针对基于交错正交幅度调制的相干光滤波器组多载波(CO-FBMC-OQAM)通信系统的信道均衡问题, 提出了一种基于人工神经网络(CD-net)的信道均衡方法。CD-net融合了卷积神经网络和深度神经网络的优点, 能够完成CO-FBMC-OQAM通信系统的信道均衡任务。在CD-net中, 卷积神经网络模块首先对接收端畸变信号进行特征提取, 再由深度神经网络模块完成信号解调和信道均衡, 并恢复出原始信息。数值仿真结果表明: 与传统信道均衡方法相比, CD-net方法在误码率性能方面具有更大的优势, 能够较好地解决CO-FBMC-OQAM系统的信道失真问题。

This paper proposes an channel equalization method based on artificial neural network(CD-net), which for deals with the channel equalization problem of coherent optical filter bank multicarrier with offset quadrature amplitude modulation (CO-FBMC-OQAM)communication system. CD-net combines the advantages of convolutional neural network and deep neural network to perform the channel equalization task for the CO-FBMC-OQAM communication system. In CD-net, the convolutional neural network modular first performs feature extraction of the distorted signal at the receiving end. Then the deep neural network modular performs signal demodulation and channel equalization, and recovers the original information. The numerical simulation results show that compared with the traditional channel equalization method, the CD-net method has a greater advantage in bit error rate performance, which can better solve the channel distortion problem of the CO-FBMC-OQAM communication system.