  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.
