A Self-Supervised Transformer-Enhanced Hypergraph Neural Network with Meta-Optimization for Robust Cooperative Spectrum Sensing

Abstract

Effective spectrum use has become a key issue in the current wireless systems since the IoT and 5G/6G systems are rapidly expanding. CSS in Cognitive Radio Networks enhances spectrum-access but has low detection and high false-alarm rates in low Signal-to-Noise Ratio (SNR) environments. A new Self-supervised Transformer-enhanced Hypergraph Neural Network with Meta-optimization (ST-HGNN-MetaCSS) framework is suggested to resolve this issue. The model combines the application of hypergraphs, multi-scale temporal feature extraction, transformer-based attention, contrastive self-supervised learning, meta-optimization into adaptive learning. At RadioML2016.10b test, the model achieves an accuracy of 72% at -20 dB SNR which is 14% better than CNN and OMSGNNA. It attains 92% to 94%, 98% − 99% at zero dB and high SNR, respectively and is never below baselines. It attains ~ 0.95 precision/recall, 0.94–0.96 F1-score, AUC of 0.97–0.98, and reduces false alarm rate to 0.06. The framework is a little expensive to calculate and provides sound and healthy spectrum analysis.