Abstract
Visible light communications (VLC) has been regarded as a promising technology for high-speed indoor wireless accessing since it can offer both lighting and network. However, the spectral efficiency of the VLC system based on orthogonal frequency division multiplexing (OFDM) is always smaller than RF-OFDM because light-emitting diodes (LED) require real-value signals. Pilots occupy the spectrum in proportion for channel estimation(CE) to improve communication quality. Based on this consideration, we firstly present the idea of introducing deep learning technology into the CE scheme in the VLC system and propose a CE scheme based on Deep Neural Networks(DNN) perform as well as conventional CE schemes with fewer pilots. The result of experiments validates the feasibility of DNN-based CE.
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