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

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This paper investigates evaluation methodologies and machine reasoning schemes to analyze dynamic electromagnetic spectrum. We research practical and scalable classification of radio frequency signals across high frequency, very high frequency, ultra high frequency and super high frequency bands. The multi-band software defined radio, software defined mobile networks, and global navigation system receivers accomplish reconfigurable communication. Resilient communication, as well as high-fidelity analysis of extreme and congested electromagnetic spectra, are open problems due to challenges in classification of interference, distortions and adaptive jamming from spatially distributed transmitters and jammers. This paper documents high-fidelity characterization and dynamically reconfigured machine reasoning schemes to ensure the cognitive capabilities of communication systems. Low-fidelity experimental studies substantiate the spectrum evaluation methodology and demonstrate results.