Abstract
A real time scenario of dynamic primary user (PU) is considered in additive Laplacian noise. Two transitions or status changes of PU in the fixed sensing time are considered. The last status change point (LSCP) is estimated with maximum likelihood estimation by using dynamic programming. We consider Cumulative Sum (CuSum) based weighted samples for detection. We consider three detection schemes such as sample mean detector, energy detection and improved absolute value cumulation detection. We derive closed form expressions of detection probability \((P_D)\) and false alarm probability \((P_F)\) for all the three schemes. We present our results with receiver operating characteristic (ROC) for the considered schemes. We also present simulation results, which are closely matching with their analytical counterparts. We compare the ROC of the considered system with the ROC of conventional techniques. In the conventional techniques, all the samples in the sensing time are used for detection without LSCP estimation and weight. It is found that the considered system outperforms the conventional schemes.
Similar content being viewed by others
References
Yang, L., Fang, J., Duan, H., & Li, H. (2020). Fast compressed power spectrum estimation: toward a practical solution for wideband spectrum sensing. IEEE Trans. Wirel. Commun., 19(1), 520–532.
Liu, M., Zhao, N., Li, J., & Leung, V. C. M. (2019). Spectrum sensing based on maximum generalized correntropy under symmetric alpha stable noise. IEEE Trans. Veh. Technol., 68(10), 10262–10266.
Hu, F., Chen, B., & Zhu, K. (2018). Full spectrum sharing in cognitive radio networks toward 5g: a survey. IEEE Access, 6, 15754–15776.
Venkateshkumar, U., & Ramakrishnan, S. (2020). Detection of spectrum hole from n-number of primary users using machine learning algorithms. J. Eng., 2020(5), 175–188.
MacDonald, S., Popescu, D. C., & Popescu, O. (2017). Analyzing the performance of spectrum sensing in cognitive radio systems with dynamic pu activity. IEEE Commun. Lett., 21(9), 2037–2040.
Beaulieu, N. C., & Chen, Y. (2010). Improved energy detectors for cognitive radios with randomly arriving or departing primary users. IEEE Signal Process. Lett., 17(10), 867–870.
Kay, S. M. (1998). Fundamentals of Statistical Signal Processing. Englewood Cliffs, NJ, USA: Prentice-Hall.
Chen, L., Zhao, N., Chen, Y., Yu, F. R., & Wei, G. (2018). Over-the-air computation for cooperative wideband spectrum sensing and performance analysis. IEEE Trans. Veh. Technol., 67(11), 10603–10614.
Yoo, S. K., Sofotasios, P. C., Cotton, S. L., Muhaidat, S., Badarneh, O. S., & Karagiannidis, G. K. (2019). Entropy and energy detection-based spectrum sensing over \(\cal{F}\) -composite fading channels. IEEE Trans. Commun., 67(7), 4641–4653.
Chen, Y. (2010). Improved energy detector for random signals in gaussian noise. IEEE Trans. Wirel. Commun., 9(2), 558–563.
Düzenli, T., & Akay, O. (2016). A new spectrum sensing strategy for dynamic primary users in cognitive radio. IEEE Commun. Lett., 20(4), 752–755.
Shen, J., & Alsusa, E. (2013). An efficient multiple lags selection method for cyclostationary feature based spectrum-sensing. IEEE Signal Process. Lett., 20(2), 133–136.
Tang, L., Chen, Y., Hines, E. L., & Alouini, M. (2012). Performance analysis of spectrum sensing with multiple status changes in primary user traffic. IEEE Commun. Lett., 16(6), 874–877.
B. Hu, N.C. Beaulieu, On characterizing multiple access interference in th-uwb systems with impulsive noise models, in 2008 IEEE Radio and Wireless Symposium (2008), pp. 879–882
Shongwe, T., Vinck, A. J. H., & Ferreira, H. C. (2015). A study on impulse noise and its models. SAIEE Afr. Res. J., 106(3), 119–131.
Shao, M., & Nikias, C. L. (1993). Signal processing with fractional lower order moments: stable processes and their applications. Proc. IEEE, 81(7), 936–1010.
Zhou, F., Beaulieu, N. C., Li, Z., & Si, J. (2016). Feasibility of maximum eigenvalue cooperative spectrum sensing based on Cholesky factorisation. IET Commun., 10(2), 199–206.
Win, M. Z., & Scholtz, R. A. (2000). Ultra-wide bandwidth time-hopping spread-spectrum impulse radio for wireless multiple-access communications. IEEE Trans. Commun., 48(4), 679–689.
Y. Ye, Y. Li, G. Lu, F. Zhou, H. Zhang, Performance of spectrum sensing based on absolute value cumulation in laplacian noise, in in 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall) (2000), pp. 1–5
Ye, Y., Li, Y., Lu, G., & Zhou, F. (2000). Improved energy detection with laplacian noise in cognitive radio. IEEE Syst. J., 13(1), 18–29.
Geddes, K. O., Glasser, M. L., Moore, R. A., et al. (1990). Evaluation of classes of definite integrals involving elementary functions via differentiation of special functions. Alegbra Eng. Commun. Comput., 1(2), 149–165.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
A. Proof of \(E[Z'(\mathbf{y} )|H'_o]\)
From (7), we have
Expanding the above expression, we get
As \(w_N,w_{N-1},w_{N-2}\cdots\) are all i.i.ds, hence
\(E\big [w_N\big ]=E[w_{N-1}\big ]=\cdots E\big [w_{L_{scp}+1}\big ]=0.\) The above expression of \(E[Z'(\mathbf{y} )|H'_o]\) can be further simplified as
B. Proof of \(var[Z'(\mathbf{y} )|H'_o]\)
We have
As \(w_N,w_{N-1},w_{N-2}\cdots\) are all i.i.ds, hence \(var\big [w_N\big ]=var[w_{N-1}\big ]=\cdots var\big [w_{L_{scp}+1}\big ]=2b^2.\) The above expression of \(var[Z'(\mathbf{y} )|H'_o]\) can be further simplified as
As \(L_{scp}=\hat{N_2}\), the final expression becomes
C. Proof of \(E[Z'(\mathbf{y} )|H'_1]\)
We have
Expanding the above expression, we get
As \(w_N,w_{N-1},w_{N-2}\cdots\) are all i.i.ds, hence \(E\big [w_N\big ]=E[w_{N-1}\big ]=\cdots E\big [w_{L_{scp}+1}\big ]=0.\) The above expression of \(E[Z'(\mathbf{y} )|H'_1]\) can be further simplified as
As \(L_{scp}=\hat{N_2'}\), the final expression becomes
D. Proof of \(var[Z'(\mathbf{y} )|H'_1]\)
We have
As \(w_N,w_{N-1},w_{N-2}\cdots\) are all i.i.ds, hence \(var\big [w_N\big ]=var[w_{N-1}\big ]=\cdots var\big [w_{L_{scp}+1}\big ]=2b^2.\) The above expression of \(var[Z'(\mathbf{y} )|H'_1]\) can be further simplified as
Rights and permissions
About this article
Cite this article
Sinha, K., Trivedi, Y.N. Spectrum Sensing Techniques Based on Last Status Change Point Estimation for Dynamic Primary User in Additive Laplacian Noise. Wireless Pers Commun 122, 2131–2143 (2022). https://doi.org/10.1007/s11277-021-08984-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-021-08984-1