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

TL;DR

This paper demonstrates an FPGA-implemented opto-electronic reservoir computer trained online for real-time wireless channel equalisation, achieving significantly lower error rates and adaptability to changing channels.

Contribution

It introduces a novel online training method for an opto-electronic reservoir computer applied to wireless communication channel equalisation, with FPGA implementation for real-time processing.

Findings

Error rates up to 100 times lower than previous methods.

Effective adaptation to drifting and switching channels.

Successful real-time implementation on FPGA hardware.

Abstract

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