Control Aware Communication

Control-aware communication focuses on optimizing communication strategies to enhance the performance of control systems, particularly in resource-constrained environments like autonomous driving and human-robot collaboration. Current research emphasizes the development of joint optimization frameworks, often employing deep reinforcement learning (DRL) and model-based communication (MBC) to dynamically adjust communication frequency and content based on control needs. This approach aims to reduce communication overhead while maintaining or improving control performance, leading to more efficient and robust systems in various applications.