# Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation

arXiv: https://arxiv.org/abs/1610.06268 · HTML: https://mlsanity.com/paper/1610.06268/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [Master memory function for delay-based reservoir computers with single-variable dynamics](https://mlsanity.com/paper/2108.12643/) (2021)
- [Efficient Optimisation of Physical Reservoir Computers using only a Delayed Input](https://mlsanity.com/paper/2401.14371/) (2024)
- [Time-shift selection for reservoir computing using a rank-revealing QR algorithm](https://mlsanity.com/paper/2211.17095/) (2022)
- [Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors](https://mlsanity.com/paper/2106.16087/) (2021)
- [A tensor network approach for chaotic time series prediction](https://mlsanity.com/paper/2505.17740/) (2025)
- [Next-generation reservoir computing validated by classification task](https://mlsanity.com/paper/2512.12903/) (2025)

## Abstract (arXiv)

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementation are comparable to other state of the art algorithms for tasks such as speech recognition or chaotic time series prediction, but these are often constrained by the offline training methods commonly employed. Here we investigated the online learning approach by training an opto-electronic reservoir computer using a simple gradient descent algorithm, programmed on an FPGA chip. Our system was applied to wireless communications, a quickly growing domain with an increasing demand for fast analogue devices to equalise the nonlinear distorted channels. We report error rates up to two orders of magnitude lower than previous implementations on this task. We show that our system is particularly well-suited for realistic channel equalisation by testing it on a drifting and a switching channels and obtaining good performances