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Channel Power Control of Genetic-Nonlinear Algorithm Based on Impairment Aware in Optical Network

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Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

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Abstract

The physical-layer impairments may weaken the signal quality in optical transmission link. And the signal quality degradation may substantially dent the performance of optical communications. This paper, taking into account the physical-layer impairments in performance optimization, proposes genetic-nonlinear algorithm to adjust channel power and optimize OSNR (Optical Signal Noise Ratio). Simulation results confirm the validity of the controlling strategy with the better dynamic and stable performance for adjustment of transmission power at the device nodes.

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Acknowledgments

This work was supported by the Research Foundation of China Mobile, and the Foundation of Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services, the National Natural Science Foundation of China (No. 61302064), the China Postdoctoral Science Foundation (No. 2013M540862)

Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Dongyan Zhao

  2. Institute of Advanced Network Technology and New Services, University of Science and Technology Beijing, Beijing, China

    Dongyan Zhao, Yichuan Zheng, Xiaoyu Wang & Jian Sun

  3. The University-Enterprise R & D Center of Measuring and Testing Technology & Instrument and Meter Engineering, College of Electronic Science, Northeast Petroleum University, Heilongjiang, 163318, China

    Shuo Cheng & Jian Sun

Authors
  1. Dongyan Zhao
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  2. Shuo Cheng
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  3. Yichuan Zheng
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  4. Xiaoyu Wang
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  5. Jian Sun
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Corresponding author

Correspondence to Dongyan Zhao .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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© 2016 Springer Science+Business Media Singapore

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Zhao, D., Cheng, S., Zheng, Y., Wang, X., Sun, J. (2016). Channel Power Control of Genetic-Nonlinear Algorithm Based on Impairment Aware in Optical Network. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_73

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_73

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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