Journal
JOURNAL OF LIGHTWAVE TECHNOLOGY
Volume 38, Issue 6, Pages 1323-1329Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2020.2973718
Keywords
Artificial neural networks; Vertical cavity surface emitting lasers; Equalizers; Signal processing algorithms; Optical interconnections; Iterative algorithms; Short reach optical interconnects; VCSEL; PAM; MMF; neural network based equalization
Funding
- National Natural Science Foundation of China [61605111]
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Advanced nonlinear digital signal processing technologies, which bring significant performance gain for high-speed optical interconnects, are highly constrained by huge complexity in the actual deployment. Fully connected neural network-based equalizer has shown powerful efficacy to deal with the complex linear and nonlinear impairments for VCSEL-enabled multi-mode optical interconnects, but it also contains a number of redundancies with little impact on performance improvement. In this article, we experimentally demonstrate a compressed neural network equalization using the iterative pruning algorithm for 112-Gbps VCSEL-enabled PAM-4 and PAM-8 transmissions over 100-m MMF. We also study the impact of threshold and pruning span on the performance of proposed algorithms. The results show up to 71% connection compression by use of the iterative pruning algorithms and maximum 28.4% improvement compared with the one-shot pruning algorithm.
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