GREEN: A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems
IEEE Transactions on Mobile Computing
In the scenario of limited energy supply, Unmanned Aerial Vehicles (UAVs) enabled communication systems must make efficient use of energy in order to provide long-term service. In this paper, we propose a global energy efficiency maximization (GREEN) strategy for multi-UAV enabled communication systems. In such systems, a group of UAVs communicates with their associated ground terminals (GTs) by using a UAV-enabled interference channel (UAV-IC). In particular, we optimize the UAVs' trajectory control by jointly considering both the communication throughput and the total energy consumption of the whole system. We aim to maximize the global energy efficiency (GEE) of a task for multi-UAV communications, in which the problem is challenging to optimally solve due to its non-convex nature and strongly coupled variables. To tackle this problem, first, we investigate and propose a global energy-efficient optimization problem based on the fly-hover-communicate protocol. Second, we extend our proposed solution from the single UAV-enabled system to multiple UAV-GT pairs cases. In addition, we consider the general scenario in which the UAVs also communicate while flying. Based on the successive convex approximation technique and the path discretization method, the GREEN strategy is designed for optimizing UAV trajectories in this scenario. The simulation results show that the proposed strategy can achieve significantly higher GEE than the benchmark schemes for multi-UAV enabled communications.
Autonomous aerial vehicles, Communication systems, Energy consumption, Energy efficiency, energy efficiency, Internet of Things (IoT), Optimization, Throughput, trajectory optimization, Unmanned aerial vehicle (UAV), Wireless communication
N. Lin, Y. Fan, L. Zhao, X. Li and M. Guizani, "GREEN: A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems," in IEEE Transactions on Mobile Computing,, 2022, pp. 1-18. doi: 10.1109/TMC.2022.3207791.
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