Abstract
This paper presents a mathematical analysis of the accuracy of estimating Primary User's (PU's) mean duty cycle u, as well as the mean off- and on-times, where the estimation accuracy is expressed in terms of the Cramer-Rao bound on the mean squared estimation error. For estimating u, we derive the mean squared estimation error for uniform, non-uniform, and weighted sample stream averaging, as well as maximum likelihood (ML) estimation. The estimation accuracy of the mean PU off- and on-times is studied when ML estimation is employed. Besides, the impact of spectrum sensing errors on the estimation accuracy is studied analytically for the averaging estimators, while simulation results are used for the ML estimators. Furthermore, we develop algorithms for the blind estimation of the traffic parameters based on the derived theoretical estimation accuracy expressions.
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