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Sparsity-Tuned Elastic Net-Pruned Volterra Equalization for 80 Gb/s PAM4 transmissions for Optical Inter-Data Centers Interconnects
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Sparsity-Tuned Elastic Net-Pruned Volterra Equalization for 80 Gb/s PAM4 transmissions for Optical Inter-Data Centers Interconnects

Govind Sharan Yadav, Chun-Yen Chuang, Kai-Ming Feng, Yan JHIH-HENG Yan, Jason Jyehong ChenYoung-Kai Chen
Journal of Lightwave Technology
2022

摘要

Computational complexity digital signal processing Fiber nonlinear optics fiber nonlinearities Machine learning optical communications Optical distortion Optical fibers Optical interconnections Optical transmitters PAM System performance Atomic and Molecular Physics and Optics
Volterra equalizations (VE) have delivered significant performance improvement on optical signals, but their extremely high demands on computation complexity severely impede the possibility of large-scale deployment in resource-constrained optical interconnects. In this paper, we propose and experimentally demonstrate a novel sparsity tuned elastic net-pruned Volterra equalization (SENVE) technique to remove the redundancy in the structure of conventional VE (baseline VE) to save complexity without degrading system performance. With a three-stage configuration: pretraining, pruning, and retraining, our SENVE scheme can: (1) realize the compressed structure from a larger VE to reduce computation complexity and (2) accomplish budget-conscious and hardware- friendly structured sparsity of VE to efficiently accelerate the VEs evaluation. We experientially demonstrate the proposed scheme on an 80-Gbps four-level pulse-amplitude modulation (PAM4) signal in O-band for 40-km single-mode fiber (SMF) transmission with a 30-GHz externally modulated laser (EML). Under optimal pruning but without retraining scenarios, the experimental results show that, compared with baseline VE at the KP4-FEC limit, the proposed SENVE can achieve up to 68.7% complexity reduction without degrading performance over 40 km transmission at an received optical power (ROP) of 5 dBm. Moreover, we also find our proposed SENVE with retraining can achieve up to 90.8% and 35.8% complexity reduction, with respect to baseline VE and L1-regularization VE, without sacrificing system performance under the same over-pruning scenarios.

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