Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 18, Issue 4, Pages 2329-2345Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2019.2902559
Keywords
UAV communication; rotary-wing UAV; energy model; energy-efficient communication; trajectory optimization; path discretization
Funding
- Australia Research Council Discovery Early Career Researcher Award [DE190101436]
- National Natural Science Foundation of China [61871137]
- Open Research Fund of the National Mobile Communications Research Laboratory, Southeast University [2019D08]
- Australian Research Council [DE190101436] Funding Source: Australian Research Council
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This paper studies unmanned aerial vehicle (UAV)enabled wireless communication, where a rotary-wing UAV is dispatched to communicate with multiple ground nodes (GNs). We aim to minimize the total UAV energy consumption, including both propulsion energy and communication related energy, while satisfying the communication throughput requirement of each GN. To this end, we first derive a closed-form propulsion power consumption model for rotary-wing UAVs, and then formulate the energy minimization problem by jointly optimizing the UAV trajectory and communication time allocation among GNs, as well as the total mission completion time. The problem is difficult to be optimally solved, as it is non-convex and involves infinitely many variables over time. To tackle this problem, we first consider the simple fly-hover-communicate design, where the UAV successively visits a set of hovering locations and communicates with one corresponding GN while hovering at each location. For this design, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations, by leveraging the travelling salesman problem with neighborhood and convex optimization techniques. Next, we consider the general case, where the UAV also communicates while flying. We propose a new path discretization method to transform the original problem into a discretized equivalent with a finite number of optimization variables, for which we obtain a high-quality suboptimal solution by applying the successive convex approximation technique. The numerical results show that the proposed designs significantly outperform the benchmark schemes.
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