Online Parameter Estimation for Temporal Spectrum Sensing

We develop a computationally efficient online parameter estimation algorithm for temporal spectrum sensing of a cognitive radio channel using a hidden bivariate Markov model. The online estimator is based on a block-recursive parameter estimation algorithm developed by Rydén for hidden Markov models. This approach requires the score function only. We develop an efficient method for computing the score function recursively and extend Rydén's approach to hidden bivariate Markov models. The advantage of the hidden bivariate Markov model over the hidden Markov model is its ability to characterize non-geometric state sojourn time distributions, which can be crucial in spectrum sensing. Based on the hidden bivariate Markov model, an estimate of the future state of the primary user can be obtained, which can be used to reduce harmful interference and improve channel utilization. Moreover, the online estimator can adapt to changes in the statistical characteristics of the primary user. We present numerical results that demonstrate the performance of temporal spectrum sensing using the proposed online parameter estimator.

I. Introduction

RADIO spectrum has become an increasingly scarce communication resource due to the explosive development of wireless technologies. At the same time, spectrum measurement studies have shown that significant portions of the wireless spectrum are highly underutilized [8]. Cognitive radio enables secondary (unlicensed) users to utilize spectrum holes of primary (licensed) users, either because the primary user (PU) is idle or because the PU's location is sufficiently far from the secondary user (SU). In the first scenario, the SU performs temporal spectrum sensing to detect idle periods of the PU. In the second scenario, the SU determines the maximum transmission power that can be used in a given frequency band without causing harmful interference to PUs [16], [19], [27].