Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement Learning
Device-to-device (D2D) communication is a promising technology in fifth-generation (5G) wireless networks, offering enhanced system capacity, spectrum performance, and energy efficiency. However, D2D links can introduce interference with cellular links, posing challenges in spectrum allocation and n...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2024/2780845 |
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| author | Kwame Opuni-Boachie Obour Agyekum Alex Yaw Boakye Benedict Appati Jochebed Akoto Opoku Justice Owusu Agyemang Gordon Owusu Boateng James Dzisi Gadze |
| author_facet | Kwame Opuni-Boachie Obour Agyekum Alex Yaw Boakye Benedict Appati Jochebed Akoto Opoku Justice Owusu Agyemang Gordon Owusu Boateng James Dzisi Gadze |
| author_sort | Kwame Opuni-Boachie Obour Agyekum |
| collection | DOAJ |
| description | Device-to-device (D2D) communication is a promising technology in fifth-generation (5G) wireless networks, offering enhanced system capacity, spectrum performance, and energy efficiency. However, D2D links can introduce interference with cellular links, posing challenges in spectrum allocation and network quality assurance. This paper presents a novel approach using multiagent reinforcement learning with a proximal policy optimization algorithm to address the resource allocation problem in D2D networks. The proposed algorithm aims to optimize overall throughput and maximize the signal-to-interference noise ratio (SINR) while ensuring low computational complexity. The study introduces the following two key techniques: staggered training and decentralized execution. Staggered training improves agent performance and minimizes computational complexity by training agents one at a time in a sequential manner. This allows agents to learn from each other’s mistakes and avoid local minima. Decentralized execution enhances scalability and system robustness by enabling agents to learn and act independently without relying on communication with other agents. In the event of agent failure, the remaining agents can continue operating. The findings of this work demonstrate a significant improvement in energy efficiency (EE) and an enhancement in the quality of service (QoS) of the network. Overall, the algorithm proves to be a promising solution for resource allocation in multiagent D2D networks, offering notable improvements in EE and QoS while maintaining scalability for large networks. |
| format | Article |
| id | doaj-art-047cdddcd7b749849078baa6b2ad3632 |
| institution | Kabale University |
| issn | 2090-715X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-047cdddcd7b749849078baa6b2ad36322025-02-03T07:23:26ZengWileyJournal of Computer Networks and Communications2090-715X2024-01-01202410.1155/2024/2780845Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement LearningKwame Opuni-Boachie Obour Agyekum0Alex Yaw Boakye1Benedict Appati2Jochebed Akoto Opoku3Justice Owusu Agyemang4Gordon Owusu Boateng5James Dzisi Gadze6Department of Telecommunication EngineeringDepartment of Telecommunication EngineeringDepartment of Telecommunication EngineeringDepartment of Telecommunication EngineeringDepartment of Telecommunication EngineeringSchool of Computer Science and EngineeringDepartment of Telecommunication EngineeringDevice-to-device (D2D) communication is a promising technology in fifth-generation (5G) wireless networks, offering enhanced system capacity, spectrum performance, and energy efficiency. However, D2D links can introduce interference with cellular links, posing challenges in spectrum allocation and network quality assurance. This paper presents a novel approach using multiagent reinforcement learning with a proximal policy optimization algorithm to address the resource allocation problem in D2D networks. The proposed algorithm aims to optimize overall throughput and maximize the signal-to-interference noise ratio (SINR) while ensuring low computational complexity. The study introduces the following two key techniques: staggered training and decentralized execution. Staggered training improves agent performance and minimizes computational complexity by training agents one at a time in a sequential manner. This allows agents to learn from each other’s mistakes and avoid local minima. Decentralized execution enhances scalability and system robustness by enabling agents to learn and act independently without relying on communication with other agents. In the event of agent failure, the remaining agents can continue operating. The findings of this work demonstrate a significant improvement in energy efficiency (EE) and an enhancement in the quality of service (QoS) of the network. Overall, the algorithm proves to be a promising solution for resource allocation in multiagent D2D networks, offering notable improvements in EE and QoS while maintaining scalability for large networks.http://dx.doi.org/10.1155/2024/2780845 |
| spellingShingle | Kwame Opuni-Boachie Obour Agyekum Alex Yaw Boakye Benedict Appati Jochebed Akoto Opoku Justice Owusu Agyemang Gordon Owusu Boateng James Dzisi Gadze Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement Learning Journal of Computer Networks and Communications |
| title | Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement Learning |
| title_full | Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement Learning |
| title_fullStr | Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement Learning |
| title_full_unstemmed | Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement Learning |
| title_short | Resource Allocation in D2D-Enabled 5G Networks Using Multiagent Reinforcement Learning |
| title_sort | resource allocation in d2d enabled 5g networks using multiagent reinforcement learning |
| url | http://dx.doi.org/10.1155/2024/2780845 |
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