Abstract
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.
Highlights
A wireless meshes network (WMN) architecture according to [1] shows that a WMN consists of mesh routers and mesh clients
This paper addresses the channel assignment problem and investigates optimal channel and power assignment in wireless mesh networks using learning automata
Let the action set of all automata denoted by A with |A|=r such that r is the number of combinations that can be assigned to the channels to available radios in a node
Summary
A wireless meshes network (WMN) architecture according to [1] shows that a WMN consists of mesh routers and mesh clients. This paper addresses the channel assignment problem and investigates optimal channel and power assignment in wireless mesh networks using learning automata In this approach, the nodes do not require any knowledge about network topology, and heuristically learn contention for better channel and power selection in a distributed fashion. A distributed learning automaton based algorithm that dynamically adapts nodes channel selection and power level according to measured payoff is proposed. A zero can be chosen for min (ui) and a large constant value for max (ui), but choosing a very large value for maximum compared to average utility values will significantly decrease the speed of convergence In this scheme, each node tries to maximize its utility function by maximizing the normalized payoff via current assigned channels and power level. ) is the payoff vector such that βi (t) is the payoff to ith automata
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