Dynamic Spectrum Access in Cognitive Radio Networks: A Reinforcement Learning Approach

This study also evaluates the role of RL algorithms like Q-learning, DQN and Policy Gradient Methods including DDPG in relation to implementation within DRSA for CRNs. By using metrics such as average reward, convergence time and spectrum efficiency a realistic evaluation has been done in the simulated environment. The findings show that DDPG outperformed other algorithms in terms of average rewards, fast convergence and high spectrum utilization efficiency proving its adaptability under dynamic settings. It is in line with the wider trend of using sophisticated approaches that emphasize high-level learning, collaborative optimization models and efficient distributed methods for the studies related to spectrum. This study gives helpful information on RL algorithms’ pros and cons in the field of DSM, highlighting their ability to redefine wireless communication frameworks. This provides a basis for creating intelligent and self-optimization communication networks, that are of immense importance in addressing spectrum scarcity, creation innovative wireless technologies.