In this work, a mode identification system for superimposed signals in the same band is presented. More precisely, a signal processing technique, namely the Wigner-Ville distribution, combined with non parametric (k-Nearest Neighbors and Parzen) and Neural Network classifiers is proposed for identifying the transmission modes in an indoor wireless environment. A reconfigurable terminal based on Software Defined Radio technology is considered aiming at the identification of the presence of two co-existent communication modes such as Bluetooth, based on Frequency Hopping -Code Division Multiple Access, and IEEE WLAN 802.11b, based on Direct Sequence -Code Division Multiple Access. Results in terms of error classification probability, expressed as relative error frequency, will be provided with a comparison among the classifiers.
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