Modern Spectrum Sensing Techniques for Cognitive Radio Networks: Practical Implementation and Performance Evaluation
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).
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:77747", author = "Antoni Ivanov and Nikolay Dandanov and Nicole Christoff and Vladimir Poulkov", title = "Modern Spectrum Sensing Techniques for Cognitive Radio Networks: Practical Implementation and Performance Evaluation", 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.", volume = "12", number = "7", pages = "572-6", }
