Adaptive UAV Communications for URLLC: From Preplanned Designs to Real-Time Intelligence

Unmanned aerial vehicles (UAVs) are emerging as a key enabler of next-generation wireless networks, particularly for applications that require ultra-reliable and low-latency communication (URLLC), such as emergency response, industrial automation, and autonomous systems. In these scenarios, maintaining reliable connectivity under strict transmission time constraints is challenging due to dynamic environments, mobility, and limited onboard energy. In particular, communication performance and energy are closely coupled with UAV movement, making trajectory design a critical component of system operation. Most existing approaches rely on offline joint communication and trajectory optimization, where the UAV trajectory and communication parameters are optimized prior to execution based on assumed system information. Although effective under ideal assumptions, such designs cannot adapt to real-time variations in user demand, channel conditions, or environmental disturbances, which are particularly critical in URLLC settings. To address these challenges, this article investigates model predictive control (MPC) as an adaptive framework for UAV-enabled communications. Using a receding-horizon strategy, MPC enables the UAV to continuously update its trajectory based on real-time information, improving reliability and robustness in dynamic environments. Representative application scenarios are discussed to highlight the role of MPC in UAV-enabled URLLC systems. Furthermore, a case study is presented to illustrate key design trade-offs and performance insights under finite blocklength-based URLLC transmission, followed by a discussion on open challenges and future research directions for practical and scalable MPC-enabled UAV communication systems.