  Non-cooperative communications using non-orthogonal multicarrier signals are
challenging since self-created inter carrier interference (ICI) exists, which
would prevent successful signal classification. Deep learning (DL) can deal
with the classification task without domain-knowledge at the cost of training
complexity since neural network hyperparameters have to be extensively tuned.
Previous work showed that a tremendously trained convolutional neural network
(CNN) classifier can efficiently identify feature-diversity dominant signals
while it failed when feature-similarity dominates. Therefore, a pre-processing
strategy, which can amplify signal feature diversity is of great importance.
This work applies single-level wavelet transform to manually extract
time-frequency features from non-orthogonal signals. Composite statistical
features are investigated and the wavelet enabled two-dimensional
time-frequency feature grid is further simplified into a one-dimensional
feature vector via proper statistical transform. The dimensionality reduced
features are fed to an error-correcting output codes (ECOC) model, consisting
of multiple binary support vector machine (SVM) learners, for multiclass signal
classification. Low-cost experiments reveal 100% classification accuracy for
feature-diversity dominant signals and 90% for feature-similarity dominant
signals, which is nearly 28% accuracy improvement when compared with the CNN
classification results.
