MODULATION IDENTIFICATION USING NEURAL NETWORKS FOR COGNITIVE RADIOS

This paper presents a signal modulation classifier d sign using artificial neural networks. We analyze system -l vel issues including carrier synchronization, bandwidth estimation, and modulation classification. This is an extension of previous work with the addition of sta nd rdfree signal classification as well as an in-depth a nalysis of the feature space used in the neural network. The r esults show promising classification statistics with over 80% success rates in the presence of noise even with hi gher-order digital modulations.

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