Energy Tradeoff in Ground-to-UAV Communication via Trajectory Design

Unmanned aerial vehicles (UAVs) have a great potential for improving the performance of wireless communication systems due to their high mobility. In this correspondence, we study a UAV-enabled data collection system, where a UAV is dispatched to collect a given amount of data from a ground terminal (GT) at fixed location. Intuitively, if the UAV flies closer to the GT, the uplink transmission energy of the GT required to send the target data can be more reduced. However, such UAV movement may consume more propulsion energy of the UAV, which needs to be properly controlled to save its limited on-board energy. As a result, the transmission energy reduction of the GT is generally at the cost of higher propulsion energy consumption of the UAV, which leads to a new fundamental energy tradeoff in ground-to-UAV wireless communication. To characterize this tradeoff, we consider two practical UAV trajectories, namely circular flight and straight flight. In each case, we first derive the energy consumption expressions of the UAV and GT and then find the optimal GT transmit power and UAV trajectory that achieve different Pareto optimal tradeoffs between them. Numerical results are provided to corroborate our study.

[1]  Walid Saad,et al.  Optimal transport theory for power-efficient deployment of unmanned aerial vehicles , 2016, 2016 IEEE International Conference on Communications (ICC).

[2]  Qingqing Wu,et al.  Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks , 2017, IEEE Transactions on Wireless Communications.

[3]  Joonhyuk Kang,et al.  Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning , 2016, IEEE Transactions on Vehicular Technology.

[4]  Kandeepan Sithamparanathan,et al.  Optimal LAP Altitude for Maximum Coverage , 2014, IEEE Wireless Communications Letters.

[5]  Xin Wang,et al.  Energy-Efficient Cooperative Relaying for Unmanned Aerial Vehicles , 2016, IEEE Transactions on Mobile Computing.

[6]  Rui Zhang,et al.  Placement Optimization of UAV-Mounted Mobile Base Stations , 2016, IEEE Communications Letters.

[7]  Rui Zhang,et al.  Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network , 2017, IEEE Wireless Communications Letters.

[8]  Halim Yanikomeroglu,et al.  3-D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage , 2017, IEEE Wireless Communications Letters.

[9]  Rui Zhang,et al.  Throughput Maximization for UAV-Enabled Mobile Relaying Systems , 2016, IEEE Transactions on Communications.

[10]  Yik-Chung Wu,et al.  Wirelessly Powered Two-Way Communication With Nonlinear Energy Harvesting Model: Rate Regions Under Fixed and Mobile Relay , 2017, IEEE Transactions on Wireless Communications.

[11]  Sofie Pollin,et al.  Optimal UAV Positioning for Terrestrial-Aerial Communication in Presence of Fading , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[12]  Geoffrey Ye Li,et al.  An Overview of Sustainable Green 5G Networks , 2016, IEEE Wireless Communications.

[13]  Rui Zhang,et al.  Wireless communications with unmanned aerial vehicles: opportunities and challenges , 2016, IEEE Communications Magazine.

[14]  Halim Yanikomeroglu,et al.  Efficient 3-D placement of an aerial base station in next generation cellular networks , 2016, 2016 IEEE International Conference on Communications (ICC).

[15]  Qingqing Wu,et al.  Joint Trajectory and Communication Design for UAV-Enabled Multiple Access , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[16]  Rui Zhang,et al.  Energy-Efficient UAV Communication With Trajectory Optimization , 2016, IEEE Transactions on Wireless Communications.

[17]  Guowang Miao,et al.  Energy-Efficient Uplink Multi-User MIMO , 2013, IEEE Transactions on Wireless Communications.

[18]  Walid Saad,et al.  Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage , 2016, IEEE Communications Letters.