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Joint Antenna Selection and Beamforming Design for Active RIS-aided ISAC Systems

Wei Ma, Peichang Zhang, Junjie Ye, Rouyang Guan, Xiao-Peng Li, Lei Huang

TL;DR

This paper addresses the challenge of energy consumption in active RIS-aided ISAC systems by introducing antenna selection at the base station to reduce RF chains. It proposes a two-stage design: (i) a discrete cuckoo search-based antenna selection to pick high-gain antennas, and (ii) an alternating optimization framework that jointly designs transmit beamforming with WMMSE and RIS beamforming with FP, under radar sensing and power constraints. The approach is complemented by a semidefinite relaxation for transmit design and a convex quadratic programming formulation for RIS design, with convergence and complexity analyses provided. Simulation results demonstrate that the proposed antenna selection substantially reduces RF chains with minimal performance loss, and that the joint WMMSE-FP design achieves notable improvements in weighted sum-rate while satisfying sensing requirements.

Abstract

Active reconfigurable intelligent surface (A-RIS) aided integrated sensing and communications (ISAC) system has been considered as a promising paradigm to improve spectrum efficiency. However, massive energy-hungry radio frequency (RF) chains hinder its large-scale deployment. To address this issue, an A-RIS-aided ISAC system with antenna selection (AS) is proposed in this work, where a target is sensed while multiple communication users are served with specifically selected antennas. Specifically, a cuckoo search-based scheme is first utilized to select the antennas associated with high-gain channels. Subsequently, with the properly selected antennas, the weighted sum-rate (WSR) of the system is optimized under the condition of radar probing power level, power budget for the A-RIS and transmitter. To solve the highly non-convex optimization problem, we develop an efficient algorithm based on weighted minimum mean square error (WMMSE) and fractional programming (FP). Simulation results show that the proposed AS scheme and the algorithm are effective, which reduce the number of RF chains without significant performance degradation.

Joint Antenna Selection and Beamforming Design for Active RIS-aided ISAC Systems

TL;DR

This paper addresses the challenge of energy consumption in active RIS-aided ISAC systems by introducing antenna selection at the base station to reduce RF chains. It proposes a two-stage design: (i) a discrete cuckoo search-based antenna selection to pick high-gain antennas, and (ii) an alternating optimization framework that jointly designs transmit beamforming with WMMSE and RIS beamforming with FP, under radar sensing and power constraints. The approach is complemented by a semidefinite relaxation for transmit design and a convex quadratic programming formulation for RIS design, with convergence and complexity analyses provided. Simulation results demonstrate that the proposed antenna selection substantially reduces RF chains with minimal performance loss, and that the joint WMMSE-FP design achieves notable improvements in weighted sum-rate while satisfying sensing requirements.

Abstract

Active reconfigurable intelligent surface (A-RIS) aided integrated sensing and communications (ISAC) system has been considered as a promising paradigm to improve spectrum efficiency. However, massive energy-hungry radio frequency (RF) chains hinder its large-scale deployment. To address this issue, an A-RIS-aided ISAC system with antenna selection (AS) is proposed in this work, where a target is sensed while multiple communication users are served with specifically selected antennas. Specifically, a cuckoo search-based scheme is first utilized to select the antennas associated with high-gain channels. Subsequently, with the properly selected antennas, the weighted sum-rate (WSR) of the system is optimized under the condition of radar probing power level, power budget for the A-RIS and transmitter. To solve the highly non-convex optimization problem, we develop an efficient algorithm based on weighted minimum mean square error (WMMSE) and fractional programming (FP). Simulation results show that the proposed AS scheme and the algorithm are effective, which reduce the number of RF chains without significant performance degradation.
Paper Structure (12 sections, 2 theorems, 39 equations, 9 figures, 2 algorithms)

This paper contains 12 sections, 2 theorems, 39 equations, 9 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. RELATED WORK
  3. System Model and Problem Formulation
  4. System Model
  5. Problem Formulation
  6. Joint BS and A-RIS beamforming design with AS
  7. Cuckoo Search Policy for AS
  8. WMMSE for Transmit Beamformer Design
  9. Fractional Programming for RIS Beamforming
  10. Convergence and Complexity Discussions
  11. Simulation Results
  12. Conclusions

Key Result

Theorem 1

The WSR problem of transmitter design is equivalent to a weighted MMSE minimization problem when the weighted MMSE coefficients are selected as With these weighted MMSE coefficients $\omega_k$, the KKT-conditions of the equivalent problem and the original problem can be satisfied simultaneously.

Figures (9)

  • Figure 1: An A-RIS-aided DFRC system model with the transmitter equipped with $M$ antenna and $M_s$ RF chains.
  • Figure 2: The simulated A-RIS aided DFRC scenario comprising of an DFRC BS with $M$-antenna and $M_s$ RF chains, an $N$-element active RIS, $4$ single antenna users, and one target.
  • Figure 3: Convergence behaviour of the proposed algorithm: WSR versus the iteration number.
  • Figure 4: DFRC transmit beampatterns for different algorithms.
  • Figure 5: WSR versus different AS numbers.
  • ...and 4 more figures

Theorems & Definitions (3)

  • Theorem 1
  • proof
  • Theorem 2