Utilizing Machine Learning for Signal Classification and Noise Reduction in Amateur Radio

TL;DR

This work addresses the challenge of reliable amateur radio communication in noisy and interference-rich environments by applying machine learning to signal classification and noise reduction. It leverages spectrogram-based representations and CNN/RNN architectures, validated through SDR-collected datasets with diverse operating conditions, preprocessing, augmentation, and rigorous evaluation metrics such as $SNR$, $MSE$, and $BER$. The study demonstrates that ML-based denoising and signal classification can outperform traditional methods, achieving improved signal clarity, robustness, and real-time applicability in practical SDR deployments. The findings highlight the potential of adaptive, data-driven radio processing to enhance reliability in contesting, emergency, and everyday amateur radio operations, while outlining pathways to address scalability, domain integration, and security concerns.

Abstract

In the realm of amateur radio, the effective classification of signals and the mitigation of noise play crucial roles in ensuring reliable communication. Traditional methods for signal classification and noise reduction often rely on manual intervention and predefined thresholds, which can be labor-intensive and less adaptable to dynamic radio environments. In this paper, we explore the application of machine learning techniques for signal classification and noise reduction in amateur radio operations. We investigate the feasibility and effectiveness of employing supervised and unsupervised learning algorithms to automatically differentiate between desired signals and unwanted interference, as well as to reduce the impact of noise on received transmissions. Experimental results demonstrate the potential of machine learning approaches to enhance the efficiency and robustness of amateur radio communication systems, paving the way for more intelligent and adaptive radio solutions in the amateur radio community.