Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics

Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jaziri, Aymen [verfasserIn] Nasri, Ridha [verfasserIn] Chahed, Tijani [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Mobile networks Tracking traffic peaks Network performance metrics Coverage map Measurement imperfections of mobile equipments

Übergeordnetes Werk:	Enthalten in: International journal of wireless information networks - New York, NY [u.a.] : Springer Science + Business Media B.V., 1994, 24(2017), 4 vom: 11. Feb., Seite 389-403
Übergeordnetes Werk:	volume:24 ; year:2017 ; number:4 ; day:11 ; month:02 ; pages:389-403

Links:	Volltext

DOI / URN:	10.1007/s10776-017-0335-6

Katalog-ID:	SPR01319626X

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520			\|a Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost.
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allfields	10.1007/s10776-017-0335-6 doi (DE-627)SPR01319626X (SPR)s10776-017-0335-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Jaziri, Aymen verfasserin aut Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost. Mobile networks (dpeaa)DE-He213 Tracking traffic peaks (dpeaa)DE-He213 Network performance metrics (dpeaa)DE-He213 Coverage map (dpeaa)DE-He213 Measurement imperfections of mobile equipments (dpeaa)DE-He213 Nasri, Ridha verfasserin aut Chahed, Tijani verfasserin aut Enthalten in International journal of wireless information networks New York, NY [u.a.] : Springer Science + Business Media B.V., 1994 24(2017), 4 vom: 11. Feb., Seite 389-403 (DE-627)320567982 (DE-600)2016171-2 1572-8129 nnns volume:24 year:2017 number:4 day:11 month:02 pages:389-403 https://dx.doi.org/10.1007/s10776-017-0335-6 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 24 2017 4 11 02 389-403
spelling	10.1007/s10776-017-0335-6 doi (DE-627)SPR01319626X (SPR)s10776-017-0335-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Jaziri, Aymen verfasserin aut Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost. Mobile networks (dpeaa)DE-He213 Tracking traffic peaks (dpeaa)DE-He213 Network performance metrics (dpeaa)DE-He213 Coverage map (dpeaa)DE-He213 Measurement imperfections of mobile equipments (dpeaa)DE-He213 Nasri, Ridha verfasserin aut Chahed, Tijani verfasserin aut Enthalten in International journal of wireless information networks New York, NY [u.a.] : Springer Science + Business Media B.V., 1994 24(2017), 4 vom: 11. Feb., Seite 389-403 (DE-627)320567982 (DE-600)2016171-2 1572-8129 nnns volume:24 year:2017 number:4 day:11 month:02 pages:389-403 https://dx.doi.org/10.1007/s10776-017-0335-6 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 24 2017 4 11 02 389-403
allfields_unstemmed	10.1007/s10776-017-0335-6 doi (DE-627)SPR01319626X (SPR)s10776-017-0335-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Jaziri, Aymen verfasserin aut Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost. Mobile networks (dpeaa)DE-He213 Tracking traffic peaks (dpeaa)DE-He213 Network performance metrics (dpeaa)DE-He213 Coverage map (dpeaa)DE-He213 Measurement imperfections of mobile equipments (dpeaa)DE-He213 Nasri, Ridha verfasserin aut Chahed, Tijani verfasserin aut Enthalten in International journal of wireless information networks New York, NY [u.a.] : Springer Science + Business Media B.V., 1994 24(2017), 4 vom: 11. Feb., Seite 389-403 (DE-627)320567982 (DE-600)2016171-2 1572-8129 nnns volume:24 year:2017 number:4 day:11 month:02 pages:389-403 https://dx.doi.org/10.1007/s10776-017-0335-6 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 24 2017 4 11 02 389-403
allfieldsGer	10.1007/s10776-017-0335-6 doi (DE-627)SPR01319626X (SPR)s10776-017-0335-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Jaziri, Aymen verfasserin aut Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost. Mobile networks (dpeaa)DE-He213 Tracking traffic peaks (dpeaa)DE-He213 Network performance metrics (dpeaa)DE-He213 Coverage map (dpeaa)DE-He213 Measurement imperfections of mobile equipments (dpeaa)DE-He213 Nasri, Ridha verfasserin aut Chahed, Tijani verfasserin aut Enthalten in International journal of wireless information networks New York, NY [u.a.] : Springer Science + Business Media B.V., 1994 24(2017), 4 vom: 11. Feb., Seite 389-403 (DE-627)320567982 (DE-600)2016171-2 1572-8129 nnns volume:24 year:2017 number:4 day:11 month:02 pages:389-403 https://dx.doi.org/10.1007/s10776-017-0335-6 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 24 2017 4 11 02 389-403
allfieldsSound	10.1007/s10776-017-0335-6 doi (DE-627)SPR01319626X (SPR)s10776-017-0335-6-e DE-627 ger DE-627 rakwb eng 620 ASE 53.00 bkl Jaziri, Aymen verfasserin aut Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost. Mobile networks (dpeaa)DE-He213 Tracking traffic peaks (dpeaa)DE-He213 Network performance metrics (dpeaa)DE-He213 Coverage map (dpeaa)DE-He213 Measurement imperfections of mobile equipments (dpeaa)DE-He213 Nasri, Ridha verfasserin aut Chahed, Tijani verfasserin aut Enthalten in International journal of wireless information networks New York, NY [u.a.] : Springer Science + Business Media B.V., 1994 24(2017), 4 vom: 11. Feb., Seite 389-403 (DE-627)320567982 (DE-600)2016171-2 1572-8129 nnns volume:24 year:2017 number:4 day:11 month:02 pages:389-403 https://dx.doi.org/10.1007/s10776-017-0335-6 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_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 ASE AR 24 2017 4 11 02 389-403
language	English
source	Enthalten in International journal of wireless information networks 24(2017), 4 vom: 11. Feb., Seite 389-403 volume:24 year:2017 number:4 day:11 month:02 pages:389-403
sourceStr	Enthalten in International journal of wireless information networks 24(2017), 4 vom: 11. Feb., Seite 389-403 volume:24 year:2017 number:4 day:11 month:02 pages:389-403
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Mobile networks Tracking traffic peaks Network performance metrics Coverage map Measurement imperfections of mobile equipments
dewey-raw	620
isfreeaccess_bool	false
container_title	International journal of wireless information networks
authorswithroles_txt_mv	Jaziri, Aymen @@aut@@ Nasri, Ridha @@aut@@ Chahed, Tijani @@aut@@
publishDateDaySort_date	2017-02-11T00:00:00Z
hierarchy_top_id	320567982
dewey-sort	3620
id	SPR01319626X
language_de	englisch
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author	Jaziri, Aymen
spellingShingle	Jaziri, Aymen ddc 620 bkl 53.00 misc Mobile networks misc Tracking traffic peaks misc Network performance metrics misc Coverage map misc Measurement imperfections of mobile equipments Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics
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topic_title	620 ASE 53.00 bkl Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics Mobile networks (dpeaa)DE-He213 Tracking traffic peaks (dpeaa)DE-He213 Network performance metrics (dpeaa)DE-He213 Coverage map (dpeaa)DE-He213 Measurement imperfections of mobile equipments (dpeaa)DE-He213
topic	ddc 620 bkl 53.00 misc Mobile networks misc Tracking traffic peaks misc Network performance metrics misc Coverage map misc Measurement imperfections of mobile equipments
topic_unstemmed	ddc 620 bkl 53.00 misc Mobile networks misc Tracking traffic peaks misc Network performance metrics misc Coverage map misc Measurement imperfections of mobile equipments
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title	Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics
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title_full	Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics
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title_sort	tracking traffic peaks in mobile networks using statistics of performance metrics
title_auth	Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics
abstract	Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost.
abstractGer	Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost.
abstract_unstemmed	Abstract In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost.
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container_issue	4
title_short	Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics
url	https://dx.doi.org/10.1007/s10776-017-0335-6
remote_bool	true
author2	Nasri, Ridha Chahed, Tijani
author2Str	Nasri, Ridha Chahed, Tijani
ppnlink	320567982
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hochschulschrift_bool	false
doi_str	10.1007/s10776-017-0335-6
up_date	2024-07-03T18:04:18.109Z
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