Gao, Z;
Wang, Y;
Liu, X;
Zhou, F;
Wong, KK;
(2020)
FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems.
IEEE Wireless Communications Letters
, 9
(3)
pp. 340-343.
10.1109/LWC.2019.2954511.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LWC.2019.2954511 |
| Publisher version: | https://doi.org/10.1109/LWC.2019.2954511 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Channel estimation, m-MIMO, visible light communication, FFDNet, deep learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10094118 |
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