Eigenvalue-Domain Neural Network Demodulator for Eigenvalue-Modulated Signal

• Published in: Journal of lightwave technology Vol. 39; no. 13; pp. 4307 - 4317
• Main Authors: Mishina, Ken, Sato, Shingo, Yoshida, Yuki, Hisano, Daisuke, Maruta, Akihiro
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: artificial neural networks; Communications systems; Demodulation; Demodulators; Eigenvalues; Eigenvalues and eigenfunctions; Encoding; fiber nonlinear optics; machine learning; Neural networks; Nonlinear optics; Optical communication; Optical fiber communication; Optical modulation; Optical pulses; optical solitons; Schrodinger equation; Sensitivity enhancement; Time domain analysis; Training
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2021.3074744

Optical eigenvalue communication is a promising technique for overcoming the Kerr nonlinear limit in optical communication systems. The optical eigenvalue associated with the nonlinear Schrödinger equation remains invariant during fiber-based nonlinear dispersive transmission. However, practical applications involving use of such systems are limited by the occurrence of fiber loss and amplified noise that induce eigenvalue distortion. Thus, several time-domain neural-network-based approaches have been proposed and demonstrated to enhance receiver sensitivity toward eigenvalue-modulated signals. However, despite the substantial improvement in power margin realized using time-domain neural-network-based demodulators compared to their conventional counterparts, these devices require rigorous training for each transmission distance owing to changes in time-domain pulses during transmission. This paper presents a method for demodulation of eigenvalue-modulated signals using an eigenvalue-domain neural network and demonstrates its utility through simulation and experimental results. Simulation results obtained in this study reveal that the proposed demodulator demonstrates superior generalization performance compared to its time-domain counterpart with regard to the transmission distance. Moreover, experimental results demonstrate successful demodulation over distances from zero to 3000 km without training for each distance.