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Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at
Tassadaq Nawaz1, Ali Alzahrani1
1Department of Computer Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Sensors (Basel, Switzerland)
|August 26, 2023
Summary
This study introduces a robust algorithm for cognitive radio spectrum characterization, enhancing jamming detection and classification accuracy for secure communications. The novel method effectively identifies legitimate signals and stealthy jamming attacks in wideband environments.
Area of Science:
- Electrical Engineering
- Computer Science
- Signal Processing
Background:
- Spectrum scarcity necessitates dynamic spectrum access solutions.
- Cognitive radios (CRs) offer intelligent spectrum management capabilities.
- CRs' adaptability suits advanced jamming and anti-jamming system design.
Purpose of the Study:
- To present a novel, robust algorithm for spectrum characterization in wideband cognitive radio terminals.
- To accurately classify narrowband signals as either legitimate or stealthily jammed.
Main Methods:
- Utilizing cyclostationary feature detection to measure spectral correlation density.
- Extracting cyclic and angular frequency profiles as feature sets.
- Employing an artificial neural network for signal classification.
Main Results:
- The algorithm demonstrated superior classification accuracy compared to existing methods.
- Effective characterization of narrowband signals in wideband spectrum sensing.
- Successful identification of both multi-tone and modulated stealthy jamming attacks.
Conclusions:
- The proposed algorithm offers a robust solution for spectrum characterization in cognitive radio systems.
- High classification accuracy supports advanced jamming and anti-jamming applications.
- Potential applications span both commercial and military communication systems.
Keywords:
artificial neural networkscognitive radioscyclostationary spectral analysissignals classificationsstealthy jammerMore Related Videos
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