Genetic Algorithm Applied to Planning IEEE 802.11g Networks
Hamid Barkouk1, El Mokhtar En-Naimi2, Aziz Mahboub3

1Hamid Barkouk*, Faculty, Sciences and Technologies, Tangier, Morocco at the University of Abdelmalek Essaadi.
2El Mokhtar En-Naimi, Faculty, Department of Computer Sciences of Sciences and Technologies of Tangier.
3Aziz Mahboub, Faculty, Department of Computer Sciences, of Sciences and Technologies of Tangier.

Manuscript received on March 24, 2021. | Revised Manuscript received on March 30, 2021. | Manuscript published on April 30, 2021. | PP: 59-64 | Volume-10 Issue-4, April 2021. | Retrieval Number: 100.1/ijeat.D23550410421 | DOI: 10.35940/ijeat.D2355.0410421
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Abstract: The problem of planning local wireless network IEEE 802.11g consists of automatically positioning and setting up wireless access points (APs) in order to provide access to the local network with the desired coverage and the required quality of service (QOS).In addition to the complexity of predicting the Quality of Service (QoS) of a network from the variables of the problem (positions, parameters and frequency of the APs), the planning of WLAN networks faces several difficulties. In particular, the location of APs and the allocation of frequencies. There is no single model to solve the problem of designing wireless local networks. Depending on the situations and the hypotheses studied, different criteria can be considered and expressed in terms of constraints to be observed or in terms of objectives to be optimized. The first distinction is to separate the financial criteria from the network quality criteria. The nature of these two criteria being fundamentally different. Then there are a variety of service quality criteria, but we can still group them into three main categories: coverage criteria, interference criteria and capacity criteria.. In this article, we will use an optimization method based on an algorithm of stochastic optimization, which is also based on the mechanisms of natural selection and of genetic. It is genetic algorithm. Our goal consist of minimizing the total interaction between the APs to perform the good choices when deploying a network 802.11g in a way that gives users signal-to-interference ratios (SIR) greater than the required threshold ß. 
Keywords: Access point, optimization, genetic algorithm, frequency assignment, power management, load balancing, and signal-to-interference ratio
Scope of the Article: Genetic algorithm