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Application of Nonlinear Classification Algorithm in Communication Interference Evaluation

  • Yifan Chen
  • Zheng Dou
  • Hui Han
  • Xianglong Zhou
  • Yun LinEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 279)

Abstract

Traditional methods of communication interference assessment belong third-party assessments that fail to meet the needs of real-time assessments. This paper proposes an interference level evaluation method under the nonlinear classification algorithm. Firstly, building data set with the eigenvalues that affect the interference effect, and then simulation verify by BP neural network and support vector machine. The simulation results verify the feasibility in communication interference assessment and providing the possibility for real-time evaluation.

Keywords

Interference level assessment BP neural network Support vector machine 

Notes

Acknowledgment

This work is supported by the National Natural Science Foundation of China (61771154) and the Fundamental Research Funds for the Central Universities (HEUCFG201830).

This paper is also funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation.

Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.

We gratefully thank of very useful discussions of reviewers.

References

  1. 1.
    Lin, X.H., Xue, G.Y., Liu, P.: Novel data acquisition method for interference suppression in dual-channel SAR. Prog. Electromagnet. Res. 144(1), 79–92 (2014)CrossRefGoogle Scholar
  2. 2.
    Khanduri, A.C., Bédard, C., Stathopoulos, T.: Modelling wind-induced interference effects using back propagation neural networks. J. Wind Eng. Ind. Aerodyn. 72(1), 71–79 (1997)CrossRefGoogle Scholar
  3. 3.
    Liu, P., Jin, F., Zhang, X., et al.: Research on the multi-attribute decision-making under risk with interval probability based on prospect theory and the uncertain linguistic variables. Knowl.-Based Syst. 24(4), 554–561 (2011)CrossRefGoogle Scholar
  4. 4.
    Lu, D., Baprawski, J., Yao, K.: BER simulation of digital communication systems with intersymbol interference and non-Gaussian noise using improved importance sampling. In: Conference Record, Military Communications in a Changing World, IEEE Military Communications Conference, MILCOM 1991, vol. 1, pp. 273–277. IEEE (2002)Google Scholar
  5. 5.
    Albu, F., Martinez, D.: The application of support vector machines with Gaussian kernels for overcoming co-channel interference. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing Ix, 1999, pp. 49–57. IEEE (1999)Google Scholar
  6. 6.
    Han, G.Q., Li, Y.Z., Xing, S.Q., et al.: Research on an evaluation method for new deceptive jamming effect on SAR. J. Astronaut. 32(9), 1994–2001 (2011)Google Scholar
  7. 7.
    Yang, W., Xu, G.: Method and system for interference assessment and reduction in a wireless communication system: EP, US 7068977 B1[P] (2006)Google Scholar
  8. 8.
    Poisel, R.A.: Information Warfare and Electronic Warfare Systems (2013)Google Scholar
  9. 9.
    Li, J., Cheng, J.H., Shi, J.Y., et al.: Brief introduction of back propagation (BP) neural network algorithm and its improvement, vol. 169, pp. 553–558 (2012)CrossRefGoogle Scholar
  10. 10.
    Kecman, V.: Support vector machines – an introduction. In: Support Vector Machines: Theory and Applications, pp. 1–28. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    He, H.X., Yan, W.M.: Structural damage detection with wavelet support vector machine: introduction and applications. Struct. Control Health Monit. 14(1), 162–176 (2007)CrossRefGoogle Scholar
  12. 12.
    Giorgetti, A., Chiani, M., Win, M.Z.: The effect of narrowband interference on wideband wireless communication systems. IEEE Trans. Commun. 53(12), 2139–2149 (2005)CrossRefGoogle Scholar
  13. 13.
    Zhang, Y., Zhao, D.N., Jiang, G.J.: The simulation design and implementation of military short wave communication anti-jamming performance. Acta Simulata Systematica Sinica (2003)Google Scholar
  14. 14.
    Song, W., Chiu, W., Goldsman, D.: Importance sampling techniques for estimating the bit error rate in digital communication systems. In: 2005 Proceedings of the Winter Simulation Conference, pp. 1–14. IEEE (2006)Google Scholar
  15. 15.
    Xu, C., Xu, C.: Optimization analysis of dynamic sample number and hidden layer node number based on BP neural network. In: Yin, Z., Pan, L., Fang, X. (eds.) Proceedings of the Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). AISC, vol. 212, pp. 687–695. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37502-6_82CrossRefGoogle Scholar
  16. 16.
    Miura, A., Watanabe, H., Hamamoto, N., et al.: On interference level in satellite uplink for satellite/ terrestrial integrated mobile communication system. IEICE Tech. Rep. 110, 105–110 (2010)Google Scholar
  17. 17.
    Tu, Y., Lin, Y., Wang, J., et al.: Semi-supervised learning with generative adversarial networks on digital signal modulation classification. CMC-Comput. Mater. Continua 55(2), 243–254 (2018)Google Scholar
  18. 18.
    Zhou, J.T., Zhao, H., Peng, X., Fang, M., Qin, Z., Goh, R.S.M.: Transfer Hashing: From Shallow to Deep. IEEE Trans. Neural Netw. Learn. Syst.  https://doi.org/10.1109/tnnls.2018.2827036CrossRefGoogle Scholar
  19. 19.
    Zheng, Z., Sangaiah, A.K., Wang, T.: Adaptive communication protocols in flying ad-hoc network. IEEE Commun. Mag. 56(1), 136–142 (2018)CrossRefGoogle Scholar
  20. 20.
    Zhao, N., Richard Yu, F., Sun, H., Li, M.: Adaptive power allocation schemes for spectrum sharing in interference-alignment-based cognitive radio networks. IEEE Trans. Veh. Technol. 65(5), 3700–3714 (2016)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Yifan Chen
    • 1
  • Zheng Dou
    • 1
  • Hui Han
    • 2
  • Xianglong Zhou
    • 1
  • Yun Lin
    • 1
    Email author
  1. 1.College of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina
  2. 2.State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE)LuoyangChina

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