DocumentCode :
2577135
Title :
Observability and reconstructibility of hidden Markov models: Implications for control and network congestion control
Author :
Liu, Andrew R. ; Bitmead, Robert R.
Author_Institution :
Dept. of Mech. & Aerosp. Eng., Univ. of California, San Diego, La Jolla, CA, USA
fYear :
2010
fDate :
15-17 Dec. 2010
Firstpage :
918
Lastpage :
923
Abstract :
This paper addresses the observability and reconstructibility of the hidden Markov model. A rank condition for observability of the time-invariant hidden Markov model is proven. This condition is reminiscent of deterministic linear systems theory. Additionally, the externally controlled case is studied by way of simulations of a hidden Markov model which represents a computer network with sources running Transmission Control Protocol (TCP). This permits the comparison of congestion control methods based on their quantified reconstructibility properties. Simulation results elucidate the dual purpose of the control signal, which simultaneously regulates the system while ensuring persistent reconstructibility.
Keywords :
computer networks; hidden Markov models; linear systems; observability; telecommunication congestion control; time-varying systems; transport protocols; computer network; deterministic linear system; hidden Markov model observability; network congestion control; rank condition; reconstructibility property; time invariant hidden Markov model; transmission control protocol; Entropy; Feedback control; Hidden Markov models; Observability; Random variables; Stochastic processes; Throughput;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
ISSN :
0743-1546
Print_ISBN :
978-1-4244-7745-6
Type :
conf
DOI :
10.1109/CDC.2010.5717730
Filename :
5717730
Link To Document :
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