Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing
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 propos...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9078057/ |
| _version_ | 1818870177641529344 |
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| author | Fei Song Jun Li Chuan Ma Yijin Zhang Long Shi Dushantha Nalin K. Jayakody |
| author_facet | Fei Song Jun Li Chuan Ma Yijin Zhang Long Shi Dushantha Nalin K. Jayakody |
| author_sort | Fei Song |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T12:02:53Z |
| format | Article |
| id | doaj.art-6e3a63a925164cbe86c9031af24417b4 |
| institution | Directory Open Access Journal |
| issn | 2644-1330 |
| language | English |
| last_indexed | 2024-12-19T12:02:53Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj.art-6e3a63a925164cbe86c9031af24417b42022-12-21T20:22:25ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302020-01-01121522610.1109/OJVT.2020.29900729078057Dynamic Virtual Resource Allocation for 5G and Beyond Network SlicingFei Song0https://orcid.org/0000-0002-2040-2168Jun Li1https://orcid.org/0000-0002-1042-6297Chuan Ma2https://orcid.org/0000-0001-6198-9498Yijin Zhang3https://orcid.org/0000-0002-1491-209XLong Shi4https://orcid.org/0000-0001-6124-5173Dushantha Nalin K. Jayakody5https://orcid.org/0000-0002-7004-2930School of Electronic and Optical Engineering, Nanjing University of Science Technology, Nanjing, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science Technology, Nanjing, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science Technology, Nanjing, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science Technology, Nanjing, ChinaScience and Math Cluster, Singapore University of Technology and Design, Singapore, SingaporeSchool of Computer Science and Robotics, National Research Tomsk Polytechnic University, Tomsk, RussiaThe 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.https://ieeexplore.ieee.org/document/9078057/Network slicingRAN slicingconstrained Markov decision process (CMDP)resource allocation |
| spellingShingle | Fei Song Jun Li Chuan Ma Yijin Zhang Long Shi Dushantha Nalin K. Jayakody Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing IEEE Open Journal of Vehicular Technology Network slicing RAN slicing constrained Markov decision process (CMDP) resource allocation |
| title | Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing |
| title_full | Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing |
| title_fullStr | Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing |
| title_full_unstemmed | Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing |
| title_short | Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing |
| title_sort | dynamic virtual resource allocation for 5g and beyond network slicing |
| topic | Network slicing RAN slicing constrained Markov decision process (CMDP) resource allocation |
| url | https://ieeexplore.ieee.org/document/9078057/ |
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