Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers

TL;DR

This paper introduces an open set wireless signal classifier that combines deep learning with expert feature classifiers, improving accuracy and efficiency in identifying known and unknown signals in shared spectrum environments.

Contribution

It proposes a hybrid classifier that leverages expert features and deep learning, reducing computational complexity and enhancing detection of unknown signals.

Findings

Achieves 95% accuracy at 15 dB SNR for known signals.

Reduces computational complexity by 2 to 7 times.

Detects unknown classes with nearly 100% accuracy.

Abstract

In shared spectrum with multiple radio access technologies, wireless standard classification is vital for applications such as dynamic spectrum access (DSA) and wideband spectrum monitoring. However, interfering signals and the presence of unknown classes of signals can diminish classification accuracy. To reduce interference, signals can be isolated in time, frequency, and space, but the isolation process adds distortion that reduces the accuracy of deep learning classifiers. We find that the distortion can be partially mitigated by augmenting the classifier training data with the signal isolation steps. To address unknown signals, we propose an open set hybrid classifier, which combines deep learning and expert feature classifiers to leverage the reliability and explainability of expert feature classifiers and the lower computational complexity of deep learning classifiers. The hybrid classifier reduces the computational complexity by 2 to 7 times on average compared to the expert feature classifiers, while achieving an accuracy of 95% at 15 dB SNR for known signal classes. The hybrid classifier manages to detect unknown classes at nearly 100% accuracy, due to the robustness of the expert feature classifiers.