigurable intelligent surfaces (RISs) have emerged as a transformative technology for sixth-generation (6G) communication networks, offering the ability to dynamically shape wireless propagation environments and thus efficiently enhance received signal quality. However, practical implementation of RIS faces challenges, including potential failures of individual elements (pixels), which can degrade the performance significantly. This paper leverages autoencoders and end-to-end (E2E) learning in RIS-aided systems to jointly optimize the RIS phase profiles and receiver angle-of-departure (AoD) estimation in the presence of pixel failures. The proposed E2E approach demonstrates resilience against practical pixel errors while is shown to achieve performance close to the fundamental bounds, thereby advancing the state-of-the-art in RIS-aided systems towards the 6G era.

End-to-End Learning for RIS Profile Design and Channel Parameter Estimation under Pixel Failures

Mehmet C. Ilter, Tampere University; Furkan Keskin, Chalmers University; José Miguel Mateos-Ramos, Chalmers University of Technology, Sweden; Christian Häger, Chalmers University of Technology; Mikko Valkama, Tampere University; Henk Wymeersch, Chalmers University of Technology