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Optimizing the end-to-end transmission scheme for hybrid satellite and multihop networks.

Authors :
Zong, Liang
Wang, Han
Du, Wencai
Zhao, Chenglin
Luo, Gaofeng
Source :
Neural Computing & Applications. Feb2023, Vol. 35 Issue 4, p3063-3074. 12p.
Publication Year :
2023

Abstract

Satellite networks can communicate with the outside world from anywhere in the world, and multihop networks are suitable for occasions in which infrastructure is lacking or for emergencies. Heterogeneous networks formed by satellite and multihop networks can further expand the communication range of wireless networks; this expansion is conducive to communication with the outside world in remote areas and in emergency situations. However, the formation of heterogeneous networks also brings new challenges to wireless network research. To improve the transmission performance of heterogeneous networks composed of satellite and multihop networks, this paper first introduces the heterogeneous network model of satellite and multihop networks, then analyzes the bandwidth delay products of heterogeneous networks and proposes an end-to-end transmission control algorithm for heterogeneous networks. The algorithm incorporates different congestion window settings in the slow start through a threshold and through the size of the receiver notification window by increasing the amount of data transmitted in the slow start to improve the throughput of the satellite link. The algorithm then differentiates packet losses in congestion avoidance through the sizes of unacknowledged data in the heterogeneous network, using different threshold settings for different unacknowledged data sizes. The simulation results show that the proposed algorithm has some advantages over the TCP Hybla, TCP Veno and TCP Reno schemes in terms of the throughput of the satellite link, the download response time of the multihop network and the queue delay of nodes. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09410643
Volume :
35
Issue :
4
Database :
Academic Search Index
Journal :
Neural Computing & Applications
Publication Type :
Academic Journal
Accession number :
161516446
Full Text :
https://doi.org/10.1007/s00521-021-06156-7