Novel Blind Encoder Parameter Estimation for Turbo Codes

Author

Debessu, Yonas G. ; Wu, Hsiao-Chun ; Jiang, Hong

Author_Institution

Sch. of Electr. Eng. & Comput. Sci., Louisiana State Univ., Baton Rouge, LA, USA

Volume

16

Issue

12

fYear

2012

fDate

12/1/2012 12:00:00 AM

Firstpage

1917

Lastpage

1920

Abstract

A novel blind parameter-estimation method, which identifies a turbo encoder, is proposed in this paper. The blind estimator is designed using an iterative expectation-maximization (EM) algorithm. To facilitate this innovative blind estimation scheme, we transform the recursive systematic convolutional (RSC) encoder into a non-systematic convolutional encoder preceded by a feedback encoder. The effect of the separate feedback encoder on the state sequence of the forward convolutional encoder will be studied. Besides, the effectiveness of our proposed new scheme will be evaluated by Monte Carlo simulations.

Keywords

Monte Carlo methods; convolutional codes; expectation-maximisation algorithm; feedback; iterative methods; recursive estimation; turbo codes; EM algorithm; Monte Carlo simulations; RSC encoder; blind encoder parameter estimation method; feedback encoder; forward convolutional encoder; iterative expectation-maximization algorithm; nonsystematic convolutional encoder; recursive systematic convolutional encoder transform; state sequence; turbo codes; Convolution; Convolutional codes; Estimation; Monte Carlo methods; Parameter estimation; Signal to noise ratio; Turbo codes; Blind decoder; expectation-maximization; maximum likelihood; turbo codes;

fLanguage

English

Journal_Title

Communications Letters, IEEE

Publisher

ieee

ISSN

1089-7798

Type

jour

DOI

10.1109/LCOMM.2012.102612.121473

Filename

6343245