Abstract
In order to alleviate the impact of network congestion on the spatial network running traditional contact graph routing (CGR) algorithm and DTN protocol, we propose a flow intelligent control method based on deep convolutional neural network (CNN). The method includes two stages of offline learning and online prediction to intelligently predict the traffic congestion trend of the spatial network. A CGR update mechanism is also proposed to intelligently update the CGR to select a better contact path and achieve a higher congestion avoidance rate. The proposed method is evaluated in the prototype system. The experimental results show that it is superior to the existing CGR algorithm in terms of transmission delay, receiver throughput and packet loss probability.
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Acknowledgement
This paper is supported by National Key R&D Program of China under Grant No. 2018YFA0701604, NSFC under Grant No. 61802014, No. U1530118, and National High Technology of China (“863 program”) under Grant No. 2015AA015702.
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Li, K., Zhou, H., Zhang, H., Tu, Z., Li, G. (2020). Deep Learning Based Intelligent Congestion Control for Space Network. In: Yu, Q. (eds) Space Information Networks. SINC 2019. Communications in Computer and Information Science, vol 1169. Springer, Singapore. https://doi.org/10.1007/978-981-15-3442-3_2
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DOI: https://doi.org/10.1007/978-981-15-3442-3_2
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