Electronic polarization-division demultiplexing based on artificial neural networks in optical communication systems

Yuichiro Kurokawa; Takeru Kyono; Moriya Nakamura

doi:10.1117/12.2544848

24 February 2020 Electronic polarization-division demultiplexing based on artificial neural networks in optical communication systems

Yuichiro Kurokawa, Takeru Kyono, Moriya Nakamura

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Proceedings Volume 11299, AI and Optical Data Sciences; 1129910 (2020) https://doi.org/10.1117/12.2544848
Event: SPIE OPTO, 2020, San Francisco, California, United States

Abstract

In coherent optical-fiber communication systems, polarization-division multiplexing is employed to double the transmission capacity. Polarization tracking based on digital signal processing (DSP) is used to cope with the polarization fluctuations of the light wave, which are caused by disturbances of the optical fibers. Usually, the polarization demultiplexing and polarization tracking are performed by using butterfly-structured finite impulse response (FIR) filters. We have proposed and investigated novel methods of polarization tracking using artificial neural networks (ANNs). An ANN can perform polarization demultiplexing because an ANN includes butterfly structures. Adaptive control of the weights of the ANN can be achieved by using decision directed least mean squares (DD-LMS) algorithm. Furthermore, the ANNs can potentially compensate waveform distortion caused by optical nonlinear effects such as self phase modulation (SPM) and cross-phase modulation (XPM). In this paper, we investigated the polarization tracking performance of the ANN under various conditions of polarization fluctuation speed by numerical simulations, comparing with that of FIR filters. Furthermore, we investigated the tracking performance depending on the number of input layer and hidden layer units of the ANN. The results show that the ANN can efficiently track the polarization fluctuation.

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Yuichiro Kurokawa, Takeru Kyono, and Moriya Nakamura "Electronic polarization-division demultiplexing based on artificial neural networks in optical communication systems", Proc. SPIE 11299, AI and Optical Data Sciences, 1129910 (24 February 2020); https://doi.org/10.1117/12.2544848

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KEYWORDS

Polarization

Digital signal processing

Finite impulse response filters

Detection and tracking algorithms

Artificial neural networks

Multiplexing

Telecommunications

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