DOAJ Open Access 2021

A Layer-Reduced Neural Network Based Digital Backpropagation Algorithm for Fiber Nonlinearity Mitigation

Pinjing He Aiying Yang Peng Guo Yaojun Qiao Xiangjun Xin

Abstrak

A layer-reduced neural network based digital backpropagation algorithm called smoothing learned digital backpropagation (smoothing-LDBP), is proposed in this paper. The smoothing-LDBP smooths the power terms in nonlinear activation functions to limit the bandwidth. The limited bandwidth of the power terms generates fewer in-band distortions, thus reduces the required layer for a given equalization performance. Simulation results show that the required layers of smoothing-LDBP are reduced by approximately 62% at 6.7% HD-FEC compared with learned digital backpropagation. Owing to the layer reduction, the latency and the complexity are reduced by 69% and 51%, respectively. The layer-reduced property of smoothing-LDBP is also validated by a proof-of-concept experiment.

Topik & Kata Kunci

Applied optics. Photonics Optics. Light

Penulis (5)

Pinjing He

Aiying Yang

Peng Guo

Yaojun Qiao

Xiangjun Xin

Format Sitasi

APA MLA BibTeX

He, P., Yang, A., Guo, P., Qiao, Y., Xin, X. (2021). A Layer-Reduced Neural Network Based Digital Backpropagation Algorithm for Fiber Nonlinearity Mitigation. https://doi.org/10.1109/JPHOT.2021.3087592

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Informasi Jurnal

Tahun Terbit: 2021
Sumber Database: DOAJ
DOI: 10.1109/JPHOT.2021.3087592
Akses: Open Access ✓