Deep Reinforcement Learning-Based Network Slicing for Beyond 5G
With the advent of 5G era, network slicing has received a great deal of attention as a means to support a variety of wireless services in a flexible manner. Network slicing is a technique to divide a single physical resource network into multiple slices supporting independent services. In beyond 5G...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9676621/ |
| _version_ | 1828125743180152832 |
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| author | Kyungjoo Suh Sunwoo Kim Yongjun Ahn Seungnyun Kim Hyungyu Ju Byonghyo Shim |
| author_facet | Kyungjoo Suh Sunwoo Kim Yongjun Ahn Seungnyun Kim Hyungyu Ju Byonghyo Shim |
| author_sort | Kyungjoo Suh |
| collection | DOAJ |
| description | With the advent of 5G era, network slicing has received a great deal of attention as a means to support a variety of wireless services in a flexible manner. Network slicing is a technique to divide a single physical resource network into multiple slices supporting independent services. In beyond 5G (B5G) systems, the main goal of network slicing is to assign the physical resource blocks (RBs) such that the quality of service (QoS) requirements of eMBB, URLLC, and mMTC services are satisfied. Since the goal of each service category is dearly distinct and the computational burden caused by the increased number of time slots is huge, it is in general very difficult to assign RB properly. In this paper, we propose a deep reinforcement learning (DRL)-based network slicing technique to find out the resource allocation policy maximizing the long-term throughput while satisfying the QoS requirements in the B5G systems. Key ingredient of the proposed technique is to reduce the action space by eliminating undesirable actions that cannot satisfy the QoS requirements. Numerical results demonstrate that the proposed technique is effective in maximizing the long-term throughput and handling the coexistence of use cases in the B5G environments. |
| first_indexed | 2024-04-11T15:25:07Z |
| format | Article |
| id | doaj.art-ce41bfed1484424bb9de9f0dbcf1080c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T15:25:07Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ce41bfed1484424bb9de9f0dbcf1080c2022-12-22T04:16:17ZengIEEEIEEE Access2169-35362022-01-01107384739510.1109/ACCESS.2022.31417899676621Deep Reinforcement Learning-Based Network Slicing for Beyond 5GKyungjoo Suh0https://orcid.org/0000-0001-8054-5171Sunwoo Kim1https://orcid.org/0000-0003-2622-4136Yongjun Ahn2https://orcid.org/0000-0003-0914-9330Seungnyun Kim3https://orcid.org/0000-0001-6435-9029Hyungyu Ju4https://orcid.org/0000-0002-2841-3400Byonghyo Shim5https://orcid.org/0000-0001-5051-1763Department of Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul, South KoreaDepartment of Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul, South KoreaWith the advent of 5G era, network slicing has received a great deal of attention as a means to support a variety of wireless services in a flexible manner. Network slicing is a technique to divide a single physical resource network into multiple slices supporting independent services. In beyond 5G (B5G) systems, the main goal of network slicing is to assign the physical resource blocks (RBs) such that the quality of service (QoS) requirements of eMBB, URLLC, and mMTC services are satisfied. Since the goal of each service category is dearly distinct and the computational burden caused by the increased number of time slots is huge, it is in general very difficult to assign RB properly. In this paper, we propose a deep reinforcement learning (DRL)-based network slicing technique to find out the resource allocation policy maximizing the long-term throughput while satisfying the QoS requirements in the B5G systems. Key ingredient of the proposed technique is to reduce the action space by eliminating undesirable actions that cannot satisfy the QoS requirements. Numerical results demonstrate that the proposed technique is effective in maximizing the long-term throughput and handling the coexistence of use cases in the B5G environments.https://ieeexplore.ieee.org/document/9676621/Network slicingresource allocationdeep reinforcement learning |
| spellingShingle | Kyungjoo Suh Sunwoo Kim Yongjun Ahn Seungnyun Kim Hyungyu Ju Byonghyo Shim Deep Reinforcement Learning-Based Network Slicing for Beyond 5G IEEE Access Network slicing resource allocation deep reinforcement learning |
| title | Deep Reinforcement Learning-Based Network Slicing for Beyond 5G |
| title_full | Deep Reinforcement Learning-Based Network Slicing for Beyond 5G |
| title_fullStr | Deep Reinforcement Learning-Based Network Slicing for Beyond 5G |
| title_full_unstemmed | Deep Reinforcement Learning-Based Network Slicing for Beyond 5G |
| title_short | Deep Reinforcement Learning-Based Network Slicing for Beyond 5G |
| title_sort | deep reinforcement learning based network slicing for beyond 5g |
| topic | Network slicing resource allocation deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/9676621/ |
| work_keys_str_mv | AT kyungjoosuh deepreinforcementlearningbasednetworkslicingforbeyond5g AT sunwookim deepreinforcementlearningbasednetworkslicingforbeyond5g AT yongjunahn deepreinforcementlearningbasednetworkslicingforbeyond5g AT seungnyunkim deepreinforcementlearningbasednetworkslicingforbeyond5g AT hyungyuju deepreinforcementlearningbasednetworkslicingforbeyond5g AT byonghyoshim deepreinforcementlearningbasednetworkslicingforbeyond5g |