Journal
IEEE WIRELESS COMMUNICATIONS LETTERS
Volume 9, Issue 3, Pages 340-343Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2019.2954511
Keywords
Channel estimation; m-MIMO; visible light communication; FFDNet; deep learning
Categories
Funding
- National Natural Science Foundation of China [61661028, 61701214]
- Excellent Youth Foundation of Jiangxi Province [2018ACB21012]
- National Key Research and Development Project [2018YFB1404303, 2018YFB14043033]
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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.
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