基于深度神经网络的水声FBMC通信信号检测方法

DNN based signal detection for underwater acoustic FBMC communications

针对传统水声滤波器组多载波(Filter Bank Multi-Carrier,FBMC)通信接收端需经过信道估计和均衡才可恢复出发送符号,系统复杂度高且信道估计精度不佳等问题。文章将深度神经网络融入到水声多载波通信当中,提出一种基于深度神经网络的水声FBMC信号检测方法。在训练阶段通过大量的数据迭代、调试超参数和优化算法来改善深度神经网络参数,使其具有预期的估计效果。利用训练完成的深度神经网络模型取代传统FBMC通信系统接收端的信道估计、均衡等模块,自适应地学习水声信道状态信息,同时避免了固有的虚部干扰影响。在测试阶段直接将频域序列作为网络的输入来预测发送的二进制序列,仿真结果表明所提出的基于深度神经网络的FBMC信号检测方法相比传统信道估计算法有更好的误码率性能。

The traditional filter bank multi-carrier(FBMC)communication receiver can only recover the transmitted symbols after channel estimation and equalization,thus the system complexity is high and the channel estimation accuracy is poor.In this paper,deep neural network(DNN)is integrated into underwater acoustic multi-carrier communication,and a DNN based signal detection method for underwater acoustic FBMC communications(DNN-FBMC)is proposed.In the training stage,the DNN parameters are improved by a large number of data iteration,hyper-parameter selection and the optimization algorithm,so that the expected estimation effect is achieved.The trained DNN model is used to replace the channel estimation and equilibrium modules and others in the receiver of traditional FBMC communication system.Meantime,the channel state information is learned adaptively and the inherent imaginary interference is avoided.In the test stage,the frequency domain sequence is considered as the input of network to predict the sending binary sequence directly.The simulation results show that the proposed DNN-FBMC signal detection method has better BER performance than traditional channel estimation algorithm.