Reconfigurable Intelligent Surface Empowered Full Duplex Systems: Opportunities and Challenges

TL;DR

This paper investigates the integration of reconfigurable intelligent surfaces, especially STAR-RIS, with full-duplex systems to enhance wireless communication by improving transmission capacity and self-interference cancellation, supported by simulations and deep learning optimization.

Contribution

It introduces the novel application of STAR-RIS in full-duplex systems, demonstrating their potential to improve performance and reduce interference in future wireless networks.

Findings

STAR-RIS enhances transmission capacity in FD systems.

STAR-RIS provides effective self-interference cancellation.

Deep learning optimization improves system performance.

Abstract

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology in wireless communications. Simultaneously transmitting and reflecting RIS (STAR-RISs) in particular have garnered significant attention due to their dual capabilities of simultaneous transmission and reflection, underscoring their potential applications in critical scenarios within the forthcoming sixth-generation (6G) technology landscape. Moreover, full-duplex (FD) systems have emerged as a breakthrough research direction in wireless transmission technology due to their high spectral efficiency. This paper explores the application potential of STAR-RIS in FD systems for future wireless communications, presenting an innovative technology that provides robust self-interference cancellation (SIC) capabilities for FD systems. We utilize the refraction functionality of STAR-RIS enhances the transmission capacity of FD systems, while its reflection functionality is used to eliminate self interference within the FD system. We delve into the applications of two different types of STAR-RIS in FD systems and compare their performance through simulations. Furthermore, we discuss the performance differences of STAR-RIS empowered FD systems under various configurations in a case study, and demonstrate the superiority of the proposed deep learning-based optimization algorithm. Finally, we discuss possible future research directions for STAR-RIS empowered FD systems.