Toward an Efficient and Dynamic Allocation of Radio Access Network Slicing Resources for 5G Era
With the development of 5G technology and Internet of Things (IoT), more and more devices are connected through 5G wirelessly. Radio access network (RAN) slicing, as a key feature of 5G, enables a flexible bandwidth resource allocation policy, and facilitates various types of services to operate on...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10232995/ |
| _version_ | 1797688494087208960 |
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| author | Xiaolei Chang Tian Ji Runsu Zhu Zhenzhou Wu Chenxi Li Yong Jiang |
| author_facet | Xiaolei Chang Tian Ji Runsu Zhu Zhenzhou Wu Chenxi Li Yong Jiang |
| author_sort | Xiaolei Chang |
| collection | DOAJ |
| description | With the development of 5G technology and Internet of Things (IoT), more and more devices are connected through 5G wirelessly. Radio access network (RAN) slicing, as a key feature of 5G, enables a flexible bandwidth resource allocation policy, and facilitates various types of services to operate on different network slices. However, RAN slicing resources is scarce, thus effective management of wireless bandwidth resources in RAN slicing becomes indispensable to improve user satisfaction. Extensive research has investigated into RAN slicing, but they do not take user mobility into consideration. While RAN slicing allocation has a great impact on user experience in mobile 5G scenarios, user mobility poses great challenges to network management and causing unsatisfaction of users. In this paper, we propose a new RAN slicing allocation strategy based on machine learning, to maximize spectrum efficiency while guaranteeing the Service Satisfaction Ratio (SSR) of various slicing services. To further alleviate the SSR fluctuation brought by user mobility, we study into the temporal characteristics of user mobility and preprocess the state sequences using Long Short-Term Memory (LSTM) networks. Finally, these sequences are taken as the input of an Advantage Actor Critic (A2C) reinforcement learning network to develop a RAN slicing allocation policy. We conduct comprehensive simulations, and the results show that the performance of the proposed mechanism outperforms the traditional mechanism in ensuring SSR and enhancing the spectrum efficiency. |
| first_indexed | 2024-03-12T01:32:46Z |
| format | Article |
| id | doaj.art-f3970d6fe00d414c9480c11e33cdc5be |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T01:32:46Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f3970d6fe00d414c9480c11e33cdc5be2023-09-11T23:02:47ZengIEEEIEEE Access2169-35362023-01-0111950379505010.1109/ACCESS.2023.330929410232995Toward an Efficient and Dynamic Allocation of Radio Access Network Slicing Resources for 5G EraXiaolei Chang0https://orcid.org/0009-0000-2740-6252Tian Ji1Runsu Zhu2Zhenzhou Wu3https://orcid.org/0009-0009-2721-0183Chenxi Li4https://orcid.org/0009-0006-3459-104XYong Jiang5https://orcid.org/0000-0002-4260-1395Tsinghua University, Beijing, ChinaTsinghua University, Beijing, ChinaLonghua District Government Service Data Administration, Shenzhen, ChinaResearch Institute of Tsinghua University in Shenzhen, Shenzhen, ChinaResearch Institute of Tsinghua University in Shenzhen, Shenzhen, ChinaTsinghua Shenzhen International Graduate School, Shenzhen, ChinaWith the development of 5G technology and Internet of Things (IoT), more and more devices are connected through 5G wirelessly. Radio access network (RAN) slicing, as a key feature of 5G, enables a flexible bandwidth resource allocation policy, and facilitates various types of services to operate on different network slices. However, RAN slicing resources is scarce, thus effective management of wireless bandwidth resources in RAN slicing becomes indispensable to improve user satisfaction. Extensive research has investigated into RAN slicing, but they do not take user mobility into consideration. While RAN slicing allocation has a great impact on user experience in mobile 5G scenarios, user mobility poses great challenges to network management and causing unsatisfaction of users. In this paper, we propose a new RAN slicing allocation strategy based on machine learning, to maximize spectrum efficiency while guaranteeing the Service Satisfaction Ratio (SSR) of various slicing services. To further alleviate the SSR fluctuation brought by user mobility, we study into the temporal characteristics of user mobility and preprocess the state sequences using Long Short-Term Memory (LSTM) networks. Finally, these sequences are taken as the input of an Advantage Actor Critic (A2C) reinforcement learning network to develop a RAN slicing allocation policy. We conduct comprehensive simulations, and the results show that the performance of the proposed mechanism outperforms the traditional mechanism in ensuring SSR and enhancing the spectrum efficiency.https://ieeexplore.ieee.org/document/10232995/Internet of Thingsnetwork slicingreinforcement learningradio access networks |
| spellingShingle | Xiaolei Chang Tian Ji Runsu Zhu Zhenzhou Wu Chenxi Li Yong Jiang Toward an Efficient and Dynamic Allocation of Radio Access Network Slicing Resources for 5G Era IEEE Access Internet of Things network slicing reinforcement learning radio access networks |
| title | Toward an Efficient and Dynamic Allocation of Radio Access Network Slicing Resources for 5G Era |
| title_full | Toward an Efficient and Dynamic Allocation of Radio Access Network Slicing Resources for 5G Era |
| title_fullStr | Toward an Efficient and Dynamic Allocation of Radio Access Network Slicing Resources for 5G Era |
| title_full_unstemmed | Toward an Efficient and Dynamic Allocation of Radio Access Network Slicing Resources for 5G Era |
| title_short | Toward an Efficient and Dynamic Allocation of Radio Access Network Slicing Resources for 5G Era |
| title_sort | toward an efficient and dynamic allocation of radio access network slicing resources for 5g era |
| topic | Internet of Things network slicing reinforcement learning radio access networks |
| url | https://ieeexplore.ieee.org/document/10232995/ |
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