Exploiting NOMA Transmissions in Multi-UAV-assisted Wireless Networks: From Aerial-RIS to Mode-switching UAVs Article Swipe
Songhan Zhao
,
Shimin Gong
,
Bo Gu
,
Lanhua Li
,
Bin Lyu
,
Dinh Thai Hoang
,
Changyan Yi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.20484
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.20484
In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs' trajectory planning and operating modes, the ARIS's passive beamforming, and the GUs' transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs' trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.20484
- https://arxiv.org/pdf/2412.20484
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405956480
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405956480Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2412.20484Digital Object Identifier
- Title
-
Exploiting NOMA Transmissions in Multi-UAV-assisted Wireless Networks: From Aerial-RIS to Mode-switching UAVsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-29Full publication date if available
- Authors
-
Songhan Zhao, Shimin Gong, Bo Gu, Lanhua Li, Bin Lyu, Dinh Thai Hoang, Changyan YiList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.20484Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2412.20484Direct 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/2412.20484Direct OA link when available
- Concepts
-
Noma, Mode (computer interface), Wireless, Computer science, Computer network, Wireless network, Drone, Telecommunications, Real-time computing, Telecommunications link, Human–computer interaction, Biology, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
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.benchmark | 175 |
| abstract_inverted_index.decompose | 120 |
| abstract_inverted_index.dual-mode | 56, 179 |
| abstract_inverted_index.frequency | 70 |
| abstract_inverted_index.operating | 97 |
| abstract_inverted_index.stability | 168 |
| abstract_inverted_index.switching | 57, 142, 180 |
| abstract_inverted_index.Meanwhile, | 177 |
| abstract_inverted_index.adaptively | 74 |
| abstract_inverted_index.co-channel | 47 |
| abstract_inverted_index.optimizing | 91 |
| abstract_inverted_index.reflection | 77 |
| abstract_inverted_index.strategies | 151 |
| abstract_inverted_index.throughput | 88, 188 |
| abstract_inverted_index.trajectory | 94, 138 |
| abstract_inverted_index.beamforming | 147 |
| abstract_inverted_index.efficiently | 164 |
| abstract_inverted_index.intelligent | 8 |
| abstract_inverted_index.multi-agent | 129 |
| abstract_inverted_index.performance | 171, 189 |
| abstract_inverted_index.strategies, | 143 |
| abstract_inverted_index.strategies. | 108 |
| abstract_inverted_index.transceiver | 72 |
| abstract_inverted_index.beamforming, | 102 |
| abstract_inverted_index.hierarchical | 113 |
| abstract_inverted_index.interference | 48 |
| abstract_inverted_index.optimization | 156 |
| abstract_inverted_index.subproblems. | 126 |
| abstract_inverted_index.transmission | 106, 149 |
| abstract_inverted_index.Specifically, | 127 |
| abstract_inverted_index.deterministic | 131 |
| abstract_inverted_index.reinforcement | 115 |
| abstract_inverted_index.transmission. | 80 |
| abstract_inverted_index.transmissions | 43 |
| abstract_inverted_index.non-orthogonal | 28 |
| abstract_inverted_index.reconfigurable | 7 |
| abstract_inverted_index.(ARIS)-assisted | 10 |
| abstract_inverted_index.controllability | 38 |
| abstract_inverted_index.optimization-driven | 112 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |