Cognitive Radio for Satellite TT & C System: A General Dataset Using Software-defined Radio (RML24)

The rapid evolution of satellite communication systems necessitates the use of cognitive radio technologies for enhanced adaptability. However, current public datasets predominantly focus on terrestrial signal characteristics and fail to incorporate critical real-world channel impairments, significantly limiting their utility for space applications. This paper presents RML24, the first open-source benchmark dataset specifically designed for deep learning-based satellite signal processing. Unlike conventional datasets, RML24 integrates authentic Telemetry, Tracking, and Command (TTC) system waveforms with practical channel effects through a hybrid data collection approach combining software-defined radio (SDR) platforms and RF transceivers, providing a robust benchmark for satellite signal identification and demodulation. RML24 contains over 1.3 million samples across 22 modulation types. Comprehensive experimental results demonstrate that RML24 aligns more closely with the distribution of real-world signals. Compared to traditional training models, it achieves a 40% improvement in recognition performance under high signal-to-noise ratio (SNR) conditions.RML24 aims to advance intelligent, adaptive deep learning-driven satellite systems by offering a high-quality dataset tailored for satellite communications.