Open AccessWireless ultraviolet scattering channel estimation method based on deep learning
Author(s) -
Taifei Zhao,
Xinzhe Lv,
Haijun Zhang,
Shuang Zhang
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.438422
Subject(s) - non line of sight propagation , computer science , deep learning , channel (broadcasting) , artificial intelligence , convolutional neural network , bit error rate , transmission (telecommunications) , wireless , artificial neural network , mean squared error , algorithm , wireless network , interference (communication) , telecommunications , mathematics , statistics
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.