Resilient Communication for Software Defined Radio: Machine Reasoning and Electromagnetic Spectrum Evaluation.

Area of Science:

• Electrical Engineering
• Computer Science
• Signal Processing

Background:

• Dynamic electromagnetic spectrum analysis is crucial for modern communication systems.
• Challenges include classifying interference, distortions, and jamming in high-density signal environments.
• Existing methods struggle with resilience and high-fidelity analysis of complex spectra.

Purpose of the Study:

• To investigate novel evaluation methodologies for dynamic electromagnetic spectrum analysis.
• To develop scalable machine reasoning schemes for radio frequency signal classification.
• To enhance cognitive capabilities in communication systems for resilient and high-fidelity spectrum utilization.

Main Methods:

• Utilizing multi-band software defined radio and software defined mobile networks.
• Implementing advanced machine reasoning schemes for signal classification across HF, VHF, UHF, and SHF bands.
• Conducting low-fidelity experimental studies to validate the proposed spectrum evaluation methodology.

Main Results:

• Demonstrated practical and scalable classification of radio frequency signals.
• Achieved high-fidelity characterization of dynamic electromagnetic spectra.
• Substantiated the effectiveness of the developed spectrum evaluation methodology through experimental validation.

Conclusions:

• The proposed methodologies and machine reasoning schemes offer a path towards resilient and cognitive communication systems.
• Effective analysis and classification of dynamic spectra are achievable even in extreme and congested environments.
• This work provides a foundation for future advancements in intelligent spectrum management.