Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing
| Parent link: | IEEE Open Journal of Vehicular Technology Vol. 1.— 2020.— [P. 215-226] |
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| Körperschaft: | |
| Weitere Verfasser: | , , , , , |
| Zusammenfassung: | Title screen The fifth generation and beyond wireless communication will support vastly heterogeneous services and user demands such as massive connection, low latency and high transmission rate. Network slicing has been envisaged as an efficient technology to meet these diverse demands. In this paper, we propose a dynamic virtual resources allocation scheme based on the radio access network (RAN) slicing for uplink communications to ensure the quality-of-service (QoS). To maximum the weighted-sum transmission rate performance under delay constraint, formulate a joint optimization problem of subchannel allocation and power control as an infinite-horizon average-reward constrained Markov decision process (CMDP) problem. Based on the equivalent Bellman equation, the optimal control policy is first derived by the value iteration algorithm. However, the optimal policy suffers from the widely known curse-of-dimensionality problem. To address this problem, the linear value function approximation (approximate dynamic programming) is adopted. Then, the subchannel allocation Q-factor is decomposed into the per-slice Q-factor. Furthermore, the Q-factor and Lagrangian multipliers are updated by the use of an online stochastic learning algorithm. Finally, simulation results reveal that the proposed algorithm can meet the delay requirements and improve the user transmission rate compared with baseline schemes. |
| Sprache: | Englisch |
| Veröffentlicht: |
2020
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.1109/OJVT.2020.2990072 |
| Format: | Elektronisch Buchkapitel |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665167 |
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| 200 | 1 | |a Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing |f Song Fey, Li Iyun, Ma Chuan [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 33 tit.] | ||
| 330 | |a The fifth generation and beyond wireless communication will support vastly heterogeneous services and user demands such as massive connection, low latency and high transmission rate. Network slicing has been envisaged as an efficient technology to meet these diverse demands. In this paper, we propose a dynamic virtual resources allocation scheme based on the radio access network (RAN) slicing for uplink communications to ensure the quality-of-service (QoS). To maximum the weighted-sum transmission rate performance under delay constraint, formulate a joint optimization problem of subchannel allocation and power control as an infinite-horizon average-reward constrained Markov decision process (CMDP) problem. Based on the equivalent Bellman equation, the optimal control policy is first derived by the value iteration algorithm. However, the optimal policy suffers from the widely known curse-of-dimensionality problem. To address this problem, the linear value function approximation (approximate dynamic programming) is adopted. Then, the subchannel allocation Q-factor is decomposed into the per-slice Q-factor. Furthermore, the Q-factor and Lagrangian multipliers are updated by the use of an online stochastic learning algorithm. Finally, simulation results reveal that the proposed algorithm can meet the delay requirements and improve the user transmission rate compared with baseline schemes. | ||
| 461 | |t IEEE Open Journal of Vehicular Technology | ||
| 463 | |t Vol. 1 |v [P. 215-226] |d 2020 | ||
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a network slicing | |
| 610 | 1 | |a RAN slicing | |
| 610 | 1 | |a constrained Markov decision process (CMDP) | |
| 610 | 1 | |a resource allocation | |
| 610 | 1 | |a сети | |
| 610 | 1 | |a марковский процесс | |
| 610 | 1 | |a принятие решений | |
| 610 | 1 | |a распределения | |
| 610 | 1 | |a ресурсы | |
| 610 | 1 | |a беспроводная связь | |
| 610 | 1 | |a динамическое программирование | |
| 610 | 1 | |a передача данных | |
| 610 | 1 | |a алгоритмы | |
| 701 | 0 | |a Song Fey | |
| 701 | 0 | |a Li Iyun | |
| 701 | 0 | |a Ma Chuan | |
| 701 | 0 | |a Chzhan Itszin | |
| 701 | 0 | |a Shi Long | |
| 701 | 1 | |a Dzhayakodi (Jayakody) Arachshiladzh |b D. N. K. |c specialist in the field of electronics |c Professor of Tomsk Polytechnic University |f 1983- |g Dushanta Nalin Kumara |3 (RuTPU)RU\TPU\pers\37962 |9 20606 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Научно-образовательный центр "Автоматизация и информационные технологии" |3 (RuTPU)RU\TPU\col\27515 |
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| 856 | 4 | 0 | |u https://doi.org/10.1109/OJVT.2020.2990072 |
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