Spatio-Temporal Network Dynamics Framework for Energy-Efficient Ultra-Dense Cellular Networks

TL;DR

This paper develops a framework combining stochastic geometry and mean-field game theory to analyze and optimize energy efficiency in ultra-dense cellular networks, providing a tractable power control policy with significant efficiency gains.

Contribution

It introduces a novel mean-field interference convergence proof and derives a closed-form energy efficiency optimal power control policy for ultra-dense networks.

Findings

Asymptotic convergence of mean-field interference to zero in UDNs.

Proposed power control policy achieves over 1.5 times higher energy efficiency.

Framework effectively models interference and energy optimization in dense cellular deployments.

Abstract

This article investigates the performance of an ultra-dense network (UDN) from an energy-efficiency (EE) standpoint leveraging the interplay between stochastic geometry (SG) and mean-field game (MFG) theory. In this setting, base stations (BSs) (resp. users) are uniformly distributed over a two-dimensional plane as two independent homogeneous Poisson point processes (PPPs), where users associate to their nearest BSs. The goal of every BS is to maximize its own energy efficiency subject to channel uncertainty, random BS location, and interference levels. Due to the coupling in interference, the problem is solved in the mean-field (MF) regime where each BS interacts with the whole BS population via time-varying MF interference. As a main contribution, the asymptotic convergence of MF interference to zero is rigorously proved in a UDN with multiple transmit antennas. It allows us to derive a closed-form EE representation, yielding a tractable EE optimal power control policy. This proposed power control achieves more than 1.5 times higher EE compared to a fixed power baseline.