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Rate adaptation in IEEE 802.11 WLAN based on neural network

Mohamed Issa , Abdanaser (2011) Rate adaptation in IEEE 802.11 WLAN based on neural network. Masters thesis, Universiti Tun Hussein Onn Malaysia.


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This thesis presents an adaptive Auto Rate Fallback (ARF) scheme to improve the performance of aggregate throughput in IEEE 802.11 Wireless Local Area Network (WLAN) with multiple nodes. We will exploit artificial neural network (ANN) as an adaptive framework in this thesis. IEEE 802.11 WLANs provide multiple transmission rates to improve the system throughput by adapting the transmission rate to the current wireless channel conditions. The Auto Rate Fallback (ARF) scheme is a simple and heuristic link adaptation approach and compliant with IEEE 802.11 standard, also most of commercial devices implement it but it's suffer from random packet collisions especially when the number of nodes increases and consequently cause a decline of the overall throughput. In this thesis we propose ARF rate adaptation in WLAN 802.11 based in neural network. The proposed rate adaptation scheme, appropriately adjust the data transmission rate based on the estimated wireless channel condition, specifically by dynamically adjusting the system parameters that determine the transmission rates according to the contention situations including the amount of contending nodes and traffic intensity. Through extensive simulation runs by using the Qualnet 5.0 simulator, proposed scheme is evaluated to show that, this scheme yields higher throughput performance than the ARF scheme.

Item Type: Thesis (Masters)
Subjects: T Technology > T Technology (General)
Depositing User: Normajihan Abd. Rahman
Date Deposited: 07 May 2012 00:53
Last Modified: 07 May 2012 00:53
URI: http://eprints.uthm.edu.my/id/eprint/2388
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