On the Impact of Control Signaling in RIS-Empowered Wireless Communications
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Abstract (arXiv)
The research on Reconfigurable Intelligent Surfaces (RISs) has dominantly been focused on physical-layer aspects and analyses of the achievable adaptation of the wireless propagation environment. Compared to that, questions related to system-level integration of RISs have received less attention. We address this research gap by analyzing the necessary control/signaling operations that are necessary to integrate RIS as a new type of wireless infrastructure element. We build a general model for evaluating the impact of control operations along two dimensions: i) the allocated bandwidth of the control channels (in-band and out-of-band), and ii) the rate selection for the data channel (multiplexing or diversity). Specifically, the second dimension results in two generic transmission schemes, one based on channel estimation and the subsequent optimization of the RIS, while the other is based on sweeping through predefined RIS phase configurations. We analyze the communication performance in multiple setups built along these two dimensions. While necessarily simplified, our analysis reveals the basic trade-offs in RIS-assisted communication and the associated control operations. The main contribution of the paper is a methodology for systematic evaluation of the control overhead in RIS-aided networks, regardless of the specific control schemes used.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2023.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networkscited by 7
- 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimizationcited by 0
- 2026Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approachcited by 0
- 2026Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach30 Jun 2026
- 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization21 Dec 2025
- 2025Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networks31 Mar 2025