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In this paper, the authors 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, the authors 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.
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