An Interpretable Mapping from a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization

TL;DR

This paper introduces a novel AI-based approach that maps a communication system to a neural network to achieve optimal joint equalization without altering existing DSP structures, improving performance in optical systems.

Contribution

It presents a new method of representing a communication system as a neural network for joint equalization, leveraging AI for optimization without modifying DSP architecture.

Findings

Achieves a 0.76 dB gain for 65 GBaud 16QAM signals with 16 WSSs.

Demonstrates the effectiveness of the neural network mapping through extensive simulations.

Validates the approach's potential for optical transceiver optimization.

Abstract

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.