  We introduce a new technique for narrow-band (NB) signal classification in
sparsely populated wide-band (WB) spectrum using supervised learning approach.
For WB spectrum acquisition, Nyquist rate sampling is required at the
receiver's analog-to-digital converter (ADC), hence we use compressed sensing
(CS) theory to alleviate such high rate sampling requirement at the receiver
ADC. From the estimated WB spectrum, we then extract various spectral features
of each of the NB signal. These features are then used to train and classify
each NB signal into its respective modulation using the random forest
classifier. In the end, we evaluate the performance of the proposed algorithm
under different empirical setups and verify its superior performance in
comparison to a recently proposed signal classification algorithm.
