31-08-2017, 10:31 AM
Cognitive radios require accurate spectrum detection decisions to minimize interference for them as well as for primary and / or secondary users of the spectrum. In dynamic spectrum environments, where interference can appear or disappear on any channel at any time, robust spectrum detection is a challenge, especially if only blind methods are available. Blind detection methods for the operation of a single spectrum sample vector are more sensitive to detecting changes in the interference environment, while sequential test methods use more data to increase the reliability of detection decisions but are insensitive to the dynamics of the spectrum. This paper reviews the Bayesian approach to sequential testing and analyzes the effect of parameter estimation on detection performance. A two-dimensional hidden Markov model of reduced complexity is proposed to improve the sensitivity of sequential tests to spectrum dynamics. The efficacy of this method is established by comparison with pure sequential tests and detection of single spectrum sample vector.