Spectrum Awareness at the Edge: Modulation Classification using Smartphones

Abstract

As spectrum becomes crowded and spread over wide ranges, there is a growing need for efficient spectrum management techniques that need minimal, or even better, no human intervention. Identifying and classifying wireless signals of interest through deep learning is a first step, albeit with many practical pitfalls in porting laboratory-tested methods into the field. Towards this aim, this paper proposes using Android smartphones with TensorFlow Lite as an edge computing device that can run GPU-trained deep Convolutional Neural Networks (CNNs) for modulation classification. Our approach intelligently identifies the SNR region of the signal with high reliability (over 99%) and chooses grouping of modulation labels that can be predicted with high (over 95%) detection probability. We demonstrate that while there are no significant differences between the GPU and smartphone in terms of classification accuracy, the latter takes much less time (down to $\frac{1}{870}{\mathrm {x}}$), memory space ($\frac{1}{3}$ of the original size), and consumes minimal power, which makes our approach ideal for ubiquitous smartphone-based signal classification.