Blind Spectrum Sensing using Bayesian Sequential Testing with Dynamic Update

Cognitive radios require accurate spectrum sensing decisions to minimize interference both to themselves and to primary and/or other secondary spectrum users. In dynamic spectrum environments, where interference may appear or disappear on any channel at any time instant, robust spectrum sensing is challenging particularly if only blind methods are available. Blind sensing methods for single spectrum sample vector operation are most sensitive at detecting changes in the interference environment, whereas sequential testing methods use more data to increase the reliability of detection decisions but are insensitive to spectrum dynamics. This paper reviews the Bayesian sequential testing approach and analyses the effect of parameter estimation on detection performance. A reduced complexity, two dimensional hidden Markov modeling method is proposed to improve the sensitivity of sequential testing to spectrum dynamics. The efficacy of this method is established by comparison with pure sequential testing and single spectrum sample vector detection.