A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network
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Abstract (arXiv)
Thanks to the strong ability against the inter-cell interference, cell-free network is considered as a promising technique to improve network capacity. However, further capacity improvement requires to deploy more base stations (BSs) with high cost and power consumption. To address this issue, inspired by the recently developed reconfigurable intelligent surface (RIS) technique, we propose the concept of RIS-aided cell-free network to improve the capacity with low cost and power consumption. The key idea is to replace some of the required BSs by low-cost and energy-efficient RISs. Then, in a wideband RIS-aided cell-free network, we formulate the problem of joint precoding design at BSs and RISs to maximize the network capacity. Due to the non-convexity and high complexity of the formulated problem, we develop an alternating optimization framework to solve this challenging problem. In particular, we decouple this problem via fractional programming, and solve the subproblems alternatively. Note that most of the scenarios considered in existing works are special cases of the general scenario studied in this paper, and the proposed joint precoding framework can serve as a general solution to maximize the capacity in most existing RIS-aided scenarios. Finally, simulation results demonstrate that, compared with the conventional cell-free network, the network capacity under the proposed scheme can be improved significantly.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2020.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 1.
5 papers.
- 2023Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systemscited by 3
- 2022Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Networkcited by 0
- 2022Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO Systemcited by 0
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approachcited by 0
- 2025Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimizationcited by 0
- 2025Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization1 Dec 2025
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach11 Apr 2023
- 2023Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems10 Feb 2023
- 2022Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System24 Dec 2022
- 2022Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network8 Jan 2022