C-RAN中以网络为中心的聚类中断概率分析

Yidi Shao, Yixin Du, Yi Jiang, W. Huang, Kai Sun
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引用次数: 0

摘要

云无线接入网(C-RAN)中的BBU(基带单元)池实现了基带处理单元的集中部署,集中的信号处理促进了远程无线电头(rrh)之间CoMP (coordination multi- point)的实现。以网络为中心的聚类模式被认为是提高网络覆盖概率的有效解决方案。聚类大小和频率重用距离作为网络中可调节的参数,对提高服务质量起着重要作用。然而,由于无线网络的不确定性和由此产生的聚合干扰分布,对无线网络的性能评估是一个严峻的挑战。本文的目标是研究集群大小和频率重用距离对用户中断概率的影响。采用以网络为中心的六角形聚类方法,每个用户与集群中最近的RRH相关联。为了减轻簇内干扰,我们假设同一簇内的RRHs可以完美地获取信道状态信息(CSI),并且可以通过波束形成消除簇内干扰。通过将RRHs和用户建模为二项点过程(BPP),得到了停机概率的积分形式。通过蒙特卡罗仿真验证了所导出的停运概率表达式的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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