  Smart jammer nodes can disrupt communication between a transmitter and a
receiver in a wireless network, and they leave traces that are undetectable to
classical jammer identification techniques, hidden in the time-frequency plane.
These traces cannot be effectively identified through the use of the classical
Fourier transform based time-frequency transformation (TFT) techniques with a
fixed resolution. Inspired by the adaptive resolution property provided by the
wavelet transforms, in this paper, we propose a jammer identification
methodology that includes a pre-processing step to obtain a multi-resolution
image, followed by the use of a classifier. Support vector machine (SVM) and
deep convolutional neural network (DCNN) architectures are investigated as
classifiers to automatically extract the features of the transformed signals
and to classify them. Three different jamming attacks are considered, the
barrage jamming that targets the complete transmission bandwidth, the
synchronization signal jamming attack that targets synchronization signals and
the reference signal jamming attack that targets the reference signals in an
LTE downlink transmission scenario. The performance of the proposed approach is
compared with the classical Fourier transform based TFT techniques,
demonstrating the efficacy of the proposed approach in the presence of smart
jammers.
