Spatial Mappings for Planning and Optimization of Cellular Networks

TL;DR

This paper advances spatial mapping techniques for cellular network planning by integrating centroidal Voronoi algorithms, non-conformal mappings, and power optimization to better adapt to dynamic, heterogeneous service demands.

Contribution

It introduces new tools like centroidal Voronoi algorithms and non-conformal mappings into existing spatial mapping frameworks for improved cellular network optimization.

Findings

Enhanced planning accuracy with the proposed methods

Effective adaptation to dynamic service demand patterns

Promising results in power optimization

Abstract

In cellular networks, users are grouped into different cells and served by different access points (base stations) that provide wireless access to services and applications. In general, the service demand is very heterogeneous, non-uniformly distributed, and dynamic. Consequently, radio access networks create very irregular topologies with more access points where service demand is concentrated. While this dynamism requires networks with the ability to adapt to time-varying conditions, the non-uniformity of the service demand makes the planning, analysis, and optimization difficult. In order to help with these tasks, a framework based on canonical domains and spatial mappings (e.g., conformal mapping) have recently been proposed. The idea is to carry out part of the planning in a canonical (perfectly symmetric) domain that is connected to the physical one (real-scenario) by means of a spatial transformation designed to map the access points consistently with the service demand. This paper continues the research in that direction by introducing additional tools and possibilities to that framework, namely the use of centroidal Voronoi algorithms and non-conformal composite mappings. Moreover, power optimization is also introduced to the framework. The results show the usability and effectiveness of the proposed method and its promising research perspectives.