Wavelet Classification for Over-the-Air Non-Orthogonal Waveforms

TL;DR

This paper introduces a wavelet-based feature extraction method combined with ECOC-SVM for classifying non-orthogonal multicarrier signals, achieving high accuracy especially in challenging feature-similarity scenarios.

Contribution

It proposes a wavelet transform-based pre-processing approach that significantly improves classification accuracy over CNNs for non-orthogonal signals.

Findings

Achieved 100% accuracy on feature-diversity signals.

Achieved 90% accuracy on feature-similarity signals.

Improved accuracy by 28% over CNN-based methods.

Abstract

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.