Cell size determination in WCDMA systems using an evolutionary programming approach

Authors:
José Antonio Portilla-Figueras;Sancho Salcedo-Sanz;Alicia Oropesa-García;Carlos Bousoño-Calzón
Affiliations:
Department of Signal Theory and Communications, Universidad de Alcalá, Carretera Madrid-Barcelona Km 33, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, Carretera Madrid-Barcelona Km 33, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain;Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain
Venue:
Computers and Operations Research
Year:
2008

Citing 8
Cited 1

Intelligence through simulated evolution: forty years of evolutionary programming

Intelligence through simulated evolution: forty years of evolutionary programming
Wideband CDMA For Third Generation Mobile Communications: Universal Personal Communications

Wideband CDMA For Third Generation Mobile Communications: Universal Personal Communications
Wcdma for Umts

Wcdma for Umts
Radio Network Planning and Optimisation for Umts

Radio Network Planning and Optimisation for Umts
An overview of evolutionary algorithms for parameter optimization

Evolutionary Computation
Class-based downlink capacity estimation of a WCDMA network in a multiservice context

Computer Communications
Evolutionary programming made faster

IEEE Transactions on Evolutionary Computation
An introduction to simulated evolutionary optimization

IEEE Transactions on Neural Networks

Novel heuristics for cell radius determination in WCDMA systems and their application to strategic planning studies

EURASIP Journal on Wireless Communications and Networking

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper deals with the problem of the cell size determination in WCDMA-based mobile networks, in multiservice environments. The objective is to obtain the maximum cell size, given a set of services with their corresponding constraints, in terms of quality of service (QoS), binary rate, etc. To achieve this, we have to find the optimal services' load factors which maximizes the cell radius of the system under traffic criteria. We apply an evolutionary programming algorithm to solve the problem, which codifies and evolves the services' load factors. We have compared our approach with an existing algorithm in several multiservice scenarios, improving its solutions in terms of cell size.