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.
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References
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)
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)
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)
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)
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)
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)
Yang, W., Xu, G.: Method and system for interference assessment and reduction in a wireless communication system: EP, US 7068977 B1[P] (2006)
Poisel, R.A.: Information Warfare and Electronic Warfare Systems (2013)
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)
Kecman, V.: Support vector machines – an introduction. In: Support Vector Machines: Theory and Applications, pp. 1–28. Springer, Heidelberg (2005)
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)
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)
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)
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)
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_82
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)
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)
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.2018.2827036
Zheng, Z., Sangaiah, A.K., Wang, T.: Adaptive communication protocols in flying ad-hoc network. IEEE Commun. Mag. 56(1), 136–142 (2018)
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)
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.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, Y., Dou, Z., Han, H., Zhou, X., Lin, Y. (2019). Application of Nonlinear Classification Algorithm in Communication Interference Evaluation. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_49
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DOI: https://doi.org/10.1007/978-3-030-19086-6_49
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