@article{oai:muroran-it.repo.nii.ac.jp:00010380,
 author = {ZHANG, Chaofeng and DONG,  Mianxiong and 董, 冕雄 and OTA,  Kaoru and 太田, 香},
 issue = {2},
 journal = {IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING},
 month = {},
 note = {application/pdf, Recently, the installation of 5G networks offers a variety of real-time, high-performance and human-oriented customized services. However, the current laying 5G structure is unable to meet all of the growing communication needs by these new emerging services. In this paper, we propose a DQL (Deep Q-learning Network) based intelligent resource management method for 5G architecture, to improve the quality of service (QoS) under limited communication resources. In the environment of network function virtualization (NFV), we aim at improving the efficient usage of spectrum resources. In this two-step solution, our first goal is to guarantee the maximum communication quality with the smallest number of infrastructures. Then, a DQL-based wireless resource allocation algorithm is designed to realize the elaborate operation. Unlike previous studies, our system can provide the allocation policy in a more subdivided way and finally maximize the usage of bandwidth resources. The simulation also shows that our proposed MSIO improves 3.12% in the performance of the maximum coverage importance problem and the ARODQ algorithm improves 4.05% than other standard solutions.},
 pages = {428--435},
 title = {Fine-Grained Management in 5G: DQL Based Intelligent Resource Allocation for Network Function Virtualization in C-RAN},
 volume = {6},
 year = {2020},
 yomi = {トウ, メンユウ and オオタ, カオル}
}