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http://dx.doi.org/10.6109/jkiice.2019.23.8.1011

Reinforcement Learning for Node-disjoint Path Problem in Wireless Ad-hoc Networks  

Jang, Kil-woong (Department of Data Informatics, Korea Maritime and Ocean University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.8, 2019 , pp. 1011-1017 More about this Journal
Abstract
This paper proposes reinforcement learning to solve the node-disjoint path problem which establishes multipath for reliable data transmission in wireless ad-hoc networks. The node-disjoint path problem is a problem of determining a plurality of paths so that the intermediate nodes do not overlap between the source and the destination. In this paper, we propose an optimization method considering transmission distance in a large-scale wireless ad-hoc network using Q-learning in reinforcement learning, one of machine learning. Especially, in order to solve the node-disjoint path problem in a large-scale wireless ad-hoc network, a large amount of computation is required, but the proposed reinforcement learning efficiently obtains appropriate results by learning the path. The performance of the proposed reinforcement learning is evaluated from the viewpoint of transmission distance to establish two node-disjoint paths. From the evaluation results, it showed better performance in the transmission distance compared with the conventional simulated annealing.
Keywords
Node-disjoint path problem; reinforcement learning; Q-learning; wireless ad-hoc networks;
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Times Cited By KSCI : 1  (Citation Analysis)
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