DocumentCode :
1329918
Title :
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
Link To Document :
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