Blind Signal Classification via Sparse Coding

We propose a novel RF signal classification method based on sparse coding, an unsupervised learning method popular in computer vision. In particular, we employ a convolutional sparse coder that can extract high-level features of an unknown received signal by maximal similarity matching against an over-complete dictionary of filter patterns. Such dictionary can be either generated or learned in an unsupervised fashion from measured signal examples conveying no ground-truth labels. The computed sparse code is then applied to train SVM classifiers for discriminating RF signals. As a result, the proposed approach can achieve blind signal classification that requires no prior knowledge (e.g., MCS, pulse shaping) about the signals present in an arbitrary RF channel. Since modulated RF signals undergo pulse shaping to aid the matched filter detection, our method exploits variability in relative similarity against the dictionary atoms as the key discriminating factor for classification. Our experimental results indicate that we can blindly separate different classes of digitally modulated signals with a 0.703 recall and 0.246 false alarm at 20dB SNR. Provided a small labeled dataset for supervised classifier training, we could improve the classification performance to a 0.878 recall and 0.141 false alarm.