A Deep Learning-Based CSS Modulation for NLOS Visible Light Communications

With the development of smart cities, visible light communication (VLC) with its unique advantages is increasingly regarded as a viable complement to traditional radio frequency-based wireless communications. In practical applications, line-of-sight VLC is susceptible to blocking/shadowing, resulting in communication interruptions. Even though non-line-of-sight (NLOS) transmission can effectively address this issue, propagating signals are often subject to significant attenuation and multipath effects, which can degrade the quality of communications. In this paper, we propose a NLOS VLC system with chirp spread spectrum modulation, which leverages reflected light to overcome blocking. Additionally, a spatial shift convolutional neural networks (${\rm{S}}^2{\text{-CNN}}$) demodulator is used to mitigate the signal linear and nonlinear transmission impairments introduced in NLOS propagation, thus achieving effective joint signal compensation and recovery. Experimental results demonstrate that, ${\rm{S}}^2{\text{-CNN}}$-based demodulator can effectively compensate for linear and nonlinear distortions, achieving a transmission rate of more than 10 Mbps over a 2.7-m NLOS link, demonstrating higher reliability and robustness.