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Robust Consensus-Based Distributed Beamforming for Wideband Cell-free Multi-RIS MISO Systems

Konstantinos D. Katsanos, George C. Alexandropoulos

TL;DR

This work tackles sum-rate maximization in wideband cell-free MISO systems aided by multiple RISs under imperfect CSI. It introduces a consensus-based distributed optimization framework that jointly optimizes active precoding and RIS phase (via capacitors) using stochastic decomposition and dynamic average consensus to achieve RIS consensus across BSs. The proposed method yields superior performance compared with centralized schemes that rely on Lorentzian-type frequency-selectivity models, especially at higher transmit powers, while reducing coordination overhead. The approach is scalable and practical for 6G-era networks, enabling robust, distributed cooperation among BSs and shared RISs with imperfect channel knowledge.

Abstract

The cell-free networking paradigm constitutes a revolutionary architecture for future generations of wireless networks, which has been recently considered in synergy with Reconfigurable Intelligent Surfaces (RISs), a promising physical-layer technology for signal propagation programmability. In this paper, we focus on wideband cell-free multi-RIS-empowered Multiple-Input Single-Output (MISO) systems and present a decentralized cooperative active and passive beamforming scheme, aiming to provide an efficient alternative towards the cooperation overhead of available centralized schemes depending on central processing unit. Considering imperfect channel information availability and realistic frequency selectivity behavior of each RIS's element response, we devise a distributed optimization approach based on consensus updates for the RISs' phase configurations. Our simulation results showcase that the proposed distributed design is superior to centralized schemes that are based on various Lorentzian-type wideband modeling approaches for the RISs.

Robust Consensus-Based Distributed Beamforming for Wideband Cell-free Multi-RIS MISO Systems

TL;DR

This work tackles sum-rate maximization in wideband cell-free MISO systems aided by multiple RISs under imperfect CSI. It introduces a consensus-based distributed optimization framework that jointly optimizes active precoding and RIS phase (via capacitors) using stochastic decomposition and dynamic average consensus to achieve RIS consensus across BSs. The proposed method yields superior performance compared with centralized schemes that rely on Lorentzian-type frequency-selectivity models, especially at higher transmit powers, while reducing coordination overhead. The approach is scalable and practical for 6G-era networks, enabling robust, distributed cooperation among BSs and shared RISs with imperfect channel knowledge.

Abstract

The cell-free networking paradigm constitutes a revolutionary architecture for future generations of wireless networks, which has been recently considered in synergy with Reconfigurable Intelligent Surfaces (RISs), a promising physical-layer technology for signal propagation programmability. In this paper, we focus on wideband cell-free multi-RIS-empowered Multiple-Input Single-Output (MISO) systems and present a decentralized cooperative active and passive beamforming scheme, aiming to provide an efficient alternative towards the cooperation overhead of available centralized schemes depending on central processing unit. Considering imperfect channel information availability and realistic frequency selectivity behavior of each RIS's element response, we devise a distributed optimization approach based on consensus updates for the RISs' phase configurations. Our simulation results showcase that the proposed distributed design is superior to centralized schemes that are based on various Lorentzian-type wideband modeling approaches for the RISs.
Paper Structure (11 sections, 23 equations, 1 figure)

This paper contains 11 sections, 23 equations, 1 figure.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. RIS Element Frequency Response
  4. Received Signal Model
  5. Design Objective and Problem Formulation
  6. Distributed Sum-Rate Maximization
  7. Precoding Optimization at Each $b$-th BS
  8. RIS Phase Configuration Optimization at Each $b$-th BS
  9. Overall Distributed Solution
  10. Numerical Results and Discussion
  11. Conclusion

Figures (1)

  • Figure 1: Achievable sum-rate performance for the considered wideband cell-free MISO system with $B=4$ BSs, each with $N=2$ antennas, and $R=2$ RISs with $M=144$ unit elements each.