OIRS-Assisted NLOS Visible Light Communication Systems: Modeling, Optimization, and Experimental Validation
Bohui Xu, Bangjiang Lin, Jian Chen, Bowen Zheng, Guojun Pang, Jiabin Luo, Zabih Ghassemlooy
Abstract
As part of Internet of Things networks, visible light communication (VLC) technology has excelled in recent years. Compared to radio frequency (RF) technologies, VLC offers unlicensed spectrum, low interference, high security, and energy efficiency, making it ideal for short-range communication. However, its performance is severely limited under the non-line-of-sight (NLOS) links. Intelligent reflecting surface (IRS), as an innovative technology, can dynamically and passively adjust the wireless environment. In this article, we propose the use of optical IRSs (OIRSs) to enhance VLC performance under NLOS scenarios. Specifically, we develop ZEMAX-based nonsequential ray tracing to accurately model light propagation in OIRS-assisted indoor VLC environments. Then, we establish a system model and formulate an optimization problem aimed at maximizing the received optical power. To solve this, we propose a genetic-enhanced sine–cosine algorithm with elite strategy and dynamic opposition learning (GESCA-ED), which optimizes the rotational degrees of freedom of each OIRS element. Furthermore, we design a hardware-based experimental platform featuring a configurable OIRS control system, which is integrated with GESCA-ED for real-time optimization in an actual NLOS environment. Experimental results demonstrate significant improvements in received optical power, bit error rate (BER), and transmission distance, achieving a BER of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 10^{-6}$ </tex-math></inline-formula> over a 3.8-m NLOS link. These results confirm that the proposed system effectively extends signal coverage, enabling communication between mobile users regardless of their spatial positions.