Dr Shuowen Zhang attained the 2022 IEEE Communications Society Young Author Best Paper Award for her paper “Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective” published in the IEEE Transactions on Communications in March 2019! This award honors the authors of an especially meritorious paper dealing with a subject related to the IEEE Communications Society Society’s technical scope and who, upon the date of submission of the paper, is less than 30 years of age.
Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, Dr Zhang’s research team first provide an overview on the two main research paradigms in cellular UAV communications, namely, cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, they focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. They aim to minimize the UAV's mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target, which needs to be satisfied throughout its mission. To tackle this challenging non-convex optimization problem, they first propose an efficient method to verify its feasibility via checking the connectivity between two given vertices on an equivalent graph. Next, by examining the GBS-UAV association sequence over time, they obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging the techniques from graph theory and convex optimization. The proposed methods are analytically shown to be capable of achieving a flexible tradeoff between complexity and performance, and yielding a solution in polynomial time with the performance arbitrarily close to that of the optimal solution.
