UAV-Assisted Enhanced Coverage and Capacity in Dynamic MU-mMIMO IoT Systems: A Deep Reinforcement Learning Approach Article Swipe
MohammadMahdi Ghadaksaz
,
Mobeen Mahmood
,
Tho Le‐Ngoc
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.06726
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.06726
This study focuses on a multi-user massive multiple-input multiple-output (MU-mMIMO) system by incorporating an unmanned aerial vehicle (UAV) as a decode-and-forward (DF) relay between the base station (BS) and multiple Internet-of-Things (IoT) devices. Our primary objective is to maximize the overall achievable rate (AR) by introducing a novel framework that integrates joint hybrid beamforming (HBF) and UAV localization in dynamic MU-mMIMO IoT systems. Particularly, HBF stages for BS and UAV are designed by leveraging slow time-varying angular information, whereas a deep reinforcement learning (RL) algorithm, namely deep deterministic policy gradient (DDPG) with continuous action space, is developed to train the UAV for its deployment. By using a customized reward function, the RL agent learns an optimal UAV deployment policy capable of adapting to both static and dynamic environments. The illustrative results show that the proposed DDPG-based UAV deployment (DDPG-UD) can achieve approximately 99.5% of the sum-rate capacity achieved by particle swarm optimization (PSO)-based UAV deployment (PSO-UD), while requiring a significantly reduced runtime at approximately 68.50% of that needed by PSO-UD, offering an efficient solution in dynamic MU-mMIMO environments.
Related Topics
Concepts
Software deployment
Reinforcement learning
Computer science
Particle swarm optimization
Relay
Real-time computing
Base station
Function (biology)
Distributed computing
Simulation
Artificial intelligence
Computer network
Algorithm
Evolutionary biology
Biology
Physics
Power (physics)
Operating system
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.06726
- https://arxiv.org/pdf/2404.06726
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394777944
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394777944Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2404.06726Digital Object Identifier
- Title
-
UAV-Assisted Enhanced Coverage and Capacity in Dynamic MU-mMIMO IoT Systems: A Deep Reinforcement Learning ApproachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-10Full publication date if available
- Authors
-
MohammadMahdi Ghadaksaz, Mobeen Mahmood, Tho Le‐NgocList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.06726Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2404.06726Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2404.06726Direct OA link when available
- Concepts
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Software deployment, Reinforcement learning, Computer science, Particle swarm optimization, Relay, Real-time computing, Base station, Function (biology), Distributed computing, Simulation, Artificial intelligence, Computer network, Algorithm, Evolutionary biology, Biology, Physics, Power (physics), Operating system, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.MU-mMIMO | 60, 176 |
| abstract_inverted_index.achieved | 147 |
| abstract_inverted_index.adapting | 121 |
| abstract_inverted_index.capacity | 146 |
| abstract_inverted_index.designed | 71 |
| abstract_inverted_index.devices. | 32 |
| abstract_inverted_index.gradient | 89 |
| abstract_inverted_index.learning | 82 |
| abstract_inverted_index.maximize | 38 |
| abstract_inverted_index.multiple | 29 |
| abstract_inverted_index.offering | 170 |
| abstract_inverted_index.particle | 149 |
| abstract_inverted_index.proposed | 134 |
| abstract_inverted_index.solution | 173 |
| abstract_inverted_index.sum-rate | 145 |
| abstract_inverted_index.systems. | 62 |
| abstract_inverted_index.unmanned | 14 |
| abstract_inverted_index.(DDPG-UD) | 138 |
| abstract_inverted_index.(PSO-UD), | 155 |
| abstract_inverted_index.developed | 96 |
| abstract_inverted_index.efficient | 172 |
| abstract_inverted_index.framework | 48 |
| abstract_inverted_index.function, | 109 |
| abstract_inverted_index.objective | 35 |
| abstract_inverted_index.requiring | 157 |
| abstract_inverted_index.(MU-mMIMO) | 9 |
| abstract_inverted_index.DDPG-based | 135 |
| abstract_inverted_index.achievable | 41 |
| abstract_inverted_index.algorithm, | 84 |
| abstract_inverted_index.continuous | 92 |
| abstract_inverted_index.customized | 107 |
| abstract_inverted_index.deployment | 117, 137, 154 |
| abstract_inverted_index.integrates | 50 |
| abstract_inverted_index.leveraging | 73 |
| abstract_inverted_index.multi-user | 5 |
| abstract_inverted_index.(PSO)-based | 152 |
| abstract_inverted_index.beamforming | 53 |
| abstract_inverted_index.deployment. | 103 |
| abstract_inverted_index.introducing | 45 |
| abstract_inverted_index.illustrative | 129 |
| abstract_inverted_index.information, | 77 |
| abstract_inverted_index.localization | 57 |
| abstract_inverted_index.optimization | 151 |
| abstract_inverted_index.time-varying | 75 |
| abstract_inverted_index.Particularly, | 63 |
| abstract_inverted_index.approximately | 141, 163 |
| abstract_inverted_index.deterministic | 87 |
| abstract_inverted_index.environments. | 127, 177 |
| abstract_inverted_index.incorporating | 12 |
| abstract_inverted_index.reinforcement | 81 |
| abstract_inverted_index.significantly | 159 |
| abstract_inverted_index.multiple-input | 7 |
| abstract_inverted_index.multiple-output | 8 |
| abstract_inverted_index.Internet-of-Things | 30 |
| abstract_inverted_index.decode-and-forward | 20 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |