Cognitive Radio

Cognitive radio aims to improve spectrum efficiency by allowing secondary users to opportunistically access underutilized frequency bands without interfering with primary users. Current research heavily utilizes machine learning, particularly deep neural networks (like CNNs and LSTMs), and reinforcement learning (including multi-agent approaches and soft actor-critic methods) to optimize spectrum sensing, resource allocation, and interference mitigation. These advancements are significant for addressing spectrum scarcity in increasingly crowded wireless environments and improving the performance of various communication systems, including cognitive radio networks and UAV communications.