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An Adaptive Covariance Matrix Based on Combined Fully Blind Self Adapted Method for Cognitive Radio Spectrum Sensing

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Abstract

The main aim of this paper is to analysis and simulate existing methods such as energy detection, Maximum–Minimum Eigen value detectors (MME), MME with blind two stage detector and compare with the proposed adaptive covariance threshold method. This comparison is being made keeping in mind the complexity and accurateness in the terms of the sensing receiver operating characteristics curve. The influence of signal bandwidth of signal in comparison to the bandwidth of observation is done for every detector. As of the MME detector, the ratio between the bandwidth of the signal and the bandwidth of the observation is observed to be 0.5 when the reasonable values are used. The performance of the adaptive covariance threshold with combined full blind self-adapted detector are simulated on Matlab. The proposed method of detector shows a superior performance values when compared to three individual detectors. The performance metrics of proposed method are performed better than other three individual detector.

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References

  1. Staple, G., & Werbach, K. (2004). The end of spectrum scarcity [spectrum allocation and utilization]. IEEE Spectrum, 41(3), 48–52.

    Article  Google Scholar 

  2. Rajendran, S., Calvo-Palomino, R., Fuchs, M., Van den Bergh, B., Cordobés, H., Giustiniano, D., et al. (2018). Electrosense: Open and big spectrum data. IEEE Communications Magazine, 56(1), 210–217.

    Article  Google Scholar 

  3. Patil, K., Prasad, R., & Skouby, K., (2011). A survey of worldwide spectrum occupancy measurement campaigns for cognitive radio. In 2011 International conference on devices and communications (ICDeCom), (pp. 1–5). IEEE.

  4. Taher, T.M., Bacchus, R.B., Zdunek, K.J., & Roberson, D.A. (2011). Long-term spectral occupancy findings in Chicago. In 2011 IEEE international symposium on dynamic spectrum access networks (DySPAN) (pp. 100–107). IEEE.

  5. Mitola, J. (1999). Cognitive radio for flexible mobile multimedia communications. In 1999 IEEE international workshop on mobile multimedia communications, 1999 (MoMuC’99), (pp. 3–10). IEEE.

  6. Zeng, Y., Liang, Y. C., Hoang, A. T., & Zhang, R. (2010). A review on spectrum sensing for cognitive radio: Challenges and solutions. EURASIP Journal on Advances in Signal Processing, 2010, 2.

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Kapoor, S., Rao, S.V.R.K., & Singh, G. (2011). Opportunistic spectrum sensing by employing matched filter in cognitive radio network. In 2011 International conference on communication systems and network technologies (CSNT), (pp. 580–583). IEEE.

  9. Satheesh, A., Aswini, S.H., Lekshmi, S.G., Sagar, S., & Kumar, M.H. (2013). Spectrum sensing techniques a comparison between energy detector and cyclostationarity detector. In 2013 International conference on control communication and computing (ICCC), (pp. 388–393). IEEE.

  10. Mishra, S.M., Ten Brink, S., Mahadevappa, R., & Brodersen, R.W., (2007). Cognitive technology for ultra-wideband/WiMax coexistence. In 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks, 2007. DySPAN 2007, (pp. 179–186). IEEE.

  11. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. California: Sage.

    Google Scholar 

  12. Shen, L., Wang, H., Zhang, W., & Zhao, Z. (2011). Blind spectrum sensing for cognitive radio channels with noise uncertainty. IEEE Transactions on Wireless Communications, 10(6), 1721–1724.

    Article  Google Scholar 

  13. Khajavi, N.T., Sadeghi, S., & Sadough, S.M.S. (2010). An improved blind spectrum sensing technique for cognitive radio systems. In 5th international symposium on telecommunications (IST), 2010 (pp. 13–17). IEEE.

  14. Axell, E., Leus, G., Larsson, E. G., & Poor, H. V. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Processing Magazine, 29(3), 101–116.

    Article  Google Scholar 

  15. Hamid, M., Barbé, K., Björsell, N., & Van Moer, W. (2012). Spectrum sensing through spectrum discriminator and maximum minimum eigenvalue detector: A comparative study. In IEEE international instrumentation and measurement technology conference (I2MTC), 2012 (pp. 2252–2256). IEEE.

  16. Hamid, M., Bjorsell, N., Van Moer, W., Barbe, K., & Slimane, S. B. (2013). Blind spectrum sensing for cognitive radios using discriminant analysis: A novel approach. IEEE Transactions on Instrumentation and Measurement, 62(11), 2912–2921.

    Article  Google Scholar 

  17. Zeng, Y., & Liang, Y. C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, 57(6), 1784–1793.

    Article  Google Scholar 

  18. Annamalai, A., & Olaluwe, A. (2014). Energy detection of unknown deterministic signals in κ–μ and η–μ generalized fading channels with diversity receivers. In 2014 international conference on computing, networking and communications (ICNC), (pp. 761–765). IEEE.

  19. Smitha, K.G., Vinod, A.P., & Nair, P.R. (2012). Low power DFT filter bank based two-stage spectrum sensing. In 2012 International conference on innovations in information technology (IIT), (pp. 173–177). IEEE.

  20. Maleki, S., Pandharipande, A., & Leus, G. (2010). Two-stage spectrum sensing for cognitive radios. In 2010 IEEE international conference on acoustics speech and signal processing (ICASSP), (pp. 2946–2949). IEEE.

  21. Nair, P. R., Vinod, A. P., Smitha, K. G., & Krishna, A. K. (2012). Fast two-stage spectrum detector for cognitive radios in uncertain noise channels. IET Communications, 6(11), 1341–1348.

    Article  MathSciNet  Google Scholar 

  22. Ejaz, W., Hasan, N. U., & Kim, H. S. (2012). SNR-based adaptive spectrum sensing for cognitive radio networks. International Journal of Innovative Computing, Information and Control, 8(9), 6095–6105.

    Google Scholar 

  23. Ejaz, W., Ul Hasan, N., Azam, M. A., & Kim, H. S. (2012). Improved local spectrum sensing for cognitive radio networks. EURASIP Journal on Advances in Signal Processing, 2012(1), 242.

    Article  Google Scholar 

  24. Geethu, S., & Narayanan, G. L. (2012). A novel high speed two stage detector for spectrum sensing. Procedia Technology, 6, 682–689.

    Article  Google Scholar 

  25. Yue, W. J., Zheng, B. Y., Meng, Q. M., & Yue, W. J. (2010). Combined energy detection and one-order cyclostationary feature detection techniques in cognitive radio systems. The Journal of China Universities of Posts and Telecommunications, 17(4), 18–25.

    Article  Google Scholar 

  26. Lee, W. Y., & Akyildiz, I. F. (2008). Optimal spectrum sensing framework for cognitive radio networks. IEEE Transactions on Wireless Communications, 7(10), 3845–3857.

    Article  Google Scholar 

  27. Hamid, M., Björsell, N., & Slimane, S. B. (2016). Energy and eigenvalue based combined fully blind self-adapted spectrum sensing algorithm. IEEE Transactions on Vehicular Technology, 65(2), 630–642.

    Article  Google Scholar 

  28. Charan, C., & Pandey, R. (2017). An adaptive spectrum-sensing algorithm for cognitive radio networks based on the sample covariance matrix. Defence Science Journal, 67(3), 325–331.

    Article  Google Scholar 

  29. Hamid, M., & Björsell, N. (2011). A novel approach for energy detector sensing time and periodic sensing interval optimization in cognitive radios. In Proceedings of the 4th international conference on cognitive radio and advanced spectrum management (p. 58).

  30. Gayathri Devi, S., Selvam, K., & Rajagopalan, S. P. (2011). An abstract to calculate big O factors of time and space complexity of machine code. International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011), Chennai, 2011, (pp. 844–847).

  31. D’Alessio, L., & Rigol, M. (2014). Long-time behavior of isolated periodically driven interacting lattice systems. Physical Review X, 4(4), 041048.

    Article  Google Scholar 

  32. Couillet, R., & Debbah, M. (2011). Random matrix methods for wireless communications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  33. Hamid, M., Björsell, N., & Slimane, S. B. (2015). Signal bandwidth impact on maximum–minimum Eigen value detection. IEEE Communications Letters, 19(3), 395–398.

    Article  Google Scholar 

  34. Leon-Garcia, A. (2017). Probability, statistics, and random processes for electrical engineering. person 3 ed (28 December 2017).

  35. Golub, G. H., & Van Loan, C. F. (2012). Matrix computations (Vol. 3). Baltimore: JHU Press.

    MATH  Google Scholar 

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Authors and Affiliations

  1. Madhav Institute of Technology and Science, Gwalior, India

    Rakesh Singh Rajput & Rekha Gupta

  2. ABV Indian Institute of Information Technology and Management, Gwalior, India

    Aditya Trivedi

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  1. Rakesh Singh Rajput
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  2. Rekha Gupta
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  3. Aditya Trivedi
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Correspondence to Rakesh Singh Rajput.

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Rajput, R.S., Gupta, R. & Trivedi, A. An Adaptive Covariance Matrix Based on Combined Fully Blind Self Adapted Method for Cognitive Radio Spectrum Sensing. Wireless Pers Commun 114, 93–111 (2020). https://doi.org/10.1007/s11277-020-07352-9

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  • DOI: https://doi.org/10.1007/s11277-020-07352-9

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