Multi-User Signal Classification via Spectral Correlation

With the proliferation of wireless devices being used, the RF spectrum´s capacity continues to dwindle. In recent years, a new technology called Cognitive Radio has been advocated to solve the impending spectral drought. The premise of Cognitive Radio is that it can modify its signal to either avoid currently occupied frequency bands or alter its transmission parameters so as to cohabit the frequency band without interfering with the primary user. However, if the widespread use of Cognitive Radios and Dynamic Access Networks becomes a reality, it would enable multiple users to occupy the same frequency band There have yet to be any works published regarding how to classify the signals of multiple users, a barrier which will have great implications in the future use of Cognitive Radio. In addition to future commercial applications for multiuser signal classification, there is currently a need for this technology in the military. Military communication devices are used in scenarios where the RF spectrum is filled with jamming and interference from enemies. A method to detect and classify what signals are being used to jam and interfere would solve a significant roadblock for the military. Cyclic spectral analysis has proven to be a key tool in Cognitive Radios, giving them the ability to determine the parameters of the present signal, thus being able to modify its own transmission accordingly. Using this analysis as a foundation, we revisit the signal classification problem and propose a novel multi-user signal classification scheme using spectral correlation.