Interference mitigation in wideband radios using spectrum correlation and neural network

Technologies such as cognitive radio and dynamic spectrum access rely on spectrum sensing which provides wireless devices with information about the radio spectrum in the surrounding environment. One of the main challenges in wireless communications is the interference caused by malicious users on the shared spectrum. In this manuscript, an artificial intelligence enabled cognitive radio framework is proposed at system-level as part of a cyclic spectrum intelligence algorithm for interference mitigation in wideband radios. It exploits the cyclostationary feature of signals to differentiate users with different modulation schemes and an artificial neural network as classifier to detect potential malicious users. A dataset consisting of experimental modulated and dynamic signals is recorded by spectrum measurements with an in-house software defined radio testbed and then processed. Cyclostationary features are extracted for each detected signal and fed to a neural network classifier as training and testing data in a complex and dynamic scenario. Results highlight a classification rate of $\sim 1$similar to 1 in most of cases, even at low transmission power. A comparison with two previous works with hand-crafted features, which employ an energy detector-based classifier and a naive Bayes-based classifier, respectively, is discussed.