Adaptive deep-learning equalizer based on constellation partitioning scheme with reduced computational complexity in UVLC system

Visible light communication (VLC) system has emerged as a promising solution for high-speed underwater data transmission. To tackle with the linear and nonlinear impairments, deep learning inspired equalization is introduced into VLC. Despite their success in accuracy, deep learning approaches often come with high computational budget. In this paper, we propose an adaptive deep-learning equalizer based on complex-valued neural network and constellation partitioning scheme for 64 QAM-CAP modulated underwater VLC (UVLC) system. Inspired by the fact that symbols modulated at different levels experience various extent of nonlinear distortion, we adaptively partition the received symbols in constellation and design compact equalization networks for specific regions to reduce computation consumption. Experiments demonstrate that the partitioned equalizer can achieve the bit error rate below the 7% hard-decision forward error correction (HD-FEC) limit of 3.8 × 10^-3 at 2.85 Gbps similar to the standard complex-valued network, yet with 56.1% total computational complexity reduction. This work paves the path for online data processing in high speed UVLC system.