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

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This study introduces RML24, a novel dataset for deep learning in satellite communications. It addresses the need for space-specific signal data to advance adaptive cognitive radio systems.

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Area of Science:

  • Satellite Communications
  • Cognitive Radio
  • Deep Learning

Background:

  • Satellite communication systems require adaptive technologies like cognitive radio.
  • Existing datasets lack space-environment signal features, hindering research.
  • There is a critical need for specialized datasets for satellite signal processing.

Purpose of the Study:

  • Introduce RML24, the first open-source benchmark dataset for deep learning in satellite Telemetry, Tracking, and Command (TT&C) signal processing.
  • Provide a realistic dataset that bridges the gap between terrestrial and space communication signal characteristics.
  • Facilitate advancements in intelligent, adaptive satellite systems through accessible data and code.

Main Methods:

  • Generated over 1.3 million signal samples using Software Defined Radio (SDR) and Radio Frequency (RF) platforms.
  • Included 22 modulation schemes common in TT&C systems.
  • Incorporated simulated satellite channel models, RF link effects, and propagation phenomena for realism.

Main Results:

  • RML24 is the first open-source dataset tailored for deep learning in satellite TT&C signal processing.
  • The dataset features realistic signal characteristics, including channel models and RF effects.
  • Comprehensive annotations support deep learning tasks like parameter estimation and demodulation.

Conclusions:

  • RML24 provides a high-quality, specialized resource to accelerate deep learning research in satellite communications.
  • The dataset enables the development of more adaptive and intelligent cognitive radio systems for space.
  • Public availability of RML24 and associated code promotes broader research and innovation in the field.