SkipNet: an adaptive neural network equalization algorithm for future passive optical networking
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Published Version
Date
2024-10-08
Authors
Murphy, Stephen L.
Townsend, Paul D.
Antony, Cleitus
Journal Title
Journal ISSN
Volume Title
Publisher
Optica Publishing Group
Published Version
Abstract
In this paper, we propose an original adaptive neural network equalizer (NNE) algorithm named SkipNet, which is suitable for rapid training on a packet-by-packet basis for burst-mode non-linear equalization in upstream PON transmission. SkipNet uses the simple LMS algorithm and avoids complex neural network training algorithms such as backpropagation and mini-batch training. We demonstrate SkipNet on captured continuous mode 100 Gbit/s PAM4 signals using an SOA preamplifier to achieve the challenging 29 dB PON optical loss budget. The adaptive SkipNet equalizer is shown to overcome combinations of severe SOA patterning effects and fiber dispersion impairments to achieve >29dB dynamic range back-to-back and >22.9dB dynamic range for up to 81.6 ps/nm accumulated dispersion. It can adapt in as little as 250 training symbols to each impairment scenario, which is equivalent to existing FFE/DFE solutions, while matching the non-linear performance of previously proposed static NNE solutions. To the best of our knowledge, SkipNet is the first ever adaptive NNE framework that can realistically be trained and adapted on a packet-by-packet basis and within strict PON packet preamble lengths.
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Keywords
Adaptive neural network equalizer (NNE) algorithm , SkipNet , Burst-mode non-linear equalization , Upstream PON transmission
Citation
Murphy, S. L., Townsend, P. D. and Antony, C. (2024) 'SkipNet: an adaptive neural network equalization algorithm for future passive optical networking', Journal of Optical Communications and Networking, 16(11), pp. 1082-1092. https://doi.org/10.1364/JOCN.528490