基于深度学习的OFDM半相干水声通信方法

针对正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)水声通信中常用的相干和非相干通信分别面临的对多普勒敏感和频谱效率低的问题,提出一种高阶幅度键控调制的半相干通信技术,将OFDM符号时频帧结构中全部频点采用高阶幅度键控调制方式,并利用信号幅度信息完成半相干信道估计。通过两种基于深度学习的算法优化半相干信道估计这一非线性过程,较非相干通信有效提高了频谱效率,较一定信噪比下的相干通信提高了鲁棒性,降低了误比特率和系统复杂度,并利用元学习算法降低深度学习算法对训练数据的依赖。最后,提取海试信道数据,完成OFDM半相干水声通信系统仿真,验证了所提方法在频谱效率和系统误比特率性能方面较非相干和相干通信的优势,当信道长度改变时,基于元学习的算法依然可以获得较好的性能。

OFDM semi-coherent underwater acoustic communication method based on deep learning

To solve the problems of Doppler sensitivity and low spectral efficiency of the coherent and non-coherent communication commonly used in orthogonal frequency division multiplexing (OFDM) underwater acoustic communication, a semi-coherent communication technique with M-ary amplitude shift keying is proposed, in which, all the frequencies in the OFDM symbolic time-frequency frame structure are modulated with M-ary amplitude shift keying and the semi-coherent channel estimation is accomplished with the signal amplitude information. By optimizing the nonlinear process of semi-coherent channel estimation with two deep learning algorithms, the spectral efficiency is improved compared to the non-coherent communication and the robustness is improved; meanwhile, the bit error rate and system complexity are reduced compared to the coherent communication at a certain signal to noise ratio. Moreover, the meta-learning-based algorithm is used to reduce the dependence of the deep learning algorithm on the training data. Finally, the simulation of OFDM semi-coherent underwater acoustic communication system is completed by using the actual channel data obtained from sea trial, and the results verify the advantages of the proposed method over non-coherent and coherent communication in terms of spectral efficiency and system bit error rate. And, the meta-learning-based algorithm can still obtain better performance when the channel length changes.