Blind wideband spectrum sensing in cognitive radio networks based on direction of arrival estimation model and generalised autoregressive conditional heteroscedasticity noise modelling

Abstract

A new method for wideband spectrum sensing in cognitive radio networks is proposed. Since the problem of estimating the number of occupied channels can be considered as the problem of estimating the number of signal sources in array signal processing, so the model used for direction of arrival (DOA) estimation is utilised here for spectrum-sensing modelling. In the proposed algorithm, wideband spectrum is divided into subchannels, where each subchannel resembles a sensor in array processing for DOA estimation. Furthermore, the detection problem in practical situations is complicated because noise is most likely non-Gaussian and non-stationary unlike the assumption of previously presented algorithms. Therefore, in the proposed algorithm, a generalised autoregressive conditional heteroscedasticity model is used to model the additive noise. The number and locations of the occupied subchannels will be jointly estimated using the maximum likelihood approach. The introduced method is blind as there is no need for initial information about the primary users’ signals and noise variance. The results indicate the efficiency of the proposed method.