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 Chen, Young Kai Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
JournalJournal of Lightwave Technology
DOIs
StateAccepted/In press - 2022

Keywords

  • 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

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