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An efficient algorithm for multiuser sum-rate maximization of large-scale active RIS-aided MIMO system

Qian Zhang, Mingjie Shao, Qiang Li, Ju Liu

TL;DR

This work tackles sum-rate maximization in a multiuser downlink aided by a large-scale active RIS, a nonconvex problem due to coupling between precoding and RIS coefficients. It introduces a BSUM-based algorithm grounded in a WMMSE-like reformulation with auxiliary variables, achieving (semi) closed-form updates for each block and reducing computational complexity relative to general solvers. The $m{\phi}$-update uses a proximal distance approach with an efficient projection onto the RIS/BR constraint set, while the $\bm{w}$-update applies distance majorization, all within a homotopy framework to enhance convergence. Empirical results demonstrate orders-of-magnitude speedups while maintaining SR performance, highlighting the method’s scalability to massive MIMO and large RIS deployments.

Abstract

Active reconfigurable intelligent surface (RIS) is a new RIS architecture that can reflect and amplify communication signals. It can provide enhanced performance gain compared to the conventional passive RIS systems that can only reflect the signals. On the other hand, the design problem of active RIS-aided systems is more challenging than the passive RIS-aided systems and its efficient algorithms are less studied. In this paper, we consider the sum rate maximization problem in the multiuser massive multiple-input single-output (MISO) downlink with the aid of a large-scale active RIS. Existing approaches for handling this problem usually resort to general optimization solvers and can be computationally prohibitive. We propose an efficient block successive upper bound minimization (BSUM) method, of which each step has a (semi) closed-form update. Thus, the proposed algorithm has an attractive low per-iteration complexity. By simulation, our proposed algorithm consumes much less computation than the existing approaches. In particular, when the MIMO and/or RIS sizes are large, our proposed algorithm can be orders-of-magnitude faster than existing approaches.

An efficient algorithm for multiuser sum-rate maximization of large-scale active RIS-aided MIMO system

TL;DR

This work tackles sum-rate maximization in a multiuser downlink aided by a large-scale active RIS, a nonconvex problem due to coupling between precoding and RIS coefficients. It introduces a BSUM-based algorithm grounded in a WMMSE-like reformulation with auxiliary variables, achieving (semi) closed-form updates for each block and reducing computational complexity relative to general solvers. The -update uses a proximal distance approach with an efficient projection onto the RIS/BR constraint set, while the -update applies distance majorization, all within a homotopy framework to enhance convergence. Empirical results demonstrate orders-of-magnitude speedups while maintaining SR performance, highlighting the method’s scalability to massive MIMO and large RIS deployments.

Abstract

Active reconfigurable intelligent surface (RIS) is a new RIS architecture that can reflect and amplify communication signals. It can provide enhanced performance gain compared to the conventional passive RIS systems that can only reflect the signals. On the other hand, the design problem of active RIS-aided systems is more challenging than the passive RIS-aided systems and its efficient algorithms are less studied. In this paper, we consider the sum rate maximization problem in the multiuser massive multiple-input single-output (MISO) downlink with the aid of a large-scale active RIS. Existing approaches for handling this problem usually resort to general optimization solvers and can be computationally prohibitive. We propose an efficient block successive upper bound minimization (BSUM) method, of which each step has a (semi) closed-form update. Thus, the proposed algorithm has an attractive low per-iteration complexity. By simulation, our proposed algorithm consumes much less computation than the existing approaches. In particular, when the MIMO and/or RIS sizes are large, our proposed algorithm can be orders-of-magnitude faster than existing approaches.
Paper Structure (7 sections, 25 equations, 4 figures, 1 algorithm)

This paper contains 7 sections, 25 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Proposed Algorithm
  4. Reformulation of the Objective Function
  5. Precoding Algorithm
  6. Simulation Results and Conclusion
  7. Proof of Fact 2

Figures (4)

  • Figure 1: Illustration for single-cell active RIS-aided multiuser MIMO downlink scenario.
  • Figure 2: Multiuser sum rate of different algorithms.
  • Figure 3: Average runtime under different $N$; $M=256$.
  • Figure 4: Average runtime under different $M$; $N=128$.