Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning Article Swipe
Yu Min Park
,
Yan Kyaw Tun
,
Choong Seon Hong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.08708
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.08708
The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academia and industry. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission, which makes the existing terrestrial networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To address this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems, namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity.
Related Topics
Concepts
Beamforming
Computer science
Pairing
Noma
Base station
Decoding methods
Wireless network
Transmission (telecommunications)
Spectral efficiency
Optimization problem
Wireless
Mathematical optimization
Distributed computing
Computer network
Telecommunications
Algorithm
Telecommunications link
Mathematics
Quantum mechanics
Superconductivity
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.08708
- https://arxiv.org/pdf/2311.08708
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388748023
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388748023Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2311.08708Digital Object Identifier
- Title
-
Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-15Full publication date if available
- Authors
-
Yu Min Park, Yan Kyaw Tun, Choong Seon HongList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.08708Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.08708Direct 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/2311.08708Direct OA link when available
- Concepts
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Beamforming, Computer science, Pairing, Noma, Base station, Decoding methods, Wireless network, Transmission (telecommunications), Spectral efficiency, Optimization problem, Wireless, Mathematical optimization, Distributed computing, Computer network, Telecommunications, Algorithm, Telecommunications link, Mathematics, Quantum mechanics, Superconductivity, PhysicsTop 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.major | 125 |
| abstract_inverted_index.makes | 114 |
| abstract_inverted_index.order | 194 |
| abstract_inverted_index.owing | 255 |
| abstract_inverted_index.phase | 76, 106 |
| abstract_inverted_index.quick | 249 |
| abstract_inverted_index.share | 48 |
| abstract_inverted_index.shift | 107 |
| abstract_inverted_index.solve | 230 |
| abstract_inverted_index.study | 133 |
| abstract_inverted_index.total | 161 |
| abstract_inverted_index.under | 215 |
| abstract_inverted_index.users | 46 |
| abstract_inverted_index.which | 6, 52, 113, 246 |
| abstract_inverted_index.6G/B5G | 3, 25 |
| abstract_inverted_index.MAPPO, | 245 |
| abstract_inverted_index.MINLP. | 184 |
| abstract_inverted_index.above, | 131 |
| abstract_inverted_index.active | 173 |
| abstract_inverted_index.allows | 44 |
| abstract_inverted_index.beyond | 11 |
| abstract_inverted_index.design | 142 |
| abstract_inverted_index.employ | 225 |
| abstract_inverted_index.indoor | 147 |
| abstract_inverted_index.issue. | 127 |
| abstract_inverted_index.larger | 67 |
| abstract_inverted_index.number | 68 |
| abstract_inverted_index.order, | 170 |
| abstract_inverted_index.order. | 219 |
| abstract_inverted_index.system | 59 |
| abstract_inverted_index.users. | 70 |
| abstract_inverted_index.vector | 241 |
| abstract_inverted_index.K-means | 227 |
| abstract_inverted_index.achieve | 88 |
| abstract_inverted_index.address | 186 |
| abstract_inverted_index.current | 12 |
| abstract_inverted_index.gaining | 16 |
| abstract_inverted_index.jointly | 166, 237 |
| abstract_inverted_index.namely: | 207 |
| abstract_inverted_index.network | 26 |
| abstract_inverted_index.optimal | 217 |
| abstract_inverted_index.pairing | 137, 210, 233 |
| abstract_inverted_index.passive | 176 |
| abstract_inverted_index.problem | 154, 181, 203 |
| abstract_inverted_index.propose | 244 |
| abstract_inverted_index.However, | 22, 98, 178 |
| abstract_inverted_index.academia | 19 |
| abstract_inverted_index.cellular | 29 |
| abstract_inverted_index.decoding | 169, 193, 218 |
| abstract_inverted_index.enhanced | 95 |
| abstract_inverted_index.existing | 116 |
| abstract_inverted_index.improved | 89 |
| abstract_inverted_index.improves | 53 |
| abstract_inverted_index.increase | 24 |
| abstract_inverted_index.interest | 17 |
| abstract_inverted_index.multiple | 45 |
| abstract_inverted_index.networks | 30, 118 |
| abstract_inverted_index.optimize | 102 |
| abstract_inverted_index.original | 202 |
| abstract_inverted_index.pairing, | 172 |
| abstract_inverted_index.problem. | 234 |
| abstract_inverted_index.quality, | 27 |
| abstract_inverted_index.signals, | 85 |
| abstract_inverted_index.spectral | 55, 92 |
| abstract_inverted_index.stations | 36 |
| abstract_inverted_index.wireless | 4 |
| abstract_inverted_index.Motivated | 128 |
| abstract_inverted_index.STAR-RISs | 86, 99 |
| abstract_inverted_index.advantage | 63 |
| abstract_inverted_index.amplitude | 78, 104 |
| abstract_inverted_index.coverage, | 90 |
| abstract_inverted_index.decisions | 250 |
| abstract_inverted_index.decompose | 200 |
| abstract_inverted_index.formulate | 151 |
| abstract_inverted_index.increased | 91 |
| abstract_inverted_index.industry. | 21 |
| abstract_inverted_index.introduce | 191 |
| abstract_inverted_index.networks, | 5, 14 |
| abstract_inverted_index.networks. | 197 |
| abstract_inverted_index.objective | 157 |
| abstract_inverted_index.reflected | 82 |
| abstract_inverted_index.Meanwhile, | 42 |
| abstract_inverted_index.challenge, | 188 |
| abstract_inverted_index.clustering | 228 |
| abstract_inverted_index.considered | 123 |
| abstract_inverted_index.efficiency | 56 |
| abstract_inverted_index.formulated | 180 |
| abstract_inverted_index.maximizing | 159 |
| abstract_inverted_index.optimizing | 167 |
| abstract_inverted_index.reflection | 110 |
| abstract_inverted_index.resources, | 51 |
| abstract_inverted_index.supporting | 65 |
| abstract_inverted_index.throughput | 162 |
| abstract_inverted_index.Beamforming | 213 |
| abstract_inverted_index.beamforming | 141, 240 |
| abstract_inverted_index.challenging | 126 |
| abstract_inverted_index.complexity. | 259 |
| abstract_inverted_index.complicated | 120 |
| abstract_inverted_index.constrained | 38 |
| abstract_inverted_index.development | 1 |
| abstract_inverted_index.efficiency, | 93 |
| abstract_inverted_index.environment | 254 |
| abstract_inverted_index.subproblem, | 223 |
| abstract_inverted_index.terrestrial | 34, 117 |
| abstract_inverted_index.transmitted | 84 |
| abstract_inverted_index.beamforming, | 174 |
| abstract_inverted_index.beamforming. | 177 |
| abstract_inverted_index.conventional | 28 |
| abstract_inverted_index.environment. | 148 |
| abstract_inverted_index.manipulating | 74 |
| abstract_inverted_index.optimization | 153, 214 |
| abstract_inverted_index.reliability. | 97 |
| abstract_inverted_index.requirements | 8 |
| abstract_inverted_index.subproblems, | 206 |
| abstract_inverted_index.Additionally, | 71 |
| abstract_inverted_index.communication | 96 |
| abstract_inverted_index.corresponding | 108 |
| abstract_inverted_index.economically. | 41 |
| abstract_inverted_index.intelligently | 73 |
| abstract_inverted_index.transmission, | 112 |
| abstract_inverted_index.geographically | 39 |
| abstract_inverted_index.optimizations, | 242 |
| abstract_inverted_index.simultaneously | 101 |
| abstract_inverted_index.Multi-STAR-RISs | 144 |
| abstract_inverted_index.correlation-based | 226 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.1690742 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |