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Abstract <p>This paper presents a cost-effective practice-oriented approach to real-time radio signal classification based on a HackRF One software-defined radio receiver. For model training, a custom dataset comprising spectrograms of radio signals tailored to target various application scenarios is created. The developed system is capable of detecting the presence of a signal and classifying its type, provided that the corresponding class is included in the collected dataset. Laboratory tests confirm the stable performance of the classifier under real-time conditions, with an average accuracy of 99.3%. The results demonstrate the potential of the proposed solution for radio monitoring and spectrum analysis under limited computational resources.</p>

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Real-Time Radio Signal Classification Based on Spectrograms

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • L. H. Kirakosyan,
  • M. V. Navoyan,
  • V. G. Melkonyan,
  • A. S. Sardaryan,
  • S. S. Sargsyan

摘要

Abstract

This paper presents a cost-effective practice-oriented approach to real-time radio signal classification based on a HackRF One software-defined radio receiver. For model training, a custom dataset comprising spectrograms of radio signals tailored to target various application scenarios is created. The developed system is capable of detecting the presence of a signal and classifying its type, provided that the corresponding class is included in the collected dataset. Laboratory tests confirm the stable performance of the classifier under real-time conditions, with an average accuracy of 99.3%. The results demonstrate the potential of the proposed solution for radio monitoring and spectrum analysis under limited computational resources.