Collaborative Spectrum Sensing for Cognitive Radio: Diversity Combining Approach

ABSTRACT

In this paper it is shown that cyclostationary spectrum sensing for Cognitive Radio networks, applying multiple cyclic frequencies for single user detection can be interpreted (with some assumptions) in terms of optimal incoherent diversity addition for “virtual diversity branches” or SIMO radar. This approach allows proposing, by analogy to diversity combining, suboptimal algorithms which can provide near optimal characteristics for the Neyman-Pearson Test (NPT) for single user detection. The analysis is based on the Generalized Gaussian (Klovsky-Middleton) Channel Model, which allows obtaining the NPT noise immunity characteristics: probability of misdetection error (PM) and probability of false alarm (Pfa) or Receiver Operational Characteristics (ROC) in the most general way. Some quasi-optimum algorithms such as energetic receiver and selection addition algorithm are analyzed and their comparison with the noise immunity properties (ROC) of the optimum approach is provided as well. Finally, the diversity combining approach is applied for the collaborative spectrum sensing and censoring. It is shown how the diversity addition principles are applied for distributed detection algorithms, called hereafter as SIMO radar or distributed SIMO radar, implementing Majority Addition (MA) approach and Weighted Majority Addition (WMA) principle.

In this paper it is shown that cyclostationary spectrum sensing for Cognitive Radio networks, applying multiple cyclic frequencies for single user detection can be interpreted (with some assumptions) in terms of optimal incoherent diversity addition for “virtual diversity branches” or SIMO radar. This approach allows proposing, by analogy to diversity combining, suboptimal algorithms which can provide near optimal characteristics for the Neyman-Pearson Test (NPT) for single user detection. The analysis is based on the Generalized Gaussian (Klovsky-Middleton) Channel Model, which allows obtaining the NPT noise immunity characteristics: probability of misdetection error (PM) and probability of false alarm (Pfa) or Receiver Operational Characteristics (ROC) in the most general way. Some quasi-optimum algorithms such as energetic receiver and selection addition algorithm are analyzed and their comparison with the noise immunity properties (ROC) of the optimum approach is provided as well. Finally, the diversity combining approach is applied for the collaborative spectrum sensing and censoring. It is shown how the diversity addition principles are applied for distributed detection algorithms, called hereafter as SIMO radar or distributed SIMO radar, implementing Majority Addition (MA) approach and Weighted Majority Addition (WMA) principle.

KEYWORDS

Spectrum Sensing, Cognitive Radio, Diversity Combining, Collaborative Sensing, Majority Diversity Addition, Sequential Analysis

Spectrum Sensing, Cognitive Radio, Diversity Combining, Collaborative Sensing, Majority Diversity Addition, Sequential Analysis

Cite this paper

nullO. Filio-Rodriguez, V. Kontorovich, S. Primak and F. Ramos-Alarcon, "Collaborative Spectrum Sensing for Cognitive Radio: Diversity Combining Approach,"*Wireless Sensor Network*, Vol. 3 No. 1, 2011, pp. 24-37. doi: 10.4236/wsn.2011.31004.

nullO. Filio-Rodriguez, V. Kontorovich, S. Primak and F. Ramos-Alarcon, "Collaborative Spectrum Sensing for Cognitive Radio: Diversity Combining Approach,"

References

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[14] V. V. Veeravalli, T. Basar and H. V. Poor, “Decentralized Sequential Detection with a Fusion Center Performing Sequential Tests,” IEEE Transactions on Information Theory, Vol. 39, No. 2, March 1993, pp. 433-442, doi:10. 1109/18.212274

[15] O. Filio, S. Primak and V. Kontorovich, “Impact of Scatering Environment on the Performance of Spectrum Sensing in Multiantenna Cognitive Radio Systems,” Submitted to ICC 2011 Cognitive Radio and Network Symposium.

[16] D. Middleton, “A Statistical Theory of Reverberation and Similar First Order Scattered Fields,” IEEE Transaction on Information Theory, Vol. 13, No. 3, July 1967, pp. 393- 414. doi:10.1109/TIT.1967.1054045

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[20] R. Zhang, T-J Lim et al., “Multiantenna Based Spectrum Sensing for Cognitive Radios: A GLRT Ap-proach,” IEEE Transactions on Communications, Vol. 58, No. 1, January 2010, pp. 84-88. doi:10.1109/TCOMM.2010.01.080158

[21] M. Shi, Y. Bar-Ness and W. Du, “A Simple Method to Enhance the Detection of Second Order Cyclostationarity,” Proceedings of the GLOBECOM’08, New Orleans, 2008, pp. 1-6. doi:10.1109/GLOCOM.2008.ECP.679

[22] Y. Okunev, “Phase and Phase-Difference Modulation in Digital Communications,” Artech-House, London, 1997.

[23] D. Middleton, “Introduction to the Statistical Theory of Communication,” IEEE Press, 1999.

[24] K. Paulraj et al. “Introduction to Space-Time Wireless Communications,” Cambridge University Press, Cambridge, 2006.

[25] V. Kontorovich and S. Primak, “Performance Analysis of OSTBC over Generalized Gaussian MIMO Channels,” Proceedings of the IWCMC’09, Leipzig, 19-22 June 2009.

[26] A. Wald, “Sequential Analysis,” Dover Publi-cations, New York, 1973.

[27] Y. Zeng, Y-C. Liang, et al., “A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions,” EURASIP Journal on Advances in Signal Processing - Special Issue on Advanced Signal Processing for Cognitive Radio Networks, Vol. 2010, January 2010.

[28] N. H. Kamil and X. Yuan, “Detection Proposal Schemes for Spectrum Sensing in Cognitive Radio,” Wireless Sensor Networks, No. 2, 2010, pp. 365-372.

[29] Y. A. Goldstein and V. Ya. Kontorovich, “Noise Immunity of Weighted Discrete Addition of Di-versity Signals,” Telecommunications and Radio Engi-neering, Vol. 26/27, No. 11, 1972, pp. 104-106.

[30] R. Alvarez, H. Jardon and V. Kontorovich, “Micro-Di- versity Properties of Cellular Radio Networks,” Proceedings of Commsphere’95, Israel, 22-27January 1995, pp. 180-184.

[31] D. da Costa et al., “An Improved Closed-Form Approximation to the Sum of Arbitrary Na-kagami M Variates,” IEEE Transactions on Vehicular Technology, Vol. 57, No. 6, 2008, pp. 3854-3863.

[32] Z. Khan, et al., “On the Selection of the Best Detection Per-formance Sensors for Cognitive Radio Networks,” IEEE Signal Processing Letters, Vol. 17, No. 4, April 2010, pp. 359-362. doi:10.1109/LSP.2010.2041581

[33] V. Kontorovich, S. Primak, A. Alcocer-Ochoa and R. Parra-Michel, “MIMO Channel Orthogonalization Apply- ing Universal Eigenbasis,” IET Signal Processing, Vol. 2, No. 2, 2008, pp. 87-96. doi:10.1049/iet-spr:20070 126

[34] L. Ruan and V. K. N. Lau, “Power Control and Performance Analysis of Cognitive Radio Systems under Dynamic Spectrum Activity and Imperfect Knowledge of System State,” IEEE Transactions on Wireless Communications, Vol. 8, No. 9, September 2009, pp. 4616-4622. doi:10.1109/ TWC.2009.080789

[1] S. Haykin, “Cognitive Radio: Brain Empowered Wireless Communication,” IEEE Journal on Selected Areas in Communications, Vol. 23, No. 2, February 2005, pp. 201- 220. doi:10.1109/JSAC.2004.839380

[2] X. Kang, Y-C. Liang, H. Krishna and L. Zhang, “Sensing- Based Spec-trum Sharing in Cognitive Radio Networks,” IEEE Transactions on Vehicular Technology, Vol. 58, No. 8, October 2008, pp. 4649-4654. doi:10.1109/TVT.2009. 2018258

[3] R. Zhang, Sh. Cui and Y-C. Liang, “On Ergodic Sum Capacity of Fading Cognitive Mul-tiple-Access and Broadcast Channels,” IEEE Transactions on Information Theory, Vol. 55, No. 11, November 2009, pp. 5161-5178. doi:10. 1109/TIT.2009.2030449

[4] J. Lunden, V. Keivunen, A. Huttunen and H. Vincent Poor, “Collaborative Cyclostationarity Spectrum Sensing for Cognitive Radio Systems,” IEEE Transactions on Signal Processing, Vol. 57, No. 11, November 2009, pp. 4182- 4195. doi:10.1109/TSP.2009.2025152

[5] A. V. Dan-dawate and G. B. Giannakis, “Statistical Tests for Presence of Cyclostationarity,” IEEE Transactions on Signal Processing, Vol. 42, No. 9, September 1994, pp. 2355-2369. doi:10.1109/78.317857

[6] W. A. Gardner, A. Napolitano and L. Paura, “Cyclostationarity: Half a Century of Research,” Signal Processing, Vol. 86, No. 4, April 2006, pp. 639-697. doi:10.1016/ j.sigpro.2005.06.016

[7] M. Simon and M.-S. Alouini, “Digital Communications over Fading Channels: A Unified Approach to Performance Analysis,” John Wiley & Sons, Hoboken, 2005.

[8] D. D. Klovsky, “Digital Data Transmission over Radio Channels,” Sviaz, Moscow, 1982.

[9] A. Sendonaris, E. Erkip and B. Aazhang, “User Cooperation Diversity, Parts I and II,” IEEE Transactions on Communications, Vol. 51, No. 11, November 2003, pp. 1928-1948. doi:10.1109/TCOMM.2003.819238

[10] V. Kontorovich and S. Primak, “Autocovariance Receiver for DTSM: Performance Characteristics for a Generalized MIMO Channel,” Proceedings of the IWCMC’09, 19-22 June 2009, Leipzig.

[11] A. Papoulis, “Probability, Random Variables and Stochastic Processes,” Mc-Graw Hill, New York, 1991.

[12] A. M. Hussein, “Multisensor Distributed Sequential Detection”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 30, No. 3, 1994, pp. 698-708. doi:10.1109/7. 303740

[13] R. Vishwantan and V. Aalo, “On Counting Rules in Distributed Detection,” IEEE Transactions on Acoustic, Speech and Signal Proceedings, Vol. 37, No. 5, May 1989, pp. 772-775. doi:10.1109/29.17574

[14] V. V. Veeravalli, T. Basar and H. V. Poor, “Decentralized Sequential Detection with a Fusion Center Performing Sequential Tests,” IEEE Transactions on Information Theory, Vol. 39, No. 2, March 1993, pp. 433-442, doi:10. 1109/18.212274

[15] O. Filio, S. Primak and V. Kontorovich, “Impact of Scatering Environment on the Performance of Spectrum Sensing in Multiantenna Cognitive Radio Systems,” Submitted to ICC 2011 Cognitive Radio and Network Symposium.

[16] D. Middleton, “A Statistical Theory of Reverberation and Similar First Order Scattered Fields,” IEEE Transaction on Information Theory, Vol. 13, No. 3, July 1967, pp. 393- 414. doi:10.1109/TIT.1967.1054045

[17] P. Beckman and A. Spizzidino, “The Scattering of Electromagnetic Waves from Rough Surfaces,” Pergamon Press, Oxford, 1963.

[18] I. S. Gradshteyn and I. M. Ryzhik, “Table of Integrals, Series and Products,” Academic Press, Massa-chusetts, 2000.

[19] M. Nakagami, “The M-Distribution: A General Formula of Intensity Distribution of Rapid Fadings,” In: W. G. Hoffman, Ed., Statistical Methods in Radio Wave Propagation, Pergamon Press, Oxford, 1960.

[20] R. Zhang, T-J Lim et al., “Multiantenna Based Spectrum Sensing for Cognitive Radios: A GLRT Ap-proach,” IEEE Transactions on Communications, Vol. 58, No. 1, January 2010, pp. 84-88. doi:10.1109/TCOMM.2010.01.080158

[21] M. Shi, Y. Bar-Ness and W. Du, “A Simple Method to Enhance the Detection of Second Order Cyclostationarity,” Proceedings of the GLOBECOM’08, New Orleans, 2008, pp. 1-6. doi:10.1109/GLOCOM.2008.ECP.679

[22] Y. Okunev, “Phase and Phase-Difference Modulation in Digital Communications,” Artech-House, London, 1997.

[23] D. Middleton, “Introduction to the Statistical Theory of Communication,” IEEE Press, 1999.

[24] K. Paulraj et al. “Introduction to Space-Time Wireless Communications,” Cambridge University Press, Cambridge, 2006.

[25] V. Kontorovich and S. Primak, “Performance Analysis of OSTBC over Generalized Gaussian MIMO Channels,” Proceedings of the IWCMC’09, Leipzig, 19-22 June 2009.

[26] A. Wald, “Sequential Analysis,” Dover Publi-cations, New York, 1973.

[27] Y. Zeng, Y-C. Liang, et al., “A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions,” EURASIP Journal on Advances in Signal Processing - Special Issue on Advanced Signal Processing for Cognitive Radio Networks, Vol. 2010, January 2010.

[28] N. H. Kamil and X. Yuan, “Detection Proposal Schemes for Spectrum Sensing in Cognitive Radio,” Wireless Sensor Networks, No. 2, 2010, pp. 365-372.

[29] Y. A. Goldstein and V. Ya. Kontorovich, “Noise Immunity of Weighted Discrete Addition of Di-versity Signals,” Telecommunications and Radio Engi-neering, Vol. 26/27, No. 11, 1972, pp. 104-106.

[30] R. Alvarez, H. Jardon and V. Kontorovich, “Micro-Di- versity Properties of Cellular Radio Networks,” Proceedings of Commsphere’95, Israel, 22-27January 1995, pp. 180-184.

[31] D. da Costa et al., “An Improved Closed-Form Approximation to the Sum of Arbitrary Na-kagami M Variates,” IEEE Transactions on Vehicular Technology, Vol. 57, No. 6, 2008, pp. 3854-3863.

[32] Z. Khan, et al., “On the Selection of the Best Detection Per-formance Sensors for Cognitive Radio Networks,” IEEE Signal Processing Letters, Vol. 17, No. 4, April 2010, pp. 359-362. doi:10.1109/LSP.2010.2041581

[33] V. Kontorovich, S. Primak, A. Alcocer-Ochoa and R. Parra-Michel, “MIMO Channel Orthogonalization Apply- ing Universal Eigenbasis,” IET Signal Processing, Vol. 2, No. 2, 2008, pp. 87-96. doi:10.1049/iet-spr:20070 126

[34] L. Ruan and V. K. N. Lau, “Power Control and Performance Analysis of Cognitive Radio Systems under Dynamic Spectrum Activity and Imperfect Knowledge of System State,” IEEE Transactions on Wireless Communications, Vol. 8, No. 9, September 2009, pp. 4616-4622. doi:10.1109/ TWC.2009.080789