A performance study of novel Sequential Energy Detection methods for spectrum sensing

Abstract

We study the sequential energy detection problem in the context of spectrum sensing for cognitive radio networks. We formulate a novel Sequential Energy Detector and provide a comprehensive study of its performance. The sensitivity of the Sequential Test to primary signal variance estimation is addressed for the first time ever in this paper. Specifically, we develop an Iterative Hybrid Bayesian method to robustly estimate the primary signal variance. Through extensive simulations it is demonstrated that our Sequential version of the energy detector delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection and False Alarm. Our simulations also demonstrate the enhanced robustness gained via the use of the new Variance Estimator which converges in only 10 iterations on average and delivers a performance within 10% of that with perfect knowledge of the actual primary signal variance.

Sequential Likelihood Ratio Test under Incomplete Signal Model for Spectrum Sensing

“…We denote the logarithmic likelihood ratio of the sequential energy detector as Λ SeqEng,t . The sequential update of the logarithmic likelihood ratio can be expressed [11] as…”

“…Besides the energy detector, the sequential energy detector in [11] is briefly described for comparison. We denote the logarithmic likelihood ratio of the sequential energy detector as Λ SeqEng,t .…”

“…The sequential detector [18][19][20] is designed using the iterative formulation of likelihood ratio, which requires the complete statistics of the signal of interest. Variations of the sequential detector include the sequential energy detector [11] and the sequential change detector [21]. The sequential energy detector [11] uses sequential likelihood ratio of the sample energy for decision.…”

“…Variations of the sequential detector include the sequential energy detector [11] and the sequential change detector [21]. The sequential energy detector [11] uses sequential likelihood ratio of the sample energy for decision. The sequential change detector [21] continuously monitors the signal of interest and detects abrupt spectral changes.…”

“…One of the important applications arises in the spectrum sensing for the cognitive radios [1][2][3][4][5][6][7][8][9][10][11][12]. With the increasing number of wireless applications, the demand for the spectra increases and the efficient utilization of spectrum is crucial in supporting the applications.…”