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Performance Analysis of Blind Eigenvalue with Multiple Antenna-Based Spectrum Sensing in Cognitive Radio

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Ambient Communications and Computer Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 696))

Abstract

Spectrum sensing (SS) is the important functions of cognitive radio networks (CRNs) which decides whether the band or sub-band of spectrum is available or not for secondary users (SUs), i.e., cognitive radios (CRs). In this paper, the authors considered the problem of the spectrum sensing, i.e., detection of the primary users under the case of unknown signal and noise levels. The detection method of primary user is based on blind eigenvalue, as well as multiple antenna system is considered for spectrum sensing. On the increment of one, more receivers gave the better performance of detection as compared to increasing thousands of received signal samples or increasing covariance variable for eigenvalue-based spectrum sensing.

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References

  1. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021 White Paper, Document ID: 1454457600805266, Updated: March 28, (2017).

    Google Scholar 

  2. Sharma, S. K., Lagunas, E., Chatzinotas, S., Ottersten, B.: Application of comprehensive sensing in cognitive radio communications: A Survey. IEEE communications surveys & tutorials, 18 (3), 1838–1860, (2016).

    Google Scholar 

  3. FCC: Spectrum Policy Task Force Report. ET Docket No. 02-155, Nov 02, (2002).

    Google Scholar 

  4. Mitola, J., Maguire, G. Q.: Cognitive radios: making software radios more personal. IEEE Pers. Communication, 6(4), 13–18, (1999).

    Google Scholar 

  5. Muchandi, N., Khanai, R.: Cognitive radio spectrum sensing: a survey, IEEE international conference on electrical, electronics and optimization techniques, 3233–3237, (2016).

    Google Scholar 

  6. Wang, B., Liu, K. J. R.: Advances in cognitive radio networks: a survey. IEEE journal of selected topics in signal processing, 5(1) 5–23, (2011).

    Google Scholar 

  7. Ghasemi, A., Sousa, E. S.: Spectrum sensing in cognitive radio networks: The cooperative processing tradeoff. Wireless Commun. And Mobile Comput., 7(9), 1049–1060, (2007).

    Google Scholar 

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

    Google Scholar 

  9. Simon, M. K., Alouini, M. S.: Digital Communication over Fading Channels, 2nd edition, John Wiley & Sons, Inc, (2004).

    Google Scholar 

  10. Digham, F., Alouini, M., Simon, M.: On the energy detection of unknown signals over fading channels. In Proc. IEEE Int. Conf. Commun., 5, 3575–3579, (2003).

    Google Scholar 

  11. Verma, P. K., Soni, S. K., Jain, P.: On the performance of energy detection-based CR with SC diversity over IG channel. International journal of electronics, 104(12), 1945–1956, (2017).

    Google Scholar 

  12. Kostylev, V.: Energy detection of a signal with random amplitude. In Proc. IEEE ICC, 1606–1610, (2002).

    Google Scholar 

  13. Ruttik, K., Koufos, K., Jantti, R.: Detection of unknown signals in a fading environment. IEEE communications letter, 13(7), 498–500, (2009).

    Google Scholar 

  14. Tandra, R., Sahai, A.: Fundamental limits on detection in low SNR under noise uncertainty. International Conference on Wireless Networks, Communications and Mobile Computing, Maui, HI, USA, 464–469, (2005).

    Google Scholar 

  15. Digham, F.F., Alouini, M. S., Simon, M. K.: On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55(1), 1–7, (2007).

    Google Scholar 

  16. Salahdine, F., Ghazi, H. E., Kaabouch, N., Fihri, W. F.: Matched filter detection with dynamic threshold for cognitive radio networks. IEEE International conference on wireless networks and mobile communications, 1–6, (2015).

    Google Scholar 

  17. Zhang, X., Chai, R., Gao, F.: Matched filter based spectrum sensing and power level detection for cognitive radio network. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 1267–1270, (2014).

    Google Scholar 

  18. Upadhyay, S., Deshmukh, S.: Blind parameter estimation based matched filter detection for cognitiveradio networks. International Conference on Communications and Signal Processing (ICCSP), 904–908, (2015).

    Google Scholar 

  19. Gojariya, T. M., Bansode, R. S.: Cyclostationarity-based spectrum sensing using beamforming algorithm in cognitive radio networks. International Conference & Workshop on Electronics & Telecommunication Engineering, 63–69, (2016).

    Google Scholar 

  20. Ansari, A.H., Gulhane, S. M.: Cyclostationary method based spectrum sensing and analysis using different windowing method. IEEE International conference on energy system and applications, 684–688, (2015).

    Google Scholar 

  21. Tian, Z., Giannakis, G. B.: A Wavelet Approach to Wideband Spectrum Sensing Cognitive Radios. IEEE 1st Int. Conf. on Cognitive Radio Oriented Wireless Networks and Communications, Mykonos Island, 1–5, (2006).

    Google Scholar 

  22. Verma, R., Mahapatro, A.: Cognitive radio: Energy detection using wavelet packet transform for spectrum sensing. Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, 168–172, (2017).

    Google Scholar 

  23. Zhao, Y., Wu, Y., Wang, J., Zhong, X., Mei, L.: Wavelet transform for spectrum sensing in Cognitive Radio networks. IEEE International Conference on Audio, Language and Image Processing, 565–569, (2014).

    Google Scholar 

  24. Ariananda, D. D., Lakshmanan, M. K., Nikookar, H.: A study on the application of wavelet packet transforms to cognitive radio spectrum estimation. 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 1–6, (2009).

    Google Scholar 

  25. Zeng, Y., Liang, Y.C.: Eigenvalue based spectrum sensing algorithms for cognitive radio. IEEE Trans. Commun., 57(6), 1784–1793, (2009).

    Google Scholar 

  26. Widianto, M. H., Suratman, F. Y., Meylani, L.: Evaluation spectrum sensing in Cognitive Radio based on signal covariance matrix. Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast), 84–88, (2016).

    Google Scholar 

  27. Pillay, N., Xu, H. J.: Blind Eigen value based spectrum sensing for cognitive radio networks. IET communications, 6(11), 1388–1396, (2012).

    Google Scholar 

  28. Yang, X., Lei, K., Hu, L., Cao, X., Huang, X.: Eigenvalue ratio based blind spectrum sensing algorithm for multiband cognitive radios with relatively small samples. Electronics letters, 53(16), 1150–1152, (2017).

    Google Scholar 

  29. Charan, C., Pandey, R.,: Eigen value-based reliable spectrum sensing scheme for cognitive radio networks. Int. Conference on Nascent Technologies in Engineering, 1–5, (2017).

    Google Scholar 

  30. Zayen, B., Hayar, A., Kansanen, K.: Blind Spectrum Sensing for Cognitive Radio based on signal Space Dimension Estimation. IEEE International Conference on Communications, 1–5, (2009).

    Google Scholar 

  31. Liu, C., Li, H., Wang, J., Jin, M.: Optimal Eigenvalue Weighting Detection for Multi-Antenna CognitiveRadio Networks. IEEE Transactions on Wireless Communications, 16(4), 2083–2096, (2017).

    Google Scholar 

  32. Ujjinimated, R., Patil, S. R.: Spectrum sensing in cognitive radio networks with unknown noise levels. IET communications, 7(15), 1708–1714, (2013).

    Google Scholar 

  33. Nadler, B.: On the distribution of the ratio of largest Eigen value to the trace of a Wishart matrix. J. multivariate anal, 102, 363–371, (2010).

    Google Scholar 

  34. Kortun, A., Sellathurai, M., Ratnarajah, T., Zhong, C.: Distribution of the Ratio of the Largest Eigenvalue to the Trace of Complex Wishart Matrices. IEEE Transactions on Signal Processing, 60(10), 5527–5532, 2012.

    Google Scholar 

  35. Jones, S. R., Howard, S. D., Clarkson, I. V. L., Bialkowski, K. S., Cochran, D.: Computing the largest eigenvalue distribution for complex Wishart matrices. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3439–3443, (2017).

    Google Scholar 

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Correspondence to Pappu Kumar Verma .

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Verma, P.K., Kumar, R., Soni, S.K., Jain, P. (2018). Performance Analysis of Blind Eigenvalue with Multiple Antenna-Based Spectrum Sensing in Cognitive Radio. In: Perez, G., Tiwari, S., Trivedi, M., Mishra, K. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-10-7386-1_13

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  • DOI: https://doi.org/10.1007/978-981-10-7386-1_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7385-4

  • Online ISBN: 978-981-10-7386-1

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