Game theory with Tuna-Sandcat optimisation-based robust and energy efficient spectrum sensing against malicious users in cognitive radio network

In 5G Het-net, Spectrum Sensing (SS) efficiency and Energy Efficiency (EE) are balanced by the Cognitive Radio Network (CRN). By taking into account different SS circumstances, this research aims to achieve EE. This work proposes a Tuna-Sandcat Optimisation-based Spectrum Sensing (TSI-SCSA) method, integrating Stackelberg game theory to enhance spectrum sensing efficiency and energy efficiency in CRNs while mitigating the impact of MUs providing false information. The approach ensures robust decision-making and optimal channel selection, even under malicious conditions. The performance of the proposed algorithm is evaluated over conventional algorithms for analyses such as spectrum is busy, missed detection, false alarm, useful detection and throughput. The proposed TSI-SCSA model achieves minimal mission detection value of 0.25 at iteration 100, when compared to existing techniques for efficient spectrum sensing in CRN.