UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization

By Jie Xu, Yong Zeng, Rui Zhang. IEEE Transactions on Wireless Communications, 2017

531 citations recorded by the source corpus.

This paper studies a new unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV-mounted mobile energy transmitter is dispatched to deliver wireless energy to a set of energy receivers (ERs) at known locations on the ground. We investigate how the UAV should optimally exploit its mobility via trajectory design to maximize the amount of energy transferred to all ERs during a finite charging period. First, we consider the maximization of the sum energy received by all ERs by optimizing the UAV’s trajectory subject to its maximum speed constraint. Although this problem is non-convex, we obtain its optimal solution, which shows that the UAV should hover at one single fixed location during the whole charging period. However, the sum-energy maximization incurs a “near-far” fairness issue, where the received energy by the ERs varies significantly with their distances to the UAV’s optimal hovering location. To overcome this issue, we consider a different problem to maximize the minimum received energy among all ERs, which, however, is more challenging to solve than the sum-energy maximization. To tackle this problem, we first consider an ideal case by ignoring the UAV’s maximum speed constraint, and show that the relaxed problem can be optimally solved via the Lagrange dual method. The obtained trajectory solution implies that the UAV should hover over a set of fixed locations with optimal hovering time allocations among them. Then, for the general case with the UAV’s maximum speed constraint considered, we propose a new successive hover-and-fly trajectory motivated by the optimal trajectory in the ideal case and obtain efficient trajectory designs by applying the successive convex programing optimization technique. Finally, numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmark schemes.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/345fcf51ce716a00142866f0d68ad89cc4980ec2

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Citations in the corpus

Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, background): “2927313 data collection [9]–[12], device-to-device communication networks [13], wireless power transfer networks [14] and caching networks [15].”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “To address these issues, several works have exploited the adjustable mobility of UAVs in the 3D space [80]–[82].”
Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems (2018, background): “In fact, according to field measurements, reflection and scattering of the UAV transmit signal occurs in air-to-ground communication links [26], [27] although this is often neglected in the literature [5]–[7], [28]–[30].”
UAV Communications for 5G and Beyond: Recent Advances and Future Trends (2019, methodology): “The authors of [91] further considered the UAV-enabled multiuser WPT system in [92], where the problems of sumenergy maximization and min-energy maximization were respectively conceived by optimizing the UAV’s trajectory subject to the practical speed constraint.”
IRS-Assisted Secure UAV Transmission via Joint Trajectory and Beamforming Design (2022, methodology): “Employing the UAV’s mobility, Xu et al. solved the sum energy maximization problem for a UAV-enabled multiuser wireless power transfer system in [10] by optimizing the UAV’s trajectory.”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, methodology): “In [82], a UAV-enabled wireless power transfer (WPT) system was considered, where the trajectory of a UAV-mounted energy transmitter was optimized for maximizing the total amount of energy transmitted to the ground energy receivers.”
Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing (2020, unknown): “As a future development, in [8], the authors deployed the UAV as the mobile energy transmitter (ET) in a wireless power transfer (WPT) system.”
Throughput Maximization for UAV-Enabled Wireless Powered Communication Networks (2018, background): “Remark 1: It is worth noting that similar multilocationhovering solutions have been proposed in the UAV-enabled multiuser WPT system [29] and the UAV-enabled multiuser communication system with TDMA transmission [31], when the UAV’s flight period becomes sufficiently long.”
Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends (2023, methodology): “Several research works have been carried out for UAV charging through different WPT techniques [69, 70].”
3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting (2019, unknown): “Besides UAV-enabled WSNs, UAV trajectory design has been widely investigated in other wireless networks, such as UAV-assisted terrestrial communication [13]–[16], relay systems [17], cellular networks [18], radio access networks [19], and wireless power transfer networks [20].”
Energy Minimization for Wireless Communication With Rotary-Wing UAV (2018, methodology): “of joint power allocation and UAV trajectory optimization was proposed for the UAV-enabled mobile relaying system, which has been extended to various other setups such as UAV-enabled data collection [27], multi-UAV coordinated/cooperative communication [28], [30], and UAV-enabled wireless power tran”
UAV-Assisted Emergency Networks in Disasters (2019, background): “in [8], in which a UAV-enabled energy transmitter delivered wireless energy to multiple ground energy receivers.”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, background): “To tackle this issue, the problem of maximizing the minimum energy harvested among all ERs was also considered in [151], where a successive hover-and-ﬂy trajectory was shown to be optimal.”
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence (2019, unknown): “cles (UAVs) became very popular [16]–[18].”