Abstract
Optimizing the coexistence performance of a cognitive radio (CR) greatly depends on the extent of its awareness of the radio environment. The more knowledge a CR can acquire about primary user (PU) systems, the better it can plan its radio activities. Ideally, it would like to classify the PU systems with respect to ‘known standards’ so that the documented knowledge of PU systems can be usefully utilized. Intensive research has been done in the area of signal classification but merely with respect to modulations. We present a novel neuro-fuzzy signal classifier (NFSC) to classify signals with respect to known standards. The NFSC demonstrates excellent performance by using modern A/D based wideband data acquisition as well as traditional heterodyne narrowband transceivers in real-time coexistence environments.
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