Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning
Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performanc...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2733 |
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| author | Ming Sun Yihe Zhong Xiaoou He Jie Zhang |
| author_facet | Ming Sun Yihe Zhong Xiaoou He Jie Zhang |
| author_sort | Ming Sun |
| collection | DOAJ |
| description | Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performance of the multi-cell NOMA system. In addition, due to the complex and dynamically changing wireless communication environment and the lack of the near-optimal labels, conventional supervised learning methods cannot be directly applied. To address these challenges, this paper proposes a framework of MDRL-UL that integrates the multi-agent deep reinforcement learning with the unsupervised learning to allocate the channel and power resources in a near-optimal manner. In the framework, a multi-agent deep reinforcement learning neural network (MDRLNN) is proposed for channel allocation, while an attention-based unsupervised learning neural network (ULNN) is proposed for power allocation. Furthermore, the joint action (JA) derived from the MDRLNN for channel allocation is used as a representation to be fed into the ULNN for power allocation. In order to maximize the energy efficiency of the multi-cell NOMA system, the expectation of the energy efficiency is used to train both the MDRLNN and the ULNN. Simulation results indicate that the proposed MDRL-UL can achieve higher energy efficiency and transmission rates than other algorithms. |
| format | Article |
| id | doaj-art-20a343e892df4941922aec50bc84d31d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-20a343e892df4941922aec50bc84d31d2025-08-20T03:52:57ZengMDPI AGSensors1424-82202025-04-01259273310.3390/s25092733Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised LearningMing Sun0Yihe Zhong1Xiaoou He2Jie Zhang3College of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, ChinaCollege of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, ChinaCollege of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, ChinaCollege of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, ChinaAmong the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performance of the multi-cell NOMA system. In addition, due to the complex and dynamically changing wireless communication environment and the lack of the near-optimal labels, conventional supervised learning methods cannot be directly applied. To address these challenges, this paper proposes a framework of MDRL-UL that integrates the multi-agent deep reinforcement learning with the unsupervised learning to allocate the channel and power resources in a near-optimal manner. In the framework, a multi-agent deep reinforcement learning neural network (MDRLNN) is proposed for channel allocation, while an attention-based unsupervised learning neural network (ULNN) is proposed for power allocation. Furthermore, the joint action (JA) derived from the MDRLNN for channel allocation is used as a representation to be fed into the ULNN for power allocation. In order to maximize the energy efficiency of the multi-cell NOMA system, the expectation of the energy efficiency is used to train both the MDRLNN and the ULNN. Simulation results indicate that the proposed MDRL-UL can achieve higher energy efficiency and transmission rates than other algorithms.https://www.mdpi.com/1424-8220/25/9/2733non-orthogonal multiple access (NOMA)channel allocationpower allocationmulti-agent deep reinforcement learningunsupervised learningattention mechanism |
| spellingShingle | Ming Sun Yihe Zhong Xiaoou He Jie Zhang Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning Sensors non-orthogonal multiple access (NOMA) channel allocation power allocation multi-agent deep reinforcement learning unsupervised learning attention mechanism |
| title | Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning |
| title_full | Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning |
| title_fullStr | Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning |
| title_full_unstemmed | Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning |
| title_short | Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning |
| title_sort | channel and power allocation for multi cell noma using multi agent deep reinforcement learning and unsupervised learning |
| topic | non-orthogonal multiple access (NOMA) channel allocation power allocation multi-agent deep reinforcement learning unsupervised learning attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/9/2733 |
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