Adaptive Communication

Adaptive communication focuses on dynamically adjusting communication parameters—like bandwidth, latency, and data encoding—to optimize performance in diverse settings, ranging from multi-agent systems to wireless networks. Current research emphasizes machine learning techniques, including reinforcement learning and deep learning models, to predict and adapt to changing conditions, such as network congestion or varying data rates. This field is crucial for improving efficiency and robustness in various applications, from autonomous driving and collaborative robotics to next-generation communication networks, by enabling more reliable and resource-efficient information exchange.