A Novel Pre-Equalization Scheme for Visible Light Communications with Direct Learning Architecture

Visible light communication is receiving increasing attention as a promising indoor wireless access technology with broad prospects. In this paper, we propose a novel pre-equalization algorithm with direct learning architecture for compensating the nonlinear distortions of visible light communication systems. Particularly, we introduce a deep reinforcement learning based design to directly optimize the parameters by interacting with the environment without any auxiliary networks. We demonstrate that the proposed approach outperforms the deep learning based pre-equalizer with indirect learning in terms of convergence speed, nonlinear compensation and noise immunity. Moreover, we find that the main contribution to the performance gain of the proposed method over the deep learning based preequalizer comes from the hidden layer.