Advertisement

Cluster Computing

, Volume 22, Supplement 3, pp 7569–7576 | Cite as

Interrupt protection control of anti-interference nodes in network based on band sampling decision filter modulation

  • Kun Zhang
  • Chong ShenEmail author
  • Mengxing Huang
  • Haifeng Wang
  • Hanwen Li
  • Qian Gao
Article

Abstract

In network real-time communication, interrupt protection control needs to be conducted for interference nodes due to the multipath interference in the link layer. However, there have always been some incorrect operations in selecting optimal nodes, such as large errors and non-optimal masking results. Therefore, an interrupt protection method for anti-interference nodes in network real-time communication based on band sampling decision filter modulation is proposed in this paper. In this method, a multipath transmission link model of network real-time communication is constructed; an impulse response function of network communication is established; copy autocorrelation processing for signals of communication node sampling is conducted to realize the transformation from time domain to frequency domain; band sampling decision filter is used to conduct noise suppression for interference nodes; an interrupt decision is conducted for filtering output signals in network real-time communication according to the least mean square error criterion, and whether to conduct interrupt mask for nodes is decided according to the threshold, so as to realize the interrupt protection control for anti-interference nodes in network real-time communication. The simulation results show that by using this method to conduct interrupt protection for communication nodes, the output bit error rate is in general lower than those got by the traditional methods, and the highest bit error rate does not exceed 0.5. It tends to be flat from − 4SNR/dB, and completely unaffected by the interference nodes after 4SNR/dB. The method proposed in this paper restrains the interference nodes, and effectively improves the anti-interference capability and the communication quality.

Keywords

Network real-time communication Anti-interference Interrupt protection control Sampling decision 

Notes

Acknowledgements

This research was financially supported by the National Natural Science Foundation of China (No. 61461017); the Hainan Natural Science Foundation Innovation Research Team Project (No. 2017CXTD0004); the Hainan Province Key Research and Development Projects (No. ZDYF2016002); the Innovative Research Project of Postgraduates in Hainan Province (No. Hyb2017-07); the Open Topic of State Key Laboratory of Marine Resources Utilization in South China Sea of Hainan University (No. 2016013A); the Key Laboratory of Sanya Project (No. L1410); the Scientific and Technological Cooperative Project for College and Region of Sanya (No. 2017YD26).

References

  1. 1.
    Hou, F., Tan, J., Fan, X., et al.: A novel method for sparse channel estimation using super-resolution dictionary. EURASIP J. Adv. Signal Process. 2014(1), 1–11 (2014)CrossRefGoogle Scholar
  2. 2.
    Helmy, A., Hedayat, A., Al-Dhahir, N.: Robust weighted sum-rate maximization for the multi-stream MIMO interference channel with sparse equalization. IEEE Trans. Commun. 60(10), 3645–3659 (2015)CrossRefGoogle Scholar
  3. 3.
    Müller, L.F., Oliveira, R.R. et al. Survivor: an enhanced controller placement strategy for improving SDN survivability. In: IEEE Global Communications Conference (GLOBECOM), Austin, pp. 1909–1915 (2014)Google Scholar
  4. 4.
    Din, D.R., Huang, J.S.: Multicast backup reprovisioning problem for Hamiltonian cycle-based protection on WDM networks. Opt. Fiber Technol. 20(2), 142–157 (2014)CrossRefGoogle Scholar
  5. 5.
    Choi, J.S.: Design and implementation of a PCE-based software-defined provisioning framework for carrier-grade MPLS-TP networks. Photon Netw. Commun. 29(1), 96–105 (2014)CrossRefGoogle Scholar
  6. 6.
    Ma, J.H., Ji, L.X.: Study on agent immune network monitoring system model. Comput. Simulat. 30(5), 213–216 (2013)Google Scholar
  7. 7.
    Zhang, W.M., Chen, Q.Z.: Network intrusion detection algorithm based on HHT with shift hierarchical control. Comput. Sci. 41(12), 107–111 (2014)MathSciNetGoogle Scholar
  8. 8.
    Wu, Y., Weng, X.L.: Positioning analysis of undamaged node in large scale network intrusion. Comput. Simul. 32(10), 301–304 (2015)Google Scholar
  9. 9.
    Hu, Z., Xiong, M., Shang, H., et al.: Anti-interference measurement methods of the coupled transmission-line capacitance parameters based on the harmonic components. IEEE Trans. Power Deliv. 31(6), 2464–2472 (2016)CrossRefGoogle Scholar
  10. 10.
    Chen, J., Yuan, Z.H., Chun, Y.B.: Control method design of aircraft stability. Comput. Simul. 34(5), 39–43 (2017)Google Scholar
  11. 11.
    Shams, F., Bacci, G., Luise, M.: Energy-efficient power control for multiple-relay cooperative networks using, Q-learning. IEEE Trans. Wirel. Commun. 14(3), 1567–1580 (2015)CrossRefGoogle Scholar
  12. 12.
    Cui, G., Li, Z., Yang, C., et al.: Study on the interference between cathodic protection systems of gas station and long distance trunk pipeline. Anti-Corros. Methods Mater. 63(5), 405–413 (2016)CrossRefGoogle Scholar
  13. 13.
    Galinina, O., Pyattaev, A., Andreev, S., et al.: 5G Multi-RAT LTE-WiFi ultra-dense small cells: performance dynamics, architecture, and trends. IEEE J. Sel. Areas Commun. 33(6), 1224–1240 (2015)CrossRefGoogle Scholar
  14. 14.
    Liao, J., Jiang, C.H., Guo, C., et al.: Classification anonymity algorithm based on weight attributes entropy. Comput. Sci. 44(7), 42–46 (2017)Google Scholar
  15. 15.
    Fischer, S., Handrick, R., Aschrafi, A., et al.: Unveiling the principle of microRNA-mediated redundancy in cellular pathway regulation. RNA Biol. 12(3), 238–247 (2015)CrossRefGoogle Scholar
  16. 16.
    Kramer, F.J., Böhrnsen, F., Moser, N., et al.: The submental island flap for the treatment of intraoral tumor-related defects: No effect on recurrence rates. Oral Oncol. 51(7), 668–673 (2015)CrossRefGoogle Scholar
  17. 17.
    Xing, H., Liu, L., Zhang, R.: Secrecy wireless information and power transfer in fading wiretap channel. IEEE J. Sel. Areas Commun. 65(1), 180–190 (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Kun Zhang
    • 1
    • 2
    • 3
  • Chong Shen
    • 1
    • 3
    Email author
  • Mengxing Huang
    • 1
    • 3
  • Haifeng Wang
    • 1
    • 2
  • Hanwen Li
    • 2
  • Qian Gao
    • 1
    • 3
  1. 1.State Key Laboratory of Marine Resources Utilization in South China SeaHainan UniversityHaikouChina
  2. 2.College of Ocean Information EngineeringHainan Tropical Ocean UniversitySanyaChina
  3. 3.College of Information Science and TechnologyHainan UniversityHaikouChina

Personalised recommendations