Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 28, Issue 11, Pages 2686-2698Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2016.2598655
Keywords
Artificial neural networks; channel equalization; field-programmable gate array (FPGA); online learning; optoelectronic systems; reservoir computing (RC)
Categories
Funding
- Belgian Science Policy Office [IAP P7-35]
- Fonds de la Recherche Scientifique FRS-FNRS
- Action de la Recherche Concertee of the Academie Universitaire Wallonie-Bruxelles [AUWB-2012-12/17-ULB9]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available