alexa Learning Automata Based Channel Assignment with Power C
ISSN: 2167-0919

Journal of Telecommunications System & Management
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Research Article

Learning Automata Based Channel Assignment with Power Control in Multi-Radio Multi-Channel Wireless Mesh Networks

Beheshtifard Z1* and Meybodi MR2

1Department of Computer Engineering and Information Technology, Islamic Azad University, Qazvin Branch, Qazvin, Iran

2Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

*Corresponding Author:
Ziaeddin Beheshtifard
Department of Computer Engineering and Information Technology
Islamic Azad University, Qazvin Branch
Qazvin, Iran
Tel: 4515658145
E-mail: [email protected]

Received Date: August 01, 2016; Accepted Date: September 02, 2016; Published Date: September 14, 2016

Citation: Beheshtifard Z, Meybodi MR (2016) Learning Automata Based Channel Assignment with Power Control in Multi-Radio Multi-Channel Wireless Mesh Networks. J Telecommun Syst Manage 5:139. doi:10.4172/2167-0919.1000139

Copyright: © 2016 Beheshtifard Z, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Wireless channel assignment is one of the major challenging issues in multi hop wireless mesh networks (WMN) when there is the need to design them in a distributed fashion, specifically for multi-radio multi-channel (MRMC) systems. In this work, a new learning automata based channel and power assignment scheme which adaptively improve network overall throughput by expecting network dynamics was proposed. First, a utility function which reflected the user’s preference for the signal to interference and noise ratio (SINR) was applied, and then the transmitter power. The distributed channel assignment and power control problem is formulated as a multiple payoff stochastic game of automata. In this game, each user evaluates a channel and power selection strategy by computing a utility value. This evaluation is performed using a stochastic iterative procedure. The utility function that potentially reflected a measure of satisfaction of every node was used by every node as an environmental response for the current selected strategy chosen by the nodes. According to dynamics of system, the proposed algorithm assigned channels and powers to radio interface such that it minimized interference in the neighborhood of a node. The stability of the system was analyzed via appropriate Lyapunov-like trajectory; it was shown that the stability and optimum point of the system converged.


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