Energy detection sensing of unknown signals over Weibull fading channels

Energy detection is a widely used method of spectrum sensing in cognitive radio and Radio Detection And Ranging (RADAR) systems. This paper is devoted to the analytical evaluation of the performance of an energy detector over Weibull fading channels. This is a flexible fading model that has been shown capable of providing accurate characterization of multipath fading in, e.g., typical cellular radio frequency range of 800/900 MHz. A novel analytic expression for the corresponding average probability of detection is derived in a simple algebraic representation which renders it convenient to handle both analytically and numerically. As expected, the performance of the detector is highly dependent upon the severity of fading as even small variation of the fading parameters affect significantly the value of the average probability of detection. This appears to be particularly the case in severe fading conditions. The offered results are useful in evaluating the effect of multipath fading in energy detection-based cognitive radio communication systems and therefore they can be used in quantifying the associated trade-offs between sensing performance and energy efficiency in cognitive radio networks.

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

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

[3]  Hai Jiang,et al.  Performance of Energy Detection: A Complementary AUC Approach , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[4]  Daniel Benevides da Costa,et al.  On the weibull autocorrelation and power spectrum functions: field trials and validation , 2006, IEEE Communications Letters.

[5]  Amir Ghasemi,et al.  Impact of User Collaboration on the Performance of Sensing-Based Opportunistic Spectrum Access , 2006, IEEE Vehicular Technology Conference.

[6]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[7]  Norman C. Beaulieu,et al.  Novel Analysis for Performance Evaluation of Energy Detection of Unknown Deterministic Signals Using Dual Diversity , 2011, 2011 IEEE Vehicular Technology Conference (VTC Fall).

[8]  Paschalis C. Sofotasios,et al.  Underlay cooperative cognitive networks with imperfect Nakagami-m fading channel information and strict transmit power constraint: Interference statistics and outage probability analysis , 2014, Journal of Communications and Networks.

[9]  W. Weibull A Statistical Distribution Function of Wide Applicability , 1951 .

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

[11]  Vijay K. Bhargava,et al.  Cognitive Wireless Communication Networks , 2007 .

[12]  Mohamed-Slim Alouini,et al.  Digital Communication Over Fading Channels: A Unified Approach to Performance Analysis , 2000 .

[13]  C. Tellambura,et al.  Energy detection of primary signals over η - μ fading channels , 2009, 2009 International Conference on Industrial and Information Systems (ICIIS).

[14]  Jess Marcum,et al.  A statistical theory of target detection by pulsed radar , 1948, IRE Trans. Inf. Theory.

[15]  Paschalis C. Sofotasios,et al.  Outage behaviour of cooperative underlay cognitive networks with inaccurate channel estimation , 2013, 2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN).

[16]  Hai Jiang,et al.  Energy Detection Based Cooperative Spectrum Sensing in Cognitive Radio Networks , 2011, IEEE Transactions on Wireless Communications.

[17]  Daniel Benevides da Costa,et al.  Multiuser and Multirelay Cognitive Radio Networks Under Spectrum-Sharing Constraints , 2014, IEEE Transactions on Vehicular Technology.

[18]  Albert H. Nuttall,et al.  Some integrals involving the QM function (Corresp.) , 1975, IEEE Trans. Inf. Theory.

[19]  Paschalis C. Sofotasios,et al.  Bit error rate of underlay multi-hop cognitive networks in the presence of multipath fading , 2013, 2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN).

[20]  A. Nuttall Some Integrals Involving the (Q sub M)-Function , 1974 .

[21]  R. M. A. P. Rajatheva,et al.  Analysis of Equal Gain Combining in Energy Detection for Cognitive Radio over Nakagami Channels , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[22]  R. M. A. P. Rajatheva,et al.  Energy Detection of Unknown Signals in Fading and Diversity Reception , 2011, IEEE Transactions on Communications.

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

[24]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[25]  Paschalis C. Sofotasios,et al.  Exact bit-error-rate analysis of underlay decode-andforward multi-hop cognitive networks with estimation errors , 2013, IET Commun..

[26]  David W. Matolak,et al.  Generation of multivariate Weibull random variates , 2008, IET Commun..

[27]  A. Ghasemi,et al.  Collaborative spectrum sensing for opportunistic access in fading environments , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[28]  Hai Jiang,et al.  Spectrum Sensing via Energy Detector in Low SNR , 2011, 2011 IEEE International Conference on Communications (ICC).

[29]  A. Rabbachin,et al.  UWB Energy Detection in the Presence of Multiple Narrowband Interferers , 2007, 2007 IEEE International Conference on Ultra-Wideband.

[30]  Hai Jiang,et al.  Relay Based Cooperative Spectrum Sensing in Cognitive Radio Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[31]  Hai Jiang,et al.  Performance of an Energy Detector over Channels with Both Multipath Fading and Shadowing , 2010, IEEE Transactions on Wireless Communications.

[32]  D. B. da Costa,et al.  Joint statistics for two correlated Weibull variates , 2005, IEEE Antennas and Wireless Propagation Letters.

[33]  R. M. A. P. Rajatheva,et al.  On the energy detection of unknown deterministic signal over Nakagami channelswith selection combining , 2009, 2009 Canadian Conference on Electrical and Computer Engineering.

[34]  S. Haykin,et al.  Modern Wireless Communications , 1939, Nature.

[35]  George K. Karagiannidis,et al.  Channel capacity and second-order statistics in Weibull fading , 2004, IEEE Communications Letters.

[36]  Kalle Ruttik,et al.  Detection of Unknown Signals in a Fading Environment , 2009, IEEE Communications Letters.

[37]  Amir Ghasemi,et al.  Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs , 2008, IEEE Communications Magazine.

[38]  M.C. Stefanovic,et al.  Outage Probability of Sir-Based Dual Selection Diversity over Correlated Weibull Fading Channels , 2007, 2007 8th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services.

[39]  Vladimir I. Kostylev,et al.  Energy detection of a signal with random amplitude , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[40]  Paschalis C. Sofotasios,et al.  Analytic performance evaluation of underlay relay cognitive networks with channel estimation errors , 2013, 2013 International Conference on Advanced Technologies for Communications (ATC 2013).