A measure for the length of probabilistic dependence

Author

Kong, Hongwei ; Shwedyk, Ed

Author_Institution

Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada

fYear

1997

fDate

29 Jun-4 Jul 1997

Firstpage

469

Abstract

A very useful model to describe a random process with memory is the Markov model. The MLSE receiver based on a finite order Markov model is studied in Kong and Shwedyk (1995). It is found that the order of the Markov model, i.e., the length of probabilistic dependence, is the crucial parameter that determines the receiver complexity. An information-theoretic measure for the length of probabilistic dependence is proposed

Keywords

Markov processes; computational complexity; information theory; maximum likelihood estimation; probability; random processes; receivers; MLSE receiver; Markov model; finite order Markov model; information-theoretic measure; memory; probabilistic dependence length; random process; receiver complexity; Electronic switching systems; Fading; Gaussian processes; Hidden Markov models; Length measurement; Markov processes; Maximum likelihood estimation; Mutual information; Probability density function; Random processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory. 1997. Proceedings., 1997 IEEE International Symposium on

Conference_Location

Ulm

Print_ISBN

0-7803-3956-8

Type

conf

DOI

10.1109/ISIT.1997.613406

Filename

613406

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3078222