Minimum Throughput Maximization for Multi-UAV Enabled WPCN: A Deep Reinforcement Learning Method

Tang, Jie; Song, Jingru; Ou, Jun-Hui; Luo, Jingci; Zhang, Xiu Yin; Wong, Kai-Kit

doi:10.1109/access.2020.2964042

Cited by 62 publications

(40 citation statements)

References 36 publications

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“…Tang et al [ 20 ] has proposed a multi agent deep Q-learning (DQL) model where UAVs are providing energy supply to IoT devices. The static IoT devices are forming disjoint clusters, each of them is served by a UAV that tried to maximize throughput by joint optimization of UAV trajectory and time resource assignment.…”

Section: Related Workmentioning

confidence: 99%

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Fotouhi

Ding

Hassan

2021

Sensors

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In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.

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Section: Related Workmentioning

confidence: 99%

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Fotouhi

Ding

Hassan

2021

Sensors

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“…By solving the system optimization model, the optimal deployment and transmission power of each UC relay UAV is obtained [115]. When the throughput optimization problem involves the constraints of the drone's flight speed and the IoT device's upstream transmission power, the use of multi-agent deep learning (DQL) strategy and a anoverl algorithm can solve the 3D path planning and Joint optimization of channel resources [116]. Current communication systems are based on traditional information theory principles to transmit long messages, and achieving ultra-high reliability of short messages is the core challenge of future infinite communication systems.…”

Section:  Uav-internet Of Thingsmentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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“…The simulation results depicted that the proposed optimization framework can achieve superior performance compared to other benchmark solutions. The joint optimization of the multi-UAV three-dimensional (3-D) trajectory and the time resource allocation that leads to throughput maximization in a WPC network was handled in [115]. In this respect, a multi-agent deep Q-learning (DQL) approach was presented.…”

Section: Airborne-based Intelligent Iiotmentioning

confidence: 99%

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

Michailidis

Potirakis

Kanatas

2020

IoT

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During the last few years, various Industrial Internet of Things (IIoT) applications have emerged with numerous network elements interconnected using wired and wireless communication technologies and equipped with strategically placed sensors and actuators. This paper justifies why non-terrestrial networks (NTNs) will bring the IIoT vision closer to reality by providing improved data acquisition and massive connectivity to sensor fields in large and remote areas. NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services. Additionally, this paper describes indicative delay-tolerant massive IIoT and delay-sensitive mission-critical IIoT applications spanning a large number of vertical markets with diverse and stringent requirements. As the heterogeneous nature of NTNs and the complex and dynamic communications scenarios lead to uncertainty and a high degree of variability, conventional wireless communication technologies cannot sufficiently support ultra-reliable and low-latency communications (URLLC) and offer ubiquitous and uninterrupted interconnectivity. In this regard, this paper sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in the provision of challenging NTN-based IIoT services and provides a thorough review of the relevant research works. By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics with significantly lower complexity compared to typical optimization methods.

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Minimum Throughput Maximization for Multi-UAV Enabled WPCN: A Deep Reinforcement Learning Method

Cited by 62 publications

References 36 publications

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

UAV Swarm Intelligence: Recent Advances and Future Trends

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

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