Robust Spectrum Sensing and Signal Classification: Enhanced Out-of-Distribution Detection Incorporating a Voting Mechanism

Abstract

This paper introduces an innovative methodology for spectrum sensing and signal classification, leveraging generative artificial intelligence and incorporating out-of-distribution (OOD) detection mechanisms. Our proposed approach demonstrates remarkable resilience in challenging, low signal-to-noise ratio (SNR) environments. Through comprehensive simulations, our method demonstrates remarkable results even under challenging conditions with low SNR scenarios. For example, our proposed method achieves 100% accuracy in spectrum sensing and a notable 95.8% accuracy in signal classification at -14dB SNR, which is a fairly low SNR. What sets our approach apart is its capability to identify and classify novel signal classes not encountered during the training phase. This exceptional feature significantly enhances system robustness and adaptability, particularly in dynamic, real-world applications such as the broader landscape of cognitive radio technologies, aiming to enhance adaptability and responsiveness in dynamic spectrum environments. The ability to successfully operate in diverse and unanticipated signal scenarios positions our methodology as a valuable contribution to the advancement of spectrum sensing and signal classification technologies, with promising implications for future wireless communication systems.

Keywords

• Cognitive radio
• deep learning
• out-of-distribution (OOD) detection
• signal classification
• spectrum sensing