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Periodic multimedia spectrum sensing method based on high-order anti-jamming mechanism in cognitive wireless networks

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Abstract

The cognitive radio network provides high bandwidth for mobile users to reconstruct wireless architecture and dynamic spectrum access technology. The spectrum allocation is the key to cognitive radio spectrum resources for the relative scarcity of radio spectrum resources. The spectrum allocation algorithm must have faster convergence speed to adapt to the time-varying characteristics of cognitive radio networks. In this paper, a spectrum sensing method is proposed based on high-order anti-jamming mechanism for cognitive radio spectrum allocation. Firstly, the combination of cognitive anti-jamming spectrum sensing, channel estimation, learning comprehension, time-hopping frequency hopping, spectrum access control and other technologies can improve the anti-jamming ability. Secondly, cognitive radio can effectively improve the utilization of spectrum resources according to the different benefits of different users on different channels, achieve the best matching between cognitive users and channels and flexible spectrum allocation. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.

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References

  1. Ahmed IK, Fapojuwo AO (2017) Stackelberg equilibria of an anti-jamming game in cooperative cognitive radio networks[J]. IEEE Transactions on Cognitive Communications & Networking PP(99):1–1

    Google Scholar 

  2. Arunkumar N, Mohammed MA, Abd Ghani MK et al (2018) K-means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput. https://doi.org/10.1007/s00500-018-3618-7

  3. Arunkumar N, Mohammed MA, Mostafa SA, Ibrahim DA, Rodrigues JJPC, de Albuquerque VHC (2018) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurrency Computat Pract Exper:e4962. https://doi.org/10.1002/cpe.4962

  4. Bagyalakshmi G, Rajkumar G, Arunkumar N, Easwaran M, Narasimhan K, Elamaran V, Solarte M, Hernández I, Ramirez-Gonzalez G (2018) Network vulnerability analysis on brain signal/image databases using Nmap and Wireshark tools. IEEE Access 6:57144–57151

    Article  Google Scholar 

  5. Balogun V, Krings A (2013) On the impact of jamming attacks on cooperative spectrum sensing in cognitive radio networks[J]. Wirel Pers Commun 72(4):2229–2249

    Article  Google Scholar 

  6. Balogun V, Krings A (2013) On the impact of jamming attacks on cooperative spectrum sensing in cognitive radio networks[C]. In: Eighth Cyber Security & Information Intelligence Research Workshop

  7. Cadeau W, Li X (2012) Anti-jamming performance of cognitive radio networks under multiple uncoordinated jammers in fading environment[C]. In: Information Sciences & Systems

  8. Dongdong J, Arunkumar N, Wenyu Z, Beibei L, Xinlei Z, Guangjian Z (2019) Semantic clustering fuzzy c means spectral model based comparative analysis of cardiac color ultrasound and electrocardiogram in patients with left ventricular heart failure and cardiomyopathy. Futur Gener Comput Syst 92:324–328

    Article  Google Scholar 

  9. Elamaran V, Arunkumar N, Hussein AF, Solarte M, Ramirez-Gonzalez G (2018) Spectral fault recovery analysis revisited with Normal and abnormal heart sound signals. IEEE Access 6:62874–62879

    Article  Google Scholar 

  10. Elamaran V, Arunkumar N, Babu GV, Balaji VS, Gómez J, Figueroa C, Ramirez-Gonzalez G (2018) Exploring DNS, HTTP, and ICMP response time computations on brain signal/image databases using a packet sniffer tool. IEEE Access 6:59672–59678

    Article  Google Scholar 

  11. Fang S, Liu Y, Ning P (2016) Wireless communications under broadband reactive jamming attacks[J]. IEEE Transactions on Dependable & Secure Computing 13(3):394–408

    Article  Google Scholar 

  12. Haoyu L, Jianxing L, Arunkumar N, Hussein AF, Jaber MM (2018) An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.12.001

  13. Jiajie L, Narasimhan K, Elamaran V, Arunkumar N, Solarte M, Ramirez-Gonzalez G (2018) Clinical decision support system for alcoholism detection using the analysis of EEG signals. IEEE Access 6:61457–61461

    Article  Google Scholar 

  14. Khamparia A, Singh A, Anand D et al (2018) A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders. Neural Comput & Applic. https://doi.org/10.1007/s00521-018-3896-0

  15. Lakshmanaprabu SK, Mohanty S, Shankar K, Arunkumar N, Ramirez G (2019) Optimal deep learning model for classification of lung Cancer on CT images. Futur Gener Comput Syst 92:374–382

    Article  Google Scholar 

  16. Liu MY, Li SY, Liu Q (2009) Cognitive Radio Spectrum Sensing Based on Index Belief Degree Function[C]. In: International Conference on Wireless Communications

  17. Lo BF, Akyildiz IF (2013) Multiagent jamming-resilient control channel game for cognitive radio ad hoc networks[C]. IEEE International Conference on Communications

  18. Machuzak S, Jayaweera SK (2016) Reinforcement learning based anti-jamming with wideband autonomous cognitive radios[C]. In: IEEE/CIC International Conference on Communications in China

  19. Mohammadi J, Stańczak S, Zheng M (2015) Joint spectrum sensing and jamming detection with correlated channels in cognitive radio networks[C]. In: IEEE International Conference on Communication Workshop

  20. Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Rajendra Acharya U (2018) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput & Applic:1–7. https://doi.org/10.1007/s00521-018-3689-5

  21. Rajendra Achary U, YukiHagiwara, Deshpande SN, Suren S, Koh JEW, Oh SL, Arunkumar N, Ciaccio EJ, Lim CM (2019) Characterization of focal EEG signals: a review. Futur Gener Comput Syst 91:290–299

    Article  Google Scholar 

  22. Santamaria-Granados L, Munoz-Organero M, Ramirez-Gonzalez G, Abdulhay E, Arunkumar N (2018) Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS). IEEE Access. https://doi.org/10.1109/ACCESS.2018.2883213

  23. Su H, Wang Q, Ren K et al (2011) Jamming-Resilient Dynamic Spectrum Access for Cognitive Radio Networks[C]. In: IEEE International Conference on Communications

  24. Wang Q, Ren K, Ning P (2011) Anti-jamming communication in cognitive radio networks with unknown channel statistics.[C]. In: IEEE International Conference on Network Protocols

  25. Wang B, Wu Y, Liu KJR et al (2011) An anti-jamming stochastic game for cognitive radio networks[J]. IEEE Journal on Selected Areas in Communications 29(4):877–889

    Article  Google Scholar 

  26. Wang Q, Ren K, Ning P et al (2016) Jamming-resistant multiradio multichannel opportunistic Spectrum access in cognitive radio networks[J]. IEEE Trans Veh Technol 65(10):8331–8344

    Article  Google Scholar 

  27. Wu Z, Wang H, Arunkumar N (2019) Bayesian analysis model for the use of anesthetic analgesic drugs in cancer patients based on geometry reconstruction. Futur Gener Comput Syst 93:170–175

    Article  Google Scholar 

  28. Zhang L, Pei Q, Li H (2013) Anti-jamming Scheme Based on Zero Pre-shared Secret in Cognitive Radio Network[C]. In: Eighth International Conference on Computational Intelligence & Security

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Acknowledgements

Mine IOT converged communication network architecture and its transmission technology and equipment(2017YFC0804405).

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Authors and Affiliations

  1. School of network education, Beijing University of Posts and Telecommunications, Beijing, China

    Yanli Ji

  2. Information and Electronic Technology Lab, Beijing University of Posts and Telecommunications, Beijing, China

    Weidong Wang & Yinghai Zhang

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  1. Yanli Ji
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  2. Weidong Wang
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  3. Yinghai Zhang
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Correspondence to Yanli Ji.

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Ji, Y., Wang, W. & Zhang, Y. Periodic multimedia spectrum sensing method based on high-order anti-jamming mechanism in cognitive wireless networks. Multimed Tools Appl 79, 35171–35182 (2020). https://doi.org/10.1007/s11042-019-7533-4

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  • DOI: https://doi.org/10.1007/s11042-019-7533-4

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