Skip to main content
Top

Hint

Swipe to navigate through the articles of this issue

Published in: Wireless Personal Communications 2/2022

06-07-2022

A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems

Author: Garima Mahendru

Published in: Wireless Personal Communications | Issue 2/2022

Login to get access
share
SHARE

Abstract

Cognitive Radio is a novel concept that has invoked a paradigm shift in wireless communication and promises to solve the problem of spectrum underutilization. Spectrum sensing plays a pivotal role in a cognitive radio system by detecting the vacant spectrum for establishing a communication link. For any spectrum sensing method, detection probability and error probability portray a significant part in quantifying the detection performance. At low SNR, it becomes cumbersome to differentiate noise and signal due to which sensing method loses robustness and reliability. In this paper, mathematical modeling and critical measurement of detection probabilities has been done for energy detection-based spectrum sensing at low SNR in uncertain noisy environment. A mathematical model has been proposed to compute double thresholds for reliable sensing when the observed energy is less than the uncertainty in the noise power. A novel parameter “Threshold Wall” has been formulated for optimum threshold selection to overcome sensing failure. Comparative simulation and analytical result measurements have been presented that reveals improved sensing performance.Please check inserted city is correct for affiliation 1.Noida, it is correct
Literature
1.
go back to reference Lutu, A., Perino, D., Bagnulo, M., Frias-Martinez, E., & Khangosstar, J. (2020). A characterization of the COVID-19 pandemic impact on a mobile network operator traffic. In Proceedings of the ACM internet measurement conference 2020 Oct 27 (pp. 19–33). Lutu, A., Perino, D., Bagnulo, M., Frias-Martinez, E., & Khangosstar, J. (2020). A characterization of the COVID-19 pandemic impact on a mobile network operator traffic. In Proceedings of the ACM internet measurement conference 2020 Oct 27 (pp. 19–33).
2.
go back to reference Federal Communications Commission Spectrum Policy Task Force Report, FCC 02-155 Federal Communications Commission (2002). Federal Communications Commission Spectrum Policy Task Force Report, FCC 02-155 Federal Communications Commission (2002).
3.
go back to reference McHenry, M. (2005). NSF spectrum occupancy measurements project summary. Tech. Rep. McHenry, M. (2005). NSF spectrum occupancy measurements project summary. Tech. Rep.
4.
go back to reference Faruk, N., Bello, O. W., Sowande, O. A., Onidare, S. O., Muhammad, M. Y., & Ayeni, A. A. (2016). Large scale spectrum survey in rural and urban environments within the 50 MHz–6 GHz bands. Measurement, 91, 228–238. CrossRef Faruk, N., Bello, O. W., Sowande, O. A., Onidare, S. O., Muhammad, M. Y., & Ayeni, A. A. (2016). Large scale spectrum survey in rural and urban environments within the 50 MHz–6 GHz bands. Measurement, 91, 228–238. CrossRef
5.
go back to reference Federal Communications Commission Notice of Proposed Rulemaking and Order, Facilitating Opportunities for Flexible, Efficient and Reliable Spectrum Use Employing Cognitive Radio Technologies, FCC 03-322 Federal Communications Commission (2003). Federal Communications Commission Notice of Proposed Rulemaking and Order, Facilitating Opportunities for Flexible, Efficient and Reliable Spectrum Use Employing Cognitive Radio Technologies, FCC 03-322 Federal Communications Commission (2003).
6.
go back to reference Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: making software radios more personal. IEEE Personal Communications, 6, 13–18. CrossRef Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: making software radios more personal. IEEE Personal Communications, 6, 13–18. CrossRef
7.
go back to reference Mitola, J. (2000). Cognitive Radio: An Integrated Architecture for Software Defined Radio, Royal Institute Technology, Stockholm, Sweden (Ph.D. Diss.). Mitola, J. (2000). Cognitive Radio: An Integrated Architecture for Software Defined Radio, Royal Institute Technology, Stockholm, Sweden (Ph.D. Diss.).
8.
go back to reference Haykin, S., Thomson, D. J., & Reed, J. H. (2009). Spectrum sensing for cognitive radio. IEEE Proceedings, 97(5), 849–877. CrossRef Haykin, S., Thomson, D. J., & Reed, J. H. (2009). Spectrum sensing for cognitive radio. IEEE Proceedings, 97(5), 849–877. CrossRef
9.
go back to reference Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531. CrossRef Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531. CrossRef
10.
go back to reference Chen, Y. (2010). Improved energy detector for random signals in Gaussian noise. IEEE Transactions on Wireless Communications, 9(2), 558–563. CrossRef Chen, Y. (2010). Improved energy detector for random signals in Gaussian noise. IEEE Transactions on Wireless Communications, 9(2), 558–563. CrossRef
11.
go back to reference Cabric, D., Tkachenko, A., & Brodersen, R. W. (2006). Experimental study of spectrum sensing based on energy detection and netwotk cooperation. ACM Int. Workshop on Technology and Policy for Accessing Spectrum. Cabric, D., Tkachenko, A., & Brodersen, R. W. (2006). Experimental study of spectrum sensing based on energy detection and netwotk cooperation. ACM Int. Workshop on Technology and Policy for Accessing Spectrum.
12.
go back to reference Lunden, J., Koivunen, V., Huttunen, A., & Poor, H. V. (2009). Collaborative cyclo-stationary spectrum sensing for cognitive radio systems. IEEE Transactions on Signal Processing, 57(11), 4182–4195. MathSciNetCrossRef Lunden, J., Koivunen, V., Huttunen, A., & Poor, H. V. (2009). Collaborative cyclo-stationary spectrum sensing for cognitive radio systems. IEEE Transactions on Signal Processing, 57(11), 4182–4195. MathSciNetCrossRef
13.
go back to reference Yan, T., Xu, F., Wei, N., & Yang, Z. (2018). An improved cyclostationary feature detection algorithm. In International conference on computer engineering and networks (pp. 544–555). Springer. Yan, T., Xu, F., Wei, N., & Yang, Z. (2018). An improved cyclostationary feature detection algorithm. In International conference on computer engineering and networks (pp. 544–555). Springer.
14.
go back to reference Khan, I., & Singh, P. (2014). Double threshold feature detector for cooperative spectrum sensing in cognitive radio networks. In 2014 annual IEEE India conference (INDICON) (pp. 1–5). IEEE. Khan, I., & Singh, P. (2014). Double threshold feature detector for cooperative spectrum sensing in cognitive radio networks. In 2014 annual IEEE India conference (INDICON) (pp. 1–5). IEEE.
15.
go back to reference Salahdine, F., El Ghazi, H., Kaabouch, N., & Fihri, W.F. (2015). Matched filter detection with dynamic threshold for cognitive radio networks. In Proceedings of the international conference on wireless networks and mobile communications, Marrakech (pp. 1–6), Morocco, 20–23 October 2015. Salahdine, F., El Ghazi, H., Kaabouch, N., & Fihri, W.F. (2015). Matched filter detection with dynamic threshold for cognitive radio networks. In Proceedings of the international conference on wireless networks and mobile communications, Marrakech (pp. 1–6), Morocco, 20–23 October 2015.
17.
go back to reference Kumar, K. S., Saravanan, R., & Muthaiah, R. (2013). Cognitive Radio spectrum sensing algorithms based on eigenvalue and covariance methods. International Journal of Engineering Technology, 5, 595–601. Kumar, K. S., Saravanan, R., & Muthaiah, R. (2013). Cognitive Radio spectrum sensing algorithms based on eigenvalue and covariance methods. International Journal of Engineering Technology, 5, 595–601.
18.
go back to reference Zeng, Y., & Liang, Y. C. (2007). Covariance based signal detections for cognitive radio. In Proceedings of the 2007 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks , Dublin, Ireland (pp. 202–207). Zeng, Y., & Liang, Y. C. (2007). Covariance based signal detections for cognitive radio. In Proceedings of the 2007 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks , Dublin, Ireland (pp. 202–207).
19.
go back to reference Balaji, V., Kabra, P., Saieesh, P., Hota, C., & Raghurama, G. (2015). Cooperative spectrum sensing in cognitive radios using perceptron learning for IEEE 802.22 WRAN. Elsevier Procedia Computing Science, 54, 14–23. CrossRef Balaji, V., Kabra, P., Saieesh, P., Hota, C., & Raghurama, G. (2015). Cooperative spectrum sensing in cognitive radios using perceptron learning for IEEE 802.22 WRAN. Elsevier Procedia Computing Science, 54, 14–23. CrossRef
20.
go back to reference Sharma, S. K., Lagunas, E., Chatzinotas, S., & Ottersten, B. (2016). Application of compressive sensing in cognitive radio communications: A survey. IEEE Communication Survey Tutorial, 18, 1838–1860. CrossRef Sharma, S. K., Lagunas, E., Chatzinotas, S., & Ottersten, B. (2016). Application of compressive sensing in cognitive radio communications: A survey. IEEE Communication Survey Tutorial, 18, 1838–1860. CrossRef
21.
go back to reference Salahdine, F., Kaabouch, N., & El Ghazi, H. (2016). A survey on compressive sensing techniques for cognitive radio networks. Journal of Physics Communication, 20, 61–73. CrossRef Salahdine, F., Kaabouch, N., & El Ghazi, H. (2016). A survey on compressive sensing techniques for cognitive radio networks. Journal of Physics Communication, 20, 61–73. CrossRef
22.
go back to reference Arjoune, Y., & Kaabouch, N. (2019). A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions. Sensors., 19(1), 126. CrossRef Arjoune, Y., & Kaabouch, N. (2019). A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions. Sensors., 19(1), 126. CrossRef
23.
go back to reference Nguyen, M. T., & Boveiri, H. R. (2020). Energy-efficient sensing in robotic networks. Measurement, 158, 107708. CrossRef Nguyen, M. T., & Boveiri, H. R. (2020). Energy-efficient sensing in robotic networks. Measurement, 158, 107708. CrossRef
24.
go back to reference Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communication Surveys and Tutorials, 11(1), 116–130. CrossRef Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communication Surveys and Tutorials, 11(1), 116–130. CrossRef
25.
go back to reference Abdulsattar, M. A., & Hussein, Z. A. (2012). Energy detection technique for spectrum sensing in cognitive radio: A survey. International Journal of Computer Networks & Communications., 4(5), 223. CrossRef Abdulsattar, M. A., & Hussein, Z. A. (2012). Energy detection technique for spectrum sensing in cognitive radio: A survey. International Journal of Computer Networks & Communications., 4(5), 223. CrossRef
26.
go back to reference Cabric, D., Mishra, S. M., & Brodersen, R. W. (2004). Implementation issues in spectrum sensing for cognitive radios. In Conference record of the thirty-eighth asilomar conference on signals, systems and computers (Vol. 1, pp. 772–776). Ieee. Cabric, D., Mishra, S. M., & Brodersen, R. W. (2004). Implementation issues in spectrum sensing for cognitive radios. In Conference record of the thirty-eighth asilomar conference on signals, systems and computers (Vol. 1, pp. 772–776). Ieee.
27.
go back to reference Tandra, R., & Sahai, A. (2008). SNR walls for signal detection, IEEE 1. Selection of Topics Signal Process., 2, 4–17. CrossRef Tandra, R., & Sahai, A. (2008). SNR walls for signal detection, IEEE 1. Selection of Topics Signal Process., 2, 4–17. CrossRef
28.
go back to reference Yu, G., & Xi, W. (2012). A novel energy detection scheme based on dynamic threshold in cognitive radio systems. Journal of Computational Information Systems, 8, 2245–2252. Yu, G., & Xi, W. (2012). A novel energy detection scheme based on dynamic threshold in cognitive radio systems. Journal of Computational Information Systems, 8, 2245–2252.
29.
go back to reference Arar, A. M., Masri, A. M., Ghannam, H. O., & Tumar, I. K. (2017). A proposed scheme for dynamic threshold versus noise uncertainty in cognitive radio networks (DTNU). Wireless Personal Communication Journal, 96, 4543–4555. CrossRef Arar, A. M., Masri, A. M., Ghannam, H. O., & Tumar, I. K. (2017). A proposed scheme for dynamic threshold versus noise uncertainty in cognitive radio networks (DTNU). Wireless Personal Communication Journal, 96, 4543–4555. CrossRef
30.
go back to reference Xie, S., & Shen, L. (2012). Double-threshold energy detection of spectrum sensing for cognitive radio under noise uncertainty environment. In 2012 International Conference on Wireless Communications and Signal Processing (WCSP), Huangshan (pp. 1–5). Xie, S., & Shen, L. (2012). Double-threshold energy detection of spectrum sensing for cognitive radio under noise uncertainty environment. In 2012 International Conference on Wireless Communications and Signal Processing (WCSP), Huangshan (pp. 1–5).
31.
go back to reference Verma, P., Singh, B. (2015). Simulation study of double threshold energy detection method for cognitive radios. In 2015 2nd international conference on signal processing and integrated networks (SPIN) (pp. 232–236). IEEE. Verma, P., Singh, B. (2015). Simulation study of double threshold energy detection method for cognitive radios. In 2015 2nd international conference on signal processing and integrated networks (SPIN) (pp. 232–236). IEEE.
32.
go back to reference Liu, F., Wang, J., & Han, Y. (2013). An adaptive double thresholds scheme for spectrum sensing in cognitive radio networks. In IEEE international conference, (ICSPCC) (pp. 1–5). Liu, F., Wang, J., & Han, Y. (2013). An adaptive double thresholds scheme for spectrum sensing in cognitive radio networks. In IEEE international conference, (ICSPCC) (pp. 1–5).
33.
go back to reference Capriglione, D., Cerro, G., Ferrigno, L., & Miele, G. (2019). Performance analysis of a two-stage spectrum sensing scheme for dynamic spectrum access in TV bands. Measurement, 135, 661–671. CrossRef Capriglione, D., Cerro, G., Ferrigno, L., & Miele, G. (2019). Performance analysis of a two-stage spectrum sensing scheme for dynamic spectrum access in TV bands. Measurement, 135, 661–671. CrossRef
35.
go back to reference Shah, H. A., & Koo, I. (2018). Reliable machine learning based spectrum sensing in cognitive radio networks. Wireless Communications and Mobile Computing., 12, 2018. Shah, H. A., & Koo, I. (2018). Reliable machine learning based spectrum sensing in cognitive radio networks. Wireless Communications and Mobile Computing., 12, 2018.
36.
go back to reference Liu, X., Zheng, K., Chi, K., & Zhu, Y. H. (2020). Cooperative spectrum sensing optimization in energy-harvesting cognitive radio networks. IEEE Transactions on Wireless Communications., 19(11), 7663–7676. CrossRef Liu, X., Zheng, K., Chi, K., & Zhu, Y. H. (2020). Cooperative spectrum sensing optimization in energy-harvesting cognitive radio networks. IEEE Transactions on Wireless Communications., 19(11), 7663–7676. CrossRef
39.
go back to reference Mahendru, G., Shukla, A. K., Banerjee, P., Patnaik, L. M. (2019). Adaptive double threshold based spectrum sensing to overcome sensing failure in presence of noise uncertainty. In 2019 6th international conference on signal processing and integrated networks (SPIN) (pp. 466–471). IEEE. Mahendru, G., Shukla, A. K., Banerjee, P., Patnaik, L. M. (2019). Adaptive double threshold based spectrum sensing to overcome sensing failure in presence of noise uncertainty. In 2019 6th international conference on signal processing and integrated networks (SPIN) (pp. 466–471). IEEE.
40.
go back to reference Rop, K. V., Langat, P. K., & Ouma, H. A. (2021). Spectrum sensing on high density cognitive radio vehicular ad hoc network. Journal of Communications., 16(7), 259–266. CrossRef Rop, K. V., Langat, P. K., & Ouma, H. A. (2021). Spectrum sensing on high density cognitive radio vehicular ad hoc network. Journal of Communications., 16(7), 259–266. CrossRef
41.
go back to reference Hossain, M. A., Schukat, M., & Barrett, E. (2021). Enhancing the spectrum sensing performance of cluster-based cooperative cognitive radio networks via sequential multiple reporting channels. Wireless Personal Communications., 116(3), 2411–2433. CrossRef Hossain, M. A., Schukat, M., & Barrett, E. (2021). Enhancing the spectrum sensing performance of cluster-based cooperative cognitive radio networks via sequential multiple reporting channels. Wireless Personal Communications., 116(3), 2411–2433. CrossRef
42.
go back to reference Nasser, A., Chaitou, M., Mansour, A., Yao, K. C., & Charara, H. (2021). A deep neural network model for hybrid spectrum sensing in cognitive radio. Wireless Personal Communications., 118(1), 281–299. CrossRef Nasser, A., Chaitou, M., Mansour, A., Yao, K. C., & Charara, H. (2021). A deep neural network model for hybrid spectrum sensing in cognitive radio. Wireless Personal Communications., 118(1), 281–299. CrossRef
43.
go back to reference Chabbra, K., Mahendru, G., & Banerjee, P. (2014). Effect of dynamic threshold & noise uncertainty in energy detection spectrum sensing technique for cognitive radio systems, In International conference on signal processing and integrated networks (SPIN) (pp. 377–381). Chabbra, K., Mahendru, G., & Banerjee, P. (2014). Effect of dynamic threshold & noise uncertainty in energy detection spectrum sensing technique for cognitive radio systems, In International conference on signal processing and integrated networks (SPIN) (pp. 377–381).
44.
go back to reference Jothiraj, S., Balu, S., & Rangaraj, N. (2021). An efficient adaptive threshold-based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks. International Journal of Communication Systems., 34(10), e4829. CrossRef Jothiraj, S., Balu, S., & Rangaraj, N. (2021). An efficient adaptive threshold-based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks. International Journal of Communication Systems., 34(10), e4829. CrossRef
Metadata
Title
A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems
Author
Garima Mahendru
Publication date
06-07-2022
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 2/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-09825-5

Other articles of this Issue 2/2022

Wireless Personal Communications 2/2022 Go to the issue