Digital Operating Mode Classification of Real-World Amateur Radio Transmissions

TL;DR

This paper introduces a machine learning method for classifying digital amateur radio transmission modes using spectrograms, achieving high accuracy on real-world signals with diverse impairments.

Contribution

It presents a novel ML approach trained on simulated data that effectively classifies real-world radio signals across multiple operating modes and conditions.

Findings

Achieved 93.80% accuracy on 17 modes

Achieved 85.47% accuracy on 98 parameterized signals

Validated robustness across different signal durations and SNRs

Abstract

This study presents an ML approach for classifying digital radio operating modes evaluated on real-world transmissions. We generated 98 different parameterized radio signals from 17 digital operating modes, transmitted each of them on the 70 cm (UHF) amateur radio band, and recorded our transmissions with two different architectures of SDR receivers. Three lightweight ML models were trained exclusively on spectrograms of limited non-transmitted signals with random characters as payloads. This training involved an online data augmentation pipeline to simulate various radio channel impairments. Our best model, EfficientNetB0, achieved an accuracy of 93.80% across the 17 operating modes and 85.47% across all 98 parameterized radio signals, evaluated on our real-world transmissions with Wikipedia articles as payloads. Furthermore, we analyzed the impact of varying signal durations & the number of FFT bins on classification, assessed the effectiveness of our simulated channel impairments, and tested our models across multiple simulated SNRs.