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Mobile Networks and Applications
Erschienen in: Mobile Networks and Applications 6/2014

01.12.2014

Multi-Agent Reinforcement Learning Based Opportunistic Routing and Channel Assignment for Mobile Cognitive Radio Ad Hoc Network

verfasst von: Sunita S. Barve, Parag Kulkarni

Erschienen in: Mobile Networks and Applications | Ausgabe 6/2014

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Abstract

Opportunistic spectrum access using cognitive radio technology enables exploring vacant licensed spectrum bands and thereby improving the spectrum utilization. However, it will have a significant impact on upper layer performance like routing as the reliable knowledge of topology and channel statistics are not available, especially in Mobile Cognitive Radio Ad hoc Network (MCRAN). To address specific requirements of MCRAN, this paper is proposing online opportunistic routing algorithm using multi-agent reinforcement learning. The proposed routing scheme jointly addresses, link and relay selection based on transmission success probabilities. This sophisticated learning mechanism successfully explores opportunities in partially observable and non-stationary environment of MCRAN. Simulation results show the effectiveness of this algorithm.
Vorheriger Artikel Recent Advances on Future Networks and Their Management
Nächster Artikel A Universal IP Mobility Framework with Compulsory Route Optimization for MIPv6 and PMIPv6 Coexistence

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Metadaten
Titel
Multi-Agent Reinforcement Learning Based Opportunistic Routing and Channel Assignment for Mobile Cognitive Radio Ad Hoc Network
verfasst von
Sunita S. Barve
Parag Kulkarni
Publikationsdatum
01.12.2014
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 6/2014
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-014-0551-6

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