Reinforcement Learning for Dynamic Spectrum Management in Cognitive Radio Networks

Abstract

Dynamic Spectrum Management (DSM) is essential for optimizing the utilization of limited radio spectrum resources in next-generation wireless communication systems. Cognitive Radio Networks (CRNs) have emerged as a promising solution to address spectrum scarcity by allowing unlicensed users to access underutilized frequency bands dynamically. This research paper explores the application of Reinforcement Learning (RL) algorithms in achieving intelligent and adaptive spectrum allocation in CRNs. By employing model-free learning approaches such as Q-learning, Deep Q-Networks (DQN), and Multi-Agent Reinforcement Learning (MARL), CRNs can autonomously make real-time decisions on channel selection and power allocation. The paper presents a detailed analysis of RL-based DSM models, evaluates their performance in terms of throughput, latency, and spectral efficiency, and compares them with traditional optimization approaches. Results indicate that RL-driven CRNs achieve a 35–50% improvement in spectrum utilization and reduced interference levels, leading to enhanced network efficiency and stability.