Intelligent Reflecting Surface for MIMO VLC: Joint Design of Surface Configuration and Transceiver Signal Processing

• Published in: IEEE transactions on wireless communications Vol. 22; no. 9; p. 1
• Main Authors: Sun, Shiyuan, Yang, Fang, Song, Jian, Zhang, Rui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; alternating optimization (AO); Configuration management; intelligent reflecting surface (IRS); mean square error (MSE); MIMO communication; multiple-input and multiple-output (MIMO); Optical communication; Optical receivers; Optical transmitters; Optimization; Point sources; Reconfigurable intelligent surfaces; Signal processing; Signal processing algorithms; Signal to noise ratio; Transceivers; Visible light communication; Visible light communication (VLC); Wireless communication; Wireless communication systems
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2023.3236811

With the capability of reconfiguring the wireless electromagnetic environment, intelligent reflecting surface (IRS) becomes a new paradigm for designing future wireless communication systems. In this paper, we consider optical IRS for improving the performance of visible light communication (VLC) under a multiple-input and multiple-output (MIMO) setting, where the mean square error (MSE) of the IRS-aided MIMO VLC is minimized by jointly designing the IRS and transceiver signal processing. To this end, the MIMO channel gains of the IRS-aided VLC are first derived under the point source assumption, based on which the MSE minimization problem is formulated subject to the emission power constraints and the IRS configuration constraints. Next, we propose an alternating optimization algorithm, which decomposes the original problem into three subproblems, to iteratively optimize the IRS configuration, the precoding and detection matrices for minimizing the MSE. Moreover, theoretical analysis on the performance of the proposed algorithm in high and low signal-to-noise ratio (SNR) regimes is investigated, revealing that the joint optimization process can be simplified in such special cases, and the algorithm's convergence property and computational complexity are also discussed. Finally, numerical results show that IRS-aided schemes significantly reduce the MSE as compared to their counterparts without IRS, and the proposed algorithm outperforms other baseline schemes.