Modern Spectrum Sensing Techniques for Cognitive Radio Networks: Practical Implementation and Performance Evaluation

Authors: Antoni Ivanov, Nikolay Dandanov, Nicole Christoff, Vladimir Poulkov

Abstract:

Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Keywords: Cognitive radio, dynamic spectrum access, GNU Radio, spectrum sensing.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1340534

References:

[1] A. Kumar, P. Thakur, S. Pandit, and G. Singh, “Performance analysis of different threshold selection schemes in energy detection for cognitive radio communication systems,” in Image Information Processing (ICIIP), 2017 Fourth International Conference on. IEEE, 2017, pp. 1–6.
[2] M. S. Murty and R. Shrestha, “VLSI architecture for cyclostationary feature detection based spectrum sensing for cognitive-radio wireless networks and its asic implementation,” in VLSI (ISVLSI), 2016 IEEE Computer Society Annual Symposium on. IEEE, 2016, pp. 69–74.
[3] A. Tani, R. Fantacci, and D. Marabissi, “A low-complexity cyclostationary spectrum sensing for interference avoidance in femtocell LTE-A-based networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 4, pp. 2747–2753, 2016.
[4] F. Salahdine, H. El Ghazi, N. Kaabouch, and W. F. Fihri, “Matched filter detection with dynamic threshold for cognitive radio networks,” in Wireless Networks and Mobile Communications (WINCOM), 2015 International Conference on. IEEE, 2015, pp. 1–6.
[5] C. Charan and R. Pandey, “Eigenvalue-based reliable spectrum sensing scheme for cognitive radio networks,” in Nascent Technologies in Engineering (ICNTE), 2017 International Conference on. IEEE, 2017, pp. 1–5.
[6] V. R. S. Banjade, C. Tellambura, and H. Jiang, “Approximations for Performance of Energy Detector and p-Norm Detector,” IEEE Communications Letters, vol. 19, no. 10, pp. 1678–1681, 2015.
[7] A. Blad, E. Axell, and E. G. Larsson, “Spectrum sensing of OFDM signals in the presence of CFO: New algorithms and empirical evaluation using USRP,” in Signal Processing Advances in Wireless Communications (SPAWC), 2012 IEEE 13th International Workshop on. IEEE, 2012, pp. 159–163.
[8] X. Zhai, H. Haigen, and Z. Guoxin, “Optimal threshold and weighted cooperative data combining rule in cognitive radio network,” in Communication Technology (ICCT), 2010 12th IEEE International Conference on. IEEE, 2010, pp. 1464–1467.
[9] A. F. Eduardo and R. G. G. Caballero, “Experimental evaluation of performance for spectrum sensing: Matched filter vs energy detector,” in Communications and Computing (COLCOM), 2015 IEEE Colombian Conference on. IEEE, 2015, pp. 1–6.
[10] M. Sardana and A. Vohra, “Analysis of different spectrum sensing techniques,” in Computer, Communications and Electronics (Comptelix), 2017 International Conference on. IEEE, 2017, pp. 422–425.
[11] A. Nafkha, B. Aziz, M. Naoues, and A. Kliks, “Cyclostationarity-based versus eigenvalues-based algorithms for spectrum sensing in cognitive radio systems: Experimental evaluation using GNU radio and USRP,” in Wireless and Mobile Computing, Networking and Communications (WiMob), 2015 IEEE 11th International Conference on. IEEE, 2015, pp. 310–315.
[12] A. Ivanov, A. Mihovska, K. Tonchev, and V. Poulkov, “Real-time adaptive spectrum sensing for cyclostationary and energy detectors,” IEEE Aerospace and Electronic Systems Magazine, vol. 33, no. 5-6, pp. 20–33, 2018.
[13] B. Wang and K. R. Liu, “Advances in cognitive radio networks: A survey,” IEEE Journal of selected topics in signal processing, vol. 5, no. 1, pp. 5–23, 2011.
[14] X. Zhai, H. Haigen, and Z. Guoxin, “Optimal threshold and weighted cooperative data combining rule in cognitive radio network,” in Communication Technology (ICCT), 2010 12th IEEE International Conference on. IEEE, 2010, pp. 1464–1467.
[15] H. Urkowitz, “Energy detection of unknown deterministic signals,” Proceedings of the IEEE, vol. 55, no. 4, pp. 523–531, 1967.
[16] W. A. Gardner and C. M. Spooner, “Signal interception: performance advantages of cyclic-feature detectors,” IEEE Transactions on Communications, vol. 40, no. 1, pp. 149–159, 1992.
[17] S. Andr´as, A. Baricz, and Y. Sun, “The generalized marcum q-function: an orthogonal polynomial approach,” Acta Universitatis Sapientiae Mathematica, vol. 3, no. 1, pp. 60–76, 2011.
[18] W. McGee, “Another recursive method of computing the q function (corresp.),” IEEE Transactions on Information Theory, vol. 16, no. 4, pp. 500–501, 1970.
[19] C. A. Tracy and H. Widom, “On orthogonal and symplectic matrix ensembles,” Communications in Mathematical Physics, vol. 177, no. 3, pp. 727–754, 1996.
[20] A. Bejan, “Largest eigenvalues and sample covariance matrices. tracy-widom and painleve ii: computational aspects and realization in s-plus with applications,” Preprint: http://www. vitrum. md/andrew/MScWrwck/TWinSplus. pdf, 2005.
[21] S. M. Kay, “Fundamentals of statistical signal processing. detection theory, volume ii,” 1998.
[22] The GNU Radio Foundation, “GNU Radio, the free and open software radio ecosystem,” http://gnuradio.org,
[Online]. Accessed: 28. June 2018.
[23] Analog Devices, Inc., “ADALM-PLUTO — Software-Defined Radio Active Learning Module,” http://www.analog.com/en/design-center/ evaluation-hardware-and-software/evaluation-boards-kits/adalm-pluto. html,
[Online]. Accessed: 28. June 2018.
[24] M. L´opez-Ben´ıtez and F. Casadevall, “Time-dimension models of spectrum usage for the analysis, design, and simulation of cognitive radio networks,” IEEE transactions on vehicular technology, vol. 62, no. 5, pp. 2091–2104, 2013.
[25] C. R. Stevenson, G. Chouinard, Z. Lei, W. Hu, S. J. Shellhammer, and W. Caldwell, “Ieee 802.22: The first cognitive radio wireless regional area network standard,” IEEE communications magazine, vol. 47, no. 1, pp. 130–138, 2009.