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

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.