Channel Access

Channel access, the process by which multiple devices share a limited communication channel, aims to optimize data transmission efficiency and minimize collisions. Current research heavily utilizes multi-agent reinforcement learning (MARL), often employing deep reinforcement learning (DRL) architectures and model-agnostic meta-learning (MAML), to develop adaptive and scalable channel access policies, addressing challenges like hidden terminals and dynamic bandwidth allocation. These advancements are crucial for improving the performance of wireless networks, particularly in high-density environments like the Internet of Things, by enabling efficient resource utilization and robust communication. The development of data-driven optimization techniques further enhances the adaptability and performance of these systems.