To better solve the fading and severe inter-symbol interference problems in underwater acoustic channels,a Joint Multi-branch Merging and Equalization algorithm based on Deep Learning(JMME-DL)is proposed in this paper.The algorithm jointly implements multi-branch merging and equalization with the help of the nonlinear fitting ability of the deep learning network.The merging and equalization are not independent of each other,in the implementation of the algorithm,the total error is first calculated based on the total output of the deep learning network,and then the network parameters of each part are jointly adjusted with the total error,and the dataset is constructed based on the statistical underwater acoustic channel model.Simulation results show that the proposed algorithm achieves faster convergence speed and better BER performance compared to the existing algorithms,making it better adapted to underwater acoustic channels.
关键词
水声通信/深度学习/水声信道/联合多分支合并与均衡
Key words
Underwater acoustic communication/Deep learning/Underwater acoustic channel/Joint multi-branch merging and equalization
维普
万方数据