Time Division Multiple Access

Time Division Multiple Access (TDMA) is a channel access method that divides communication time into slots, assigning slots to different users to avoid collisions. Current research focuses on improving TDMA's efficiency and adaptability in diverse applications, particularly using machine learning algorithms like Q-learning and multi-armed bandits for decentralized slot allocation and optimization in wireless sensor networks and IoT systems. This work is significant because it addresses challenges in managing increasingly complex and heterogeneous networks, leading to improved bandwidth utilization, reduced latency, and enhanced performance in applications ranging from visible light communication to drone swarms and federated learning.