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Wireless Networks
Erschienen in: Wireless Networks 6/2017

22.03.2016

A game theoretic learning solution for distributed relay selection on throughput optimization

verfasst von: Cheng Ding, Liang Shen, Dianxiong Liu, Kun Xu, Yuhua Xu

Erschienen in: Wireless Networks | Ausgabe 6/2017

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Abstract

In this paper, we study the problem of distributed relay selection in wireless networks using a game theoretic approach. Specifically, we consider a system model where one relay node can be shared by multiple source-destination pairs. Our objective is to find the relay selections of source nodes to optimize the total capacity. The relay selection problem is formulated as a congestion game with player-specific payoff functions and the existence of Nash equilibrium (NE) is demonstrated. Then we propose a stochastic learning automata (SLA) based distributed relay selection approach to obtain the NE without information exchange among source nodes. Simulation results show that the proposed distributed relay selection approach achieves satisfactory performance, when compared with other solutions.
Vorheriger Artikel A modeling framework for supporting and evaluating connectivity in cognitive radio ad hoc networks with beamforming
Nächster Artikel An EDRI-based approach for detecting and eliminating cooperative black hole nodes in MANET

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Metadaten
Titel
A game theoretic learning solution for distributed relay selection on throughput optimization
verfasst von
Cheng Ding
Liang Shen
Dianxiong Liu
Kun Xu
Yuhua Xu
Publikationsdatum
22.03.2016
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 6/2017
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-016-1250-y

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