Dimming-Aware Deep Learning Approach for OOK-Based Visible Light Communication

Abstract

Visible light communication (VLC) is a secure, low-cost, and high-rate communication method. On-off keying (OOK) is one of the modulation schemes of VLC, turning each light either on or off to generate binary signals. Recently, deep learning (DL) technologies have made a series of breakthroughs for dimming in VLC system. This task is actually quite challenging for DL, since the VLC system needs to be able to support various dimming targets on account of the different preferences from users in practical applications, resulting in an optimization problem with multiple constraints. This article presents a DL framework for the dimming-aware binary VLC system, which can meet arbitrary dimming requirements by a universal neural network, named universal auto-encoder (UAE). The proposed UAE creatively utilizes a multi-branch architecture with several carefully designed concatenated patches, and a novel multi-stage training strategy for the optimization problem with multiple dimming constraints. The experiments indicate that the proposed DL approach outperforms existing techniques in terms of the average bit error rate, the satisfaction of the dimming constraints, and the robustness for imperfect optical channels.