Open AccessGenetic Algorithm Applied to Planning IEEE 802.11g Networks
Author(s) -
Hamid Barkouk,
El Mokhtar En-Naimi,
Aziz Mahboub
Publication year - 2021
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.d2355.0410421
Subject(s) - computer science , genetic algorithm , quality of service , wireless network , interference (communication) , quality (philosophy) , ieee 802 , wireless , wi fi , network planning and design , service (business) , computer network , mathematical optimization , telecommunications , machine learning , mathematics , channel (broadcasting) , philosophy , economy , epistemology , economics
The problem of planning local wireless network IEEE802.11g consists of automatically positioning and setting upwireless access points (APs) in order to provide access to the localnetwork with the desired coverage and the required quality ofservice (QOS).In addition to the complexity of predicting theQuality of Service (QoS) of a network from the variables of theproblem (positions, parameters and frequency of the APs), theplanning of WLAN networks faces several difficulties. Inparticular, the location of APs and the allocation of frequencies.There is no single model to solve the problem of designing wirelesslocal networks. Depending on the situations and the hypothesesstudied, different criteria can be considered and expressed interms of constraints to be observed or in terms of objectives to beoptimized. The first distinction is to separate the financial criteriafrom the network quality criteria. The nature of these two criteriabeing fundamentally different. Then there are a variety of servicequality criteria, but we can still group them into three maincategories: coverage criteria, interference criteria and capacitycriteria.. In this article, we will use an optimization method basedon an algorithm of stochastic optimization, which is also based onthe mechanisms of natural selection and of genetic. It is geneticalgorithm. Our goal consist of minimizing the total interactionbetween the APs to perform the good choices when deploying anetwork 802.11g in a way that gives users signal-to-interferenceratios (SIR) greater than the required threshold ß.