Enhanced Deep Reinforcement Learning Approach for Spectrum Sensing and Allocation in Cognitive Radio Networks

This paper proposes an Enhanced Deep Reinforcement Learning Approach (EDRLA) for spectrum allocation in Cognitive Radio Networks (CRNs) to address spectrum scarcity and improve spectral efficiency. The approach enables secondary users to exploit underutilised spectrum holes left by primary users while minimising interference. EDRLA integrates Deep Reinforcement Learning (DRL) with an Adaptive War Strategy (AWS) to leverage historical channel data to learn both channel correlations and temporal dynamics. The Q-table in DRL is updated through AWS, allowing accurate identification and allocation of available spectrum. The method is evaluated through performance and comparative analyses with conventional techniques, including Enhanced Threshold Energy Detection (ET-BED), Whale Optimisation Algorithm (WOA), and Grey Wolf Optimisation (GWO). The results demonstrate that EDRLA achieves superior performance, with an average throughput of 2.8 Mbps and an energy efficiency of 4.0, highlighting its effectiveness in improving spectrum utilisation. The proposed approach combines reinforcement learning with an adaptive strategy, offering an efficient solution for dynamic spectrum management in CRNs.