Channel Estimation for Indoor Massive MIMO Visible Light Communication With Deep Residual Convolutional Blind Denoising Network

Massive multiple-input multiple-output (m-MIMO) visible light communication (VLC) has been considered as one of the promising components in the optical wireless communication system. However, the envisioned benefits may be limited due to the high computational complexity to estimate accurate channel state information (CSI). Besides, several propagation paths in the practical communication channels induces significant difficulties. In this paper, a deep residual convolutional blind denoising network (ResCBDNet) has been proposed to predict more realistic channels in indoor m-MIMO VLC communication system. Unlike the other convolutional networks which may overfit on the simplified additive white Gaussian noise model, the ResCBDNet exploits noise level estimation subnetwork to improve the generalization ability to real noise as well as interactively reduce the noise in the channel matrix by adjusting the noise level map. More specifically, we have investigated to optimize the ResCBDNet over a large range of signal-to-noise ratio (SNR). During the simulation, the sparse channel matrix has been treated as a two dimensional natural image. Simulation results validate that the proposed network is very promising in practical channel estimation for the indoor m-MIMO VLC communication system and outperforms the state-of-the-art channel estimators in terms of normalized mean square error and peak SNR.

Automated summary

In this paper, a deep residual convolutional blind denoising network (ResCBDNet) is proposed to predict more realistic channels in indoor m-MIMO VLC communication system. The ResCBDNet exploits noise level estimation subnetwork to improve the generalization ability to real noise and interactively reduce the noise in the channel matrix. Simulation results validate the advantage of the proposed network in practical channel estimation.