Improving Congestion Control Algorithm in Distributed Spaceflight TT&C Networks

Author

Changqing, Gong ; Xiaoxia, Bi ; Xiaoyan, Wang

Author_Institution

Shenyang Inst.of Aeronaut. Eng., Shenyang

fYear

2007

fDate

16-17 Aug. 2007

Firstpage

1134

Lastpage

1137

Abstract

TT&C (tracking, telemetry, and command) networks usually include some wireless links; thus there are many packet losses are due to I ink errors, but not due to network congestion. TCP congestion control algorithm has no means to distinguish these two reasons of packet losses, and often reduces its data rate mistakenly, cannot keep a reasonable data rate. For solving this problem, a quantum neural network classifier is presented in this paper, which can classify packet loss cause over TT&C networks. Based on this classifier, TCP-QNN algorithm is proposed. The result of our simulation shows that the TCP-QNN algorithm is superior to Vegas, Reno and TCP-BP algorithm.

Keywords

backpropagation; neurocontrollers; packet radio networks; pattern classification; quantum communication; telecommunication congestion control; telecommunication links; transport protocols; TCP congestion control algorithm; TCP-BP algorithm; TCP-QNN algorithm; distributed spaceflight TT&C network; packet loss; quantum neural network classifier; tracking-telemetry-command networks; wireless links; Antennas and propagation; Communication system control; Communications technology; Distributed control; Electromagnetic compatibility; Microwave antennas; Microwave propagation; Neural networks; Propagation losses; Protocols; TT&C; congestion control; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2007 International Symposium on

Conference_Location

Hangzhou

Print_ISBN

978-1-4244-1045-3

Electronic_ISBN

978-1-4244-1045-3

Type

conf

DOI

10.1109/MAPE.2007.4393469

Filename

4393469