Access Point

Access points (APs) are crucial components of wireless networks, serving as central hubs for connecting devices and managing communication. Current research focuses on optimizing AP functionality across various network architectures, including cell-free MIMO, hybrid LiFi/WiFi, and UAV-based systems, employing techniques like deep reinforcement learning (DRL), graph neural networks (GNNs), and capsule neural networks to improve efficiency, security, and fairness. These advancements aim to address challenges such as load balancing, energy efficiency, robust localization in the presence of adversarial attacks, and scalable network management in increasingly complex and dynamic environments. The resulting improvements in network performance and resource utilization have significant implications for various applications, from industrial IoT to mobile edge computing.