TOSE: A Fast Capacity Determination Algorithm Based on Random Matrix Theory

TL;DR

This paper introduces TOSE, a rapid algorithm leveraging random matrix theory to estimate average cluster capacity in large-scale wireless networks efficiently, bypassing complex eigenvalue calculations.

Contribution

The paper presents a novel, fast eigenvalue estimation algorithm based on RMT for capacity calculation, with analytical bounds and broad applicability.

Findings

TOSE is at least 1000 times faster than traditional methods.

It provides accurate capacity estimates without detailed eigenvalue derivations.

The method is distribution-agnostic and adaptable to various network shapes.

Abstract

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.