AI Inspired Intelligent Resource Management in Future Wireless Network
In order to improve network performance, including reducing computation delay, transmission delay and bandwidth consumption, edge computing and caching technologies are introduced to the fifth-generation wireless network (5G). However, the volume of edge resources is limited, while the number and co...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8966360/ |
| _version_ | 1818665350460342272 |
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| author | Sibao Fu Fan Yang Ye Xiao |
| author_facet | Sibao Fu Fan Yang Ye Xiao |
| author_sort | Sibao Fu |
| collection | DOAJ |
| description | In order to improve network performance, including reducing computation delay, transmission delay and bandwidth consumption, edge computing and caching technologies are introduced to the fifth-generation wireless network (5G). However, the volume of edge resources is limited, while the number and complexity of tasks in the network are increasing sharply. Therefore, how to provide the most efficient service for network users with limited resources is an urgent problem to be solved. Thus, improving the utilization rate of communication, computing and caching resources in the network is an important issue. The diversification of network resources brings difficulties to network management. The joint resource allocation problem is difficult to be solved by traditional approaches. With the development of Artificial Intelligence (AI) technology, these AI algorithms have been applied to joint resource allocation problems to solve complex decision-making problems. In this paper, we first summarize the AI-based joint resources allocation schemes. Then, an AI-assisted intelligent wireless network architecture is proposed. Finally, based on the proposed architecture, we use deep Q-network (DQN) algorithm to figure out the complex and high-dimensional joint resource allocation problem. Simulation results show that the algorithm has good convergence characteristics, proposed architecture and the joint resource allocation scheme achieve better performance compared to other resource allocation schemes. |
| first_indexed | 2024-12-17T05:47:14Z |
| format | Article |
| id | doaj.art-326487cf90334a3fae23f8771d0517c7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:47:14Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-326487cf90334a3fae23f8771d0517c72022-12-21T22:01:16ZengIEEEIEEE Access2169-35362020-01-018224252243310.1109/ACCESS.2020.29685548966360AI Inspired Intelligent Resource Management in Future Wireless NetworkSibao Fu0https://orcid.org/0000-0002-4115-6357Fan Yang1https://orcid.org/0000-0002-8916-8605Ye Xiao2https://orcid.org/0000-0002-1419-0074School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Laboratory of Universal Wireless Communications Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaKey Laboratory of Universal Wireless Communications Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaIn order to improve network performance, including reducing computation delay, transmission delay and bandwidth consumption, edge computing and caching technologies are introduced to the fifth-generation wireless network (5G). However, the volume of edge resources is limited, while the number and complexity of tasks in the network are increasing sharply. Therefore, how to provide the most efficient service for network users with limited resources is an urgent problem to be solved. Thus, improving the utilization rate of communication, computing and caching resources in the network is an important issue. The diversification of network resources brings difficulties to network management. The joint resource allocation problem is difficult to be solved by traditional approaches. With the development of Artificial Intelligence (AI) technology, these AI algorithms have been applied to joint resource allocation problems to solve complex decision-making problems. In this paper, we first summarize the AI-based joint resources allocation schemes. Then, an AI-assisted intelligent wireless network architecture is proposed. Finally, based on the proposed architecture, we use deep Q-network (DQN) algorithm to figure out the complex and high-dimensional joint resource allocation problem. Simulation results show that the algorithm has good convergence characteristics, proposed architecture and the joint resource allocation scheme achieve better performance compared to other resource allocation schemes.https://ieeexplore.ieee.org/document/8966360/Artificial intelligencedeep Q-networkresource managementedge computing and cachingthe fifth-generation wireless network (5G) |
| spellingShingle | Sibao Fu Fan Yang Ye Xiao AI Inspired Intelligent Resource Management in Future Wireless Network IEEE Access Artificial intelligence deep Q-network resource management edge computing and caching the fifth-generation wireless network (5G) |
| title | AI Inspired Intelligent Resource Management in Future Wireless Network |
| title_full | AI Inspired Intelligent Resource Management in Future Wireless Network |
| title_fullStr | AI Inspired Intelligent Resource Management in Future Wireless Network |
| title_full_unstemmed | AI Inspired Intelligent Resource Management in Future Wireless Network |
| title_short | AI Inspired Intelligent Resource Management in Future Wireless Network |
| title_sort | ai inspired intelligent resource management in future wireless network |
| topic | Artificial intelligence deep Q-network resource management edge computing and caching the fifth-generation wireless network (5G) |
| url | https://ieeexplore.ieee.org/document/8966360/ |
| work_keys_str_mv | AT sibaofu aiinspiredintelligentresourcemanagementinfuturewirelessnetwork AT fanyang aiinspiredintelligentresourcemanagementinfuturewirelessnetwork AT yexiao aiinspiredintelligentresourcemanagementinfuturewirelessnetwork |