Journal
IEEE PHOTONICS TECHNOLOGY LETTERS
Volume 30, Issue 15, Pages 1416-1419Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LPT.2018.2852327
Keywords
Artificial neural networks; direct detection; pulse amplitude modulation; optical access network
Funding
- MSIP/Institute for Information and Communications Technology Promotion (IITP) through the Reliable Crypto-System Standards and Core Technology Development for Secure Quantum Key Distribution Network ICT RD Program [1711057505]
- Ministry of Science and ICT (MSIT), Korea, through the Information Technology Research Center (ITRC) [IITP-2018-2015-0-00385]
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Nonlinear distortion from a directly modulated laser (DML) is one of the major limiting factors to enhance the transmission capacity beyond 10 Gb/s for an intensity modulation direct-detection optical access network. In this letter, we propose and demonstrate a low-complexity nonlinear equalizer (NLE) based on a machine-learning algorithm called artificial neural network (ANN). Experimental results for a DML-based 20-Gb/s signal transmission over an 18-km SMF-28e fiber at 1310-nm employing pulse amplitude modulation (PAM)-4 confirm that the proposed ANN-NLE equalizer can increase the channel capacity and significantly reduce the impact of nonlinear penalties.
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