Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
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Abstract (arXiv)
This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33% and 68% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 1 May 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 0; 2025: 1.
6 papers.
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
- 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
- 2021RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learningcited by 2
- 2021Convolutional Autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless Systemscited by 2
- 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
- 2025Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSIcited by 0
- 2025Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI16 Sep 2025
- 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics3 Jan 2023
- 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions2 Nov 2022
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces8 May 2022
- 2021Convolutional Autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless Systems24 Oct 2021
- 2021RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning5 Aug 2021