Deep Learning Radio Frequency Signal Classification with Hybrid Images

TL;DR

This paper introduces a hybrid image approach combining time and frequency domain data for RF signal classification using deep learning, framing it as a computer vision task to improve detection accuracy.

Contribution

It proposes a novel hybrid image pre-processing method that leverages both time and frequency domain information for RF signal classification, addressing limitations of classical approaches.

Findings

Hybrid images improve classification performance.

Classical pre-processing methods have limitations.

Deep learning can effectively leverage combined signal representations.

Abstract

In recent years, Deep Learning (DL) has been successfully applied to detect and classify Radio Frequency (RF) Signals. A DL approach is especially useful since it identifies the presence of a signal without needing full protocol information, and can also detect and/or classify non-communication waveforms, such as radar signals. In this work, we focus on the different pre-processing steps that can be used on the input training data, and test the results on a fixed DL architecture. While previous works have mostly focused exclusively on either time-domain or frequency domain approaches, we propose a hybrid image that takes advantage of both time and frequency domain information, and tackles the classification as a Computer Vision problem. Our initial results point out limitations to classical pre-processing approaches while also showing that it's possible to build a classifier that can leverage the strengths of multiple signal representations.