Reconfigurable Intelligent Surface Empowered Full Duplex Systems: Opportunities and Challenges

TL;DR

This work addresses the core challenge of achieving high spectral efficiency in full-duplex wireless systems by leveraging reconfigurable intelligent surfaces that simultaneously transmit and reflect (STAR-RIS). It introduces two STAR-RIS designs, ES-RIS and MS-RIS, and proposes a two-tier generative deep-learning framework to jointly optimize STAR-RIS configurations and FD beamforming, demonstrating improved self-interference cancellation (SIC) and data rates as the number of RIS elements grows. Case-study results indicate near-optimal configurations can be learned to achieve substantial rates (e.g., approximately $R \approx 13$ bps/Hz under favorable conditions) with ES-RIS outperforming MS-RIS at higher complexity, while larger STAR-RIS–FD distances degrade gains. The paper outlines future directions, including channel estimation, dual-sided/active RIS, integrated sensing and communication, physical-layer security, and space–air–ground network integration, highlighting STAR-RIS as a key enabler for robust, high-throughput 6GFD systems.

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.

Figures (6)

• Figure 1: The structure of FD, RIS and STAR-RIS systems.
• Figure 2: ES-RIS in FD systems.
• Figure 3: MS-RIS in FD systems.
• Figure 4: The proposed generative deep learning algorithm for STAR-RIS empowered FD systems.
• Figure 5: Received data rate at Bob with different number of STAR-RIS elements.