In view of the wireless channel complexity in refuge chamber environments, we parameterize the channel characteristics for reliable signal propagation under line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. According to the Saleh-Valenzuela channel model, the multipath components (MPCs) arrive at the receiver in cluster form. Therefore, clustering algorithms are required to identify clusters. However, the drawback of conventional KMeans and KPowerMeans algorithms is their limitation to an a priori cluster number. In this paper, we propose a clustering algorithm that uses a fitted curve to intersect the slope curve of the normalized power delay profile (PDP). The effectiveness of the proposed algorithm is verified based on the statistical measurement data by comparison with the conventional KMeans and KPowerMeans algorithms. Based on the clustering results, we explore the distribution of the cluster arrival delay and cluster power. We then analyze the probability density function (PDF) of the first MPC arrival delay in each cluster by comparison with conventional models. Finally, we investigate the path loss exponent (PLE) and shadow fading factor of the large-scale path loss. The outcomes of this paper is in the provision of theoretical support for mobile communications in refuge chamber environments.

鉴于避难室环境中无线信道的复杂性，我们参数化了在视距 (LoS) 和非视距 (NLoS) 条件下可靠信号传播的信道特性。根据 Saleh-Valenzuela 信道模型，多径分量 (MPC) 以集群形式到达接收器。因此，需要聚类算法来识别聚类。然而，传统 KMeans 和 KPowerMeans 算法的缺点是它们对先验簇数的限制。在本文中，我们提出了一种聚类算法，该算法使用拟合曲线与归一化功率延迟分布 (PDP) 的斜率曲线相交。通过与传统KMeans和KPowerMeans算法的比较，基于统计测量数据验证了该算法的有效性。基于聚类结果，我们探索了集群到达延迟和集群功率的分布。然后我们通过与传统模型的比较来分析每个集群中第一个 MPC 到达延迟的概率密度函数 (PDF)。最后，我们研究了大规模路径损耗的路径损耗指数（PLE）和阴影衰落因子。本文的成果是为避难室环境中的移动通信提供了理论支持。