Title :
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
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;
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
DOI :
10.1109/MAPE.2007.4393469
