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Joint Sparsity and Beamforming Design for RDARS-Aided Systems

Chengwang Ji, Haiquan Lu, Qiaoyan Peng, Jintao Wang, Shaodan Ma

Abstract

Reconfigurable distributed antennas and reflecting surface (RDARS) has emerged as a promising architecture for communication and sensing performance enhancement. In particular, the new selection gain can be achieved by leveraging the dynamic working mode selection between connection and reflection modes, whereas low-complexity element configuration remains an open issue. In this paper, we consider a RDARS-assisted communication system, where the connected elements are formed as a uniform sparse array for simplified mode configuration while achieving enlarged physical array aperture. The sum rate maximization problem is then formulated by jointly optimizing the active and passive beamforming matrices and sparsity of connected element array. For the special cases of a single user equipment (UE) and two UEs, the optimal sparsity designs are derived in closed-form. Then, for an arbitrary number of UEs, a weighted minimum mean-square error-based alternating optimization (AO) algorithm is proposed to tackle the non-convex optimization problem. Numerical results demonstrate the importance of optimizing the sparsity and the effectiveness of low-complexity sparsity optimization.

Joint Sparsity and Beamforming Design for RDARS-Aided Systems

Abstract

Reconfigurable distributed antennas and reflecting surface (RDARS) has emerged as a promising architecture for communication and sensing performance enhancement. In particular, the new selection gain can be achieved by leveraging the dynamic working mode selection between connection and reflection modes, whereas low-complexity element configuration remains an open issue. In this paper, we consider a RDARS-assisted communication system, where the connected elements are formed as a uniform sparse array for simplified mode configuration while achieving enlarged physical array aperture. The sum rate maximization problem is then formulated by jointly optimizing the active and passive beamforming matrices and sparsity of connected element array. For the special cases of a single user equipment (UE) and two UEs, the optimal sparsity designs are derived in closed-form. Then, for an arbitrary number of UEs, a weighted minimum mean-square error-based alternating optimization (AO) algorithm is proposed to tackle the non-convex optimization problem. Numerical results demonstrate the importance of optimizing the sparsity and the effectiveness of low-complexity sparsity optimization.
Paper Structure (13 sections, 1 theorem, 9 equations, 3 figures, 1 table)

This paper contains 13 sections, 1 theorem, 9 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Proposed Algorithm
  4. Special Cases With the Single-UE and Two-UE
  5. Single UE
  6. Two UEs
  7. Arbitrary Number of UEs
  8. Active Beamforming Optimization
  9. Passive Beamforming Optimization
  10. Sparsity Optimization
  11. Computational Complexity Analysis
  12. Numerical Results
  13. Conclusion

Key Result

Proposition 1

Under the assumption ${\varphi _n} =\frac{2\pi d}{\lambda} (n - 1)({u_{\rm ref}} - u_{\rm \mathrm{br}}^{\rm AoA})$, the optimal sparsity is obtained as

Figures (3)

  • Figure 1: An illustration of RDARS-aided communication system, where the connected elements form a uniform sparse array.
  • Figure 2: Sum rate versus the total transmit power under different cases for two UEs.
  • Figure 3: Average rate versus the total transmit power for different numbers of UEs.

Theorems & Definitions (1)

  • Proposition 1