Automatic Determination of Spectral States for Cognitive Radio

In this paper, we propose a method for automatically extracting distinct spectral states present in spectrum power measurements. The purpose is to gain information on the wireless environment for constructing efficient policies and decisions in a flexible radio context. To achieve this, we perform unsupervised classification on data obtained from spectrum power measurements on the GSM 1800 downlink band. The number of spectral states having different spectral characteristics is automatically detected by a well-known efficient rate-distortion criterion. Spectral signals that constitute the power spectrum measurements are classified into these distinct spectral states through the k-means clustering algorithm. In order to eliminate redundancies and to decrease the computational complexity, Principal Component Analysis is performed on the set of spectral signals before classification. The time-averaged frequency profile of each spectral state can be considered as the quantization or compression of the spectral information contained by the spectral signal belonging to that spectral state. The preliminary results obtained on the GSM 1800 downlink spectrum measurements reveal that the proposed method successfully extracts the distinct spectral states of the wireless environment.