Channel estimation is of crucial importance in massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) systems. In order to tackle this problem, a fast and flexible denoising convolutional neural network (FFDNet)-based channel estimation scheme for m-MIMO VLC systems was proposed. The channel matrix of the m-MIMO VLC channel is identified as a two-dimensional natural image since the channel has the characteristic of sparsity. A deep learning-enabled image denoising network FFDNet is exploited to learn from a large number of training data and to estimate the m-MIMO VLC channel. Simulation results demonstrate that our proposed channel estimation based on the FFDNet significantly outperforms the benchmark scheme based on minimum mean square error.
@article{arxiv.1911.07404,
title = {FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems},
author = {Zhipeng Gao and Yuhao Wang and Xiaodong Liu and Fuhui Zhou and Kai-Kit Wong},
journal= {arXiv preprint arXiv:1911.07404},
year = {2019}
}