Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications

Chiya Zhang; Zhukun Li; Chunlong He; Kezhi Wang; Cunhua Pan

doi:10.1109/LCOMM.2023.3292816

Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications

Chiya Zhang
, Zhukun Li^*
, Chunlong He^*
, Kezhi Wang
, Cunhua Pan

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Shenzhen University
Brunel University London
Southeast University, Nanjing

Research output: Contribution to journal › Article › peer-review

43 Scopus citations

Abstract

In this letter, we investigate the Unmanned Aerial Vehicles (UAVs)-assisted communications in three dimensional (3-D) environment, where one UAV is deployed to serve multiple user equipments (UEs). The locations and quality of service (QoS) requirement of the UEs are varying and the flying time of the UAV is unknown which depends on the battery of the UAVs. To address the issue, a proximal policy optimization 2 (PPO2)-based deep reinforcement learning (DRL) algorithm is proposed, which can control the UAV in an online manner. Specifically, it can allow the UAV to adjust its speed, direction and altitude so as to minimize the serving time of the UAV while satisfying the QoS requirement of the UEs. Simulation results are provided to demonstrate the effectiveness of the proposed framework.

Original language	English
Pages (from-to)	2398-2402
Number of pages	5
Journal	IEEE Communications Letters
Volume	27
Issue number	9
DOIs	https://doi.org/10.1109/LCOMM.2023.3292816
State	Published - 1 Sep 2023
Externally published	Yes

Keywords

3-D trajectory design
Unmanned aerial vehicles
deep reinforcement learning
uncertain flight time

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10.1109/LCOMM.2023.3292816

Cite this

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title = "Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications",

abstract = "In this letter, we investigate the Unmanned Aerial Vehicles (UAVs)-assisted communications in three dimensional (3-D) environment, where one UAV is deployed to serve multiple user equipments (UEs). The locations and quality of service (QoS) requirement of the UEs are varying and the flying time of the UAV is unknown which depends on the battery of the UAVs. To address the issue, a proximal policy optimization 2 (PPO2)-based deep reinforcement learning (DRL) algorithm is proposed, which can control the UAV in an online manner. Specifically, it can allow the UAV to adjust its speed, direction and altitude so as to minimize the serving time of the UAV while satisfying the QoS requirement of the UEs. Simulation results are provided to demonstrate the effectiveness of the proposed framework.",

keywords = "3-D trajectory design, Unmanned aerial vehicles, deep reinforcement learning, uncertain flight time",

author = "Chiya Zhang and Zhukun Li and Chunlong He and Kezhi Wang and Cunhua Pan",

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AU - Zhang, Chiya

AU - Li, Zhukun

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AU - Pan, Cunhua

PY - 2023/9/1

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N2 - In this letter, we investigate the Unmanned Aerial Vehicles (UAVs)-assisted communications in three dimensional (3-D) environment, where one UAV is deployed to serve multiple user equipments (UEs). The locations and quality of service (QoS) requirement of the UEs are varying and the flying time of the UAV is unknown which depends on the battery of the UAVs. To address the issue, a proximal policy optimization 2 (PPO2)-based deep reinforcement learning (DRL) algorithm is proposed, which can control the UAV in an online manner. Specifically, it can allow the UAV to adjust its speed, direction and altitude so as to minimize the serving time of the UAV while satisfying the QoS requirement of the UEs. Simulation results are provided to demonstrate the effectiveness of the proposed framework.

AB - In this letter, we investigate the Unmanned Aerial Vehicles (UAVs)-assisted communications in three dimensional (3-D) environment, where one UAV is deployed to serve multiple user equipments (UEs). The locations and quality of service (QoS) requirement of the UEs are varying and the flying time of the UAV is unknown which depends on the battery of the UAVs. To address the issue, a proximal policy optimization 2 (PPO2)-based deep reinforcement learning (DRL) algorithm is proposed, which can control the UAV in an online manner. Specifically, it can allow the UAV to adjust its speed, direction and altitude so as to minimize the serving time of the UAV while satisfying the QoS requirement of the UEs. Simulation results are provided to demonstrate the effectiveness of the proposed framework.

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KW - uncertain flight time

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Deep Reinforcement Learning Based Trajectory Design and Resource Allocation for UAV-Assisted Communications

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Keywords

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