[Paper Review] Simultaneously Transmitting And Reflecting (STAR) RIS Aided Wireless Communications

Xidong Mu, Yuanwei Liu+3 more|arXiv (Cornell University)|Apr 3, 2021

Advanced Wireless Communication TechnologiesEngineering45 references769 citations

TL;DR

This paper proposes a novel Simultaneously Transmitting and Reflecting (STAR) Reconfigurable Intelligent Surface (RIS) for full-space wireless signal manipulation, introducing three operating protocols—energy splitting (ES), mode switching (MS), and time switching (TS)—to enable joint active beamforming at the base station and passive beamforming at the STAR-RIS. The key contribution is a significant reduction in base station power consumption, with ES and TS protocols outperforming conventional RISs in unicast and multicast scenarios, respectively.

ABSTRACT

The novel concept of simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs) is investigated, where the incident wireless signal is divided into transmitted and reflected signals passing into both sides of the space surrounding the surface, thus facilitating a full-space manipulation of signal propagation. Based on the introduced basic signal model of `STAR', three practical operating protocols for STAR-RISs are proposed, namely energy splitting (ES), mode switching (MS), and time switching (TS). Moreover, a STAR-RIS aided downlink communication system is considered for both unicast and multicast transmission, where a multi-antenna base station (BS) sends information to two users, i.e., one on each side of the STAR-RIS. A power consumption minimization problem for the joint optimization of the active beamforming at the BS and the passive transmission and reflection beamforming at the STAR-RIS is formulated for each of the proposed operating protocols, subject to communication rate constraints of the users. For ES, the resulting highly-coupled non-convex optimization problem is solved by an iterative algorithm, which exploits the penalty method and successive convex approximation. Then, the proposed penalty-based iterative algorithm is extended to solve the mixed-integer non-convex optimization problem for MS. For TS, the optimization problem is decomposed into two subproblems, which can be consecutively solved using state-of-the-art algorithms and convex optimization techniques. Finally, our numerical results reveal that: 1) the TS and ES operating protocols are generally preferable for unicast and multicast transmission, respectively; and 2) the required power consumption for both scenarios is significantly reduced by employing the proposed STAR-RIS instead of conventional reflecting/transmiting-only RISs.

Motivation & Objective

• To address the limitation of conventional reflecting-only RISs, which restrict users to one side of the surface.
• To enable full-space signal manipulation by allowing simultaneous transmission and reflection through a novel STAR-RIS architecture.
• To design practical operating protocols (ES, MS, TS) for STAR-RISs that balance energy efficiency and spectral efficiency.
• To minimize base station power consumption via joint active and passive beamforming optimization under quality-of-service (QoS) constraints.
• To provide performance insights and design guidelines for deploying STAR-RISs in unicast and multicast communication scenarios.

Proposed method

• Proposes a basic signal model for STAR-RIS elements, where incident signals are split into transmitted and reflected components via independent transmission and reflection coefficients.
• Introduces three practical operating protocols: energy splitting (ES), mode switching (MS), and time switching (TS), each with distinct hardware and signaling constraints.
• For ES, formulates a non-convex optimization problem and solves it via a penalty-based iterative algorithm using successive convex approximation.
• For MS, extends the penalty-based method to handle mixed-integer non-convex optimization by leveraging the structure of the problem.
• For TS, decomposes the optimization into two subproblems—reflection and transmission phases—solved using convex optimization and state-of-the-art algorithms.
• Implements joint optimization of base station beamforming and STAR-RIS passive beamforming under user rate and power constraints.

Experimental results

Research questions

• RQ1 How can a Reconfigurable Intelligent Surface be designed to simultaneously transmit and reflect signals, enabling full-space signal control?
• RQ2 What are the performance trade-offs among ES, MS, and TS protocols in terms of spectral efficiency, energy efficiency, and implementation complexity?
• RQ3 How does the joint active and passive beamforming design in a STAR-RIS-aided system reduce base station power consumption compared to conventional RISs?
• RQ4 Which operating protocol—ES, MS, or TS—is most suitable for unicast versus multicast transmission under varying QoS requirements?
• RQ5 What are the key design guidelines for deploying STAR-RISs in real-world wireless networks, especially regarding channel estimation and deployment optimization?

Key findings

• The STAR-RIS achieves significantly lower base station power consumption than conventional reflecting/transmitting-only RISs in both unicast and multicast scenarios.
• For unicast transmission, the time switching (TS) protocol outperforms others at low SINR requirements (γ₀ < 12 dB), while energy splitting (ES) is superior at high SINR due to continuous user service.
• For multicast transmission, the ES protocol achieves the best performance, as it efficiently supports both users simultaneously without time division overhead.
• The performance gap between TS and other protocols increases with higher QoS requirements (γ₀ or γc), due to TS’s inefficient time allocation.
• Numerical results confirm that STAR-RISs provide a substantial gain in energy efficiency and system flexibility, especially when users are distributed on both sides of the surface.
• The study highlights the importance of protocol selection based on communication type and QoS constraints, with ES being optimal for multicast and high-SINR unicast, and TS for low-SINR unicast.