论文标题
TOSE：基于随机矩阵理论的快速容量确定算法
TOSE: A Fast Capacity Determination Algorithm Based on Random Matrix Theory
论文作者
论文摘要
无线网络容量是无线通信网络最重要的性能指标之一。未来的无线网络将由大量的基站（BS）和用户组成，并以多个群集的形式组织。不幸的是，很难确定这种未来无线网络的平均聚类能力，并且缺乏分析表达式和快速算法。在本文中，我们提出了一种快速算法，以根据随机矩阵理论（RMT）估算平均聚类能力。它可以避免大维矩阵的精确特征值推导，这些矩阵在常规能力确定方法中是不可避免的。取而代之的是，可以基于我们的TOSE算法中的RMT来实现快速特征值估计。另外，我们得出了平均簇容量的分析上限和下限。我们的数值实验表明，至少三个数量级，TOSE比常规Cholesky分解方法快。此外，Tose具有较高的通用性，因为它与BS和用户的分布以及网络区域的形状无关。
Wireless network capacity is one of the most important performance metrics for wireless communication networks. Future wireless networks will be composed of extremely large number of base stations (BSs) and users, and organized in the form of multiple clusters. Unfortunately, the determination of average cluster capacity for such future wireless networks is difficult, and lacks of both analytical expressions and fast algorithms. In this paper, we propose a fast algorithm TOSE to estimate the average cluster capacity based on the random matrix theory (RMT). It can avoid the exact eigenvalue derivations of large dimensional matrices, which are complicated and inevitable in conventional capacity determination methods. Instead, fast eigenvalue estimations can be realized based on RMT in our TOSE algorithm. In addition, we derive the analytical upper and lower bounds of the average cluster capacity. Our numerical experiments show that TOSE is faster than the conventional Cholesky decomposition method, by at least three orders of magnitude. Besides, TOSE has superior generality, since it is independent of the distributions of BSs and users, and the shape of network areas.
