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Threshold selection analysis of spectrum sensing for cognitive radio network with censoring based imperfect reporting channels

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Abstract

An appropriate threshold selection scheme is one of the main components to adjudicate the performance of energy detection spectrum sensing (EDSS) technique for cognitive radio network. In this paper, we have employed two different threshold selection approaches namely, the constant false-alarm rate (CFAR) and minimized error probability (MEP) and analyzed the threshold selection effects on the performance of cognitive user (CU) communication systems particularly, the total spectrum sensing error probability and throughput. We have derived the expressions and analyzed these performance parameters by considering an imperfect spectrum sensing and reporting channels in the cooperative spectrum sensing scenarios for additive white Gaussian noise (AWGN), Rayleigh and Nakagami-m fading environments. In addition, the censoring concept has been applied to the proposed system and compared its effect with that of the non-censoring based cognitive radio network (CRN) system under the perfect reporting (PR) and imperfect reporting (IR) channel. With the help of simulation, we have illustrated that the role of threshold selection approach is crucial to maximize the throughput and minimize the spectrum sensing error while considering the amount of error in the reporting channel. Further, from the results, the existence of trade-off between the spectrum sensing error probability and throughput is presented with threshold selection approaches. Moreover, it is also shown that there is need to switch among CFAR and MEP threshold selection approaches in the censoring scenario, to enhance the throughput and decrease the spectrum sensing error probability.

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References

  1. Zhang, R. (2009). On peak versus average interference power constraints for protecting primary users in cognitive radio networks. IEEE Transaction on Wireless Communication, 8(4), 2112–2120.

    Google Scholar 

  2. Pandit, S., & Singh, G. (2017). Spectrum sharing in cognitive radio networks: Medium access control protocol based approach. Switzerland: Springer.

    Google Scholar 

  3. 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.

    Google Scholar 

  4. Parsons, S (2014). Literature review of cognitive radio spectrum sensing. EE 359 Project, California: Stanford University.

  5. Ali, A., & Hamouda, W. (2017). Advances on spectrum sensing for cognitive radio networks: Theory and applications. IEEE Communication Surveys Tutorial, 19(2), 1277–1304.

    Google Scholar 

  6. Guo, C., Jin, M., Guo, Q., & Li, Y. (2019). Anti-eigen value-based spectrum sensing for cognitive radio. IEEE Wireless Communication Letter, 8(2), 544–547.

    Google Scholar 

  7. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceeding of the IEEE, 55(4), 523–531.

    Google Scholar 

  8. 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.

    Google Scholar 

  9. 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 

  10. 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.

    Google Scholar 

  11. Yang, X., Lei, K., Peng, S., Hu, L., Li, S., & Cao, X. (2019). Threshold setting for multiple primary user spectrum sensing via spherical detector. IEEE Wireless Communication Letter, 8(2), 488–491.

    Google Scholar 

  12. Verma, G., & Sahu, O. P. (2016). Opportunistic selection of threshold in cognitive radio networks. Wireless Personal Communication, 92(2), 711–726.

    Google Scholar 

  13. Atapattu, S., Tellambura, C., and Jiang, H. (2011).Spectrum sensing via energy detector in low SNR. Proceedings IEEE International Conference on Communications (ICC), Kyoto, Japan (pp.1–5).

  14. 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.

    Google Scholar 

  15. Renzo, M. D., Imbriglio, L., Graziosi, F., & Santucci, F. (2009). Distributed data fusion over correlated log-normal sensing and reporting channels: Application to cognitive radio networks. IEEE Transaction on Wireless Communication, 8(12), 5813–5821.

    Google Scholar 

  16. Adelantado, F., Juan, A., & Verikoukis, C. (2010). Adaptive sensing user selection mechanism in cognitive wireless networks. IEEE Communication Letters, 14(9), 800–802.

    Google Scholar 

  17. Nallagonda, S., Chandra, A., Roy, S. D., Kundu, S., Kukolev, P., & Prokes, A. (2016). Detection performance of cooperative spectrum sensing with hard decision fusion in fading channels. International Journal of Electronics, 103(2), 297–321.

    Google Scholar 

  18. Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4(1), 40–62.

    Google Scholar 

  19. Sun, C., Zhang, W. and Ben, L. K. (2007). Cooperative spectrum sensing for cognitive radios under bandwidth constraints. Proceeding of IEEE Wireless Communications and Networking Conference, Kowloon (pp. 1–5).

  20. Choi, Y. J., Park, W., Xin, Y., & Rangarajan, S. (2012). Throughput analysis of cooperative spectrum sensing in Rayleigh-faded cognitive radio systems. IET Communication, 6(9), 1104–1110.

    MathSciNet  Google Scholar 

  21. Nallagonda, S., Roy, S. D., Kundu, S., Ferrari, G., & Raheli, R. (2018). Censoring-based cooperative spectrum sensing with improved energy detectors and multiple antennas in fading channels. IEEE Transactions on Aerospace and Electronic Systems, 54(2), 537–553.

    Google Scholar 

  22. Li, M., Alhussein, O., Sofotasios, P. C., Muhaidat, S., Yoo, P. D., Liang, J., & Wang, A. (2019). Censor-based cooperative multi-antenna spectrum sensing with imperfect reporting channels. IEEE Transactions on Sustainable Computing, 5(1), 48–60.

    Google Scholar 

  23. Koley, S., Mirza, V., Islam, S., & Mitra, D. (2015). Gradient-based real-time spectrum sensing at low SNR. IEEE Communication Letter, 19(3), 391–394.

    Google Scholar 

  24. Zhang, W., Mallik, R. K., & Letaief, K. B. (2009). Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks. IEEE Transactions on Wireless Communication, 8(12), 5761–5766.

    Google Scholar 

  25. Kumar, A., Thakur, P., Pandit, S., & Singh, G. (2019). Analysis of optimal threshold selection for spectrum sensing in a cognitive radio network: An energy detection approach. Wireless Network, 25(7), 391–3931.

    Google Scholar 

  26. Kumar, A., Thakur, P., Pandit, S., & Singh, G. (2020). Intelligent threshold selection in fading environment of cognitive radio network: Advances in throughput and total error probability. International Journal of Communication Systems, 33(1), e4175.

    Google Scholar 

  27. Peh, E. C. Y., Liang, Y. C., Guan, Y. L., & Zeng, Y. (2009). Optimization of cooperative sensing in cognitive radio networks: A sensing-throughput tradeoff view. IEEE Transactions on Vehicular Technology, 58(9), 5294–5299.

    Google Scholar 

  28. Liu, X., & Tan, X. (2012). Optimization algorithm of periodical cooperative spectrum sensing in cognitive radio. International Journal of Communication Systems, 27(5), 1–16.

    Google Scholar 

  29. Tuan, P. V., & Koo, I. (2016). Throughput maximization by optimizing detection thresholds in full-duplex cognitive radio networks. IET Communications, 10(11), 1355–1364.

    Google Scholar 

  30. Lu, Y., Wang, D., & Fattouche, M. (2016). Cooperative spectrum-sensing algorithm in cognitive radio by simultaneous sensing and BER measurements. EURASIP Journal on Wireless Communications and Networking, 136, 1–22.

    Google Scholar 

  31. Li, H., & Liu, C. (2018). Cross-layer optimization for full-duplex cognitive radio network with cooperative spectrum sensing. International Journal of Communication Systems, 32(5), 1–33.

    Google Scholar 

  32. Fan, R., & Jiang, H. (2010). Optimal multi-channel cooperative sensing in cognitive radio networks. IEEE Transactions on Wireless Communications., 9(3), 1128–1138.

    Google Scholar 

  33. Tang, W., Shakir, M. Z., Imran, M. A., Tafazolli, R., & Alouini, M. S. (2012). Throughput analysis for cognitive radio networks with multiple primary users and imperfect spectrum sensing. IET Communications, 6(17), 2787–2795.

    MathSciNet  Google Scholar 

  34. Yadav, K., Prasad, B., Bhowmick, A., Roy, S. D., & Kundu, S. (2017). Throughput performance under primary user emulation attack in cognitive radio networks. International Journal of Communication Systems, 30(18), 1–9.

    Google Scholar 

  35. Sharifi, M., Sharifi, A. A., & Niya, M. J. M. (2018). Cooperative spectrum sensing in the presence of primary user emulation attack in cognitive radio network: Multi-level hypotheses test approach. Wireless Network, 24(1), 61–68.

    Google Scholar 

  36. Althunibat, S., Renzo, M. D., and Granelli, F. (2013). Optimizing the K-out-of-N rule for cooperative spectrum sensing in cognitive radio networks. Proceeding of IEEE Global Communications Conference (GLOBECOM), Atlanta (pp. 1607–1611).

  37. Althunibat, S., Renzo, M. D., & Granelli, F. (2015). Towards energy-efficient cooperative spectrum sensing for cognitive radio networks: An overview. Telecommunication Systems, 59(1), 77–91.

    Google Scholar 

  38. Hu, H., Zhang, H., & Liang, Y. C. (2016). On the spectrum-and energy-efficiency tradeoff in cognitive radio networks. IEEE Transactions on Communications, 64(2), 490–501.

    Google Scholar 

  39. Bhowmick, A., Roy, S. D., & Kundu, S. (2015). Sensing throughput trade-off for an energy efficient cognitive radio network under faded sensing and reporting channel. International Journal of Communication Systems, 29(7), 1208–1218.

    Google Scholar 

  40. Najimi, M. (2018). Sensing time optimization and sensor selection in multi-channel multi-antenna wireless cognitive sensor networks. IET Communications, 12(6), 795–801.

    Google Scholar 

  41. Zhao, N., Pu, F., Xu, X., & Chen, N. (2013). Optimization of multi-channel cooperative sensing in cognitive radio networks. IET Communications, 7(12), 1177–1190.

    Google Scholar 

  42. Gahane, L., & Sharma, P. K. (2017). Performance of improved energy detector with cognitive radio mobility and imperfect channel state information. IET Communications, 11(12), 1857–1863.

    Google Scholar 

  43. Firouzabadi, A. D., & Rabiei, A. M. (2015). Sensing-throughput optimization for multichannel cooperative spectrum sensing with imperfect reporting channels. IET Communications, 9(18), 2188–2196.

    Google Scholar 

  44. Chaudhari, S., Lundén, 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.

    MathSciNet  MATH  Google Scholar 

  45. Sakran, H., & Shokair, M. (2013). Hard and softened combination for cooperative spectrum sensing over imperfect channels in cognitive radio networks. Telecommunication System, 52(1), 61–71.

    Google Scholar 

  46. Yilmaz, H. B., Tugcu, T., & Alagoz, F. (2014). Novel quantization-based spectrum sensing scheme under imperfect reporting channel and false reports. International Journal of Communication Systems, 27(10), 1459–1475.

    Google Scholar 

  47. Mi, Y., Lu, G., Li, Y., & Bao, Z. (2019). A novel semi-soft decision scheme for cooperative spectrum sensing in cognitive radio networks. Sensors Networks, 19(11), 1–12.

    Google Scholar 

  48. Bae, S., & Kim, H. (2016). Robust cooperative sensing with ON/OFF signaling over imperfect reporting channels. IEEE Transactions on Industrial Informatics, 12(6), 2196–2205.

    Google Scholar 

  49. Liu, X., Zhang, X., Ding, H., & Peng, B. (2019). Intelligent clustering cooperative spectrum sensing based on Bayesian learning for cognitive radio network. Ad Hoc Networks, 94, 101968.

    Google Scholar 

  50. Oh, D. C., & Lee, Y. H. (2010). Cooperative spectrum sensing with imperfect feedback channel in the cognitive radio systems. International Journal of Communication Systems, 23, 763–779.

    Google Scholar 

  51. Ghorbel, M. B., Nam, H., & Alouini, M. S. (2015). Soft cooperative spectrum sensing performance under imperfect and non-identical reporting channels. IEEE Communications Letters, 19(2), 227–230.

    Google Scholar 

  52. Li, M., Wang, A., & Pan, J. S. (2016). Cognitive Wireless Networks Using the CSS Technology. Cham: Springer.

    Google Scholar 

  53. Jiang, R., & Chen, B. (2005). Fusion of censored decisions in wireless sensor networks. IEEE Transaction on Wireless Communication, 4(6), 2668–2673.

    Google Scholar 

  54. Atmaca, S., Sayli, O., Yuan, J., & Kavak, A. (2017). Throughput maximization of CSMA in cognitive radio networks with cooperative spectrum sensing. Wireless Personal Communications, 92(4), 1473–1492.

    Google Scholar 

  55. Liu, X., Zhong, W. Z., & Chen, K. Q. (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.

    Google Scholar 

  56. Juarez, M. C., & Ghogho, M. (2011). Spectrum sensing and throughput trade-off in cognitive radio under outage constraints over Nakagami fading. IEEE Communications Letters, 15(10), 1110–1113.

    Google Scholar 

  57. Rabiee, R., and Li, K. H. (2013). Throughput optimization of double-threshold based improved energy detection in cooperative sensing over imperfect reporting channels. In 2013 Proceeding of 9th International Conference on Information, Communication and Signal Processing, Tainan, (pp. 1–5).

  58. RabieeLi, R. K. H. (2015). Performance evaluation of improved double-threshold energy detector over Rayleigh-faded sensing and imperfect reporting channels. Physical Communication, 17, 58–71.

    Google Scholar 

  59. Charan, C., & Pandey, R. (2018). Intelligent selection of threshold in covariance based spectrum sensing for cognitive radio networks. Wireless Network, 24(8), 3267–3279.

    Google Scholar 

  60. Banavathu, N. R., & Khan, M. Z. A. (2019). Optimization of k-out-of-N rule for heterogeneous cognitive radio networks. IEEE Signal Processing. Letter, 26(3), 445–449.

    Google Scholar 

  61. Isukapalli, Y., & Rao, B. D. (2008). An analytically tractable approximation for the Gaussian Q-function. IEEE Communications Letters, 12(9), 669–671.

    Google Scholar 

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, 173215, India

    Alok Kumar & S. Pandit

  2. Department of Electrical and Electronics Engineering Science, Auckland Park Kingsway Campus, University of Johannesburg, Johannesburg, 2006, South Africa

    G. Singh

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  1. Alok Kumar
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Kumar, A., Pandit, S. & Singh, G. Threshold selection analysis of spectrum sensing for cognitive radio network with censoring based imperfect reporting channels. Wireless Netw 27, 961–980 (2021). https://doi.org/10.1007/s11276-020-02488-9

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