期刊
OPTICS COMMUNICATIONS
卷 399, 期 -, 页码 1-12出版社
ELSEVIER
DOI: 10.1016/j.optcom.2017.04.050
关键词
Fiber optics communications; Digital signal processing; Machine learning; Artificial neural network
类别
资金
- NSFC [61372119]
- Doctoral Scientific Fund Project of the Ministry of Education of China [20120005110010]
A bio-inspired detector based on the artificial neural network (ANN) and genetic algorithm is proposed in the context of a coherent optical transmission system. The ANN is designed to mitigate 16-quadrature amplitude modulation system impairments, including linear impairment: Gaussian white noise, laser phase noise, inphase/quadrature component imbalance, and nonlinear impairment: nonlinear phase. Without prior information or heuristic assumptions, the ANN, functioning as a machine learning algorithm, can learn and capture the characteristics of impairments from observed data. Numerical simulations were performed, and dispersion-shifted, dispersion-managed, and dispersion-unmanaged fiber links were investigated. The launch power dynamic range and maximum transmission distance for the bio-inspired method were 2.7 dBm and 240 km greater, respectively, than those of the maximum likelihood estimation algorithm. Moreover, the linewidth tolerance of the bio-inspired technique was 170 kHz greater than that of the k-means method, demonstrating its usability for digital signal processing in coherent systems.
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