Protocol Learning

Protocol learning leverages machine learning, particularly reinforcement learning (RL) and multi-agent RL, to design and optimize communication protocols, moving beyond static, pre-defined approaches. Current research focuses on applying these techniques to diverse applications, including satellite networks, industrial IoT, and 6G communication systems, often employing neural networks to learn efficient signaling and resource allocation strategies. This approach offers the potential for more adaptive and efficient protocols tailored to specific network conditions and tasks, improving performance metrics such as throughput and success rate in various communication scenarios.