Various neural network (NN)-based equalizers have been proposed as effective digital signal processing (DSP) tools for short-reach optical direct detection (DD) communication systems. However, they are mainly designed for solving nonlinear problems due to the intrinsic nonlinear activation function. In this letter, we propose novel DSP schemes to deal with both linear and nonlinear impairments in short-reach DD optical links, including cascade feedforward NN (FNN)/cascade recurrent NN (RNN)-based equalizers, and combination of feedforward equalization (FFE) and traditional non-cascade FNN/RNN-based equalizers. A 50-Gb/s pulse amplitude modulation (PAM)-4 directly modulated laser (DML)-based system is experimentally carried out and the bit-error-rate (BER) performance of the proposed DSP algorithms are compared. Experimental results show that cascade FNN/RNN and FNN/RNN-FFE could help improve the BER performance compared with traditional non-cascade FNN/RNN, since linear impairments are taken into account by either FFE or the cascade structure. With the help of the joint optimization of both linear and nonlinear effects through the network training process, cascade FNN/RNN shows great superiority over other DSP schemes. We also investigate on the impact of decision feedback equalization (DFE) concatenated with FNN/RNN. Compared with using FNN/RNN followed by FFE, no significant BER improvement can be found using DFE in place of FFE.

中文翻译：

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PAM4短距离直接检测系统的线性和非线性减损的联合均衡

已经提出了各种基于神经网络（NN）的均衡器，作为用于短距离光学直接检测（DD）通信系统的有效数字信号处理（DSP）工具。但是，由于固有的非线性激活函数，它们主要用于解决非线性问题。在这封信中，我们提出了新颖的DSP方案来处理短距离DD光链路中的线性和非线性损伤，包括基于级联前馈NN（FNN）/级联递归NN（RNN）的均衡器以及前馈均衡（FFE）的组合）和传统的非级联基于FNN / RNN的均衡器。通过实验实现了一个基于50 Gb / s脉冲幅度调制（PAM）-4直接调制激光（DML）的系统，并比较了所提出的DSP算法的误码率（BER）性能。实验结果表明，与传统的非级联FNN / RNN相比，级联的FNN / RNN和FNN / RNN-FFE可以帮助提高BER性能，因为FFE或级联结构都考虑了线性损伤。借助网络训练过程对线性和非线性效果的联合优化，级联FNN / RNN显示出优于其他DSP方案的优越性。我们还研究了与FNN / RNN串联的决策反馈均衡（DFE）的影响。与使用FNN / RNN和FFE相比，使用DFE代替FFE并没有发现明显的BER改善。借助网络训练过程对线性和非线性效果的联合优化，级联FNN / RNN显示出优于其他DSP方案的优越性。我们还研究了与FNN / RNN串联的决策反馈均衡（DFE）的影响。与使用FNN / RNN和FFE相比，使用DFE代替FFE并没有发现明显的BER改善。借助网络训练过程对线性和非线性效果的联合优化，级联FNN / RNN显示出优于其他DSP方案的优越性。我们还研究了与FNN / RNN串联的决策反馈均衡（DFE）的影响。与使用FNN / RNN和FFE相比，使用DFE代替FFE并没有发现明显的BER改善。