An Optical Intelligent Reflective Surface-Aided Indoor NLOS VLC System

—As part of Internet of thing networks, visible light communication (VLC) technology has excelled in recent years. When compared to traditional radio frequency (RF) technology, VLC offers the advantages of abundant and unlicensed spectrum bands, no RF-induced interference, inherent security, safety and energy efficiency, making it an excellent choice for short-range communication applications. However, the performance of VLC 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. To overcome the impact of NLOS on VLC performance, this paper proposes the use of optical intelligent reflecting surfaces (OIRS) as a solution to this challenge. Using ZEMAX software, we propose a non-sequential ray tracing method to construct a realistic OIRS-aided VLC channel model, which effectively simulates the propagation of light in an OIRS-assisted indoor VLC environment. We establish a system model and formulate the optimization problem as well as propose a dynamic opposite learning and elite strategy-enhanced sine-cosine algorithm (DESCA) to optimize the rotational degrees of freedom of each OIRS element and maximize the optical power at the receiver. Furthermore, we develop a hardware-based experimental platform with a configurable control system for OIRS-aided VLC and use it with DESCA in a real NLOS environment. The system demonstrates improved performance in terms of the received optical power, bit error rate (BER) and transmission distance, achieving a BER of 1×10-6 over a 3.8 m NLOS link. And the experimental results show that the proposed system extends signal coverage, enabling communication between mobile users regardless of their positions.