Copula based performance analysis for one‐hoping relay channel in wireless ad hoc network with correlated fading channels Article Swipe
YOU?
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· 2020
· Open Access
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· DOI: https://doi.org/10.1049/iet-spr.2020.0186
A wireless ad hoc network consisting of one‐hoping relay channel and some interfering nodes with possible arbitrary correlation between wireless channels coefficients is studied. By exploiting a novel approach called Copula theory, enabling us to describe joint probability distribution function for arbitrarily correlated random variables, the authors derive a closed‐form expression for success probability in different dependence structures of channels. Specifically, by applying the Farlie–Gumbel–Morgenstern (FGM) Copula and the popular Archimedean (Clayton, Gumbel, and Frank) Copulas, they analyse the network performance in terms of success probability, and show that the channels correlation improves the performance of success probability so that whenever the dependence structure tends to higher values, the performance improvement is increased. Besides, it is found that the investigation of channel correlation effects by FGM Copula is more tangible compared to Archimedean Copulas. Finally, the efficiency of the analytical results is illustrated numerically.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/iet-spr.2020.0186
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-spr.2020.0186
- OA Status
- bronze
- Cited By
- 3
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3084987822
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3084987822Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1049/iet-spr.2020.0186Digital Object Identifier
- Title
-
Copula based performance analysis for one‐hoping relay channel in wireless ad hoc network with correlated fading channelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-10Full publication date if available
- Authors
-
Farshad Rostami Ghadi, Ghosheh Abed HodtaniList of authors in order
- Landing page
-
https://doi.org/10.1049/iet-spr.2020.0186Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-spr.2020.0186Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-spr.2020.0186Direct OA link when available
- Concepts
-
Copula (linguistics), Fading, Relay, Computer science, Joint probability distribution, Gumbel distribution, Wireless ad hoc network, Random variable, Wireless, Channel (broadcasting), Outage probability, Correlation, Wireless network, Mathematics, Statistics, Computer network, Econometrics, Telecommunications, Extreme value theory, Physics, Geometry, Quantum mechanics, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.FGM | 125 |
| abstract_inverted_index.and | 10, 67, 73, 86 |
| abstract_inverted_index.for | 40, 51 |
| abstract_inverted_index.hoc | 3 |
| abstract_inverted_index.the | 45, 63, 68, 78, 89, 93, 101, 108, 118, 135, 138 |
| abstract_inverted_index.more | 128 |
| abstract_inverted_index.show | 87 |
| abstract_inverted_index.some | 11 |
| abstract_inverted_index.that | 88, 99, 117 |
| abstract_inverted_index.they | 76 |
| abstract_inverted_index.with | 14 |
| abstract_inverted_index.(FGM) | 65 |
| abstract_inverted_index.found | 116 |
| abstract_inverted_index.joint | 36 |
| abstract_inverted_index.nodes | 13 |
| abstract_inverted_index.novel | 27 |
| abstract_inverted_index.relay | 8 |
| abstract_inverted_index.tends | 104 |
| abstract_inverted_index.terms | 82 |
| abstract_inverted_index.Copula | 30, 66, 126 |
| abstract_inverted_index.Frank) | 74 |
| abstract_inverted_index.called | 29 |
| abstract_inverted_index.derive | 47 |
| abstract_inverted_index.higher | 106 |
| abstract_inverted_index.random | 43 |
| abstract_inverted_index.Gumbel, | 72 |
| abstract_inverted_index.analyse | 77 |
| abstract_inverted_index.authors | 46 |
| abstract_inverted_index.between | 18 |
| abstract_inverted_index.channel | 9, 121 |
| abstract_inverted_index.effects | 123 |
| abstract_inverted_index.network | 4, 79 |
| abstract_inverted_index.popular | 69 |
| abstract_inverted_index.results | 140 |
| abstract_inverted_index.success | 52, 84, 96 |
| abstract_inverted_index.theory, | 31 |
| abstract_inverted_index.values, | 107 |
| abstract_inverted_index.Besides, | 113 |
| abstract_inverted_index.Copulas, | 75 |
| abstract_inverted_index.Copulas. | 133 |
| abstract_inverted_index.Finally, | 134 |
| abstract_inverted_index.applying | 62 |
| abstract_inverted_index.approach | 28 |
| abstract_inverted_index.channels | 20, 90 |
| abstract_inverted_index.compared | 130 |
| abstract_inverted_index.describe | 35 |
| abstract_inverted_index.enabling | 32 |
| abstract_inverted_index.function | 39 |
| abstract_inverted_index.improves | 92 |
| abstract_inverted_index.possible | 15 |
| abstract_inverted_index.studied. | 23 |
| abstract_inverted_index.tangible | 129 |
| abstract_inverted_index.whenever | 100 |
| abstract_inverted_index.wireless | 1, 19 |
| abstract_inverted_index.(Clayton, | 71 |
| abstract_inverted_index.arbitrary | 16 |
| abstract_inverted_index.channels. | 59 |
| abstract_inverted_index.different | 55 |
| abstract_inverted_index.structure | 103 |
| abstract_inverted_index.analytical | 139 |
| abstract_inverted_index.consisting | 5 |
| abstract_inverted_index.correlated | 42 |
| abstract_inverted_index.dependence | 56, 102 |
| abstract_inverted_index.efficiency | 136 |
| abstract_inverted_index.exploiting | 25 |
| abstract_inverted_index.expression | 50 |
| abstract_inverted_index.increased. | 112 |
| abstract_inverted_index.structures | 57 |
| abstract_inverted_index.variables, | 44 |
| abstract_inverted_index.Archimedean | 70, 132 |
| abstract_inverted_index.arbitrarily | 41 |
| abstract_inverted_index.correlation | 17, 91, 122 |
| abstract_inverted_index.illustrated | 142 |
| abstract_inverted_index.improvement | 110 |
| abstract_inverted_index.interfering | 12 |
| abstract_inverted_index.performance | 80, 94, 109 |
| abstract_inverted_index.probability | 37, 53, 97 |
| abstract_inverted_index.coefficients | 21 |
| abstract_inverted_index.distribution | 38 |
| abstract_inverted_index.numerically. | 143 |
| abstract_inverted_index.one‐hoping | 7 |
| abstract_inverted_index.probability, | 85 |
| abstract_inverted_index.Specifically, | 60 |
| abstract_inverted_index.closed‐form | 49 |
| abstract_inverted_index.investigation | 119 |
| abstract_inverted_index.Farlie–Gumbel–Morgenstern | 64 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5038472266 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I86958956 |
| citation_normalized_percentile.value | 0.52621686 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |