Primary User Traffic Estimation for Dynamic Spectrum Access

TL;DR

This paper analyzes the accuracy of estimating primary user traffic parameters in dynamic spectrum access, providing theoretical bounds and algorithms for improved estimation with practical guidelines.

Contribution

It offers a mathematical analysis of estimation accuracy for PU traffic parameters, including new algorithms and bounds for energy- and delay-constrained applications.

Findings

Estimation error is lower bounded by sample correlation.

Maximum likelihood estimation can halve the required observation window.

Derived guidelines for traffic parameter estimation accuracy.

Abstract

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.