Joint Equalization of Linear and Nonlinear Impairments for PAM4 Short-Reach Direct Detection Systems

Abstract

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.