Optimizing Cognitive Radio in VANETs through Transfer and Reinforcement Learning Integration

Cognitive Radio (CR) and Vehicular Ad hoc Networks (VANETs) are transforming road safety, traffic management, and in-car services. While VANETs handle traffic and safety, CR tackles bandwidth limitations. However, high vehicle speeds make spectrum sensing in CR challenging. Machine Learning (ML), especially Reinforcement Learning (RL), is ideal as it doesn’t require prior training data but can be slow to learn. This work introduces a dynamic framework combining Q-learning (a type of RL), cooperative spectrum sensing, and transfer learning for faster convergence. Case-based reasoning (CBR) further enhances optimization. By improving vehicle learning, this approach supports efficient energy use and practical deployment of VANETs in real-world settings.