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Analysis of optimal threshold selection for spectrum sensing in a cognitive radio network: an energy detection approach

  • Alok Kumar
  • Prabhat Thakur
  • Shweta Pandit
  • G. SinghEmail author
Article
  • 18 Downloads

Abstract

The spectrum sensing is a key process of the cognitive radio technology in which the cognitive users identify the unutilized/underutilized primary users (PUs)/licensed users spectrum for its efficient utilization. The sensing performance of cognitive radio (CR) is generally measured in terms of false-alarm probability (\( P_{f} \)) and detection probability (\( P_{d} \)). IEEE 802.22 wireless regional area network is one of the typical cognitive radio standards to access unused licensed frequencies of TV band and according to this standard, the false-alarm probability of CR should be ≤ 0.1 and the detection probability must be ≥ 0.9. Further, the detection and false-alarm probabilities are greatly affected by the selected threshold value in the spectrum sensing approach and selection of threshold is a crucial step to yield the status (presence/absence) of PU. In most of the available literatures, the threshold is decided by fixing one parameter (\( P_{f} \) or \( P_{d} \)) and optimizing the other parameter (\( P_{d} \) or \( P_{f} \)). Moreover, at low SNR, while achieving one of the targeted sensing parameter, there is significant degradation in the other sensing parameter. Therefore, in this paper, we are motivated to decide the optimal threshold at low SNR (signal-to-noise ratio) in such a way where we can jointly achieve both sensing matrices (\( P_{f} \) ≤ 0.1 and \( P_{d} \ge 0.9 \)) and provided better sensing performance in comparison to that of the traditional constant false-alarm rate and constant detection rate (CDR) threshold selection approaches. Further, we have illustrated that at low SNR, the proposed optimal threshold selection approach has provided better throughput as compare to that of the threshold selected by traditional CDR approach. The proposed approach has improved throughput approximately 24.63% when compared with CDR at chosen SNR.

Keywords

Cognitive radio CFAR CDR MEP Optimal threshold Throughput 

Notes

Acknowledgements

The authors are sincerely thankful to the Associate Editor and anonymous reviewers for critical comments and suggestions to improve the quality of the manuscript.

References

  1. 1.
    Khan, A. A., Rehmani, M. H., & Rachedi, A. (2017). Cognitive-radio-based internet of things: Applications, architectures, spectrum related functionalities, and future research directions. IEEE Wireless Communication, 24(3), 17–25.CrossRefGoogle Scholar
  2. 2.
    Ding, J., Jiang, L., & He, C. (2018). User-centric energy-efficient resource management for time switching wireless powered communications. IEEE Communications Letters, 22(1), 165–168.CrossRefGoogle Scholar
  3. 3.
    Gandotra, P., Jha, R. K., & Jain, S. (2017). Green communication in next generation cellular networks: A survey. IEEE Access, 5, 11727–11758.CrossRefGoogle Scholar
  4. 4.
    FCC. (2002). Spectrum policy task force report. In Proceedings of the federal communications commission (FCC’02), Washington, DC, USA.Google Scholar
  5. 5.
    Zhao, Q., & Sadler, B. M. (2007). A survey of dynamic spectrum access: Signal processing, networking, and regulatory policy. IEEE Signal Processing Magazine, 24(3), 79–89.CrossRefGoogle Scholar
  6. 6.
    Lin, Y.-E., Liu, K.-H., & Hsieh, H.-Y. (2013). On using interference-aware spectrum sensing for dynamic spectrum access in cognitive radio networks. IEEE Transactions on Mobile Computing, 12(3), 461–474.CrossRefGoogle Scholar
  7. 7.
    Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radio more personal. IEEE Personal Communication, 6(4), 13–18.CrossRefGoogle Scholar
  8. 8.
    Agarwal, S., & De, S. (2016). eDSA: Energy-efficient dynamic spectrum access protocols for cognitive radio networks. IEEE Transactions on Mobile Communication, 15(12), 3057–3071.CrossRefGoogle Scholar
  9. 9.
    Lu, L., Zhou, X., Onunkwo, U., & Li, G. Y. (2012). Ten years of research in spectrum sensing and sharing in cognitive radio. EURASIP Journal of Wireless Communications and Networking, 28, 1–16.Google Scholar
  10. 10.
    Alkyldiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2006). Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.CrossRefzbMATHGoogle Scholar
  11. 11.
    Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2008). A survey on spectrum management in cognitive radio networks. IEEE Communication. Magazine, 46(4), 40–48.CrossRefGoogle Scholar
  12. 12.
    Thakur, P., Singh, G., & Satashia, S. N. (2016). Spectrum sharing in cognitive radio communication system using power constraints: A technical review. Perspectives in Science, 8, 651–653.CrossRefGoogle Scholar
  13. 13.
    Pandit, S., & Singh, G. (2017). Spectrum sharing in cognitive radio networks: Medium access control protocol based approach. Cham: Springer.CrossRefGoogle Scholar
  14. 14.
    Christian, I., Moh, S., Chung, I., & Lee, J. (2012). Spectrum mobility in cognitive radio networks. IEEE Communications Magazine, 50(6), 114–121.CrossRefGoogle Scholar
  15. 15.
    Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.CrossRefGoogle Scholar
  16. 16.
    Nafkha, A., & Aziz, B. (2014). Closed-form approximation for the performance of finite sample-based energy detection using correlated receiving antennas. IEEE Wireless Communications Letters, 3(6), 577–580.CrossRefGoogle Scholar
  17. 17.
    Atapattu, S., Tellambura, C., & Jiang, H. (2010). Analysis of area under the ROC curve of energy detection. IEEE Transactions on Communications, 9(3), 1216–1225.Google Scholar
  18. 18.
    Sobron, I., Diniz, P., Martins, W., & Velez, M. (2015). Energy detection technique for adaptive spectrum sensing. IEEE Transactions on Communications, 63(3), 617–627.CrossRefGoogle Scholar
  19. 19.
    Kapoor, S., Rao, S., & Singh, G. (2011). Opportunistic spectrum sensing by employing matched filter in cognitive radio network. In Proceedings of IEEE international conference on communication systems and network technologies (CSNT 2011), India (pp. 580–583).Google Scholar
  20. 20.
    Salahdine, F., Ghazi, H. E., Kaabouch, N., & Fihri, W. F. (2015). Matched filter detection with dynamic threshold for cognitive radio network. In Proceedings of international conference on wireless networks and mobile communications, Morocco (pp. 1–6).Google Scholar
  21. 21.
    Du, K.-L., & Mow, W. H. (2010). Affordable cyclostationarity-based spectrum sensing for cognitive radio with smart antenna. IEEE Transactions on Vehicular Technology, 59(4), 1877–1886.CrossRefGoogle Scholar
  22. 22.
    Zeng, Y., & Liang, Y. (2009). Spectrum-sensing algorithms for cognitive radio based on statistical co-variances. IEEE Transaction on Vehicular Technology, 58(4), 1804–1815.CrossRefGoogle Scholar
  23. 23.
    Zeng, Y., & Liang, Y. C. (2009). Eigen value-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communication, 57(6), 1784–1793.CrossRefGoogle Scholar
  24. 24.
    Yousif, E. H. G., Ratnarajah, T., & Sellathurai, M. (2016). A frequency domain approach to eigenvalue-based detection with diversity reception and spectrum estimation. IEEE Transactions on Signal Processing, 64(1), 35–47.MathSciNetCrossRefGoogle Scholar
  25. 25.
    Scott Parsons. (2014). “Literature review of cognitive radio spectrum sensing” EE 359 project. Stanford: Stanford University.Google Scholar
  26. 26.
    Ali, A., & Hamouda, W. (2017). Advances on spectrum sensing for cognitive radio networks: Theory and applications. IEEE Communication Surveys Tutorial, 19(2), 1277–1304.CrossRefGoogle Scholar
  27. 27.
    IEEE 802.22 Standard. (2005). http://www.ieee802.org/22/. Accessed July 2018.
  28. 28.
    Atapattu, S., Tellambura, C., & Jiang, H. (2011). Spectrum sensing via energy detector in low SNR. In Proceedings of IEEE international conference on communications (ICC) (pp. 1–5).Google Scholar
  29. 29.
    Kumar, A., Thakur, P., Pandit, S., & Singh, G. (2017). Fixed and dynamic threshold selection criteria in energy detection for cognitive radio communication systems. In Proceedings of 10th IEEE international conference on contemporary computing (IC3), India (pp. 1–6).Google Scholar
  30. 30.
    Verma, G., & Sahu, O. P. (2016). Intelligent selection of threshold in cognitive radio system. Telecommunication System, 63(4), 547–556.CrossRefGoogle Scholar
  31. 31.
    Koley, S., Mirza, V., Islam, S., & Mitra, D. (2015). Gradient-based real-time spectrum sensing at low SNR. IEEE Communications Letters, 19(3), 391–394.CrossRefGoogle Scholar
  32. 32.
    Verma, G., & Sahu, O. P. (2016). Opportunistic selection of threshold in cognitive radio networks. Wireless Personal Communication, 92(2), 711–726.CrossRefGoogle Scholar
  33. 33.
    Kumar, A., Thakur, P., Pandit, S., & Singh, G. (2017). Performance analysis of different threshold selection schemes in energy detection for cognitive radio communication systems. In Proceedings of 4th IEEE international conference on image information processing (ICIIP), India (pp. 153–158).Google Scholar
  34. 34.
    Gandhi, P. P., & Kassam, S. A. (1988). Analysis of CFAR processors in non-homogeneous background. IEEE Transactions on Aerospace and Electronic Systems, 24(4), 427–445.CrossRefGoogle Scholar
  35. 35.
    Kortun, A., Ratnarajah, T., Sellathurai, M., Liang, Y. C., & Zeng, Y. (2014). On the eigenvalue-based spectrum sensing and secondary user throughput. IEEE Transactions on Vehicular Technology, 63(3), 1480–1486.CrossRefGoogle Scholar
  36. 36.
    Lehtomäki, J. J., Vartiainen, J., Juntti, M., & Saarnisaari, H. (2007). CFAR outlier detection with forward methods. IEEE Transactions on Signal Processing, 55(9), 4702–4706.MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Mahdi, H., Badrawi, A., & Kirsch, N. J. (2015). An EMD based double threshold detector for spectrum sensing in cognitive radio networks. In Proceedings of 82nd IEEE international conference on vehicular technology (VTC Fall), Boston, USA (pp. 1–5).Google Scholar
  38. 38.
    Politis, C., Maleki, S., Tsinos, C. G., Liolis, K. P., Chatzinotas, S., & Ottersten, B. (2017). Simultaneous sensing and transmission for cognitive radios with imperfect signal cancellation. IEEE Transactions on Wireless Communications, 16(9), 5599–5615.CrossRefGoogle Scholar
  39. 39.
    Sarker, M. (2015). Energy detector-based spectrum sensing by adaptive threshold for low SNR in CR networks. In Proceedings of 24th wireless and optical communication conference (WOCC), Taipei, Taiwan (pp. 118–122).Google Scholar
  40. 40.
    Zhang, H., Nie, Y., Cheng, J., Leung, V. C. M., & Nallanathan, A. (2017). Sensing time optimization and power control for energy efficient cognitive small cell with imperfect hybrid spectrum sensing. IEEE Transactions on Wireless Communications, 16(2), 730–743.CrossRefGoogle Scholar
  41. 41.
    Xuping, Z., Haigen, H., & Guoxin, Z. (2010). Optimal threshold and weighted cooperative data combining rule in cognitive radio network. In Proceedings of 12th IEEE international conference on communication technology (ICCT), Nanjing, China (pp. 1464–1467).Google Scholar
  42. 42.
    Choi, H.-H., Jang, K., & Cheong, Y. (2008). Adaptive sensing threshold control based on transmission power in cognitive radio systems. In Proceedings of 3 rd international conference on cognitive radio oriented wireless networks and communication (CROWNCOM), Singapore (pp. 1–6).Google Scholar
  43. 43.
    Joshi, D. R., Popescu, D. C., & Dobre, O. A. (2010). Dynamic threshold adaptation for spectrum sensing in cognitive radio systems. In Proceedings of radio and wireless symposium (RWS), New Orleans (pp. 468–471).Google Scholar
  44. 44.
    Joshi, D. R., Popescu, D. C., & Dobre, O. A. (2011). Gradient-based threshold adaptation for energy detector in cognitive radio systems. IEEE Communications Letters, 15(1), 19–21.CrossRefGoogle Scholar
  45. 45.
    Nasreddine, J., Riihijarvi, J., & Mahonen, P. (2010). Location-based adaptive detection threshold for dynamic spectrum access. In Proceedings of IEEE international symposium on new frontiers in dynamic spectrum access network, Singapore (pp. 1–10).Google Scholar
  46. 46.
    Yu, T. H., Sekkat, O., Parera, S. R., Markovic, D., & Cabric, D. (2011). A wideband spectrum-sensing processor with adaptive detection threshold and sensing time. IEEE Transaction Circuits and Systems I: Regular Papers, 58(11), 2765–2775.MathSciNetCrossRefGoogle Scholar
  47. 47.
    Ling, X., Wu, B., Wen, H., Ho, P. H., Bao, Z., & Pan, L. (2012). Adaptive threshold control for energy detection-based spectrum sensing in cognitive radios. IEEE Wireless Communications Letters, 1(5), 448–451.CrossRefGoogle Scholar
  48. 48.
    Umebayashi, K., Hayashi, K., & Lehtomäki, J. J. (2017). Threshold-setting for spectrum sensing based on statistical information. IEEE Communications Letters, 21(7), 1585–1588.CrossRefGoogle Scholar
  49. 49.
    Ding, G., Jiao, Y., Wang, J., Zou, Y., Wu, Q., Yao, Y. D., et al. (2018). Spectrum inference in cognitive radio networks: Algorithms and applications. IEEE Communications Surveys & Tutorials, 20(1), 150–182.CrossRefGoogle Scholar
  50. 50.
    Kerdabadi, M. S., Ghazizadeh, R., Farrokhi, H., & Najimi, M. (2018). Energy consumption minimization and throughput improvement in cognitive radio networks by joint optimization of detection threshold, sensing time and user selection. Wireless Network.  https://doi.org/10.1007/s11276-018-1797-x.Google Scholar
  51. 51.
    Charan, C., & Pandey, R. (2018). Intelligent selection of threshold in covariance-based spectrum sensing for cognitive radio networks. Wireless Network, 24(8), 3267–3279.CrossRefGoogle Scholar
  52. 52.
    Benedetto, F., & Giunta, G. (2018). A novel PU sensing algorithm for constant energy signals. IEEE Transactions on Vehicular Technology, 67(1), 827–831.CrossRefGoogle Scholar
  53. 53.
    Jin, M., Guo, Q., Xi, J., Li, Y., Yu, Y., & Huang, D. D. (2015). Spectrum sensing using weighted covariance matrix in Rayleigh fading channels. IEEE Transactions on Vehicular Technology, 64(11), 5137–5148.CrossRefGoogle Scholar
  54. 54.
    Chen, A. Z., Shi, Z. P., & He, Z. Q. (2018). A robust blind detection algorithm for cognitive radio networks with correlated multiple antennas. IEEE Communications Letters, 22(3), 570–573.Google Scholar
  55. 55.
    Xiong, T., Yao, Y. D., Ren, Y., & Li, Z. (2018). Multiband spectrum sensing in cognitive radio networks with secondary user hardware limitation: random and adaptive spectrum sensing strategies. IEEE Transactions on Wireless Communications, 17(5), 3018–3029.CrossRefGoogle Scholar
  56. 56.
    Bayat, A., & Aïssa, S. (2018). Full-duplex cognitive radio with asynchronous energy-efficient sensing. IEEE Transactions on Wireless Communications, 17(2), 1066–1080.CrossRefGoogle Scholar
  57. 57.
    Makarfi, A., & Hamdi, K. (2013). Interference analysis of energy detection for spectrum sensing. IEEE Transactions on Vehicular Technology, 62(6), 2570–2578.CrossRefGoogle Scholar
  58. 58.
    Verma, P., & Singh, B. (2018). Joint optimization of sensing duration and detection threshold for maximizing the spectrum utilization. Digital Signal Processing, 74, 94–101.MathSciNetCrossRefGoogle Scholar
  59. 59.
    MacDonald, S. L., & Popescu, D. C. (2013). Impact of primary user activity on the performance of energy-based spectrum sensing in cognitive radio systems. In Proceedings of IEEE global communications conference (Globecom) (pp. 3224–3228).Google Scholar
  60. 60.
    Fu, C., Li, Y., He, Y., Jin, M., Wang, G., & Lei, P. (2017). An inter-frame dynamic double-threshold energy detection for spectrum sensing in cognitive radios. EURASIP Journal on Wireless Communication and Networking, 1, 2017.Google Scholar
  61. 61.
    Cabric, D., Tkachenko, A., & Brodersen, R.W. (2006). Experimental study of spectrum sensing based on energy detection and network cooperation. In Proceedings of ACM international workshop on technology and policy for accessing spectrum (TAPAS), Boston (pp. 1–8).Google Scholar
  62. 62.
    Liang, Y. C., Zeng, Y., Peh, E., & Hoang, A. T. (2008). Sensing-throughput tradeoff for cognitive radio networks. IEEE Transaction on Wireless Communication, 7(4), 1326–1337.CrossRefGoogle Scholar
  63. 63.
    Atapattu, S., Tellambura, C., Jiang, H., & Rajatheva, N. (2015). Unified analysis of low-SNR energy detection and threshold selection. IEEE Transactions on Vehicular Technology, 64(11), 5006–5019.CrossRefGoogle Scholar
  64. 64.
    MATLAB and Statistics Toolbox Release. (2010). The Math Works, Inc., Natick, MA.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringJaypee University of Information TechnologyWaknaghatIndia
  2. 2.Department of Electrical and Electronics Engineering SciencesUniversity of JohannesburgJohannesburgSouth Africa

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