Jammer detection algorithm for wide-band radios using spectral correlation and neural networks
Cognitive radio (CR) is a promising technology for future wireless spectrum allocation to improve the use of licensed bands. However, security challenges faced by cognitive radio technology are still a hot research topic. One of prevailing challenges is the radio frequency jamming attack, where adversaries are able to exploit on-the-fly reconfigurability potentials and learning mechanism of cognitive radios in order to devise and deploy advanced jamming tactics. Jamming attacks can significantly impact the performance of wireless communication systems and lead to significant overheads in terms of retransmission and increment of power consumption. In this context, a novel jammer detection algorithm is proposed using cyclic spectral analysis and artificial neural networks (ANN) for wide-band (WB) cognitive radios. The proposed approach assumes a WB spectrum occupied by various narrow-band (NB) signals, which can be either legitimate or jamming signals. The second order statistics, namely, the spectral correlation function (SCF) and ANN are used to classify each NB signal as a legitimate or jamming signal. The algorithm performance is shown with the help of simulations.