Performance Analysis of Cloud Radio Access Networks With Distributed Multiple Antenna Remote Radio Heads Article Swipe
Fahd Ahmed Khan
,
Huasen He
,
Jiang Xue
,
Tharmalingam Ratnarajah
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1109/tsp.2015.2446440
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1109/tsp.2015.2446440
In this paper, the performance of cloud radio access networks (CRANs) where spatially distributed remote radio heads (RRHs) aid the macro base station (MBS) in transmission is analysed. In order to reflect a realistic scenario, the MBS and the RRHs are assumed to be equipped with multiple antennas and distributed according to a Poisson point process. Both, the MBS and the RRHs, are assumed to employ maximal ratio transmission (MRT) or transmit antenna selection (TAS). Considering downlink transmission, the outage performance of three schemes is studied; first is the selection transmission (ST) scheme, in which the MBS or the RRH with the best channel is selected for transmission. In the second scheme, all the RRHs participate (ARP) and transmit the signal to the user, whereas in the third scheme, a minimal number of RRHs, to attain a desired data-rate, participate in transmission (MRP). Exact closed-form expression for the outage probability is derived for the ST scheme. For the ARP and MRP schemes, analytical approximations of the outage probability are derived which are tight at high signal-to-noise ratios. In addition, for the MRP scheme, the minimal number of RRHs required to meet a target data rate is also calculated which can be useful in characterizing the system complexity. Furthermore, the derived expressions are validated through numerical simulation. It is shown that the average diversity gains of these schemes are independent of the intensity/number of RRHs and only depend on the number of antennas on the MBS. Furthermore, the ARP scheme outperforms the ST scheme when the MBS/RRHs transmit with maximum power. However, in case of a sum power constraint and equal power allocation, the ST scheme outperforms the ARP scheme.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tsp.2015.2446440
- OA Status
- green
- Cited By
- 50
- References
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W1662669438Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tsp.2015.2446440Digital Object Identifier
- Title
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Performance Analysis of Cloud Radio Access Networks With Distributed Multiple Antenna Remote Radio HeadsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
-
2015-06-16Full publication date if available
- Authors
-
Fahd Ahmed Khan, Huasen He, Jiang Xue, Tharmalingam RatnarajahList of authors in order
- Landing page
-
https://doi.org/10.1109/tsp.2015.2446440Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.research.ed.ac.uk/en/publications/5ba70bd2-fb99-4e7f-a949-8c29b7ec6cb0Direct OA link when available
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Computer science, Telecommunications link, Transmission (telecommunications), Computer network, Base station, Poisson point process, Poisson distribution, Telecommunications, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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50Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 5, 2021: 5, 2020: 7, 2019: 8Per-year citation counts (last 5 years)
- References (count)
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20Number 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.MRP | 160, 181 |
| abstract_inverted_index.RRH | 99 |
| abstract_inverted_index.aid | 18 |
| abstract_inverted_index.all | 112 |
| abstract_inverted_index.and | 37, 48, 59, 117, 159, 234, 268 |
| abstract_inverted_index.are | 40, 62, 168, 171, 211, 227 |
| abstract_inverted_index.can | 199 |
| abstract_inverted_index.for | 106, 146, 152, 179 |
| abstract_inverted_index.sum | 265 |
| abstract_inverted_index.the | 3, 19, 35, 38, 57, 60, 78, 88, 95, 98, 101, 109, 113, 119, 122, 126, 147, 153, 157, 165, 180, 183, 204, 208, 220, 230, 238, 243, 246, 250, 254, 272, 276 |
| abstract_inverted_index.(ST) | 91 |
| abstract_inverted_index.MBS. | 244 |
| abstract_inverted_index.RRHs | 39, 114, 187, 233 |
| abstract_inverted_index.also | 196 |
| abstract_inverted_index.base | 21 |
| abstract_inverted_index.best | 102 |
| abstract_inverted_index.case | 262 |
| abstract_inverted_index.data | 193 |
| abstract_inverted_index.high | 174 |
| abstract_inverted_index.meet | 190 |
| abstract_inverted_index.only | 235 |
| abstract_inverted_index.rate | 194 |
| abstract_inverted_index.that | 219 |
| abstract_inverted_index.this | 1 |
| abstract_inverted_index.when | 253 |
| abstract_inverted_index.with | 45, 100, 257 |
| abstract_inverted_index.(ARP) | 116 |
| abstract_inverted_index.(MBS) | 23 |
| abstract_inverted_index.(MRT) | 69 |
| abstract_inverted_index.Both, | 56 |
| abstract_inverted_index.Exact | 143 |
| abstract_inverted_index.RRHs, | 61, 133 |
| abstract_inverted_index.cloud | 6 |
| abstract_inverted_index.equal | 269 |
| abstract_inverted_index.first | 86 |
| abstract_inverted_index.gains | 223 |
| abstract_inverted_index.heads | 16 |
| abstract_inverted_index.macro | 20 |
| abstract_inverted_index.order | 29 |
| abstract_inverted_index.point | 54 |
| abstract_inverted_index.power | 266, 270 |
| abstract_inverted_index.radio | 7, 15 |
| abstract_inverted_index.ratio | 67 |
| abstract_inverted_index.shown | 218 |
| abstract_inverted_index.these | 225 |
| abstract_inverted_index.third | 127 |
| abstract_inverted_index.three | 82 |
| abstract_inverted_index.tight | 172 |
| abstract_inverted_index.user, | 123 |
| abstract_inverted_index.where | 11 |
| abstract_inverted_index.which | 94, 170, 198 |
| abstract_inverted_index.(MRP). | 142 |
| abstract_inverted_index.(RRHs) | 17 |
| abstract_inverted_index.(TAS). | 74 |
| abstract_inverted_index.access | 8 |
| abstract_inverted_index.attain | 135 |
| abstract_inverted_index.depend | 236 |
| abstract_inverted_index.employ | 65 |
| abstract_inverted_index.number | 131, 185, 239 |
| abstract_inverted_index.outage | 79, 148, 166 |
| abstract_inverted_index.paper, | 2 |
| abstract_inverted_index.power. | 259 |
| abstract_inverted_index.remote | 14 |
| abstract_inverted_index.scheme | 248, 252, 274 |
| abstract_inverted_index.second | 110 |
| abstract_inverted_index.signal | 120 |
| abstract_inverted_index.system | 205 |
| abstract_inverted_index.target | 192 |
| abstract_inverted_index.useful | 201 |
| abstract_inverted_index.(CRANs) | 10 |
| abstract_inverted_index.Poisson | 53 |
| abstract_inverted_index.antenna | 72 |
| abstract_inverted_index.assumed | 41, 63 |
| abstract_inverted_index.average | 221 |
| abstract_inverted_index.channel | 103 |
| abstract_inverted_index.derived | 151, 169, 209 |
| abstract_inverted_index.desired | 137 |
| abstract_inverted_index.maximal | 66 |
| abstract_inverted_index.maximum | 258 |
| abstract_inverted_index.minimal | 130, 184 |
| abstract_inverted_index.ratios. | 176 |
| abstract_inverted_index.reflect | 31 |
| abstract_inverted_index.scheme, | 92, 111, 128, 182 |
| abstract_inverted_index.scheme. | 155, 278 |
| abstract_inverted_index.schemes | 83, 226 |
| abstract_inverted_index.station | 22 |
| abstract_inverted_index.through | 213 |
| abstract_inverted_index.whereas | 124 |
| abstract_inverted_index.However, | 260 |
| abstract_inverted_index.MBS/RRHs | 255 |
| abstract_inverted_index.antennas | 47, 241 |
| abstract_inverted_index.downlink | 76 |
| abstract_inverted_index.equipped | 44 |
| abstract_inverted_index.multiple | 46 |
| abstract_inverted_index.networks | 9 |
| abstract_inverted_index.process. | 55 |
| abstract_inverted_index.required | 188 |
| abstract_inverted_index.schemes, | 161 |
| abstract_inverted_index.selected | 105 |
| abstract_inverted_index.studied; | 85 |
| abstract_inverted_index.transmit | 71, 118, 256 |
| abstract_inverted_index.according | 50 |
| abstract_inverted_index.addition, | 178 |
| abstract_inverted_index.analysed. | 27 |
| abstract_inverted_index.diversity | 222 |
| abstract_inverted_index.numerical | 214 |
| abstract_inverted_index.realistic | 33 |
| abstract_inverted_index.scenario, | 34 |
| abstract_inverted_index.selection | 73, 89 |
| abstract_inverted_index.spatially | 12 |
| abstract_inverted_index.validated | 212 |
| abstract_inverted_index.analytical | 162 |
| abstract_inverted_index.calculated | 197 |
| abstract_inverted_index.constraint | 267 |
| abstract_inverted_index.data-rate, | 138 |
| abstract_inverted_index.expression | 145 |
| abstract_inverted_index.Considering | 75 |
| abstract_inverted_index.allocation, | 271 |
| abstract_inverted_index.closed-form | 144 |
| abstract_inverted_index.complexity. | 206 |
| abstract_inverted_index.distributed | 13, 49 |
| abstract_inverted_index.expressions | 210 |
| abstract_inverted_index.independent | 228 |
| abstract_inverted_index.outperforms | 249, 275 |
| abstract_inverted_index.participate | 115, 139 |
| abstract_inverted_index.performance | 4, 80 |
| abstract_inverted_index.probability | 149, 167 |
| abstract_inverted_index.simulation. | 215 |
| abstract_inverted_index.Furthermore, | 207, 245 |
| abstract_inverted_index.transmission | 25, 68, 90, 141 |
| abstract_inverted_index.transmission, | 77 |
| abstract_inverted_index.transmission. | 107 |
| abstract_inverted_index.approximations | 163 |
| abstract_inverted_index.characterizing | 203 |
| abstract_inverted_index.signal-to-noise | 175 |
| abstract_inverted_index.intensity/number | 231 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.95324373 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |