Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications

Abstract The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and a...
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Autor*in:	Gupta, Rohit Kumar [verfasserIn] Mahajan, Shashwat Misra, Rajiv

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	5G/6G Network slicing Industrial internet of things Generative adversarial network Reinforcement learning

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 79(2022), 5 vom: 12. Okt., Seite 5109-5138
Übergeordnetes Werk:	volume:79 ; year:2022 ; number:5 ; day:12 ; month:10 ; pages:5109-5138

Links:	Volltext

DOI / URN:	10.1007/s11227-022-04867-9

Katalog-ID:	SPR049362445

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520			\|a Abstract The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet.
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allfields	10.1007/s11227-022-04867-9 doi (DE-627)SPR049362445 (SPR)s11227-022-04867-9-e DE-627 ger DE-627 rakwb eng Gupta, Rohit Kumar verfasserin (orcid)0000-0002-1080-2651 aut Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications 2022 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 2022. Springer Nature or its licensor 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 The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. 5G/6G (dpeaa)DE-He213 Network slicing (dpeaa)DE-He213 Industrial internet of things (dpeaa)DE-He213 Generative adversarial network (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mahajan, Shashwat aut Misra, Rajiv aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 5 vom: 12. Okt., Seite 5109-5138 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:5 day:12 month:10 pages:5109-5138 https://dx.doi.org/10.1007/s11227-022-04867-9 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_101 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_206 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 79 2022 5 12 10 5109-5138
spelling	10.1007/s11227-022-04867-9 doi (DE-627)SPR049362445 (SPR)s11227-022-04867-9-e DE-627 ger DE-627 rakwb eng Gupta, Rohit Kumar verfasserin (orcid)0000-0002-1080-2651 aut Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications 2022 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 2022. Springer Nature or its licensor 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 The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. 5G/6G (dpeaa)DE-He213 Network slicing (dpeaa)DE-He213 Industrial internet of things (dpeaa)DE-He213 Generative adversarial network (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mahajan, Shashwat aut Misra, Rajiv aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 5 vom: 12. Okt., Seite 5109-5138 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:5 day:12 month:10 pages:5109-5138 https://dx.doi.org/10.1007/s11227-022-04867-9 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_101 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_206 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 79 2022 5 12 10 5109-5138
allfields_unstemmed	10.1007/s11227-022-04867-9 doi (DE-627)SPR049362445 (SPR)s11227-022-04867-9-e DE-627 ger DE-627 rakwb eng Gupta, Rohit Kumar verfasserin (orcid)0000-0002-1080-2651 aut Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications 2022 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 2022. Springer Nature or its licensor 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 The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. 5G/6G (dpeaa)DE-He213 Network slicing (dpeaa)DE-He213 Industrial internet of things (dpeaa)DE-He213 Generative adversarial network (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mahajan, Shashwat aut Misra, Rajiv aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 5 vom: 12. Okt., Seite 5109-5138 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:5 day:12 month:10 pages:5109-5138 https://dx.doi.org/10.1007/s11227-022-04867-9 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_101 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_206 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 79 2022 5 12 10 5109-5138
allfieldsGer	10.1007/s11227-022-04867-9 doi (DE-627)SPR049362445 (SPR)s11227-022-04867-9-e DE-627 ger DE-627 rakwb eng Gupta, Rohit Kumar verfasserin (orcid)0000-0002-1080-2651 aut Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications 2022 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 2022. Springer Nature or its licensor 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 The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. 5G/6G (dpeaa)DE-He213 Network slicing (dpeaa)DE-He213 Industrial internet of things (dpeaa)DE-He213 Generative adversarial network (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mahajan, Shashwat aut Misra, Rajiv aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 5 vom: 12. Okt., Seite 5109-5138 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:5 day:12 month:10 pages:5109-5138 https://dx.doi.org/10.1007/s11227-022-04867-9 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_101 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_206 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 79 2022 5 12 10 5109-5138
allfieldsSound	10.1007/s11227-022-04867-9 doi (DE-627)SPR049362445 (SPR)s11227-022-04867-9-e DE-627 ger DE-627 rakwb eng Gupta, Rohit Kumar verfasserin (orcid)0000-0002-1080-2651 aut Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications 2022 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 2022. Springer Nature or its licensor 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 The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. 5G/6G (dpeaa)DE-He213 Network slicing (dpeaa)DE-He213 Industrial internet of things (dpeaa)DE-He213 Generative adversarial network (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mahajan, Shashwat aut Misra, Rajiv aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2022), 5 vom: 12. Okt., Seite 5109-5138 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2022 number:5 day:12 month:10 pages:5109-5138 https://dx.doi.org/10.1007/s11227-022-04867-9 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_101 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_206 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 79 2022 5 12 10 5109-5138
language	English
source	Enthalten in The journal of supercomputing 79(2022), 5 vom: 12. Okt., Seite 5109-5138 volume:79 year:2022 number:5 day:12 month:10 pages:5109-5138
sourceStr	Enthalten in The journal of supercomputing 79(2022), 5 vom: 12. Okt., Seite 5109-5138 volume:79 year:2022 number:5 day:12 month:10 pages:5109-5138
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	5G/6G Network slicing Industrial internet of things Generative adversarial network Reinforcement learning
isfreeaccess_bool	false
container_title	The journal of supercomputing
authorswithroles_txt_mv	Gupta, Rohit Kumar @@aut@@ Mahajan, Shashwat @@aut@@ Misra, Rajiv @@aut@@
publishDateDaySort_date	2022-10-12T00:00:00Z
hierarchy_top_id	271350202
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author	Gupta, Rohit Kumar
spellingShingle	Gupta, Rohit Kumar misc 5G/6G misc Network slicing misc Industrial internet of things misc Generative adversarial network misc Reinforcement learning Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications
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topic_title	Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications 5G/6G (dpeaa)DE-He213 Network slicing (dpeaa)DE-He213 Industrial internet of things (dpeaa)DE-He213 Generative adversarial network (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213
topic	misc 5G/6G misc Network slicing misc Industrial internet of things misc Generative adversarial network misc Reinforcement learning
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title	Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications
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title_full	Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications
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title_sort	resource orchestration in network slicing using gan-based distributional deep q-network for industrial applications
title_auth	Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications
abstract	Abstract The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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 The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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 The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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	Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications
url	https://dx.doi.org/10.1007/s11227-022-04867-9
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author2	Mahajan, Shashwat Misra, Rajiv
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up_date	2024-07-04T00:30:23.467Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049362445</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510063400.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230218s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-022-04867-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049362445</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11227-022-04867-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gupta, Rohit Kumar</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-1080-2651</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The Industrial Internet of Things (IIoT) is an emerging and promising concept that allows intelligent manufacturing through the connectivity of 5G/6G and the interaction of industrial production units. The introduction of network slicing in 5G and beyond has made it possible to manage and allocate resources to various applications according to their requirements. In this paper, we study network slicing within a radio access network containing IIoT devices which include base stations that share the same physical infrastructure. We use deep reinforcement learning-based resource orchestration technique to achieve variable service demands of environment state-value and resource allocation as environment action-value. We describe the cognitive decision objectives to maximise the optimal policy for IIoT reward by achieving higher system throughput, spectral efficiency (SE), service level agreement (SLA), transmission packet rate with low power consumption and transmission delay. We use generative adversarial network-based deep distributional noisy Q-networks (GAN–NoisyNet) to learn the action-value distribution. Furthermore, we introduce dueling GAN–NoisyNet, which employs a duel generator that estimates the action advantage function and state-value distribution. Finally, we conduct extensive simulations to verify the performance of the proposed GAN–NoisyNet and dueling GAN–NoisyNet.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">5G/6G</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Network slicing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Industrial internet of things</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generative adversarial network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reinforcement learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mahajan, Shashwat</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Misra, Rajiv</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The journal of supercomputing</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987</subfield><subfield code="g">79(2022), 5 vom: 12. 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