Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method
This paper focuses on the unmanned aerial vehicles (UAVs)-aided mobile networks, where multiple ground mobile users (GMUs) desire to upload data to a UAV. In order to maximize the total amount of data that can be uploaded, we formulate an optimization problem to maximize the uplink throughput by opt...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/6/12/378 |
| _version_ | 1797460502888054784 |
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| author | Yuping Lu Ge Xiong Xiang Zhang Zhifei Zhang Tingyu Jia Ke Xiong |
| author_facet | Yuping Lu Ge Xiong Xiang Zhang Zhifei Zhang Tingyu Jia Ke Xiong |
| author_sort | Yuping Lu |
| collection | DOAJ |
| description | This paper focuses on the unmanned aerial vehicles (UAVs)-aided mobile networks, where multiple ground mobile users (GMUs) desire to upload data to a UAV. In order to maximize the total amount of data that can be uploaded, we formulate an optimization problem to maximize the uplink throughput by optimizing the UAV’s trajectory, under the constraints of the available energy of the UAV and the quality of service (QoS) of GMUs. To solve the non-convex problem, we propose a deep Q-network (DQN)-based method, in which we employ the iterative updating process and the Experience Relay (ER) method to reduce the negative effects sequence correlation on the training results, and the <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>ε</mi></semantics></math></inline-formula>-greedy method is applied to balance the exploration and exploitation, for achieving the better estimations of the environment and also taking better actions. Different from previous works, the mobility of the GMUs is taken into account in this work, which is more general and closer to practice. Simulation results show that the proposed DQN-based method outperforms a traditional Q-Learning-based one in terms of both convergence and network throughput. Moreover, the larger battery capacity the UAV has, the higher uplink throughput can be achieved. |
| first_indexed | 2024-03-09T17:05:59Z |
| format | Article |
| id | doaj.art-f2d96f31066948a08c64b13313344bb6 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T17:05:59Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-f2d96f31066948a08c64b13313344bb62023-11-24T14:24:39ZengMDPI AGDrones2504-446X2022-11-0161237810.3390/drones6120378Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning MethodYuping Lu0Ge Xiong1Xiang Zhang2Zhifei Zhang3Tingyu Jia4Ke Xiong5Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaEngineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaEngineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaEngineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaEngineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaEngineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaThis paper focuses on the unmanned aerial vehicles (UAVs)-aided mobile networks, where multiple ground mobile users (GMUs) desire to upload data to a UAV. In order to maximize the total amount of data that can be uploaded, we formulate an optimization problem to maximize the uplink throughput by optimizing the UAV’s trajectory, under the constraints of the available energy of the UAV and the quality of service (QoS) of GMUs. To solve the non-convex problem, we propose a deep Q-network (DQN)-based method, in which we employ the iterative updating process and the Experience Relay (ER) method to reduce the negative effects sequence correlation on the training results, and the <inline-formula><math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>ε</mi></semantics></math></inline-formula>-greedy method is applied to balance the exploration and exploitation, for achieving the better estimations of the environment and also taking better actions. Different from previous works, the mobility of the GMUs is taken into account in this work, which is more general and closer to practice. Simulation results show that the proposed DQN-based method outperforms a traditional Q-Learning-based one in terms of both convergence and network throughput. Moreover, the larger battery capacity the UAV has, the higher uplink throughput can be achieved.https://www.mdpi.com/2504-446X/6/12/378aerial base station (ABS)deep reinforcement learningQ-learningtrajectory optimization |
| spellingShingle | Yuping Lu Ge Xiong Xiang Zhang Zhifei Zhang Tingyu Jia Ke Xiong Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method Drones aerial base station (ABS) deep reinforcement learning Q-learning trajectory optimization |
| title | Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method |
| title_full | Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method |
| title_fullStr | Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method |
| title_full_unstemmed | Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method |
| title_short | Uplink Throughput Maximization in UAV-Aided Mobile Networks: A DQN-Based Trajectory Planning Method |
| title_sort | uplink throughput maximization in uav aided mobile networks a dqn based trajectory planning method |
| topic | aerial base station (ABS) deep reinforcement learning Q-learning trajectory optimization |
| url | https://www.mdpi.com/2504-446X/6/12/378 |
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