Tang, J;
Song, J;
Ou, J;
Luo, J;
Zhang, X;
Wong, KK;
(2020)
Minimum Throughput Maximization for Multi-UAV Enabled WPCN: A Deep Reinforcement Learning Method.
IEEE Access
, 8
pp. 9124-9132.
10.1109/ACCESS.2020.2964042.
Preview |
Text
08950047.pdf - Published Version Download (4MB) | Preview |
Abstract
This paper investigates joint unmanned aerial vehicle (UAV) trajectory planning and time resource allocation for minimum throughput maximization in a multiple UAV-enabled wireless powered communication network (WPCN). In particular, the UAVs perform as base stations (BS) to broadcast energy signals in the downlink to charge IoT devices, while the IoT devices send their independent information in the uplink by utilizing the collected energy. The formulated throughput optimization problem which involves joint optimization of 3D path design and channel resource assignment with the constraint of flight speed of UAVs and uplink transmit power of IoT devices, is not convex and thus is extremely difficult to solve directly. We take advantage of the multi-agent deep Q learning (DQL) strategy and propose a novel algorithm to tackle this problem. Simulation results indicate that the proposed DQL-based algorithm significantly improve performance gain in terms of minimum throughput maximization compared with the conventional WPCN scheme.
| Type: | Article |
|---|---|
| Title: | Minimum Throughput Maximization for Multi-UAV Enabled WPCN: A Deep Reinforcement Learning Method |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ACCESS.2020.2964042 |
| Publisher version: | https://doi.org/10.1109/ACCESS.2020.2964042 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Unmanned aerial vehicle (UAV), wireless powered communication network (WPCN), Internet of Things (IoT), trajectory design, deep reinforcement learning (DRL) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10091675 |
Archive Staff Only
 |
View Item |
