Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 7; p. 2165
Main Authors: Calles-Esteban, Francisco, Olmedo, Alvaro Antonio, Hellín, Carlos J., Valledor, Adrián, Gómez, Josefa, Tayebi, Abdelhamid
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.04.2024
Subjects:
Algorithms
Analysis
antenna positioning
Antennas
Antennas (Electronics)
Artificial intelligence
Automation
Genetic algorithms
optimization
Optimization techniques
Propagation
propagation losses
Web site management software
wireless communications
wireless communications
genetic algorithms
antenna positioning
optimization
propagation losses
ISSN:1424-8220, 1424-8220
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s24072165