一种从通信系统到神经网络的可解释映射用于最优收发机联合均衡
An Interpretable Mapping from a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization
摘要：
在本文中，我们提出了一种方案，利用人工智能（AI）的优化能力，最佳的双端联合均衡补偿波长选择开关（WSS）造成的光滤波损伤。与添加或替换现有数字信号处理（DSP）的某个模块相比，我们利用通信系统和神经网络（NN）之间的相似性。通过将通信系统映射到一个神经网络，其中均衡模块对应于卷积层，其他模块可以被视为静态层，可以获得最优的卷积联合均衡系数。特别地，不改变通信系统的DSP结构。大量的数值模拟进行验证所提出的方法的性能。对于65 GBaud的16 QAM信号，当WSS的数量为16时，它可以实现0.76 dB增益，-6 dB带宽为73 GHz。
英文摘要：
In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS). In contrast to adding or replacing a certain module of existing digital signal processing (DSP), we exploit the similarity between a communication system and a neural network (NN). By mapping a communication system to an NN, in which the equalization modules correspond to the convolutional layers and other modules can been regarded as static layers, the optimal transceiver-joint equalization coefficients can be obtained. In particular, the DSP structure of the communication system is not changed. Extensive numerical simulations are performed to validate the performance of the proposed method. For a 65 GBaud 16QAM signal, it can achieve a 0.76 dB gain when the number of WSSs is 16 with a -6 dB bandwidth of 73 GHz.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
