Low-complexity neural network equalization for long-haul coherent transmission with cascaded semiconductor optical amplifiers

TL;DR

This paper explores a neural network-based equalization method for long-haul coherent optical transmission systems with cascaded SOAs, demonstrating significant error rate improvements especially at low dispersion levels.

Contribution

It introduces a low-complexity neural network approach for real-time compensation of distortions in systems with cascaded SOAs, enhancing performance over existing methods.

Findings

Order-of-magnitude reduction in bit error rate at low dispersion

Performance degradation at higher dispersion levels

Neural network equalization is feasible for real-time implementation

Abstract

In this letter, we numerically investigate a long-haul coherent data transmission system with a cascade of semiconductor optical amplifiers (SOAs). We exploit low-complexity neural networks that can be implemented in real time to compensate for the accumulated distortions induced by a cascade of SOAs. This equalization provides an order-of-magnitude reduction in bit error rate at low dispersion (in the O-band), whereas higher dispersion degrades performance.