Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO
Network slicing is a critical technology for fifth-generation (5G) networks, owing to its merits in meeting the diversified requirements of users. Effective resource allocation for network slicing in Radio Access Networks (RAN) is still challenging owing to dynamic service requirements. Therein, aut...
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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/10186882/ |
| _version_ | 1797768419698802688 |
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| author | Dandan Yan Benjamin K. Ng Wei Ke Chan-Tong Lam |
| author_facet | Dandan Yan Benjamin K. Ng Wei Ke Chan-Tong Lam |
| author_sort | Dandan Yan |
| collection | DOAJ |
| description | Network slicing is a critical technology for fifth-generation (5G) networks, owing to its merits in meeting the diversified requirements of users. Effective resource allocation for network slicing in Radio Access Networks (RAN) is still challenging owing to dynamic service requirements. Therein, automatic resource allocation based on environmental changes is of significant importance for network slicing. In this study, we used deep reinforcement learning (DRL) to allocate resources for network slicing in a RAN with the aid of massive multiple-input multiple-output (MIMO). The DRL agent interacts with the environment to execute autonomous resource allocation. We considered a two-level scheduling framework that aims to maximize the quality of experience (QoE) and spectrum efficiency (SE) of slices. The proposed algorithm can find a near-optimal solution. We used the standard DRL advantage actor-critic (A2C) algorithm to implement upper-level inter-slice bandwidth resource allocation that considers service traffic dynamics in a large timescale. Lower-level scheduling is a mixed-integer stochastic optimization problem with several constraints. We combined the proportional fair scheduling algorithm and the water filling algorithm to perform resource block (RB) and power allocation in a small timescale. The results show that the QoE and SE of all slices using the A2C algorithm achieved a significant performance improvement over the other algorithms. The efficiency of the proposed method was supported by the simulation results. |
| first_indexed | 2024-03-12T20:53:56Z |
| format | Article |
| id | doaj.art-8aa126b2107e4832a40703ddd8952566 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T20:53:56Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8aa126b2107e4832a40703ddd89525662023-07-31T23:00:43ZengIEEEIEEE Access2169-35362023-01-0111758997591110.1109/ACCESS.2023.329685110186882Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMODandan Yan0https://orcid.org/0009-0008-2220-0085Benjamin K. Ng1https://orcid.org/0000-0001-5901-5694Wei Ke2https://orcid.org/0000-0003-0952-0961Chan-Tong Lam3https://orcid.org/0000-0002-8022-7744Faculty of Applied Sciences, Macao Polytechnic University, Macau, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macau, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macau, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macau, ChinaNetwork slicing is a critical technology for fifth-generation (5G) networks, owing to its merits in meeting the diversified requirements of users. Effective resource allocation for network slicing in Radio Access Networks (RAN) is still challenging owing to dynamic service requirements. Therein, automatic resource allocation based on environmental changes is of significant importance for network slicing. In this study, we used deep reinforcement learning (DRL) to allocate resources for network slicing in a RAN with the aid of massive multiple-input multiple-output (MIMO). The DRL agent interacts with the environment to execute autonomous resource allocation. We considered a two-level scheduling framework that aims to maximize the quality of experience (QoE) and spectrum efficiency (SE) of slices. The proposed algorithm can find a near-optimal solution. We used the standard DRL advantage actor-critic (A2C) algorithm to implement upper-level inter-slice bandwidth resource allocation that considers service traffic dynamics in a large timescale. Lower-level scheduling is a mixed-integer stochastic optimization problem with several constraints. We combined the proportional fair scheduling algorithm and the water filling algorithm to perform resource block (RB) and power allocation in a small timescale. The results show that the QoE and SE of all slices using the A2C algorithm achieved a significant performance improvement over the other algorithms. The efficiency of the proposed method was supported by the simulation results.https://ieeexplore.ieee.org/document/10186882/Network slicingresource allocationradio access networks (RAN)massive MIMOadvantage actor critic (A2C) |
| spellingShingle | Dandan Yan Benjamin K. Ng Wei Ke Chan-Tong Lam Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO IEEE Access Network slicing resource allocation radio access networks (RAN) massive MIMO advantage actor critic (A2C) |
| title | Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO |
| title_full | Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO |
| title_fullStr | Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO |
| title_full_unstemmed | Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO |
| title_short | Deep Reinforcement Learning Based Resource Allocation for Network Slicing With Massive MIMO |
| title_sort | deep reinforcement learning based resource allocation for network slicing with massive mimo |
| topic | Network slicing resource allocation radio access networks (RAN) massive MIMO advantage actor critic (A2C) |
| url | https://ieeexplore.ieee.org/document/10186882/ |
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