Schemes for spectrum holes sensing for cognitive radio based on the estimation of the Stokes parameters of monochromatic and quasimonochromatic polarized electromagnetic waves are developed. Statistical information that includes the variations of the polarization state in both cases (present and absent) of Primary User (PU) is accounted for. A detector based on the fluctuation of the Stokes parameters is analyzed, and its performance is compared with that of energy detectors, which use only the scalar amplitude information to sense the PU signal. The cooperative spectrum sensing based on the polarization in which the reporting channels are noisy will be investigated. The cluster technique is proposed to reduce the bit error probability due to channel impairment. A closed-form expression for the polarization detection is derived using α-μ generalized fading model, which provides directly an expression for the special cases of Nakagami-m and Weibull models as well as their derivatives. These expressions are verified using simulation. The results show that the polarization spectrum sensing gives superior performance for a wide range of SNR over the conventional energy detection method. 1. Introduction Cognitive radio (CR) technology has witnessed a growing interest over the past decade, as it promises more efficient use of the available spectrum [1, 2]. A key stage in CR is spectrum sensing, in which the Secondary User (SU) must detect the presence of a Primary User (PU) in a certain channel, and thus, deems this part of the spectrum unused, and make the decision to share it. This entails a sequence of functions that the CR system should perform, such as power control [3] and spectrum management [4]. Several techniques were proposed to improve spectrum sensing such as energy detection [5], cyclostationary feature detection [6], sensing based on smart antennas [7, 8], and wideband spectrum sensing [9, 10]. These techniques primarily make use of the amplitude, frequency, and phase information of the PU signal. It is possible, however, to improve the spectrum sensing process by exploiting the polarization state of the signal. In radar systems [11], the polarization state was used to improve the detection capability of the system. The new polarization-dependent detection statistics, which use the power and relative phase of the two orthogonal polarization components was proposed to enhance radar detection in homogenous channels [12]. The radar detection performance was enhanced based on the polarization difference between the clutter and the target [11]. The
References
[1]
S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005.
[2]
A. Sahai and D. Cabric, “Spectrum sensing: fundamental limits and practical challenges,” in Proceedings of the IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Network (DySPAN '05), Baltimore, Md, USA, November 2005.
[3]
W. Wei, P. Tao, and W. Wenbo, “Optimal power control under interference temperature constraints in cognitive radio network,” in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '07), pp. 116–120, March 2007.
[4]
I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “A survey on spectrum management in cognitive radio networks,” IEEE Communications Magazine, vol. 46, no. 4, pp. 40–48, 2008.
[5]
F. F. Digham, M.-S. Alouini, and M. K. Simon, “On the energy detection of unknown signals over fading channels,” IEEE Transactions on Communications, vol. 55, no. 1, pp. 21–24, 2007.
[6]
A. V. Dandawate and G. B. Giannakis, “Statistical tests for presence of cyclostationarity,” IEEE Transactions on Signal Processing, vol. 42, no. 9, pp. 2355–2369, 1994.
[7]
L. Zhang, Y.-C. Liang, Y. Xin, and H. V. Poor, “Robust cognitive beamforming with partial channel state information,” IEEE Transactions on Wireless Communications, vol. 8, no. 8, pp. 4143–4153, 2009.
[8]
H. Kim, J. Kim, S. Yang, M. Hong, and Y. Shin, “An effective MIMO-OFDM system for IEEE 802.22 WRAN channels,” IEEE Transactions on Circuits and Systems II, vol. 55, no. 8, pp. 821–825, 2008.
[9]
Z. Quan, S. Cui, A. H. Sayed, and H. V. Poor, “Wideband spectrum sensing in cognitive radio networks,” in Proceedings of the IEEE International Conference on Communications (ICC '08), pp. 901–906, May 2008.
[10]
Z. Tian, “Compressed wideband sensing in cooperative cognitive radio networks,” in Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '08), pp. 3756–3760, December 2008.
[11]
A. J. Poelman, “On using orthogonally polarized noncoherent receiving channels to detect target echoes in gaussian noise,” IEEE Transactions on Aerospace and Electronic Systems, vol. 11, no. 4, pp. 660–663, 1975.
[12]
G. M. Vachula and R. M. Barnes, “Polarization detection of a fluctuating radar target,” IEEE Transactions on Aerospace and Electronic Systems, vol. 19, no. 2, pp. 250–257, 1983.
[13]
R. E. Stovall, “A gaussian noise analysis of the pseudo-coherent discriminant,” Tech. Rep. Note 1978-46, M.I.T. Lincoln Laboratory, 1978.
[14]
D. P. Meyer and H. A. Mayer, Radar Target Detection: Handbook of Theory and Practice, Academic Press, New York, NY, USA, 1973.
[15]
F. Liu, C. Feng, C. Guo, Y. Wang, and D. Wei, “Polarization spectrum sensing scheme for cognitive radios,” in Proceedings of the 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM '09), September 2009.
[16]
D. Wei, C. Guo, F. Liu, and Z. Zeng, “A SINR improving scheme based on optimal polarization receiving for the cognitive radios,” in Proceedings of the IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC '09), pp. 100–104, November 2009.
[17]
F. Liu, C. Feng, C. Guo, Y. Wang, and D. Wei, “Virtual polarization detection: a vector signal sensing method for cognitive radios,” in Proceedings of the 71st IEEE Vehicular Technology Conference (VTC-Spring '10), pp. 1–5, Taipei, Taiwan, 2010.
[18]
C. Guo, X. Wu, C. Feng, and Z. Zeng, “Spectrum sensing for cognitive radios based on directional statistics of polarization vectors,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 3, pp. 379–393, 2013.
[19]
K. B. Letaief and W. Zhang, “Cooperative communications for cognitive radio networks,” Proceedings of the IEEE, vol. 97, no. 5, pp. 878–893, 2009.
[20]
L. P. Murza, “The non coherent polarimetry of noise like radiation,” Radio Engineering and Electronic Physics, vol. 23, no. 7, pp. 57–63, 1978.
[21]
G. Deschamps, “Techniques for handling elliptically polarized waves with special reference to antennas: part II—geometrical representation of the polarization of a plane electromagnetic wave,” Proceedings of the IRE, vol. 39, no. 5, pp. 540–544, 1951.
[22]
N. Goodman, “Statistical analysis based on a certain multivariate complex gaussian distribution (an introduction),” Annals of Mathematical Statistics, vol. 34, no. 1, pp. 152–177, 1963.
[23]
M. D. Yacoub, “The α-μ distribution: a physical fading model for the Stacy distribution,” IEEE Transactions on Vehicular Technology, vol. 56, no. 1, pp. 27–34, 2007.
[24]
M. K. Simon and M. S. Alouini, Digital Communication over Fading Channels, vol. 86, Wiley-IEEE Press, 2004.
[25]
A. Prudnikov, Y. A. Brychkov, and O. Marichev, Integrals and Series, vol. 4, Gordon and Breach Science, 1986.
[26]
F. W. Olver, D. W. Lozier, R. F. Boisvert, and C. W. Clark, NIST Handbook of Mathematical Functions, Cambridge University Press, 2010.
[27]
A. Ghasemi and E. S. Sousa, “Collaborative spectrum sensing for opportunistic access in fading environments,” in Proceedings of the 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN '05), pp. 131–136, November 2005.
[28]
C. Sun, W. Zhang, and K. B. Letaief, “Cluster-based cooperative spectrum sensing in cognitive radio systems,” in Proceedings of the IEEE International Conference on Communications (ICC '07), pp. 2511–2515, June 2007.
[29]
O. Younis and S. Fahmy, “Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach,” in Proceedings of the 23rd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '04), vol. 1, pp. 629–640, March 2004.
[30]
A. Ghasemi and E. S. Sousa, “Spectrum sensing in cognitive radio networks: the cooperation-processing tradeoff,” Wireless Communications and Mobile Computing, vol. 7, no. 9, pp. 1049–1060, 2007.
[31]
J. Ma, G. Zhao, and Y. Li, “Soft combination and detection for cooperative spectrum sensing in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 11, pp. 4502–4507, 2008.
