Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach
Abstract Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth determ...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-024-01111-9 |
| _version_ | 1827328900566351872 |
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| author | Fangqing Tan Quanxuan Deng Qiang Liu |
| author_facet | Fangqing Tan Quanxuan Deng Qiang Liu |
| author_sort | Fangqing Tan |
| collection | DOAJ |
| description | Abstract Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE. |
| first_indexed | 2024-03-07T15:25:03Z |
| format | Article |
| id | doaj.art-a3cef7a5be0b4d699f1686c623d6356e |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-03-07T15:25:03Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-a3cef7a5be0b4d699f1686c623d6356e2024-03-05T17:09:11ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802024-01-012024112510.1186/s13634-024-01111-9Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approachFangqing Tan0Quanxuan Deng1Qiang Liu2Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin University of Electronic TechnologyCollege of Electronic and Information Engineering, Shandong University of Science and TechnologyAbstract Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE.https://doi.org/10.1186/s13634-024-01111-9Cell-free massive MIMOAccess points clusteringPower allocationEnergy efficiency, hierarchical deep deterministic policy gradient |
| spellingShingle | Fangqing Tan Quanxuan Deng Qiang Liu Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach EURASIP Journal on Advances in Signal Processing Cell-free massive MIMO Access points clustering Power allocation Energy efficiency, hierarchical deep deterministic policy gradient |
| title | Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach |
| title_full | Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach |
| title_fullStr | Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach |
| title_full_unstemmed | Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach |
| title_short | Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach |
| title_sort | energy efficient access point clustering and power allocation in cell free massive mimo networks a hierarchical deep reinforcement learning approach |
| topic | Cell-free massive MIMO Access points clustering Power allocation Energy efficiency, hierarchical deep deterministic policy gradient |
| url | https://doi.org/10.1186/s13634-024-01111-9 |
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