# Photonic machine learning implementation for signal recovery in optical communications

arXiv: https://arxiv.org/abs/1710.01107 · HTML: https://mlsanity.com/paper/1710.01107/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Master memory function for delay-based reservoir computers with single-variable dynamics](https://mlsanity.com/paper/2108.12643/) (2021)
- [A Survey of Applied Machine Learning Techniques for Optical OFDM based Networks](https://mlsanity.com/paper/2105.03289/) (2021)
- [Beyond Terabit/s Integrated Neuromorphic Photonic Processor for DSP-Free Optical Interconnects](https://mlsanity.com/paper/2504.15044/) (2025)
- [Next-generation reservoir computing validated by classification task](https://mlsanity.com/paper/2512.12903/) (2025)

## Abstract (arXiv)

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.