Euclidean Matchings in Ultra-Dense Networks

TL;DR

This paper investigates the fundamental limits of network densification by analyzing Euclidean matchings in high-density networks, exploring optimal pairings, capacity limits, and interference constraints in d-dimensional space.

Contribution

It introduces a novel analysis of Euclidean matchings in dense networks, incorporating interference constraints and deriving scaling limits using physics-inspired methods.

Findings

Optimal matchings improve spectral efficiency in dense networks

Interference shapes like disks and triangles influence network topology

Scaling limits are derived using the replica method from physics

Abstract

In order to study the fundamental limits of network densification, we look at the spatial spectral efficiency gain achieved when densely deployed communication devices embedded in the $d$-dimensional Euclidean space are optimally `matched' in near-neighbour pairs. In light of recent success in probabilisitc modelling, we study devices distributed uniformly at random in the unit cube which enter into one-on-one contracts with each another. This is known in statistical physics as an Euclidean `matching'. Communication channels each have their own maximal data capacity given by Shannon's theorem. The length of the shortest matching then corresponds to the maximum one-hop capacity on those points. Interference is then added as a further constraint, which is modelled using shapes as guard regions, such as a disk, diametral disk, or equilateral triangle, matched to points, in a similar light to computational geometry. The disk, for example, produces the Delaunay triangulation, while the diametral disk produces a beta-skeleton. We also discuss deriving the scaling limit of both models using the replica method from the physics of disordered systems.