4.7 Article

Robust Beamforming for RIS-Assisted Wireless Communications With Discrete Phase Shifts

期刊

IEEE WIRELESS COMMUNICATIONS LETTERS
卷 10, 期 12, 页码 2619-2623

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2021.3107319

关键词

Array signal processing; Optimization; Wireless communication; Signal to noise ratio; Channel estimation; Symmetric matrices; Quantization (signal); Reconfigurable intelligent surface; robust beamforming; discrete phase shifts

资金

  1. National Natural Science Foundation of China [62101492]
  2. Fundamental Research Funds for the Central Universities [2021FZZX001-21]
  3. MOE Tier 2 [T2EP50220-0045]

向作者/读者索取更多资源

This study focuses on the robust beamforming design for RIS-assisted communication systems under imperfect CSI, aiming to jointly optimize the transmit beamforming at the AP and discrete phase shifts of RIS to minimize transmission power. Simulation results indicate that the proposed scheme approaches the performance of perfect CSI counterpart and outperforms traditional non-robust methods.
In this letter, we study the robust beamforming design for the reconfigurable intelligent surface (RIS)-assisted communication systems from a multi-antenna access point (AP) to a single-antenna user under imperfect channel state information (CSI), where the RIS has only a finite number of phase shifts at each element. In particular, considering the cascaded AP-RIS-user channel estimation error, we aim to jointly optimize the transmit beamforming at the AP and discrete phase shifts of RIS to minimize the transmission power of AP, subject to a signal-to-noise ratio constraint at the user. To tackle the non-convex optimization problem, we decouple it into two subproblems and propose a novel alternative optimization framework. More specifically, the robust beamforming of AP is first obtained through S-Procedure and semidefinite relaxation. Then we recast the discrete phase shift constraint at RIS into an equivalent convex one, and propose an efficient algorithm to obtain a near optimal solution. Finally, the two subproblems are iteratively optimized to obtain joint robust beamforming. Simulation results show that the proposed scheme can approach the performance of the perfect CSI counterpart and substantially outperform traditional non-robust methods.

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