Quickest spectrum detection using hidden Markov Model for cognitive radio

The prerequisite of accessing white spectrum is to find and locate it. Our work deals with spectrum detection and recognition under the umbrella of cognitive radio. In the procedure of spectrum recognition, a frequency sweeping device sweeps the wideband spectrum and the samples of the wideband power spectrum density (PSD) are fed into different hidden Markov models (HMMs) sequentially. The core idea of sequential detection or quickest detection is borrowed and utilized here from the classical detection theory. In our proposed approach, forward variables from different HMMs are sequentially exploited to generate the decision statistics. The decision can be made any time as long as the condition is met. The motivation of our work is to detect the availability of spectra and recognize the spectra as quickly as possible and thus shorten the time delay of detection so as to improve the spectrum utilization. The PSDs of Wi-Fi signal, CDMA signal and GSM signal are measured using a spectrum analyzer (SA). These acquired data are used to train HMMs beforehand. Meanwhile, a fourth HMM is trained by the PSD of blank spectrum. Experimental results shows this proposed approach is effective.