Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning
Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this inter...
| Main Authors: | , , , , , |
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/15/6822 |
| _version_ | 1827730869677195264 |
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| author | Chongli Zhang Tiejun Lv Pingmu Huang Zhipeng Lin Jie Zeng Yuan Ren |
| author_facet | Chongli Zhang Tiejun Lv Pingmu Huang Zhipeng Lin Jie Zeng Yuan Ren |
| author_sort | Chongli Zhang |
| collection | DOAJ |
| description | Wireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4–15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm. |
| first_indexed | 2024-03-11T00:16:40Z |
| format | Article |
| id | doaj.art-9c94b70d06cb48ceb59a0c2b6377eb85 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:16:40Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9c94b70d06cb48ceb59a0c2b6377eb852023-11-18T23:34:56ZengMDPI AGSensors1424-82202023-07-012315682210.3390/s23156822Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement LearningChongli Zhang0Tiejun Lv1Pingmu Huang2Zhipeng Lin3Jie Zeng4Yuan Ren5School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, ChinaSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, ChinaKey Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, ChinaSchool of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaShaanxi Key Laboratory of Information Communication Network and Security, School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, ChinaWireless resource utilizations are the focus of future communication, which are used constantly to alleviate the communication quality problem caused by the explosive interference with increasing users, especially the inter-cell interference in the multi-cell multi-user systems. To tackle this interference and improve the resource utilization rate, we proposed a joint-priority-based reinforcement learning (JPRL) approach to jointly optimize the bandwidth and transmit power allocation. This method aims to maximize the average throughput of the system while suppressing the co-channel interference and guaranteeing the quality of service (QoS) constraint. Specifically, we de-coupled the joint problem into two sub-problems, i.e., the bandwidth assignment and power allocation sub-problems. The multi-agent double deep Q network (MADDQN) was developed to solve the bandwidth allocation sub-problem for each user and the prioritized multi-agent deep deterministic policy gradient (P-MADDPG) algorithm by deploying a prioritized replay buffer that is designed to handle the transmit power allocation sub-problem. Numerical results show that the proposed JPRL method could accelerate model training and outperform the alternative methods in terms of throughput. For example, the average throughput was approximately 10.4–15.5% better than the homogeneous-learning-based benchmarks, and about 17.3% higher than the genetic algorithm.https://www.mdpi.com/1424-8220/23/15/6822uplinkmulti-cell multi-user systemjoint-priority-based reinforcement learning (JPRL)prioritized replay bufferthroughput |
| spellingShingle | Chongli Zhang Tiejun Lv Pingmu Huang Zhipeng Lin Jie Zeng Yuan Ren Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning Sensors uplink multi-cell multi-user system joint-priority-based reinforcement learning (JPRL) prioritized replay buffer throughput |
| title | Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning |
| title_full | Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning |
| title_fullStr | Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning |
| title_full_unstemmed | Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning |
| title_short | Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning |
| title_sort | joint optimization of bandwidth and power allocation in uplink systems with deep reinforcement learning |
| topic | uplink multi-cell multi-user system joint-priority-based reinforcement learning (JPRL) prioritized replay buffer throughput |
| url | https://www.mdpi.com/1424-8220/23/15/6822 |
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