On the Performance of Energy Detection Using Bartlett's Estimate for Spectrum Sensing in Cognitive Radio Systems

Energy detection can be used for spectrum sensing in cognitive radios when no prior knowledge about the primary signals is available. The performance of this technique, however, is strongly influenced by the available decision estimate. This paper presents accurate performance analysis of energy detection when using Bartlett's estimate as a test statistic. Both independent and identically distributed Rayleigh and Rician fading channels are investigated for unknown signals with complex envelopes. The novel contribution here is threefold. First, the quadratic form representation of Bartlett's estimate is formulated. Then, starting from the characteristic function, the cumulative distribution function is derived for each type of channel and accurate expressions are developed for the probabilities of false alarm and missed detection. Finally, a performance comparison with the raw periodogram is presented. The accuracy of the proposed analysis is confirmed using Monte Carlo trials. The results provide valuable insight into the performance of Bartlett's based energy detection. It will be seen that the additional degrees of freedom associated with Bartlett's method empower it to offer excellent performance. When compared with the raw periodogram, Bartlett's consistently leads to lower probability of miss, but its probability of false alarm is only superior at relatively higher sensing thresholds.

[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  W. Beyer CRC Standard Probability And Statistics Tables and Formulae , 1990 .

[3]  Ananthram Swami,et al.  Distributed Spectrum Sensing and Access in Cognitive Radio Networks With Energy Constraint , 2009, IEEE Transactions on Signal Processing.

[4]  M. Bartlett Periodogram analysis and continuous spectra. , 1950, Biometrika.

[5]  Weifang Wang,et al.  Spectrum sensing in cognitive radio , 2016 .

[6]  J. I. Mararm,et al.  Energy Detection of Unknown Deterministic Signals , 2022 .

[7]  A. M. Mathai Quadratic forms in random variables , 1992 .

[8]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[9]  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).

[10]  Emad Alsusa,et al.  Performance Analysis of the Periodogram-Based Energy Detector in Fading Channels , 2011, IEEE Transactions on Signal Processing.

[11]  V. Tarokh,et al.  Cognitive radio networks , 2008, IEEE Signal Processing Magazine.

[12]  Keith Q. T. Zhang,et al.  A Multitaper Spectrum Based Detector for Cognitive Radio , 2009, 2009 IEEE Wireless Communications and Networking Conference.

[13]  M. Simon Probability distributions involving Gaussian random variables : a handbook for engineers and scientists , 2002 .

[14]  Norman C. Beaulieu,et al.  Improved Energy Detectors for Cognitive Radios With Randomly Arriving or Departing Primary Users , 2010, IEEE Signal Processing Letters.

[15]  Yonghong Zeng,et al.  Blindly Combined Energy Detection for Spectrum Sensing in Cognitive Radio , 2008, IEEE Signal Processing Letters.

[16]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[17]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[18]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[19]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[20]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[21]  Thomas P. Bronez,et al.  On the performance advantage of multitaper spectral analysis , 1992, IEEE Trans. Signal Process..

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

[23]  J. Gil-Pelaez Note on the inversion theorem , 1951 .

[24]  Biing-Hwang Juang,et al.  Signal Processing in Cognitive Radio , 2009, Proceedings of the IEEE.

[25]  Joseph Lipka,et al.  A Table of Integrals , 2010 .

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