Mobility-Aware Noisy D3QN for Dynamic Spectrum Access in Cognitive Radio Networks

In this paper, a novel Double Dueling Deep Q-Network (D3QN) with noisy exploration is proposed for Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Within the proposed dueling architecture, Double Deep Q-Network (DDQN) is deployed for bias reduction and NoisyLinear layers are inserted for the efficient exploration, which have the advantages of computational scalability, and stable convergence in dynamic environments. Real-world scenarios are modeled via the WINNER II path loss model, hybrid Rician-Rayleigh fading, Gauss-Markov for Primary Users (PUs) and Random Waypoint for Secondary Users (SUs). Important performance metrics including Spectral efficiency, interference avoidance, and energy conservation are jointly optimized by an energy-aware reward function. Simulation results show that the proposed D3QN-based DSA algorithm outperforms Q-learning, DDQN, DQN with Long Short-Term Memory (DQN+LSTM), and DQN with Reservoir Computing (DQN+RC) baselines, with 80% success rates and higher cumulative rewards. These findings validate D3QN as a scalable, robust, and adaptive solution to spectrum scarcity challenges in dynamic CRNs.