Low-Complexity Multi-Task Learning Aided Neural Networks for Equalization in Short-Reach Optical Interconnects

Abstract

With the rapid development of machine learning technologies in recent years, different types of neural network (NN)-based equalizers have been proposed and proved to be efficient digital signal processing tools to deal with the nonlinear impairments in short-reach direct detection optical interconnects. However, one major concern for these NN-based equalizers is their computational complexity (CC), since only a few tens of multiplications per symbol can be practically handled considering real-time implementation. In this paper, we propose an NN-based multi-symbol equalization scheme inspired by multi-task learning. Compared with traditional single-output NN-based equalizers, the CC can be si..