LAUSR

Upcoming fifth-generation (5G) networks need to support novel ultrareliable and low-latency (URLLC) traffic that utilizes short packets. This requires a paradigm shift as traditional communication systems are designed to transmit only long data packets based on Shannon’s capacity formula, which poses a challenge for system designers. To address this challenge, this article relies on an unmanned aerial vehicle (UAV) and a reconfigurable intelligent surface (RIS) to deliver short URLLC instruction packets between ground Internet-of-Things (IoT) devices. In this context, we perform passive beamforming of RIS antenna elements as well as nonlinear and nonconvex optimization to minimize the total decoding error rate and find the UAV’s optimal position and blocklength. In this article, a novel, polytope-based method from the class of direct search methods (DSMs) named Nelder–Mead simplex (NMS) is used to solve the optimization problem based on its computational efficiency; in terms of lesser number of required iterations to evaluate objective function. The proposed approach yields better convergence performance than the traditional gradient-descent optimization algorithm and a lower computation time and equivalent performance for the blocklength variable as the exhaustive search. Moreover, the proposed approach allows ultrahigh reliability, which can be attained by increasing the number of antenna elements in RIS as well as increasing the allocated blocklengths. Simulations demonstrate the RIS’s performance gain and conclusively show that the UAV’s position is crucial for achieving ultrahigh reliability in short packet transmission.