FEATURE SELECTION FOR CYCLOSTATIONARY-BASED SIGNAL CLASSIFICATION

Abstract

Cognitive radio (CR) is a concept that imagines a radio (wireless transceiver) that contains an embedded intelligent agent that can adapt to its spectral environment. Using a software defined radio (SDR), a radio can detect the presence of other users in the spectrum and adapt accordingly, but it is important in many applications to discern between individual transmitters and this can be done using signal classification. The use of cyclostationary features have been shown to be robust to many common channel conditions. One such cyclostationary feature, the spectral correlation density(SCD),hasseenlimiteduseinsignalclassificationuntilnowbecauseitisacomputationally intensive process. This work demonstrates how feature selection techniques can be used to enable real-time classification. The proposed technique is validated using 8 common modulation formats that are generated and collected over the air.