Modeling and Analysis of Multi-Tier Clustered Millimeter-Wave Cellular Networks With User Classification for Large-Scale Hotspot Area Article Swipe
Xiangdong Jia
,
Pengshan Ji
,
Yuwan Chen
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2943687
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2943687
This paper focuses on a comprehensive framework design and performance analysis for a large-scale hotspot communication scenario where there exist large number of clustered pico base stations (P-BSs) and femto BSs (F-BSs) working on millimeter-Wave (mm-Wave). For such realistic hotspots’ deployment, to exploit the coupling between user equipments (UEs) and base stations (BSs), we model the geographical centers of UE hotspots as independent PPP around which UEs, P-BSs and F-BSs are scattered and form independent and non-homogeneous Poisson cluster processes (PCPs). However, with clustered P-BSs and F-BSs, the UEs would experience the severer intra-cluster interference so that the performance is degraded greatly. To overcome this problem in clustered networks, an effective interference management scheme is proposed by using the ratio of the first and second nearest distances of a typical UE from P-BSs so that a typical UE can be classified as either cluster-center UEs (CCUEs) or cluster-edge UEs (CEUEs). Correspondingly, the total available spectrum band is divided as CCUE-band and CEUE-band, which are occupied by P-BSs associated with CCUEs and CEUEs, respectively. Unlike P-BSs, the F-BSs randomly access the CEUE-band and CCUE-band with a given access portion factor. By using the method from stochastic geometry and the distinguishing feature of mm-Wave, such as directional beamforming with sectored antenna model and modified line of sight ball model for blockage, the UE association probability and Laplace transforms of interference are derived. This yields the derivations of downlink transmission rates. The numerical results show that the proposed clustered heterogeneous scheme with the cluster-UE classification outperforms the traditional ones without the UE classification. This approach not only captures the UE-BS coupling and the non-homogeneous nature of multi-tier networks, but also restricts the severe intra-cluster interference due to clustered BSs.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2943687
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08848389.pdf
- OA Status
- gold
- Cited By
- 12
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2976701621
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2976701621Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2019.2943687Digital Object Identifier
- Title
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Modeling and Analysis of Multi-Tier Clustered Millimeter-Wave Cellular Networks With User Classification for Large-Scale Hotspot AreaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Xiangdong Jia, Pengshan Ji, Yuwan ChenList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2943687Publisher landing page
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-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08848389.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/8600701/08848389.pdfDirect OA link when available
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Hotspot (geology), Computer science, Computer network, Geology, GeophysicsTop concepts (fields/topics) attached by OpenAlex
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12Total citation count in OpenAlex
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2024: 1, 2023: 5, 2022: 1, 2021: 1, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.also | 276 |
| abstract_inverted_index.ball | 215 |
| abstract_inverted_index.band | 155 |
| abstract_inverted_index.base | 25, 50 |
| abstract_inverted_index.form | 73 |
| abstract_inverted_index.from | 131, 193 |
| abstract_inverted_index.line | 212 |
| abstract_inverted_index.ones | 255 |
| abstract_inverted_index.only | 263 |
| abstract_inverted_index.pico | 24 |
| abstract_inverted_index.show | 241 |
| abstract_inverted_index.such | 37, 202 |
| abstract_inverted_index.that | 96, 134, 242 |
| abstract_inverted_index.this | 104 |
| abstract_inverted_index.user | 46 |
| abstract_inverted_index.with | 82, 168, 183, 206, 248 |
| abstract_inverted_index.(UEs) | 48 |
| abstract_inverted_index.CCUEs | 169 |
| abstract_inverted_index.F-BSs | 69, 176 |
| abstract_inverted_index.P-BSs | 67, 84, 132, 166 |
| abstract_inverted_index.UE-BS | 266 |
| abstract_inverted_index.exist | 19 |
| abstract_inverted_index.femto | 29 |
| abstract_inverted_index.first | 122 |
| abstract_inverted_index.given | 185 |
| abstract_inverted_index.large | 20 |
| abstract_inverted_index.model | 54, 209, 216 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.ratio | 119 |
| abstract_inverted_index.sight | 214 |
| abstract_inverted_index.there | 18 |
| abstract_inverted_index.total | 152 |
| abstract_inverted_index.using | 117, 190 |
| abstract_inverted_index.where | 17 |
| abstract_inverted_index.which | 65, 162 |
| abstract_inverted_index.would | 89 |
| abstract_inverted_index.(BSs), | 52 |
| abstract_inverted_index.CEUEs, | 171 |
| abstract_inverted_index.F-BSs, | 86 |
| abstract_inverted_index.P-BSs, | 174 |
| abstract_inverted_index.Unlike | 173 |
| abstract_inverted_index.access | 178, 186 |
| abstract_inverted_index.around | 64 |
| abstract_inverted_index.design | 7 |
| abstract_inverted_index.either | 142 |
| abstract_inverted_index.method | 192 |
| abstract_inverted_index.nature | 271 |
| abstract_inverted_index.number | 21 |
| abstract_inverted_index.rates. | 237 |
| abstract_inverted_index.scheme | 113, 247 |
| abstract_inverted_index.second | 124 |
| abstract_inverted_index.severe | 279 |
| abstract_inverted_index.yields | 231 |
| abstract_inverted_index.(CCUEs) | 145 |
| abstract_inverted_index.(F-BSs) | 31 |
| abstract_inverted_index.(P-BSs) | 27 |
| abstract_inverted_index.(PCPs). | 80 |
| abstract_inverted_index.Laplace | 224 |
| abstract_inverted_index.Poisson | 77 |
| abstract_inverted_index.antenna | 208 |
| abstract_inverted_index.between | 45 |
| abstract_inverted_index.centers | 57 |
| abstract_inverted_index.cluster | 78 |
| abstract_inverted_index.divided | 157 |
| abstract_inverted_index.exploit | 42 |
| abstract_inverted_index.factor. | 188 |
| abstract_inverted_index.feature | 199 |
| abstract_inverted_index.focuses | 2 |
| abstract_inverted_index.hotspot | 14 |
| abstract_inverted_index.nearest | 125 |
| abstract_inverted_index.portion | 187 |
| abstract_inverted_index.problem | 105 |
| abstract_inverted_index.results | 240 |
| abstract_inverted_index.severer | 92 |
| abstract_inverted_index.typical | 129, 136 |
| abstract_inverted_index.without | 256 |
| abstract_inverted_index.working | 32 |
| abstract_inverted_index.(CEUEs). | 149 |
| abstract_inverted_index.However, | 81 |
| abstract_inverted_index.analysis | 10 |
| abstract_inverted_index.approach | 261 |
| abstract_inverted_index.captures | 264 |
| abstract_inverted_index.coupling | 44, 267 |
| abstract_inverted_index.degraded | 100 |
| abstract_inverted_index.derived. | 229 |
| abstract_inverted_index.downlink | 235 |
| abstract_inverted_index.geometry | 195 |
| abstract_inverted_index.greatly. | 101 |
| abstract_inverted_index.hotspots | 60 |
| abstract_inverted_index.mm-Wave, | 201 |
| abstract_inverted_index.modified | 211 |
| abstract_inverted_index.occupied | 164 |
| abstract_inverted_index.overcome | 103 |
| abstract_inverted_index.proposed | 115, 244 |
| abstract_inverted_index.randomly | 177 |
| abstract_inverted_index.scenario | 16 |
| abstract_inverted_index.sectored | 207 |
| abstract_inverted_index.spectrum | 154 |
| abstract_inverted_index.stations | 26, 51 |
| abstract_inverted_index.CCUE-band | 159, 182 |
| abstract_inverted_index.CEUE-band | 180 |
| abstract_inverted_index.available | 153 |
| abstract_inverted_index.blockage, | 218 |
| abstract_inverted_index.clustered | 23, 83, 107, 245, 284 |
| abstract_inverted_index.distances | 126 |
| abstract_inverted_index.effective | 110 |
| abstract_inverted_index.framework | 6 |
| abstract_inverted_index.networks, | 108, 274 |
| abstract_inverted_index.numerical | 239 |
| abstract_inverted_index.processes | 79 |
| abstract_inverted_index.realistic | 38 |
| abstract_inverted_index.restricts | 277 |
| abstract_inverted_index.scattered | 71 |
| abstract_inverted_index.(mm-Wave). | 35 |
| abstract_inverted_index.CEUE-band, | 161 |
| abstract_inverted_index.associated | 167 |
| abstract_inverted_index.classified | 140 |
| abstract_inverted_index.cluster-UE | 250 |
| abstract_inverted_index.equipments | 47 |
| abstract_inverted_index.experience | 90 |
| abstract_inverted_index.management | 112 |
| abstract_inverted_index.multi-tier | 273 |
| abstract_inverted_index.stochastic | 194 |
| abstract_inverted_index.transforms | 225 |
| abstract_inverted_index.association | 221 |
| abstract_inverted_index.beamforming | 205 |
| abstract_inverted_index.deployment, | 40 |
| abstract_inverted_index.derivations | 233 |
| abstract_inverted_index.directional | 204 |
| abstract_inverted_index.independent | 62, 74 |
| abstract_inverted_index.large-scale | 13 |
| abstract_inverted_index.outperforms | 252 |
| abstract_inverted_index.performance | 9, 98 |
| abstract_inverted_index.probability | 222 |
| abstract_inverted_index.traditional | 254 |
| abstract_inverted_index.cluster-edge | 147 |
| abstract_inverted_index.geographical | 56 |
| abstract_inverted_index.interference | 94, 111, 227, 281 |
| abstract_inverted_index.transmission | 236 |
| abstract_inverted_index.communication | 15 |
| abstract_inverted_index.comprehensive | 5 |
| abstract_inverted_index.heterogeneous | 246 |
| abstract_inverted_index.intra-cluster | 93, 280 |
| abstract_inverted_index.respectively. | 172 |
| abstract_inverted_index.classification | 251 |
| abstract_inverted_index.cluster-center | 143 |
| abstract_inverted_index.distinguishing | 198 |
| abstract_inverted_index.classification. | 259 |
| abstract_inverted_index.millimeter-Wave | 34 |
| abstract_inverted_index.non-homogeneous | 76, 270 |
| abstract_inverted_index.Correspondingly, | 150 |
| abstract_inverted_index.hotspots’ | 39 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.72354601 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |