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Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms

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Abstract

The use of genetic algorithm (GA) to simplify the structures of artificial neural network-based modulation format identification is proposed in next-generation dynamic and heterogeneous fiber-optic networks. Simulation results show that with 80 asynchronous amplitude histogram bins, by virtue of GA, the identification error rate decreases from 4.24 to 1.04 %.

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Acknowledgments

The authors would like to acknowledge the support of National Natural Science Foundation of China (NSFC) under Grant Nos. (61307092, 61435006), New Century Excellent Talents in University (NCET-12-0679), National High Technology 863 Research and Development Program of China (No. 2013AA013300).

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Authors and Affiliations

  1. Institute of Photonics Technology, Jinan University, Guangzhou, 510632, Guangdong Province, People’s Republic of China

    Shuting Zhang, Yingqing Peng, Qi Sui, Jianping Li & Zhaohui Li

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  1. Shuting Zhang
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  2. Yingqing Peng
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Correspondence to Zhaohui Li.

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Zhang, S., Peng, Y., Sui, Q. et al. Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms. Photon Netw Commun 32, 246–252 (2016). https://doi.org/10.1007/s11107-016-0606-7

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  • DOI: https://doi.org/10.1007/s11107-016-0606-7

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