Joint Communication and Computing Resource Allocation for MEC-Empowered Control-Oriented UAV Systems

In emergency rescue scenarios, field robots can be dispatched to enhance rescue operations, and unmanned aerial vehicles (UAVs) can be utilized to serve field robots thanks to their flexibility and on-demand deployment. To support the robots efficiently, UAVs should be equipped with sensors, base stations (BSs), and mobile edge computing (MEC) servers. The whole process of a typical rescue task can be regarded as a sensing-communication-computing-control (SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) closed loop. In this paper, we focus on the closed-loop performance of SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> loops, which is essential for mission-critical tasks. Specifically, we propose a joint communication and computing resource allocation problem, aiming to minimize the sum linear quadratic regulator (LQR) cost of SC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> loops. We prove the convexity of the optimization problem by introducing auxiliary variables. Numerical results are provided to show that our proposed scheme can enhance the system’s control performance. Our work also shows that it is essential to jointly consider the communication, computing, and sensing capabilities in unmanned rescue tasks.