Deep Learning-based Power Allocation in Rate Splitting Optical Wireless Networks Article Swipe
Khulood D. Alazwary
,
Ahmad Adnan Qidan
,
Taisir E. H. El-Gorashi
,
Jaafar M. H. Elmirghani
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.08399
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.08399
Optical wireless communication (OWC) provides high aggregate data rates in the range of Terabits per second (Tb/s). Specifically, OWC using infrared lasers as transmitters has been considered as a strong candidate in the next generation of wireless communication. Rate splitting (RS) is a transmission scheme derived to improve spectral efficiency in dense wireless networks. In RS, the transmitted power is allocated to different messages, common and private messages, serving multiple users simultaneously, where each user can decode the desired message following a certain procedure. Moreover, two-tier precoding RS scheme has been proposed to overcome the limitations of traditional RS in multi-group scenarios. In this context, power allocation (PA) is a crucial issue, which can affect the performance of RS. Therefore, we formulate a PA optimization problem to enhance the data rates of RS-based OWC networks. However, such optimization problems are complex due to the use of different messages intended to the users. In this paper, we design and train a deep neural network (DNN) model to determine the power allocated to the messages of RS, while fulfilling the demands of users. The results show the accuracy of our trained DNN model when used in an online phase.
Related Topics
Concepts
Computer science
Optical wireless
Wireless
Context (archaeology)
Transmission (telecommunications)
Computer network
Wireless network
Power (physics)
Optical wireless communications
Aggregate (composite)
Scheme (mathematics)
Distributed computing
Telecommunications
Paleontology
Biology
Physics
Mathematical analysis
Mathematics
Materials science
Quantum mechanics
Composite material
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.08399
- https://arxiv.org/pdf/2307.08399
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384648097
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384648097Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.08399Digital Object Identifier
- Title
-
Deep Learning-based Power Allocation in Rate Splitting Optical Wireless NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-17Full publication date if available
- Authors
-
Khulood D. Alazwary, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. ElmirghaniList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.08399Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.08399Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.08399Direct OA link when available
- Concepts
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Computer science, Optical wireless, Wireless, Context (archaeology), Transmission (telecommunications), Computer network, Wireless network, Power (physics), Optical wireless communications, Aggregate (composite), Scheme (mathematics), Distributed computing, Telecommunications, Paleontology, Biology, Physics, Mathematical analysis, Mathematics, Materials science, Quantum mechanics, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.lasers | 21 |
| abstract_inverted_index.neural | 161 |
| abstract_inverted_index.online | 195 |
| abstract_inverted_index.paper, | 154 |
| abstract_inverted_index.phase. | 196 |
| abstract_inverted_index.scheme | 44, 88 |
| abstract_inverted_index.second | 15 |
| abstract_inverted_index.strong | 29 |
| abstract_inverted_index.users. | 151, 180 |
| abstract_inverted_index.(Tb/s). | 16 |
| abstract_inverted_index.Optical | 0 |
| abstract_inverted_index.certain | 82 |
| abstract_inverted_index.complex | 140 |
| abstract_inverted_index.crucial | 110 |
| abstract_inverted_index.demands | 178 |
| abstract_inverted_index.derived | 45 |
| abstract_inverted_index.desired | 78 |
| abstract_inverted_index.enhance | 127 |
| abstract_inverted_index.improve | 47 |
| abstract_inverted_index.message | 79 |
| abstract_inverted_index.network | 162 |
| abstract_inverted_index.private | 66 |
| abstract_inverted_index.problem | 125 |
| abstract_inverted_index.results | 182 |
| abstract_inverted_index.serving | 68 |
| abstract_inverted_index.trained | 188 |
| abstract_inverted_index.However, | 135 |
| abstract_inverted_index.RS-based | 132 |
| abstract_inverted_index.Terabits | 13 |
| abstract_inverted_index.accuracy | 185 |
| abstract_inverted_index.context, | 104 |
| abstract_inverted_index.infrared | 20 |
| abstract_inverted_index.intended | 148 |
| abstract_inverted_index.messages | 147, 172 |
| abstract_inverted_index.multiple | 69 |
| abstract_inverted_index.overcome | 93 |
| abstract_inverted_index.problems | 138 |
| abstract_inverted_index.proposed | 91 |
| abstract_inverted_index.provides | 4 |
| abstract_inverted_index.spectral | 48 |
| abstract_inverted_index.two-tier | 85 |
| abstract_inverted_index.wireless | 1, 36, 52 |
| abstract_inverted_index.Moreover, | 84 |
| abstract_inverted_index.aggregate | 6 |
| abstract_inverted_index.allocated | 60, 169 |
| abstract_inverted_index.candidate | 30 |
| abstract_inverted_index.determine | 166 |
| abstract_inverted_index.different | 62, 146 |
| abstract_inverted_index.following | 80 |
| abstract_inverted_index.formulate | 121 |
| abstract_inverted_index.messages, | 63, 67 |
| abstract_inverted_index.networks. | 53, 134 |
| abstract_inverted_index.precoding | 86 |
| abstract_inverted_index.splitting | 39 |
| abstract_inverted_index.Therefore, | 119 |
| abstract_inverted_index.allocation | 106 |
| abstract_inverted_index.considered | 26 |
| abstract_inverted_index.efficiency | 49 |
| abstract_inverted_index.fulfilling | 176 |
| abstract_inverted_index.generation | 34 |
| abstract_inverted_index.procedure. | 83 |
| abstract_inverted_index.scenarios. | 101 |
| abstract_inverted_index.limitations | 95 |
| abstract_inverted_index.multi-group | 100 |
| abstract_inverted_index.performance | 116 |
| abstract_inverted_index.traditional | 97 |
| abstract_inverted_index.transmitted | 57 |
| abstract_inverted_index.optimization | 124, 137 |
| abstract_inverted_index.transmission | 43 |
| abstract_inverted_index.transmitters | 23 |
| abstract_inverted_index.Specifically, | 17 |
| abstract_inverted_index.communication | 2 |
| abstract_inverted_index.communication. | 37 |
| abstract_inverted_index.simultaneously, | 71 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |