A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication

Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Singh, Sunil Kumar [verfasserIn] Mishra, Mangala Prasad Vidyarthi, Deo Prakash

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Cognitive Radio Channel aggregation Channel selection Channel fragmentation Handover

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Telecommunication systems - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1993, 84(2023), 4 vom: 30. Sept., Seite 443-455
Übergeordnetes Werk:	volume:84 ; year:2023 ; number:4 ; day:30 ; month:09 ; pages:443-455

Links:	Volltext

DOI / URN:	10.1007/s11235-023-01060-y

Katalog-ID:	SPR053695399

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allfields	10.1007/s11235-023-01060-y doi (DE-627)SPR053695399 (SPR)s11235-023-01060-y-e DE-627 ger DE-627 rakwb eng Singh, Sunil Kumar verfasserin (orcid)0000-0001-8954-6648 aut A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. Cognitive Radio (dpeaa)DE-He213 Channel aggregation (dpeaa)DE-He213 Channel selection (dpeaa)DE-He213 Channel fragmentation (dpeaa)DE-He213 Handover (dpeaa)DE-He213 Mishra, Mangala Prasad aut Vidyarthi, Deo Prakash aut Enthalten in Telecommunication systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1993 84(2023), 4 vom: 30. Sept., Seite 443-455 (DE-627)319194779 (DE-600)2024222-0 1572-9451 nnns volume:84 year:2023 number:4 day:30 month:09 pages:443-455 https://dx.doi.org/10.1007/s11235-023-01060-y 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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 AR 84 2023 4 30 09 443-455
spelling	10.1007/s11235-023-01060-y doi (DE-627)SPR053695399 (SPR)s11235-023-01060-y-e DE-627 ger DE-627 rakwb eng Singh, Sunil Kumar verfasserin (orcid)0000-0001-8954-6648 aut A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. Cognitive Radio (dpeaa)DE-He213 Channel aggregation (dpeaa)DE-He213 Channel selection (dpeaa)DE-He213 Channel fragmentation (dpeaa)DE-He213 Handover (dpeaa)DE-He213 Mishra, Mangala Prasad aut Vidyarthi, Deo Prakash aut Enthalten in Telecommunication systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1993 84(2023), 4 vom: 30. Sept., Seite 443-455 (DE-627)319194779 (DE-600)2024222-0 1572-9451 nnns volume:84 year:2023 number:4 day:30 month:09 pages:443-455 https://dx.doi.org/10.1007/s11235-023-01060-y 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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 AR 84 2023 4 30 09 443-455
allfields_unstemmed	10.1007/s11235-023-01060-y doi (DE-627)SPR053695399 (SPR)s11235-023-01060-y-e DE-627 ger DE-627 rakwb eng Singh, Sunil Kumar verfasserin (orcid)0000-0001-8954-6648 aut A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. Cognitive Radio (dpeaa)DE-He213 Channel aggregation (dpeaa)DE-He213 Channel selection (dpeaa)DE-He213 Channel fragmentation (dpeaa)DE-He213 Handover (dpeaa)DE-He213 Mishra, Mangala Prasad aut Vidyarthi, Deo Prakash aut Enthalten in Telecommunication systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1993 84(2023), 4 vom: 30. Sept., Seite 443-455 (DE-627)319194779 (DE-600)2024222-0 1572-9451 nnns volume:84 year:2023 number:4 day:30 month:09 pages:443-455 https://dx.doi.org/10.1007/s11235-023-01060-y 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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 AR 84 2023 4 30 09 443-455
allfieldsGer	10.1007/s11235-023-01060-y doi (DE-627)SPR053695399 (SPR)s11235-023-01060-y-e DE-627 ger DE-627 rakwb eng Singh, Sunil Kumar verfasserin (orcid)0000-0001-8954-6648 aut A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. Cognitive Radio (dpeaa)DE-He213 Channel aggregation (dpeaa)DE-He213 Channel selection (dpeaa)DE-He213 Channel fragmentation (dpeaa)DE-He213 Handover (dpeaa)DE-He213 Mishra, Mangala Prasad aut Vidyarthi, Deo Prakash aut Enthalten in Telecommunication systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1993 84(2023), 4 vom: 30. Sept., Seite 443-455 (DE-627)319194779 (DE-600)2024222-0 1572-9451 nnns volume:84 year:2023 number:4 day:30 month:09 pages:443-455 https://dx.doi.org/10.1007/s11235-023-01060-y 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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 AR 84 2023 4 30 09 443-455
allfieldsSound	10.1007/s11235-023-01060-y doi (DE-627)SPR053695399 (SPR)s11235-023-01060-y-e DE-627 ger DE-627 rakwb eng Singh, Sunil Kumar verfasserin (orcid)0000-0001-8954-6648 aut A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. Cognitive Radio (dpeaa)DE-He213 Channel aggregation (dpeaa)DE-He213 Channel selection (dpeaa)DE-He213 Channel fragmentation (dpeaa)DE-He213 Handover (dpeaa)DE-He213 Mishra, Mangala Prasad aut Vidyarthi, Deo Prakash aut Enthalten in Telecommunication systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1993 84(2023), 4 vom: 30. Sept., Seite 443-455 (DE-627)319194779 (DE-600)2024222-0 1572-9451 nnns volume:84 year:2023 number:4 day:30 month:09 pages:443-455 https://dx.doi.org/10.1007/s11235-023-01060-y 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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 AR 84 2023 4 30 09 443-455
language	English
source	Enthalten in Telecommunication systems 84(2023), 4 vom: 30. Sept., Seite 443-455 volume:84 year:2023 number:4 day:30 month:09 pages:443-455
sourceStr	Enthalten in Telecommunication systems 84(2023), 4 vom: 30. Sept., Seite 443-455 volume:84 year:2023 number:4 day:30 month:09 pages:443-455
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cognitive Radio Channel aggregation Channel selection Channel fragmentation Handover
isfreeaccess_bool	false
container_title	Telecommunication systems
authorswithroles_txt_mv	Singh, Sunil Kumar @@aut@@ Mishra, Mangala Prasad @@aut@@ Vidyarthi, Deo Prakash @@aut@@
publishDateDaySort_date	2023-09-30T00:00:00Z
hierarchy_top_id	319194779
id	SPR053695399
language_de	englisch
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author	Singh, Sunil Kumar
spellingShingle	Singh, Sunil Kumar misc Cognitive Radio misc Channel aggregation misc Channel selection misc Channel fragmentation misc Handover A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication
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topic_title	A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication Cognitive Radio (dpeaa)DE-He213 Channel aggregation (dpeaa)DE-He213 Channel selection (dpeaa)DE-He213 Channel fragmentation (dpeaa)DE-He213 Handover (dpeaa)DE-He213
topic	misc Cognitive Radio misc Channel aggregation misc Channel selection misc Channel fragmentation misc Handover
topic_unstemmed	misc Cognitive Radio misc Channel aggregation misc Channel selection misc Channel fragmentation misc Handover
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title	A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication
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title_full	A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication
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author_browse	Singh, Sunil Kumar Mishra, Mangala Prasad Vidyarthi, Deo Prakash
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title_sort	hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication
title_auth	A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication
abstract	Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Cognitive Radio is playing a crucial role to enhance radio spectrum utilization by applying various techniques for real-time and non-real-time services. In this work, we have proposed an aggregation and fragmentation of bandwidth-based channel allocation model which uses the Cognitive Radio concept to allocate the channels effectively. In the model, services are categorized into four heterogeneous classes. Of this, Primary new and Primary handoff services are of real-time in nature while Secondary new and Secondary handoff services are of non-real-time in nature. The network is also categorized into two: fixed network and dynamic network categories to enhance the spectrum utilization and to minimize the call block and call drop. Performance analysis, along with the comparative results, exhibit the effectiveness of the proposed model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	A hybrid dynamic aggregation and fragmentation cognitive channel allocation model for mobile communication
url	https://dx.doi.org/10.1007/s11235-023-01060-y
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author2	Mishra, Mangala Prasad Vidyarthi, Deo Prakash
author2Str	Mishra, Mangala Prasad Vidyarthi, Deo Prakash
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doi_str	10.1007/s11235-023-01060-y
up_date	2024-07-03T21:21:54.455Z
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