Abstract
Due to the strong scattering characteristics, there are serious problems of inter-symbol interference (ISI) and transmission attenuation in the none-line-of-sight (NLOS) wireless ultraviolet communication system. In this paper, a wireless ultraviolet scattering channel estimation method based on deep learning is presented. The learning model structure is designed by combining the one-dimensional convolutional neural network (1D-CNN) and the deep neural network (DNN). In the training stage, the network optimization process is improved by the differential evolution (DE) algorithm. The computer simulation results show that the proposed deep learning channel estimation scheme has better mean square error (MSE) performance and bit error rate (BER) performance compared with the traditional algorithms. Furthermore, we verify the stability of this scheme in different communication environments, and the constructed neural network model has good generalization ability.
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