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

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 11; pp. 2686 - 2698
• Main Authors: Antonik, Piotr, Duport, Francois, Hermans, Michiel, Smerieri, Anteo, Haelterman, Marc, Massar, Serge
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial Intelligence; Artificial neural networks; channel equalization; Communications systems; Computer Science; Computer Vision and Pattern Recognition; Computers; Computing time; Distance learning; Electronics; Engineering Sciences; Equalization; Field programmable gate arrays; field-programmable gate array (FPGA); Machine Learning; Nonlinear distortion; Online instruction; online learning; Optics; optoelectronic systems; Optoelectronics; Photonic; Real-time systems; reservoir computing (RC); Reservoirs; Speech recognition; Switching; Training; Wireless communication; Wireless communications; opto-electronic systems; reservoir computing; channel equalisation; FPGA; Artificial neural networks; online learning
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2016.2598655

Reservoir computing is a bioinspired computing paradigm for processing time-dependent signals. The performance of its analog implementation is 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 optoelectronic reservoir computer using a simple gradient descent algorithm, programmed on a field-programmable gate array chip. Our system was applied to wireless communications, a quickly growing domain with an increasing demand for fast analog devices to equalize 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 equalization by testing it on a drifting and a switching channel and obtaining good performances.