Distributed Channel and Power Level Selection in VANET Based on SINR using Game Model.

Selo Sulistyo; Sahirul Alam

Author(s)

Selo Sulistyo Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Indonesia

Sahirul Alam Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Indonesia

Abstract

This paper proposes a scheme of channel selection and transmission power adjustment in Vehicular Ad hoc Network (VANET) using game theoretic approach. The paradigm of VANET enables groups of vehicles to establish a mesh-like communication network. However, the mobility of vehicle, highly dynamic network environment, and the shared-spectrum concept used in VANET pose some challenges such as interferences that can decrease the quality of signal. Channel selection and transmit power adjustment are aimed to obtain the higher signal to interference and noise ratio (SINR). In this paper, game theory is implemented to model the channel and power level selection in VANET. Each vehicle represents the player and the combination of channel and power level represents the strategy used by the player to obtain the utility i.e. the SINR. Strategy selection is arranged distributive to each player using Regret Matching Learning (RML) algorithm. Each vehicle evaluates current utility obtained by selecting a strategy to define the probability of that strategy to be selected in the next power update. However, RML has a shortcoming for using assumption that hard to be implemented in real VANET environment. Therefore, modification of RML devised for this application is also proposed. The simulation model of channel and power level selection is build to evaluate the performance of the proposed scheme. The results of simulation show the improvement of VANET performance in term of SINR and throughput from the proposed scheme.

Publication Details

Page(s) 432-449

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 9, Issue: 3, Year: 2017

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Keywords

game theory, regret matching learning, signal to interference and noise ratio

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