Deep learning based end-to-end visible light communication with an in-band channel modeling strategy

Aside from ambient light noise, shot noise, and linear/nonlinear effects, strong low-frequency noise (LFN) severely affects the signal quality in LED-based visible light communication (VLC) systems, which hinders the implementation of data-driven end-to-end (E2E) deep learning approaches in real LED-VLC systems. We present a deep learning-based autoencoder to deal with this challenge. A novel modeling strategy is proposed to bypass the influence of the LFN and other low signal-to-noise ratio data when training the channel model of our E2E framework. The deep learning-based autoencoder then embeds the differentiable channel model and learns to combat the majority of channel impairments. In the E2E LED-VLC experiment, 1.875 Gbps transmission is achieved under the 7% HD-FEC threshold, 0.325 Gbps faster than the baseline. The E2E framework is robust to signal bias and amplitude variations, implying dimming support in the indoor environment. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement