Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks
In a multi-cell network, interference management between adjacent cells is a key factor that determines the performance of the entire cellular network. In particular, in order to control inter-cell interference while providing a high data rate to users, it is very important for the base station (BS)...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/4017 |
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| author | Hun Kim Jaewoo So |
| author_facet | Hun Kim Jaewoo So |
| author_sort | Hun Kim |
| collection | DOAJ |
| description | In a multi-cell network, interference management between adjacent cells is a key factor that determines the performance of the entire cellular network. In particular, in order to control inter-cell interference while providing a high data rate to users, it is very important for the base station (BS) of each cell to appropriately control the transmit power in the downlink. However, as the number of cells increases, controlling the downlink transmit power at the BS becomes increasingly difficult. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based transmit power control scheme to maximize the sum rate in multi-cell networks. In particular, the proposed scheme incorporates a long short-term memory (LSTM) architecture into the MADRL scheme to retain state information across time slots and to use that information for subsequent action decisions, thereby improving the sum rate performance. In the proposed scheme, the agent of each BS uses only its local channel state information; consequently, it does not need to receive signal messages from adjacent agents. The simulation results show that the proposed scheme outperforms the existing MADRL scheme by reducing the amount of signal messages exchanged between links and improving the sum rate. |
| format | Article |
| id | doaj-art-00734044ade64bb499a5cd07a2a082c0 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-00734044ade64bb499a5cd07a2a082c02025-08-20T03:50:17ZengMDPI AGSensors1424-82202025-06-012513401710.3390/s25134017Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular NetworksHun Kim0Jaewoo So1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of KoreaDepartment of Electronic Engineering, Sogang University, Seoul 04107, Republic of KoreaIn a multi-cell network, interference management between adjacent cells is a key factor that determines the performance of the entire cellular network. In particular, in order to control inter-cell interference while providing a high data rate to users, it is very important for the base station (BS) of each cell to appropriately control the transmit power in the downlink. However, as the number of cells increases, controlling the downlink transmit power at the BS becomes increasingly difficult. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based transmit power control scheme to maximize the sum rate in multi-cell networks. In particular, the proposed scheme incorporates a long short-term memory (LSTM) architecture into the MADRL scheme to retain state information across time slots and to use that information for subsequent action decisions, thereby improving the sum rate performance. In the proposed scheme, the agent of each BS uses only its local channel state information; consequently, it does not need to receive signal messages from adjacent agents. The simulation results show that the proposed scheme outperforms the existing MADRL scheme by reducing the amount of signal messages exchanged between links and improving the sum rate.https://www.mdpi.com/1424-8220/25/13/4017multi-cell networkmulti-agent deep reinforcement learningcentralized training with decentralized executiontransmit power control |
| spellingShingle | Hun Kim Jaewoo So Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks Sensors multi-cell network multi-agent deep reinforcement learning centralized training with decentralized execution transmit power control |
| title | Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks |
| title_full | Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks |
| title_fullStr | Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks |
| title_full_unstemmed | Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks |
| title_short | Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks |
| title_sort | distributed multi agent deep reinforcement learning based transmit power control in cellular networks |
| topic | multi-cell network multi-agent deep reinforcement learning centralized training with decentralized execution transmit power control |
| url | https://www.mdpi.com/1424-8220/25/13/4017 |
| work_keys_str_mv | AT hunkim distributedmultiagentdeepreinforcementlearningbasedtransmitpowercontrolincellularnetworks AT jaewooso distributedmultiagentdeepreinforcementlearningbasedtransmitpowercontrolincellularnetworks |