RiftNeXt™

Abstract

We propose a framework, RiftNeXtTM, to perform radio frequency (RF) scene context change detection and classification with Expert driven neural explainability. Our approach uses a deep learning based classifier to perform spectrum monitoring of Wi-Fi devices and usage patterns with an auxiliary classifier operating post-hoc to output human interpretable reasoning for classification declarations. The classification network operates on input spectrograms through a series of dilated causal convolution layers for feature extraction which are fed into classification layers. We have previously shown that dilated causal convolutions are well suited for RF applications, including RF fingerprinting, and extend their use here to new applications. The Explainability Module operates over an auxiliary dataset that is built based on domain expertise for learning how to reason over the classification network outputs. These two approaches, the deep learning classifier and Explainability Module are combined into a unique explainable deep learning approach that we apply to Wi-Fi spectrum monitoring. This paper provides results from this fused approach, leveraging the power of deep learning classification with user interpretable explainability.