Radio access technology recognition by classification of low temporal resolution power spectrum measurements

In this paper, a machine learning approach is proposed for automatic recognition of the radio access technology (RAT) based on supervised classification of spectrum power measurements in a cognitive radio context. Recognition of the active local RATs allows the cognitive terminal to make informed decisions on which wireless technology to camp, and to adapt its transmission to the wireless environment. To this end, supervised classification of signals obtained from wide band spectrum power measurements with low temporal resolution is carried out. As a first step, generic features are extracted from the power spectrum measurements. Then several supervised classification algorithms, namely the support vector machines (SVMs), k-nearest neighbors (kNN) and C4.5 are tested. The effects of reducing the number of features on classification accuracy is also tested through feature selection methods. Finally, the impact of the signal duration on classification accuracy is investigated. The obtained results demonstrate that the tested supervised classification algorithms, combined with simple feature extraction and selection methods, are good candidates for RAT recognition from low temporal resolution spectrum power measurements. Copyright © 2009 John Wiley & Sons, Ltd. In this paper, a machine learning approach is proposed for automatic recognition of the radio access technology (RAT) based on supervised classification of spectrum power measurements in a cognitive radio context. Recognition of the active local RATs allows the cognitive terminal to make informed decisions on which wireless technology to camp, and to adapt its transmission to the wireless environment. To this end, supervised classification of signals obtained from wide band spectrum power measurements with low temporal resolution is carried out.

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