Abstract
A new algorithm for jammer detection is proposed in this work for wide-band (WB) cognitive radio networks. First, the received WB signal, which is comprised of multiple narrow-band (NB) signals, is recovered from sub-Nyquist rate samples using compressed sensing. Compressed sensing allows us to alleviate Nyquist rate sampling requirements at the receiver A/D converter. After the Nyquist rate signal has been recovered, a cyclostationary feature detector is employed on this estimated signal to compute the cyclic features. Finally, the proposed algorithm uses the second order statistics, namely, the spectral correlation function (SCF), to classify each NB signal as a legitimate signal or a jamming signal. In the end, performance of the proposed algorithm is shown with the help of Monte-Carlo simulations under different empirical setups.
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