Importance sampling simulation for evaluating the lower-bound BER of the Bayesian DFE

Author

Chen, Sheng

Author_Institution

Dept. of Electron. & Comput. Sci., Southampton Univ., UK

Volume

50

Issue

2

fYear

2002

fDate

2/1/2002 12:00:00 AM

Firstpage

179

Lastpage

182

Abstract

An importance sampling (IS) simulation technique, originally derived by Iltis (1995) for Bayesian equalizers, is extended to evaluate the lower-bound bit error rate of the Bayesian decision feedback equalizer (under the assumption of correct decisions being fed back). Using a geometric translation approach, it is shown that the two subsets of opposite-class channel states are always linearly separable. A design procedure is presented, which chooses appropriate bias vectors for the simulation density to ensure asymptotic efficiency of the IS simulation

Keywords

Bayes methods; decision feedback equalisers; digital simulation; error statistics; importance sampling; Bayesian DFE; Bayesian decision feedback equalizer; IS simulation; asymptotic efficiency; bias vectors; bit error rate; design procedure; geometric translation; importance sampling simulation; linearly separable channel states; lower-bound BER; opposite-class channel states; simulation density; Bayesian methods; Bit error rate; Communications Society; Computational modeling; Computer science; Cultural differences; Decision feedback equalizers; Monte Carlo methods; State feedback; Vectors;

fLanguage

English

Journal_Title

Communications, IEEE Transactions on

Publisher

ieee

ISSN

0090-6778

Type

jour

DOI

10.1109/26.983311

Filename

983311