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Scalable and Robust ANN Based Cooperative Spectrum Sensing for Cognitive Radio Networks

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Abstract

Cognitive radio network (CRN) supports dynamic spectrum access addressing spectrum scarcity issue experienced by today’s wireless communication network. Sensing is an important task and cooperative spectrum sensing is used for improving detection performance of spectrum. The sensing information from individual secondary users is sent to fusion center to infer a common global decision regarding primary user’s presence. Various fusion schemes for decision making are proposed in the literature but they lack scalability and robustness. We have introduced artificial neural network (ANN) at fusion center thereby achieving significant improvement in detection performance and reduction in false alarm rate as compared to conventional schemes. The proposed ANN scheme is found capable to deal with scalability of CRN with consistent performance. Further, SNR of individual Secondary user is taken into consideration in decision making at fusion center. Moreover the proposed scheme is tested against security attack (malicious users) and inadvertent errors occurring at SUs are found to be robust.

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References

  1. Federal Communications Commission (FCC). (2003). Spectrum policy task force report. ET docket no. 02-135.

  2. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communication. IEEE Journal on Selected Areas Communication, 23(2), 201–220.

    Article  Google Scholar 

  3. Wyglinski, A. H., Nekovee, M., & Hou, Y. T. (2010). Cognitive radio communications and networks. New York: Academic Press.

    Google Scholar 

  4. Jaglan, R. R., Sarowa, S., Mustafa, R., Agrawal, S., & Kumar, N. (2015). Comparative study of single-user spectrum sensing techniques in cognitive radio networks. Procedia Computer Science, 58(2015), 121–128.

    Article  Google Scholar 

  5. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130.

    Article  Google Scholar 

  6. Bargavi, D., &Murthy, C. R. (2010). Performance comparison of energy, matched filter and cyclostationary based spectrum sensing. In 2014 Eleventh international workshop on signal processing advances in wireless communication (SPAWC) (pp. 1–6).

  7. Garhwal, A., & Bhattacharya, P. P. (2011). A survey on spectrum sensing techniques in cognitive radio. International Journal of Computer Science & Communication, Networks, 1, 196–206.

    Google Scholar 

  8. Jaglan, R. R., Mustafa, R., Sarowa, S., & Agrawal, S. (2016). Performance evaluation of energy detection based cooperative spectrum sensing in cognitive radio networks. In First international conference on information & communication technology for intelligent systems (Vol. 2, No. 1, pp. 585–593). Springer.

  9. Chaudhri, S., Lunden, J., Koivunen, V., & Poor, H. V. (2012). Cooperative sensing with imperfect reporting channels, hard decisions or soft decisions. IEEE Transactions on Signal Processing, 60(1), 18–28.

    Article  MathSciNet  Google Scholar 

  10. Letaief, K. B., & Zhang, W. (2009). Cooperative communications for cognitive radio networks. Proceedings of IEEE, 97(5), 878–893.

    Article  Google Scholar 

  11. Yao, C., & Wu, Q. (2014). A hybrid combination scheme for cooperative spectrum sensing in cognitive radio networks. Mathematical Problems in Engineering, 2014(1), 1–7.

    Google Scholar 

  12. Bouraoui, R., & Besbes, H. (2016). Cooperative spectrum sensing for cognitive radio networks: Fusion rules performance analysis. In International wireless communication & mobile computing conference (IWCMC) (pp. 136–143). IEEE.

  13. Chen, Y. (2010). Analytical performance of collaborative spectrum sensing using censored energy detection. IEEE Transactions on Wireless Communications, 9(12), 3856–3865.

    Article  Google Scholar 

  14. Duan, D., Yang, L., & Scharf. L. L. (2012). The optimal fusion rule for cooperative spectrum sensing from a diversity perspective. In Forty sixth Asilomar conference on signals, systems & computers (ASILOMAR) (pp. 1056–1062). IEEE.

  15. Althunibat, S., Di Renzo, M., & Granelli, F. (2013). Optimizing the k-out-of-N rule for cooperative spectrum sensing in cognitive radio networks. In Global communications conference (GLOBECOM) (pp. 1–5). IEEE.

  16. Farag, H. M., & Mohamed, E. M. (2017). Soft decision cooperative spectrum sensing with noise uncertainty reduction. Pervasive & Mobile Computing, 35(1), 146–164.

    Article  Google Scholar 

  17. Chawdhury, M., & Kader, M. F. (2013). Performance analysis of local and cooperative spectrum sensing in cognitive radio networks. International Journal of Signal Processing, Image Processing and Pattern Recognition, 6(6), 397–410.

    Article  Google Scholar 

  18. Pudi, S. K., Sundara, T. S., & Padmaja, D. N. (2013). Performance analysis of cognitive radio based on cooperative spectrum sensing. International Journal of Engineering Trends & Technology Innovation (IJETI), 4(4), 821–827.

    Google Scholar 

  19. Sriharipriya, K. C., & Baskaran, K. (2014). Collaborative spectrum sensing of cognitive radio networks with simple and effective fusion scheme. Circuits, Systems, and Signal Processing, 33(9), 2851–2865.

    Article  Google Scholar 

  20. Liu, X., Zhong, Wei-Zhi, & Chen, Kun-qi. (2015). Optimization of sensing time and cooperative user allocation for OR rule cooperative spectrum sensing in cognitive radio network. Journal of Central South University, 22(7), 2646–2654.

    Article  Google Scholar 

  21. Do, N. T., & An, B. (2015). A soft-hard combination-based cooperative spectrum sensing for cognitive radio networks. Sensors, 15(2), 4388–4407.

    Article  Google Scholar 

  22. Ma, J., Zhao, G., & Li, Y. (2008). Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 7(11), 4502–4507.

    Article  Google Scholar 

  23. Du, J., Guo, D., Zhang, B., & Su, Y. (2015). A robust cooperative spectrum sensing-assisted multiuser resource allocation scheme. Mathematical Problems in Engineering, 1, 1–12.

    MathSciNet  Google Scholar 

  24. Verma, P., & Singh, B. (2016). On the decision fusion for cooperative spectrum sensing in cognitive radio networks. Wireless Networks, 16(1), 1–10.

    Google Scholar 

  25. Chen, C., Cheng, H., & Yao, Y. D. (2011). Cooperative spectrum sensing in cognitive radio networks in the presence of the PUEA. IEEE Transactions on Wireless Communications, 10(7), 2135–2141.

    Article  Google Scholar 

  26. Li, H., Cheng, X., Li, K., Hu, C., Zhang, N., & Xue, W. (2014). Robust collaborative spectrum sensing schemes for cognitive radio networks. IEEE Transactions on Parallel and Distributed Systems, 25(8), 2190–2200.

    Article  Google Scholar 

  27. Chen, Y., Zhang, H., Hu, H., & Wang, Q. (2014). An efficient cooperative spectrum sensing algorithm based on BP neural network. In International conference on wireless communication and sensor network (pp. 297–301).

  28. Pattanayak, S., Venkateswaran, P., & Nandi, R. (2013). Artificial intelligence based model for channel status prediction: A new spectrum sensing technique for cognitive radio. International Journal on Communication, Network and System Sciences, 6(3), 139–148.

    Article  Google Scholar 

  29. Giribone, P., Revetria, R., Antonetti, M., & Tablacci, R. (2000). Use of artificial neural networks as support for energy saving procedures in telecommunications. In Twenty-second international telecommunications energy conference (INTELEC) (Vol. 2000, No. 1, pp. 159–162). IEEE.

  30. He, A., Bae, K. K., Newman, T. R., Gaeddert, J., Kim, K., Menon, R., et al. (2010). A survey of artificial intelligence for cognitive radios. IEEE Transactions on Vehicular Technology, 59(4), 1578–1592.

    Article  Google Scholar 

  31. Zhu, X. L., Liu, Y. A., Wey, W. W., & Yuan, D. M. (2008). Channel sensing algorithm based on neural networks for cognitive wireless mesh networks. In Fourth international conference on wireless communication, networks & mobile computing (WiCOM) (pp. 1–4). IEEE.

  32. Liang, Y. C., Zeng, Y., Peh, E. C. Y., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(4), 1326–1337.

    Article  Google Scholar 

  33. Chatziantoniou, E., Allen, B., & Velisavljevic, V. (2015). Threshold optimization for energy detection based spectrum sensing over hyper-rayleigh fading channels. IEEE Communications Letters, 19(6), 1077–1080.

    Article  Google Scholar 

  34. Haykin, S. (1999). Neural networks: A comprehensive foundation. Upper Saddle River, NJ: Prentice-Hall.

    MATH  Google Scholar 

  35. Karray, F. O., & De Silva, C. W. (2004). Soft computing and intelligent systems design: Theory, tools and applications. London: Pearson.

    Google Scholar 

  36. Baldo, N., & Zorzi, M. (2008). Learning and adaptation in cognitive radios using neural networks. In Fifth IEEE consumer communications and networking conference (CCNC) (pp. 998–1003).

  37. Orcay, O., & Ustundag, B. (2008). Pattern recognition in cognitive communication. In Twenty-third International symposium on computer & information sciences (ISCIS) (pp. 1–6). IEEE.

  38. www.dataschool.io/simple-guide-to-confusion-matrix-terminology. Accessed May 01, 2017.

  39. https://www.mathworks.com/products/neural-network/features.html. Accessed Jan 25, 2017.

  40. Lavanis, N., & Jalihal, D. (2017). Performance of p-norm detector in cognitive radio networks with cooperative spectrum sensing in presence of malicious users. Wireless Communications and Mobile Computing, 17(1), 1–8.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electronics and Communication, U.I.E.T., Panjab University, Chandigarh, India

    Reena Rathee Jaglan, Rashid Mustafa & Sunil Agrawal

Authors
  1. Reena Rathee Jaglan
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  2. Rashid Mustafa
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  3. Sunil Agrawal
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Correspondence to Reena Rathee Jaglan.

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Jaglan, R.R., Mustafa, R. & Agrawal, S. Scalable and Robust ANN Based Cooperative Spectrum Sensing for Cognitive Radio Networks. Wireless Pers Commun 99, 1141–1157 (2018). https://doi.org/10.1007/s11277-017-5168-1

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  • DOI: https://doi.org/10.1007/s11277-017-5168-1

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