Optimization of Linear Cooperation in Spectrum Sensing Over Correlated Log-normal Shadow Fading Channels

The objective of cooperative spectrum sensing is to collaboratively detect the presence of the primary user by the aid of multiple secondary users. It is known that the performance of such a framework substantially depends on the fading assumption. In this paper, we propose an advanced framework for linear cooperative spectrum sensing in cognitive radio networks over correlated log-normal shadow fading channels. Considering the realistic sensing and reporting channels which are not addressed in similar works, motivates us to propose a novel approximation for correlated log-normal sum based on moment generating function calculation and moment matching method. Furthermore, the linear cooperative spectrum sensing coefficients are computed based on the optimization of the deflection criterion. This results in a framework with reasonable complexity which is suitable for practical applications. Simulation results show the excellent agreement between the exact and approximated statistics and the superior performance compared with conventional equally gain combiner.

[1]  Hyung Yun Kong,et al.  Cooperative relaying in interference limited cognitive radio networks , 2010, 2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications.

[2]  Vahid Jamali,et al.  Cooperative spectrum sensing with per-user power constraints , 2012, 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC).

[3]  Nick T. Thomopoulos,et al.  Some Measures on the Standard Bivariate Lognormal Distribution , 2004 .

[4]  Venugopal V. Veeravalli,et al.  Cooperative Sensing for Primary Detection in Cognitive Radio , 2008, IEEE Journal of Selected Topics in Signal Processing.

[5]  H. Vincent Poor,et al.  Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE Transactions on Signal Processing.

[6]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[7]  L. Fenton.  The Sum of Log-Normal Probability Distributions in Scatter Transmission Systems , 1960 .

[8]  Jingxian Wu,et al.  Approximating a Sum of Random Variables with a Lognormal , 2007, IEEE Transactions on Wireless Communications.

[9]  Liljana Gavrilovska,et al.  Spectrum Sensing Framework for Cognitive Radio Networks , 2011, Wirel. Pers. Commun..

[10]  Kang G. Shin,et al.  In-band spectrum sensing in cognitive radio networks: energy detection or feature detection? , 2008, MobiCom '08.

[11]  J. Magnus,et al.  Matrix Differential Calculus with Applications in Statistics and Econometrics , 1991 .

[12]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[13]  Fortunato Santucci,et al.  Cooperative Spectrum Sensing over Correlated Log-Normal Sensing and Reporting Channels , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[14]  Amir Ghasemi,et al.  Asymptotic performance of collaborative spectrum sensing under correlated log-normal shadowing , 2007, IEEE Communications Letters.

[15]  Linda Doyle,et al.  Cyclostationary Signatures in Practical Cognitive Radio Applications , 2008, IEEE Journal on Selected Areas in Communications.

[16]  Geoffrey Ye Li,et al.  Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[17]  Edwin Patricio,et al.  Análisis del estándar IEEE 802.22 (wireless regional área network (WRAN)) y su posible implementación en Ecuador , 2006 .

[18]  Vahid Jamali,et al.  Linear cooperation for spectrum sensing over correlated log-normal shadow fading channels , 2011, 2011 The 14th International Symposium on Wireless Personal Multimedia Communications (WPMC).

[19]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[20]  Norman C. Beaulieu,et al.  An optimal lognormal approximation to lognormal sum distributions , 2004, IEEE Transactions on Vehicular Technology.

[21]  K. V. Cai,et al.  Energy detector performance in a noise fluctuating channel , 1989, IEEE Military Communications Conference, 'Bridging the Gap. Interoperability, Survivability, Security'.

[22]  Shuguang Cui,et al.  Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks , 2008, IEEE Journal of Selected Topics in Signal Processing.

[23]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[24]  S. M. Mishra,et al.  Spectrum Sensing : Fundamental Limits , 2009 .

[25]  J. Magnus,et al.  Matrix Differential Calculus with Applications in Statistics and Econometrics (Revised Edition) , 1999 .

[26]  H. Urkowitz.  Energy detection of unknown deterministic signals , 1967 .

[27]  K. B. Letaief,et al.  Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks , 2009, IEEE Transactions on Wireless Communications.

[28]  Wei Zhang,et al.  Cooperative spectrum sensing with transmit and relay diversity in cognitive radio networks - [transaction letters] , 2008, IEEE Transactions on Wireless Communications.

[29]  S. Schwartz,et al.  On the distribution function and moments of power sums with log-normal components , 1982, The Bell System Technical Journal.

[30]  Fortunato Santucci,et al.  Distributed data fusion over correlated log-normal sensing and reporting channels: Application to cognitive radio networks , 2009, IEEE Transactions on Wireless Communications.

[31]  Vahid Jamali,et al.  Cooperative spectrum sensing over correlated log-normal channels in cognitive radio networks based on clustering , 2011, Proceedings of the 11th International Conference on Telecommunications.