Unsupervised recovery method of distorted signals for visible light communication
The factors of non-linear character of LED,ambient light and channel noise lead to non-linear distortion in visible light communication system,and then the bit error rate of the visible light communication system increases.Ai-ming at this problem,a new deep neural network model is proposed,and the non-linear distorted symbols of the visible light communication system are unsupervised recovered based on this model.First of all,the mathematical model of the visible light communication system is constructed,and multiple factors of non-linear distortion are analyzed;then,the re-current neural network is utilized to learn the short term correlation of the visible light signal sequence,and the gated re-current unit is utilized to learn the long term correlation of the visible light signal sequence;Finally,a dense network is a-dopted to learn the non-linear mapping relationship between the input visible light signals and the received visible light sig-nals.Simulation results show that the proposed neural network can reduce the bit error rate of the visible light communi-cation system effectively;it also achieves better effect on different modulation orders and different bandwidths.
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