企业网络规划框架
A Framework for Enterprise Network Dimensioning
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中文总结 AI 辅助
该研究针对室内企业网络的无线节点部署问题，结合随机几何、优化与聚类方法，提出加权k-调和均值聚类策略及\texttt{SeqMinCut}算法，为企业5G规划提供了系统设计规则与策略。
中文摘要 AI 辅助
我们研究室内企业网络的无线节点（RN）部署问题。利用随机几何（SG），我们推导了测试用户设备（UE）的信干噪比（SINR）的元分布（MD），分别考虑有无室外宏基站（MBS）协作的情况，并将这些结果与整数线性规划（ILP）方法进行比较。SG可估算所需RN数量，但无法确定其位置；ILP可能产生不准确的局部最优解且计算开销大。为解决该问题，我们利用UE位置分布研究基于聚类的算法来初始化RN位置。除标准方法外，我们提出一种加权k-调和均值（WKHM）聚类策略，专门用于最大化SINR。随后，我们引入带约束的顺序最小割算法\texttt{SeqMinCut}，将多个RN合并为更大的小区，进一步提升SINR。这是首个结合基于SG的统计分析、优化和聚类，以获取企业5G系统设计见解、规划规则和部署策略的研究工作。
英文摘要
We study radio node (RN) placement for indoor enterprise networks. Using stochastic geometry (SG), we derive the meta-distribution (MD) of the SINR for a test user equipment (UE), with and without cooperation from outdoor macro base stations (MBSs), and compare these results with an integer linear programming (ILP) approach. SG provides an estimate of the required number of RNs but not their locations, while ILP can yield inaccurate local optima and requires high computational power. To address this, we investigate clustering-based algorithms for initializing RN locations using UE location distributions. Along with standard methods, we propose a weighted $k$-harmonic means (WKHM) clustering strategy tailored to maximize SINR. We then introduce a constrained sequential minimum cut algorithm, \texttt{SeqMinCut}, to merge multiple RNs into larger cells and further improve SINR. This is the first work that integrates SG-based statistical analysis, optimization, and clustering to obtain system design insights, dimensioning rules, and planning strategies for enterprise 5G.
