Fast Capacity Estimation in Ultra-dense Wireless Networks with Random Interference

TL;DR

This paper introduces fast, accurate capacity estimation methods for ultra-dense wireless networks with random interference, using Fisher matrix modeling and closed-form expressions, applicable across diverse network configurations.

Contribution

The paper proposes novel capacity estimation techniques tailored for large-scale wireless networks, leveraging Fisher matrix modeling and invariant closed-form expressions for different user-to-BS ratios.

Findings

High accuracy in capacity estimation demonstrated through simulations

Methods outperform existing approaches in speed and robustness

Applicable to various network shapes and distributions

Abstract

In wireless communication systems, the accurate and reliable evaluation of channel capacity is believed to be a fundamental and critical issue for terminals. However, with the rapid development of wireless technology, large-scale communication networks with significant random interference have emerged, resulting in extremely high computational costs for capacity calculation. In ultra-dense wireless networks with extremely large numbers of base stations (BSs) and users, we provide fast estimation methods for determining the capacity. We consider two scenarios according to the ratio of the number of users to the number of BSs, $\beta_m$. First, when $\beta_m\leq1$, the FIsher-Spiked Estimation (FISE) algorithm is proposed to determine the capacity by modeling the channel matrix with random interference as a Fisher matrix. Second, when $\beta_m>1$, based on a closed-form expression for capacity estimation requiring solely simple computations, we prove that this estimation stabilizes and remains invariant with increasing $\beta_m$. Our methods can guarantee high accuracy on capacity estimation with low complexity, which is faster than the existing methods. Furthermore, our approaches exhibit excellent generality, free of network area shapes, BS and user distributions, and sub-network locations. Extensive simulation experiments across various scenarios demonstrate the high accuracy and robustness of our methods.