Integrated optimization of heterogeneous-network management and the elusive role of macrocells

TL;DR

This paper formulates a complex optimization problem for heterogeneous wireless networks, introducing a genetic algorithm to explore the true potential of such networks and questioning the traditional role of macrocells in capacity and energy efficiency.

Contribution

It presents a new mixed-integer nonlinear programming formulation and a genetic algorithm, HetNetGA, to analyze network management without approximations, revealing insights into macrocell roles.

Findings

Results often align with expectations but sometimes challenge them.

Highlights the need to better understand macrocell contributions.

Demonstrates the potential of genetic algorithms for complex network optimization.

Abstract

We consider heterogeneous wireless networks in the physical interference model and introduce a new formulation of the mixed-integer nonlinear programming problem that addresses base-station activation and many-to-many associations while minimizing power consumption. We also introduce HetNetGA, a genetic algorithm that can tackle the problem without any approximations. Though unsuitable for practical deployment, HetNetGA enables the investigation of such networks' true possibilities. Results for scenarios involving both macrocells and picocells often align with what is expected, but sometimes are unexpected and essentially point to the need to better understand the role of macrocells in helping provide capacity while remaining energetically advantageous.