Xu, Y.-H., Liu, X., Zhou, W. et al. (1 more author) (2021) Generative adversarial LSTM networks learning for resource allocation in UAV-served M2M communications. IEEE Wireless Communications Letters, 10 (7). pp. 1601-1605. ISSN 2162-2337
Abstract
This letter investigates the resource allocation problem for multiple Unmanned Aerial Vehicles (UAVs)-served Machine-to-Machine (M2M) communications. Our goal is to maximize the sum-rate of UAVs-served M2M communications by jointly considering the transmission power, transmission mode, frequency spectrum, relay selection and the trajectory of UAVs. In order to model the uncertainty of stochastic environments, we formulate the resource allocation problem to be a Markov game, which is the generalization of Markov Decision Process (MDP) for the case of multiple agents. However, owning to the UAVs mobility poses the difficulty of perceiving the environment, we propose a Long Short-Term Memory (LSTM) with Generative Adversarial Networks (GANs) framework to better track and forecast the UAVs mobility and improving the network reward. Numerical results demonstrate that the proposed framework outperforms the conventional LSTM and Deep Q-Network (DQN) algorithms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Unmanned aerial vehicles; M2M communications; Resource allocation; Long short-term memory; Generative adversarial networks |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 May 2021 10:21 |
Last Modified: | 26 Apr 2022 00:38 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/lwc.2021.3075467 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173803 |