  Accurate estimation of licensed channel Primary User's (PU) temporal
statistics is important for Dynamic Spectrum Access (DSA) systems. With
accurate estimation of the mean duty cycle, u, and the mean off- and on-times
of PUs, DSA systems can more efficiently assign PU resources to its
subscribers, thus, increasing channel utilization. This paper presents a
mathematical analysis of the accuracy of estimating u, as well as the PU mean
off- and on-times, where the estimation accuracy is expressed as the mean
squared estimation error. The analysis applies for the traffic model assuming
exponentially distributed PU off- and on-times, which is a common model in
traffic literature. The estimation accuracy is quantified as a function of the
number of samples and observation window length, hence, this work provides
guidelines on traffic parameters estimation for both energy-constrained and
delay-constrained applications. For estimating u, we consider uniform,
non-uniform, and weighted sample stream averaging, as well as maximum
likelihood estimation. The estimation accuracy of the mean PU off- and on-times
is studied when maximum likelihood estimation is employed. Furthermore, we
develop algorithms for the blind estimation of the traffic parameters based on
the derived theoretical estimation accuracy expressions. We show that the
estimation error for all traffic parameters is lower bounded for a fixed
observation window length due to the correlation between the traffic samples.
Moreover, we prove that for estimating u, maximum likelihood estimation can
yield the same estimation error as weighted sample averaging using only half
the observation window length.
