A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks
The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming c...
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10247079/ |
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| author | Qing An Santiago Segarra Chris Dick Ashutosh Sabharwal Rahman Doost-Mohammady |
| author_facet | Qing An Santiago Segarra Chris Dick Ashutosh Sabharwal Rahman Doost-Mohammady |
| author_sort | Qing An |
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| description | The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks. |
| format | Article |
| id | doaj-art-a28b43502cb64b1da1646f37d6b88e1b |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-a28b43502cb64b1da1646f37d6b88e1b2025-08-20T02:53:07ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2023-01-01124225710.1109/TMLCN.2023.331398810247079A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO NetworksQing An0https://orcid.org/0009-0005-9070-9033Santiago Segarra1https://orcid.org/0000-0002-8408-9633Chris Dick2Ashutosh Sabharwal3https://orcid.org/0000-0003-1898-5787Rahman Doost-Mohammady4https://orcid.org/0000-0002-6963-033XDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USADepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USANVIDIA, Santa Clara, CA, USADepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USADepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USAThe large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks.https://ieeexplore.ieee.org/document/10247079/Massive MIMOresource schedulingdeep reinforcement learning |
| spellingShingle | Qing An Santiago Segarra Chris Dick Ashutosh Sabharwal Rahman Doost-Mohammady A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks IEEE Transactions on Machine Learning in Communications and Networking Massive MIMO resource scheduling deep reinforcement learning |
| title | A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks |
| title_full | A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks |
| title_fullStr | A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks |
| title_full_unstemmed | A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks |
| title_short | A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks |
| title_sort | deep reinforcement learning based resource scheduler for massive mimo networks |
| topic | Massive MIMO resource scheduling deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10247079/ |
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