Optimizasyon Teknikleri Kullanılarak Kapalı Alan 802.11ac Kablosuz WLAN Modelleme için Bir Karar Destek Aracı

Year 2020, Volume: 7 Issue: 3, 1231 - 1244, 30.09.2020

Mevlüt Ersoy , Tuncay Yiğit , Asım Sinan Yüksel

https://doi.org/10.31202/ecjse.739114

Abstract

Kablosuz Yerel Ağ (WLAN), kapalı alan ve dış ortamlarda kablosuz yayın ile Internet erişimi sağlayan bir teknolojidir. Düşük maliyetli, kullanımı kolay ve yaygın olarak kullanılmaktadır. Bununla birlikte, kapalı alanların karmaşık yapısı nedeniyle kapsamını etkileyen birçok faktör vardır. Bu nedenle, Erişim Noktalarını (AP'ler) kablosuz ağa en güçlü ve en tutarlı kapsama alanını sağlayacak şekilde yerleştirmek gerekir. Bu faktörler WLAN performansını doğrudan etkilediğinden, ağ uzmanları genellikle beklenmedik sorunlarla karşılaşırlar ve bulunduktan sonra erişim noktalarından ölçümler alarak ağ kapsamını deneme yanılma yoluyla optimize etme eğilimindedirler. WLAN optimizasyonu için anten tipi, kanallar, duvar yapıları, verim gibi çeşitli metrikler kullanılabilir. Karar destek aracımız, ağ uzmanlarının bu metrikleri kullanarak bir ağ modeli oluşturmasına yardımcı olur. Kapalı alan ortamlarında AP'lerin kapsamının belirlenmesi doğrusal olmayan bir sorundur. Bu nedenle, yazılım aracımız tavlama ve genetik algoritmanın sezgisel yaklaşımlarına dayanmaktadır. Bu araç aracılığıyla, bir kapalı alana yerleştirilecek optimum AP sayısına karar verilebilir ve optimum ağ kapsama alanı için en iyi AP yerleşimi kolayca elde edilebilir.

Keywords

WLAN Optimizasyonu, Genetik Algoritma, Tavlama Benzetimi

A Decision Support Tool for Indoor 801.11ac WLAN Modeling Using Optimization Techniques

Abstract

Wireless Local Area Network (WLAN) is a technology that provides wireless broadband Internet access in indoor or outdoor environments. It is low cost, easy to use and widely deployed. However, there are many factors that affect its coverage due to the complex structure of indoor settings. For this reason, it is necessary to locate the Access Points (APs) in such a way as to provide the wireless network with the strongest and most consistent coverage. Since these factors directly affect WLAN performance, network specialists often encounter unexpected problems and tend to optimize network coverage by trial and error, taking measurements from access points after they’re located. Several metrics such as antenna type, channels, wall structures, throughput can be used for WLAN optimization. Our decision support tool helps network specialists generate a network model by employing these metrics. Determining the coverage of APs in indoor settings is a nonlinear problem. Therefore, our tool is based on the heuristic approaches of the simulated annealing and genetic algorithm. Via this tool, the optimum number of APs to be placed in an indoor setting can be decided and the best AP placement for optimal network coverage can be easily obtained.

Keywords

WLAN Optimization, Genetic Algorithm, Simulated Annealing

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