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Coupled Transceiver-Fiber Nonlinearity Compensation Based on Machine Learning for Probabilistic Shaping System

In this article, we experimentally demonstrate the combined benefit of artificial neural network-based nonlinearity compensation and probabilistic shaping for the first time. We demonstrate that the scheme not only compensates for transceiver's nonlinearity, enabling the full benefits of shaping to be achieved, but also the combined effects of transceiver and fiber propagation nonlinearities. The performance of the proposed artificial neural network is demonstrated at 28 Gbaud for both 64-QAM and 256-QAM probabilistically shaped systems and compared to that of uniformly distributed constellations. Our experimental results demonstrate: the expected performance gains for shaping alone; an additional SNR performance gain up to 1 dB in the linear region; an additional mutual information gain of 0.2 bits per channel use in the constellation-entropy limited region. In the presence of coupled transceiver and fiber-induced nonlinearities, an additional mutual information enhancement of ~0.13 bits/symbol is experimentally observed for a fiber link of up to 500 km with the aid of the proposed artificial neural network.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.2 January 15 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 388 - 399

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
FIBER NONLINEARITY
PROBABILISTIC SHAPING
ANN
TRANSCEIVER NONLINEARITY
NONLINEAR EQUALIZER

Info Detail Spesifik

DOI: 10.1109/JLT.2020.3029336

Pernyataan Tanggungjawab

Tu T. Nguyen, Tingting Zhang, Elias Giacoumidis, Abdallah A.I. Ali, Mingming Tan, Paul Harper, Liam P. Barry, Andrew D. Ellis

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