{"title":"C-RAN中以网络为中心的聚类中断概率分析","authors":"Yidi Shao, Yixin Du, Yi Jiang, W. Huang, Kai Sun","doi":"10.1109/ICAICA52286.2021.9497983","DOIUrl":null,"url":null,"abstract":"The baseband unit (BBU) pool in the cloud radio access network (C-RAN) realizes the centralized deployment of baseband processing units, and the centralized signal processing promotes the implementation of coordination multi- point (CoMP) between remote radio heads (RRHs). Network- centric clustering mode is considered to be an effective solution to improve network coverage probability. As an adjustable parameter in the network, cluster size and frequency reuse distance play an important role in improving the quality of service. However, due to the uncertainty of wireless networks and the resulting aggregated interference distribution, it is a severe challenge to evaluate the performance of wireless networks. The goal of this paper is to study the impact of cluster size and frequency reuse distance on the outage probability of users. The network-centric hexagonal clustering method is adopted, in which each user is associated with the nearest RRH in the cluster. In order to mitigate the intra- cluster interference, we assume that the channel state information (CSI) can be perfectly obtained by the RRHs in the same cluster, and the intra-cluster interference can be eliminated by beamforming. By modeling RRHs and users as binomial point process (BPP), and the outage probability is obtained in integral form. The accuracy of the derived outage probability expression is verified by Monte Carlo simulation.","PeriodicalId":121979,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Outage Probability Analysis of Network-Centric Clustering in C-RAN\",\"authors\":\"Yidi Shao, Yixin Du, Yi Jiang, W. Huang, Kai Sun\",\"doi\":\"10.1109/ICAICA52286.2021.9497983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The baseband unit (BBU) pool in the cloud radio access network (C-RAN) realizes the centralized deployment of baseband processing units, and the centralized signal processing promotes the implementation of coordination multi- point (CoMP) between remote radio heads (RRHs). Network- centric clustering mode is considered to be an effective solution to improve network coverage probability. As an adjustable parameter in the network, cluster size and frequency reuse distance play an important role in improving the quality of service. However, due to the uncertainty of wireless networks and the resulting aggregated interference distribution, it is a severe challenge to evaluate the performance of wireless networks. The goal of this paper is to study the impact of cluster size and frequency reuse distance on the outage probability of users. The network-centric hexagonal clustering method is adopted, in which each user is associated with the nearest RRH in the cluster. In order to mitigate the intra- cluster interference, we assume that the channel state information (CSI) can be perfectly obtained by the RRHs in the same cluster, and the intra-cluster interference can be eliminated by beamforming. By modeling RRHs and users as binomial point process (BPP), and the outage probability is obtained in integral form. The accuracy of the derived outage probability expression is verified by Monte Carlo simulation.\",\"PeriodicalId\":121979,\"journal\":{\"name\":\"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAICA52286.2021.9497983\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAICA52286.2021.9497983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Outage Probability Analysis of Network-Centric Clustering in C-RAN
The baseband unit (BBU) pool in the cloud radio access network (C-RAN) realizes the centralized deployment of baseband processing units, and the centralized signal processing promotes the implementation of coordination multi- point (CoMP) between remote radio heads (RRHs). Network- centric clustering mode is considered to be an effective solution to improve network coverage probability. As an adjustable parameter in the network, cluster size and frequency reuse distance play an important role in improving the quality of service. However, due to the uncertainty of wireless networks and the resulting aggregated interference distribution, it is a severe challenge to evaluate the performance of wireless networks. The goal of this paper is to study the impact of cluster size and frequency reuse distance on the outage probability of users. The network-centric hexagonal clustering method is adopted, in which each user is associated with the nearest RRH in the cluster. In order to mitigate the intra- cluster interference, we assume that the channel state information (CSI) can be perfectly obtained by the RRHs in the same cluster, and the intra-cluster interference can be eliminated by beamforming. By modeling RRHs and users as binomial point process (BPP), and the outage probability is obtained in integral form. The accuracy of the derived outage probability expression is verified by Monte Carlo simulation.