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DC Field | Value | Language |
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dc.contributor.author | Zhang, C | - |
dc.contributor.author | Liang, S | - |
dc.contributor.author | He, C | - |
dc.contributor.author | Wang, K | - |
dc.date.accessioned | 2023-02-08T09:34:56Z | - |
dc.date.available | 2023-02-08T09:34:56Z | - |
dc.date.issued | 2021-02-16 | - |
dc.identifier | ORCID iD: Kezhi Wang https://orcid.org/0000-0001-8602-0800 | - |
dc.identifier.citation | Zhang, C. et al. (2022) 'Multi-UAV Trajectory Design and Power Control Based on Deep Reinforcement Learning', Journal of Communications and Information Networks, 2022, 7 (2), pp. 192 - 201. doi: 10.23919/JCIN.2022.9815202 | en_US |
dc.identifier.issn | 2096-1081 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25934 | - |
dc.description.abstract | In this paper,multi-unmanned aerial vehicle (multi-UAV) and multi-user system are studied, where UAVs are served as aerial base stations (BS) for ground users in the same frequency band without knowing the locations and channel parameters for the users. We aim to maximize the total throughput for all the users and meet the fairness requirement by optimizing the UAVs’ trajectories and transmission power in a centralized way. This problem is non-convex and very difficult to solve,as the locations of the user are unknown to the UAVs. We propose a deep reinforcement learning(DRL)-based solution,i.e.,soft actor-critic(SAC)to address it via modeling the problem as a Markov decision process (MDP). We carefully design the reward function that combines sparse with non-sparse reward to achieve the balance between exploitation and exploration.The simulation results show that the proposed SAC has a very good performance in terms of both training and testing. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China under Grant 62101161; Shenzhen Basic Research Program under Grant 20200811192821001 and Grant JCYJ20190808122409660; Guangdong Basic Research Program under Grant 2019A1515110358, Grant 2021A1515012097, Grant 2020ZDZX1037, Grant 2020ZDZX1021; open research fund of National Mobile Communications Research Laboratory, Southeast University under Grant 2021D16 and Grant 2022D02. | en_US |
dc.format.extent | 192 - 201 | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | China InfoCom Media Group | en_US |
dc.subject | deep reinforcement learning | en_US |
dc.subject | mobile edge computing | en_US |
dc.subject | unmanned aerial vehicle (UAV) | en_US |
dc.subject | trajectory control | en_US |
dc.subject | user association | en_US |
dc.title | Multi-UAV Trajectory Design and Power Control Based on Deep Reinforcement Learning | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.23919/JCIN.2022.9815202 | - |
dc.relation.isPartOf | Journal of Communications and Information Networks | - |
pubs.issue | 2 | - |
pubs.publication-status | Published | - |
pubs.volume | 7 | - |
dc.identifier.eissn | 2509-3312 | - |
dc.rights.holder | China InfoCom Media Group | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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File | Description | Size | Format | |
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FullText.pdf | 1.15 MB | Adobe PDF | View/Open |
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