Feasibility of Green Network Deployment for Heterogeneous Networks
Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Al-Samawi, Aida [verfasserIn] Sali, Aduwati [verfasserIn] Noordin, Nor Kamariah [verfasserIn] Othman, Mohamed [verfasserIn] Hashim, Fazirulhisyam [verfasserIn] Nisirat, Mahdi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 94(2016), 3 vom: 13. Sept., Seite 1443-1476 |
|---|---|
| Übergeordnetes Werk: |
volume:94 ; year:2016 ; number:3 ; day:13 ; month:09 ; pages:1443-1476 |
| Links: |
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| DOI / URN: |
10.1007/s11277-016-3691-0 |
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| Katalog-ID: |
SPR018580505 |
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| 520 | |a Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. | ||
| 650 | 4 | |a Wireless network planning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Decoupled GA optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Heterogeneous network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a LTE network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Heterogeneous network power consumption |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sali, Aduwati |e verfasserin |4 aut | |
| 700 | 1 | |a Noordin, Nor Kamariah |e verfasserin |4 aut | |
| 700 | 1 | |a Othman, Mohamed |e verfasserin |4 aut | |
| 700 | 1 | |a Hashim, Fazirulhisyam |e verfasserin |4 aut | |
| 700 | 1 | |a Nisirat, Mahdi |e verfasserin |4 aut | |
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10.1007/s11277-016-3691-0 doi (DE-627)SPR018580505 (SPR)s11277-016-3691-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Al-Samawi, Aida verfasserin aut Feasibility of Green Network Deployment for Heterogeneous Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. Wireless network planning (dpeaa)DE-He213 Decoupled GA optimization (dpeaa)DE-He213 Heterogeneous network (dpeaa)DE-He213 LTE network (dpeaa)DE-He213 Heterogeneous network power consumption (dpeaa)DE-He213 Sali, Aduwati verfasserin aut Noordin, Nor Kamariah verfasserin aut Othman, Mohamed verfasserin aut Hashim, Fazirulhisyam verfasserin aut Nisirat, Mahdi verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 94(2016), 3 vom: 13. Sept., Seite 1443-1476 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:94 year:2016 number:3 day:13 month:09 pages:1443-1476 https://dx.doi.org/10.1007/s11277-016-3691-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 94 2016 3 13 09 1443-1476 |
| spelling |
10.1007/s11277-016-3691-0 doi (DE-627)SPR018580505 (SPR)s11277-016-3691-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Al-Samawi, Aida verfasserin aut Feasibility of Green Network Deployment for Heterogeneous Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. Wireless network planning (dpeaa)DE-He213 Decoupled GA optimization (dpeaa)DE-He213 Heterogeneous network (dpeaa)DE-He213 LTE network (dpeaa)DE-He213 Heterogeneous network power consumption (dpeaa)DE-He213 Sali, Aduwati verfasserin aut Noordin, Nor Kamariah verfasserin aut Othman, Mohamed verfasserin aut Hashim, Fazirulhisyam verfasserin aut Nisirat, Mahdi verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 94(2016), 3 vom: 13. Sept., Seite 1443-1476 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:94 year:2016 number:3 day:13 month:09 pages:1443-1476 https://dx.doi.org/10.1007/s11277-016-3691-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 94 2016 3 13 09 1443-1476 |
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10.1007/s11277-016-3691-0 doi (DE-627)SPR018580505 (SPR)s11277-016-3691-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Al-Samawi, Aida verfasserin aut Feasibility of Green Network Deployment for Heterogeneous Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. Wireless network planning (dpeaa)DE-He213 Decoupled GA optimization (dpeaa)DE-He213 Heterogeneous network (dpeaa)DE-He213 LTE network (dpeaa)DE-He213 Heterogeneous network power consumption (dpeaa)DE-He213 Sali, Aduwati verfasserin aut Noordin, Nor Kamariah verfasserin aut Othman, Mohamed verfasserin aut Hashim, Fazirulhisyam verfasserin aut Nisirat, Mahdi verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 94(2016), 3 vom: 13. Sept., Seite 1443-1476 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:94 year:2016 number:3 day:13 month:09 pages:1443-1476 https://dx.doi.org/10.1007/s11277-016-3691-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 94 2016 3 13 09 1443-1476 |
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10.1007/s11277-016-3691-0 doi (DE-627)SPR018580505 (SPR)s11277-016-3691-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Al-Samawi, Aida verfasserin aut Feasibility of Green Network Deployment for Heterogeneous Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. Wireless network planning (dpeaa)DE-He213 Decoupled GA optimization (dpeaa)DE-He213 Heterogeneous network (dpeaa)DE-He213 LTE network (dpeaa)DE-He213 Heterogeneous network power consumption (dpeaa)DE-He213 Sali, Aduwati verfasserin aut Noordin, Nor Kamariah verfasserin aut Othman, Mohamed verfasserin aut Hashim, Fazirulhisyam verfasserin aut Nisirat, Mahdi verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 94(2016), 3 vom: 13. Sept., Seite 1443-1476 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:94 year:2016 number:3 day:13 month:09 pages:1443-1476 https://dx.doi.org/10.1007/s11277-016-3691-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 94 2016 3 13 09 1443-1476 |
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10.1007/s11277-016-3691-0 doi (DE-627)SPR018580505 (SPR)s11277-016-3691-0-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Al-Samawi, Aida verfasserin aut Feasibility of Green Network Deployment for Heterogeneous Networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. Wireless network planning (dpeaa)DE-He213 Decoupled GA optimization (dpeaa)DE-He213 Heterogeneous network (dpeaa)DE-He213 LTE network (dpeaa)DE-He213 Heterogeneous network power consumption (dpeaa)DE-He213 Sali, Aduwati verfasserin aut Noordin, Nor Kamariah verfasserin aut Othman, Mohamed verfasserin aut Hashim, Fazirulhisyam verfasserin aut Nisirat, Mahdi verfasserin aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 94(2016), 3 vom: 13. Sept., Seite 1443-1476 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:94 year:2016 number:3 day:13 month:09 pages:1443-1476 https://dx.doi.org/10.1007/s11277-016-3691-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 94 2016 3 13 09 1443-1476 |
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English |
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Enthalten in Wireless personal communications 94(2016), 3 vom: 13. Sept., Seite 1443-1476 volume:94 year:2016 number:3 day:13 month:09 pages:1443-1476 |
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Enthalten in Wireless personal communications 94(2016), 3 vom: 13. Sept., Seite 1443-1476 volume:94 year:2016 number:3 day:13 month:09 pages:1443-1476 |
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Wireless network planning Decoupled GA optimization Heterogeneous network LTE network Heterogeneous network power consumption |
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Wireless personal communications |
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Al-Samawi, Aida @@aut@@ Sali, Aduwati @@aut@@ Noordin, Nor Kamariah @@aut@@ Othman, Mohamed @@aut@@ Hashim, Fazirulhisyam @@aut@@ Nisirat, Mahdi @@aut@@ |
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In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. 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Al-Samawi, Aida |
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Al-Samawi, Aida ddc 620 bkl 53.00 misc Wireless network planning misc Decoupled GA optimization misc Heterogeneous network misc LTE network misc Heterogeneous network power consumption Feasibility of Green Network Deployment for Heterogeneous Networks |
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620 ASE 53.00 bkl Feasibility of Green Network Deployment for Heterogeneous Networks Wireless network planning (dpeaa)DE-He213 Decoupled GA optimization (dpeaa)DE-He213 Heterogeneous network (dpeaa)DE-He213 LTE network (dpeaa)DE-He213 Heterogeneous network power consumption (dpeaa)DE-He213 |
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Al-Samawi, Aida Sali, Aduwati Noordin, Nor Kamariah Othman, Mohamed Hashim, Fazirulhisyam Nisirat, Mahdi |
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Feasibility of Green Network Deployment for Heterogeneous Networks |
| abstract |
Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. |
| abstractGer |
Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. |
| abstract_unstemmed |
Abstract Green technology is a new term which is used to describe the energy efficient technologies. In the context of mobile communications industry, complying with the green technology strategy is a challenge. This is because of the tradeoff between the Quality of Service (QoS) provided and the total energy used in the transmission. Reducing the transmission energy may cause degradation in the QoS, more distinctively, in highly populated areas. This paper explores the possibility of achieving the green technology goal in planning and deployment of the HetNet mobile network with efficient network QoS. A decoupled two stage multi-objective genetic algorithm is developed to provide the network base station distribution that would satisfy both the network QoS and green network demands. In the first stage the algorithm estimates the base station parameters for more energy efficient HetNet deployment for optimum network coverage. The initial base station candidate locations are provided by a network operator in Kuala Lumpu, Malaysia. The second stage of the developed algorithm selects the number and location of RS associated with each base station optimized in the first stage to improve the network capacity. To optimize the network power, a novel arrival rate based HetNet total power consumption model is derived to investigate the parameters that affect the network power expenditure. Results show that a remarkable energy saving of about 40 % of the operator transmission power could be achieved with full network coverage. The addition of RS associated with each base station would greatly improve network capacity on the expense of its power expenditure. The relative RS to base station capacity plays major rule in reducing HetNet power expenditure. |
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| container_issue |
3 |
| title_short |
Feasibility of Green Network Deployment for Heterogeneous Networks |
| url |
https://dx.doi.org/10.1007/s11277-016-3691-0 |
| remote_bool |
true |
| author2 |
Sali, Aduwati Noordin, Nor Kamariah Othman, Mohamed Hashim, Fazirulhisyam Nisirat, Mahdi |
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Sali, Aduwati Noordin, Nor Kamariah Othman, Mohamed Hashim, Fazirulhisyam Nisirat, Mahdi |
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| doi_str |
10.1007/s11277-016-3691-0 |
| up_date |
2024-07-03T20:43:42.194Z |
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| score |
7.4018707 |