  This paper summarizes our attempts to establish a systematic approach that
overcomes a key difficulty in sensing wireless microphone signals, namely, the
inability for most existing detection methods to effectively distinguish
between a wireless microphone signal and a sinusoidal continuous wave (CW).
Such an inability 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. Having recognized the root of the difficulty, we
propose two potential solutions. The first solution focuses on the periodogram
as an estimate of the power spectral density (PSD), utilizing the property that
a CW has a line spectral component while a wireless microphone signal has a
slightly dispersed PSD. In that approach, we formulate the resulting decision
model as an one-sided test for Gaussian vectors, based on Kullback-Leibler
distance type of decision statistics. The second solution goes beyond the PSD
and looks into the spectral correlation function (SCF), proposing an augmented
SCF that is capable of revealing more features in the cycle frequency domain
compared with the conventional SCF. Thus the augmented SCF exhibits the key
difference between CW and wireless microphone signals. Both simulation results
and experimental validation results indicate that the two proposed solutions
are promising for sensing wireless microphones in TVWS.
