The wireless control plane: An overview and directions for future research

Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. T...
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Autor*in:	Mazied, EmadelDin A. [verfasserIn] ElNainay, Mustafa Y. [verfasserIn] Abdel-Rahman, Mohammad J. [verfasserIn] Midkiff, Scott F. [verfasserIn] Rizk, Mohamed R.M. [verfasserIn] Rakha, Hesham A. [verfasserIn] MacKenzie, Allen B. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Software-defined networks Wireless control plane Wireless SDN controller Deep reinforcement learning

Übergeordnetes Werk:	Enthalten in: Journal of network and computer applications - London : Academic Press, 1996, 126, Seite 104-122
Übergeordnetes Werk:	volume:126 ; pages:104-122

DOI / URN:	10.1016/j.jnca.2018.09.017

Katalog-ID:	ELV001304550

Internformat


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650		4	\|a Software-defined networks
650		4	\|a Wireless control plane
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650		4	\|a Deep reinforcement learning
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700	1		\|a Rakha, Hesham A. \|e verfasserin \|4 aut
700	1		\|a MacKenzie, Allen B. \|e verfasserin \|4 aut
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allfields	10.1016/j.jnca.2018.09.017 doi (DE-627)ELV001304550 (ELSEVIER)S1084-8045(18)30304-7 DE-627 ger DE-627 rda eng 004 DE-600 54.26 bkl 54.32 bkl Mazied, EmadelDin A. verfasserin aut The wireless control plane: An overview and directions for future research 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research. Software-defined networks Wireless control plane Wireless SDN controller Deep reinforcement learning ElNainay, Mustafa Y. verfasserin aut Abdel-Rahman, Mohammad J. verfasserin aut Midkiff, Scott F. verfasserin aut Rizk, Mohamed R.M. verfasserin aut Rakha, Hesham A. verfasserin aut MacKenzie, Allen B. verfasserin aut Enthalten in Journal of network and computer applications London : Academic Press, 1996 126, Seite 104-122 Online-Ressource (DE-627)267328176 (DE-600)1469776-2 (DE-576)259483702 1084-8045 nnns volume:126 pages:104-122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.26 Mikrocomputer 54.32 Rechnerkommunikation AR 126 104-122
spelling	10.1016/j.jnca.2018.09.017 doi (DE-627)ELV001304550 (ELSEVIER)S1084-8045(18)30304-7 DE-627 ger DE-627 rda eng 004 DE-600 54.26 bkl 54.32 bkl Mazied, EmadelDin A. verfasserin aut The wireless control plane: An overview and directions for future research 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research. Software-defined networks Wireless control plane Wireless SDN controller Deep reinforcement learning ElNainay, Mustafa Y. verfasserin aut Abdel-Rahman, Mohammad J. verfasserin aut Midkiff, Scott F. verfasserin aut Rizk, Mohamed R.M. verfasserin aut Rakha, Hesham A. verfasserin aut MacKenzie, Allen B. verfasserin aut Enthalten in Journal of network and computer applications London : Academic Press, 1996 126, Seite 104-122 Online-Ressource (DE-627)267328176 (DE-600)1469776-2 (DE-576)259483702 1084-8045 nnns volume:126 pages:104-122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.26 Mikrocomputer 54.32 Rechnerkommunikation AR 126 104-122
allfields_unstemmed	10.1016/j.jnca.2018.09.017 doi (DE-627)ELV001304550 (ELSEVIER)S1084-8045(18)30304-7 DE-627 ger DE-627 rda eng 004 DE-600 54.26 bkl 54.32 bkl Mazied, EmadelDin A. verfasserin aut The wireless control plane: An overview and directions for future research 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research. Software-defined networks Wireless control plane Wireless SDN controller Deep reinforcement learning ElNainay, Mustafa Y. verfasserin aut Abdel-Rahman, Mohammad J. verfasserin aut Midkiff, Scott F. verfasserin aut Rizk, Mohamed R.M. verfasserin aut Rakha, Hesham A. verfasserin aut MacKenzie, Allen B. verfasserin aut Enthalten in Journal of network and computer applications London : Academic Press, 1996 126, Seite 104-122 Online-Ressource (DE-627)267328176 (DE-600)1469776-2 (DE-576)259483702 1084-8045 nnns volume:126 pages:104-122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.26 Mikrocomputer 54.32 Rechnerkommunikation AR 126 104-122
allfieldsGer	10.1016/j.jnca.2018.09.017 doi (DE-627)ELV001304550 (ELSEVIER)S1084-8045(18)30304-7 DE-627 ger DE-627 rda eng 004 DE-600 54.26 bkl 54.32 bkl Mazied, EmadelDin A. verfasserin aut The wireless control plane: An overview and directions for future research 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research. Software-defined networks Wireless control plane Wireless SDN controller Deep reinforcement learning ElNainay, Mustafa Y. verfasserin aut Abdel-Rahman, Mohammad J. verfasserin aut Midkiff, Scott F. verfasserin aut Rizk, Mohamed R.M. verfasserin aut Rakha, Hesham A. verfasserin aut MacKenzie, Allen B. verfasserin aut Enthalten in Journal of network and computer applications London : Academic Press, 1996 126, Seite 104-122 Online-Ressource (DE-627)267328176 (DE-600)1469776-2 (DE-576)259483702 1084-8045 nnns volume:126 pages:104-122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.26 Mikrocomputer 54.32 Rechnerkommunikation AR 126 104-122
allfieldsSound	10.1016/j.jnca.2018.09.017 doi (DE-627)ELV001304550 (ELSEVIER)S1084-8045(18)30304-7 DE-627 ger DE-627 rda eng 004 DE-600 54.26 bkl 54.32 bkl Mazied, EmadelDin A. verfasserin aut The wireless control plane: An overview and directions for future research 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research. Software-defined networks Wireless control plane Wireless SDN controller Deep reinforcement learning ElNainay, Mustafa Y. verfasserin aut Abdel-Rahman, Mohammad J. verfasserin aut Midkiff, Scott F. verfasserin aut Rizk, Mohamed R.M. verfasserin aut Rakha, Hesham A. verfasserin aut MacKenzie, Allen B. verfasserin aut Enthalten in Journal of network and computer applications London : Academic Press, 1996 126, Seite 104-122 Online-Ressource (DE-627)267328176 (DE-600)1469776-2 (DE-576)259483702 1084-8045 nnns volume:126 pages:104-122 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.26 Mikrocomputer 54.32 Rechnerkommunikation AR 126 104-122
language	English
source	Enthalten in Journal of network and computer applications 126, Seite 104-122 volume:126 pages:104-122
sourceStr	Enthalten in Journal of network and computer applications 126, Seite 104-122 volume:126 pages:104-122
format_phy_str_mv	Article
bklname	Mikrocomputer Rechnerkommunikation
institution	findex.gbv.de
topic_facet	Software-defined networks Wireless control plane Wireless SDN controller Deep reinforcement learning
dewey-raw	004
isfreeaccess_bool	false
container_title	Journal of network and computer applications
authorswithroles_txt_mv	Mazied, EmadelDin A. @@aut@@ ElNainay, Mustafa Y. @@aut@@ Abdel-Rahman, Mohammad J. @@aut@@ Midkiff, Scott F. @@aut@@ Rizk, Mohamed R.M. @@aut@@ Rakha, Hesham A. @@aut@@ MacKenzie, Allen B. @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	267328176
dewey-sort	14
id	ELV001304550
language_de	englisch
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author	Mazied, EmadelDin A.
spellingShingle	Mazied, EmadelDin A. ddc 004 bkl 54.26 bkl 54.32 misc Software-defined networks misc Wireless control plane misc Wireless SDN controller misc Deep reinforcement learning The wireless control plane: An overview and directions for future research
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topic_title	004 DE-600 54.26 bkl 54.32 bkl The wireless control plane: An overview and directions for future research Software-defined networks Wireless control plane Wireless SDN controller Deep reinforcement learning
topic	ddc 004 bkl 54.26 bkl 54.32 misc Software-defined networks misc Wireless control plane misc Wireless SDN controller misc Deep reinforcement learning
topic_unstemmed	ddc 004 bkl 54.26 bkl 54.32 misc Software-defined networks misc Wireless control plane misc Wireless SDN controller misc Deep reinforcement learning
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title	The wireless control plane: An overview and directions for future research
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title_full	The wireless control plane: An overview and directions for future research
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author_browse	Mazied, EmadelDin A. ElNainay, Mustafa Y. Abdel-Rahman, Mohammad J. Midkiff, Scott F. Rizk, Mohamed R.M. Rakha, Hesham A. MacKenzie, Allen B.
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title_sort	the wireless control plane: an overview and directions for future research
title_auth	The wireless control plane: An overview and directions for future research
abstract	Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research.
abstractGer	Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research.
abstract_unstemmed	Software-defined networking (SDN), which has been successfully deployed in the management of complex data centers, has recently been incorporated into a myriad of 5G networks to intelligently manage a wide range of heterogeneous wireless devices, software systems, and wireless access technologies. Thus, the SDN control plane needs to communicate wirelessly with the wireless data plane either directly or indirectly. The uncertainties in the wireless SDN control plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP, and indirect WCP, I-WCP) have been incorporated into recent 5G networks; however, a discussion of their design principles and their design limitations is missing. This paper introduces an overview of the WCP design (I-WCP and D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G networks. Furthermore, to facilitate synthesizing a robust WCP, this paper proposes a generic WCP framework using deep reinforcement learning (DRL) principles and presents a roadmap for future research.
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title_short	The wireless control plane: An overview and directions for future research
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author2	ElNainay, Mustafa Y. Abdel-Rahman, Mohammad J. Midkiff, Scott F. Rizk, Mohamed R.M. Rakha, Hesham A. MacKenzie, Allen B.
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up_date	2024-07-06T20:55:03.337Z
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The wireless control plane: An overview and directions for future research

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