Photonic machine learning implementation for signal recovery in optical communications

TL;DR

This paper presents a photonic reservoir computing approach that significantly improves signal classification in optical communications, extending transmission range and reducing bit-error-rate for distorted signals.

Contribution

The authors introduce a simplified photonic reservoir computing scheme for classifying severely distorted optical signals, demonstrating substantial performance improvements.

Findings

Bit-error-rate improved by two orders of magnitude

Communication range extended by over 75%

Experimental implementation validates the approach

Abstract

Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheless, processing time-dependent high-speed signals can turn into an extremely challenging task, especially when these signals have been nonlinearly distorted. Recently, analogue hardware concepts using nonlinear transient responses have been gaining significant interest for fast information processing. Here, we introduce a simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission. To this end, we convert the direct bit detection process into a pattern recognition problem. Using an experimental implementation of our photonic reservoir computer, we demonstrate an improvement in bit-error-rate by two orders of magnitude, compared to directly classifying the transmitted signal. This improvement corresponds to an extension of the communication range by over 75%. While we do not yet reach full real-time post-processing at telecom rates, we discuss how future designs might close the gap.