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
fDate :
12/1/2012 12:00:00 AM
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;
Journal_Title :
Communications Letters, IEEE
DOI :
10.1109/LCOMM.2012.102612.121473