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

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 VE's 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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Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.14 July 15 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 4589-4598

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

OPTICAL COMMUNICATIONS
MACHINE LEARNING
DIGITAL SIGNAL PROCESSING
PAM
FIBER NONLINEARITIES

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3168805

Pernyataan Tanggungjawab

Govind Sharan Yadav, Chun-Yen Chuang, Kai-Ming Feng, Jhih-Heng Yan, Jyehong Chen, Young-Kai Chen

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