Adaptive Rate Mechanism for WLAN IEEE 802.11 Based on BPA-Artificial Neural Network

  • Jiwa AbdullahEmail author
  • A. M. I. Okaf
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 291)


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 AutoRate 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 over all throughput. In this paper we propose 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 simulator, we evaluate our proposed scheme to show that our scheme yields higher throughput performance than the ARF scheme.


WLAN 802.11 AutoRate fallback Adaptive rate mechanism Backpropagation Neural network Multilayer perceptron Performance study 


  1. 1.
    IEEE Std 802.11-1999 (2003) Wireless LAN medium access control (MAC) and physical layer (PHY) specifications, Jun 2003Google Scholar
  2. 2.
    IEEE 802.11 a/b (1999) Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. IEEE Standard, Aug 1999Google Scholar
  3. 3.
    Kamerman A, Monteban L (1997) WaveLAN-II: a high-performance wireless LAN for the unlicensed band. Bell Labs Tech J 2:118–133CrossRefGoogle Scholar
  4. 4.
    Kim J, Kim S, Choi S, Qiao D (2006) CARA: collision aware rate adaptation for IEEE 802.11 WLANs. IEEE INFOCOMGoogle Scholar
  5. 5.
    Maguolo F, Lacage M, Turletti T (2008) Efficient collision detection for auto rate fallback Algorithm. IEEE INFOCOMGoogle Scholar
  6. 6.
    Lacage M, Manshaei MH, Turletti T (2004) IEEE 802.11 rate adaptation: a practical approach. In: Proceedings of the 7th ACM international symposium on modeling, analysis and simulation of wireless and mobile systems, pp 126–134Google Scholar
  7. 7.
    Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood CliffsGoogle Scholar
  8. 8.
    Faucett L (1994) Fundamentals of neural networks architecture, algorithms, and applications. Prentice-Hall,Englewood CliffsGoogle Scholar
  9. 9.
    Matlab (2012) MATLAB and Statistics Toolbox Release 2012b, The Mathworks, Inc., Natick, Massachusetts, USGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Communication Engineering, Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia

Personalised recommendations