Reconfigurable Intelligent Surfaces with Reflection Pattern Modulation: Beamforming Design and Performance Analysis
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Abstract (arXiv)
Recent considerations for reconfigurable intelligent surfaces (RISs) assume that RISs can convey information by reflection without the need of transmit radio frequency chains, which, however, is a challenging task. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation, where the RIS can configure its reflection state for boosting the received signal power via passive beamforming and simultaneously conveying its own information via reflection. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS for the case where the RIS's state information is statistically known by the AP, and propose a high-quality suboptimal solution based on the alternating optimization technique. We analyze the asymptotic outage probability of the proposed scheme under Rayleigh fading channels, for which a closed-form expression is derived. The achievable rate of the proposed scheme is also investigated for the case where the transmitted symbol is drawn from a finite constellation. Simulation results validate the effectiveness of the proposed scheme and reveal the effect of various system parameters on the achievable rate performance. It is shown that the proposed scheme outperforms the conventional RIS-assisted system without information transfer in terms of achievable rate performance.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
3 papers.
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
- 2021RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learningcited by 2
- 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimizationcited by 0
- 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization21 Dec 2025
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces8 May 2022
- 2021RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning5 Aug 2021