Proceedings Article10.1109/ICCWORKSHOPS49005.2020.9145458
Machine Learning-Based Resource Allocation for Multi-UAV Communications System
Zheng Chang,Wenlong Guo,Xijuan Guo,Tapani Ristaniemi +3 more
- 07 Jun 2020
- pp 1-6
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TL;DR: The machine learning-based strategic resource allocation algorithm comprising of reinforcement learning and deep learning is presented to design the optimal policy of all the UAVs to maximize the system utility over all served ground users.
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Abstract: The unmanned aerial vehicle (UAV)-based wireless communication system is prominent in its flexibility and low cost for providing ubiquitous connectivity. In this work, considering a multi-UAV communications system, we propose to utilize a machine learning-based approach to tackle the trajectory design and resource allocation problems. In particular, with the objective to maximize the system utility over all served ground users, a joint user association, power allocation and trajectory design problem is formulated. To solve the problem caused by high dimensionality in state space, the machine learning-based strategic resource allocation algorithm comprising of reinforcement learning and deep learning is presented to design the optimal policy of all the UAVs. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.
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Citations
Trajectory Design and Resource Allocation for Multi-UAV Networks: Deep Reinforcement Learning Approaches
TL;DR: In this article , a machine learning-based trajectory design and resource allocation scheme for a multi-UAV communications system is presented, with the objective to maximize the system utility over all served users, a joint user association, power allocation and trajectory design problem is presented.
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Ruin Theory for Energy-Efficient Resource Allocation in UAV-Assisted Cellular Networks
Aunas Manzoor,Kitae Kim,Shashi Raj Pandey,S. M. Ahsan Kazmi,Nguyen H. Tran,Walid Saad,Choong Seon Hong +6 more
TL;DR: In this article, the problem of energy-efficient resource allocation in UAV-assisted cellular networks is studied under the reliability and latency constraints of 5G NR applications, and the framework of ruin theory is employed to allow solar-powered UAVs to capture the dynamics of harvested and consumed energies.
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Co-Channel Interference Management for Heterogeneous Networks Using Deep Learning Approach
Ishtiaq Ahmad,Sajjad Hussain,Sarmad Nozad Mahmood,Hala Mostafa,Ahmed Alkhayyat,Mohamed Marey,Ali Hashim Abbas,Zainab Abdulateef Rashed +7 more
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27
A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles
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TL;DR: It is identified that the integration of AI in UAVs has a wide array of applications ranging from path planning to resource allocation, and Reinforcement Learning based algorithms are more often used in AI-integrated UAV systems than other AI algorithms.
22
Machine Learning Driven UAV-assisted Edge Computing
Liang Zhang,Bijan Jabbari,Nirwan Ansari +2 more
- 10 Apr 2022
TL;DR: A deep reinforcement learning algorithm is proposed to solve the problem of maximizing the average aggregate quality-of-experience of all users over time slots by considering UAV path planning, user assignment, bandwidth and computing resource assignment.
10
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