Separating the wheat from the chaff: Sensing wireless microphones in TVWS

Abstract

The inability for most existing detection methods to effectively distinguish between a wireless microphone signal and a sinusoidal continuous wave (CW) interference has led to an excessively high false alarm rate and thus severely limited the utility of sensing-based cognitive transmission in the TV white space (TVWS) spectrum. Two potential solutions are proposed, in which the first solution focuses on signal's periodogram and formulates the decision model as an one-sided test based on Kullback-Leibler distance statistics, and the second solution proposes an augmented spectral correlation function (SCF) and exploits the fact that wireless microphone signal and CW exhibit different augmented SCFs. Both solutions are validated by simulation and experimental tests, shown to be capable of reliably sensing wireless microphone signals in the presence of strong noise and excessive CW interference.