Low-complexity Samples versus Symbols-based Neural Network Receiver for Channel Equalization

TL;DR

This paper introduces a low-complexity neural network that performs samples-to-symbol equalization, integrating multiple DSP functions to enhance optical link performance while maintaining low computational complexity.

Contribution

It proposes a novel samples-to-symbol neural network equalizer that combines matched filtering and downsampling, outperforming traditional samples-to-sample approaches in optical communication.

Findings

Samples-to-symbol equalizer outperforms samples-to-sample approach.

Proposed method maintains low computational complexity.

Experimental results confirm performance improvements at 32 GBd OOK signals.

Abstract

Low-complexity neural networks (NNs) have successfully been applied for digital signal processing (DSP) in short-reach intensity-modulated directly detected optical links, where chromatic dispersion-induced impairments significantly limit the transmission distance. The NN-based equalizers are usually optimized independently from other DSP components, such as matched filtering. This approach may result in lower equalization performance. Alternatively, optimizing a NN equalizer to perform functionalities of multiple DSP blocks may increase transmission reach while keeping the complexity low. In this work, we propose a low-complexity NN that performs samples-to-symbol equalization, meaning that the NN-based equalizer includes match filtering and downsampling. We compare it to a samples-to-sample equalization approach followed by match filtering and downsampling in terms of performance and computational complexity. Both approaches are evaluated using three different types of NNs combined with optical preprocessing. We numerically and experimentally show that the proposed samples-to-symbol equalization approach applied for 32 GBd on-off keying (OOK) signals outperforms the samples-domain alternative keeping the computational complexity low. Additionally, the different types of NN-based equalizers are compared in terms of performance with respect to computational complexity.