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Joint Beamforming Optimization and Mode Selection for RDARS-Aided MIMO Systems

Jintao Wang, Chengzhi Ma, Shiqi Gong, Xi Yang, Shaodan Ma

TL;DR

This work studies RDARS, a hybrid architecture that jointly leverages distribution, reflection, and selection gains to improve uplink MIMO performance. It formulates a mixed-integer, non-convex MSE-minimization problem over receive beamforming, reflection phases, and channel-aware element placement, and tackles it with an inexact BCD-PDD algorithm and a low-complexity greedy-search AO method. The proposed methods demonstrate substantial performance gains over conventional RIS and DAS systems, with insights into the optimal number and placement of connection-mode elements and the impact of channel conditions. The results offer practical deployment guidelines and scalable optimization techniques for implementing RDARS in future 6G networks.

Abstract

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution for green communications in 6G. However, its further extensive use has been greatly limited due to its fully passive characteristics. Considering the appealing distribution gains of distributed antenna systems (DAS), a flexible reconfigurable architecture called reconfigurable distributed antenna and reflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two special cases and maintains the advantages of distributed antennas while reducing the hardware cost by replacing some active antennas with low-cost passive reflecting surfaces. In this paper, we present a RDARS-aided uplink multi-user communication system and investigate the system transmission reliability with the newly proposed architecture. Specifically, in addition to the distribution gain and the reflection gain provided by the connection and reflection modes, respectively, we also consider the dynamic mode switching of each element which introduces an additional degree of freedom (DoF) and thus results in a selection gain. As such, we aim to minimize the total sum mean-square-error (MSE) of all data streams by jointly optimizing the receive beamforming matrix, the reflection phase shifts and the channel-aware placement of elements in the connection mode. Numerical results demonstrate the superiority of the proposed architecture compared to the conventional fully passive RIS or DAS. Furthermore, some insights about the practical implementation of RDARS are provided.

Joint Beamforming Optimization and Mode Selection for RDARS-Aided MIMO Systems

TL;DR

This work studies RDARS, a hybrid architecture that jointly leverages distribution, reflection, and selection gains to improve uplink MIMO performance. It formulates a mixed-integer, non-convex MSE-minimization problem over receive beamforming, reflection phases, and channel-aware element placement, and tackles it with an inexact BCD-PDD algorithm and a low-complexity greedy-search AO method. The proposed methods demonstrate substantial performance gains over conventional RIS and DAS systems, with insights into the optimal number and placement of connection-mode elements and the impact of channel conditions. The results offer practical deployment guidelines and scalable optimization techniques for implementing RDARS in future 6G networks.

Abstract

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective solution for green communications in 6G. However, its further extensive use has been greatly limited due to its fully passive characteristics. Considering the appealing distribution gains of distributed antenna systems (DAS), a flexible reconfigurable architecture called reconfigurable distributed antenna and reflecting surface (RDARS) is proposed. RDARS encompasses DAS and RIS as two special cases and maintains the advantages of distributed antennas while reducing the hardware cost by replacing some active antennas with low-cost passive reflecting surfaces. In this paper, we present a RDARS-aided uplink multi-user communication system and investigate the system transmission reliability with the newly proposed architecture. Specifically, in addition to the distribution gain and the reflection gain provided by the connection and reflection modes, respectively, we also consider the dynamic mode switching of each element which introduces an additional degree of freedom (DoF) and thus results in a selection gain. As such, we aim to minimize the total sum mean-square-error (MSE) of all data streams by jointly optimizing the receive beamforming matrix, the reflection phase shifts and the channel-aware placement of elements in the connection mode. Numerical results demonstrate the superiority of the proposed architecture compared to the conventional fully passive RIS or DAS. Furthermore, some insights about the practical implementation of RDARS are provided.
Paper Structure (31 sections, 4 theorems, 49 equations, 12 figures, 2 algorithms)

This paper contains 31 sections, 4 theorems, 49 equations, 12 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. RDARS Architecture
  4. System Model
  5. Problem Formulation
  6. Proposed Inexact BCD-PDD Algorithm
  7. Equivalent Transformation
  8. Inner Iteration of PDD
  9. ${\bf{\theta}}$-subproblem
  10. ${\bf{x}}$-subproblem
  11. ${\bf{v}}$-subproblem
  12. Outer Iteration of PDD
  13. Discussion
  14. Special Cases
  15. Single-antenna BS case
  16. ...and 16 more sections

Key Result

Lemma 1

Assume ${\bf{x}} \in \mathbb{R}^N,{\bf{v}} \in \mathbb{R}^N$ and define $\chi \triangleq \{ ({\bf{x}},{\bf{v}}) | {\bf{0}} \leq {\bf{x}} \leq {\bf{1}}, ||2{\bf{v}}\!-\!{\bf{1}}||_2^2 \leq N, \left<2{\bf{x}}\!-\!{\bf{1}},2{\bf{v}}\!-\!{\bf{1}}\right>=N, \forall {\bf{v}} \}$. Assume that ${\bf{x}},{\b

Figures (12)

  • Figure 1: Illustration of the dynamic mode switching of RDARS.
  • Figure 2: A RDARS-aided uplink MIMO communication system.
  • Figure 3: Three-dimensional coordinates of the system deployment for simulations.
  • Figure 4: Convergence behaviors of the PDD algorithm under different initialization schemes.
  • Figure 5: Performance-complexity trade-off between IBCD-PDD and GS-AO methods.
  • ...and 7 more figures

Theorems & Definitions (10)

  • Lemma 1
  • proof
  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • Proposition 3
  • proof
  • proof
  • proof