Paper Info
Title
Iterative LMMSE Channel Estimation and Decoding Based on Probabilistic Bias
Abstract
Iterative channel estimation and decoding based on probabilistic bias is investigated. In order to control the occurrence probability of transmitted symbols, biased convolutional codes (CCs) are proposed. A biased CC is obtained by puncturing the parity bit of a conventional (unbiased) CC and by inserting a fixed bit at the punctured position when the state is contained in a certain subset of all possible states. A priori information about the imposed bias is utilized for the initial linear minimum mean-squared error (LMMSE) channel estimation. This paper focuses on biased turbo codes that are constructed as the parallel concatenation of two biased CCs with interleaving, and proposes an iterative LMMSE channel estimation and decoding scheme based on approximate belief propagation. The convergence property of the iterative LMMSE channel estimation and decoding scheme is analyzed via density evolution (DE). The DE analysis allows one to design the magnitude of the bias according to the coherence time, in terms of the decoding threshold. The proposed scheme is numerically shown to outperform conventional pilot-based schemes in the moderate signal-to-noise ratio (SNR) regime, at the expense of a performance degradation in the high SNR regime.
Year
DOI
Venue
2013
10.1109/TCOMM.2013.053013.120919
IEEE Transactions on Communications
Keywords
Field
DocType
channel coding,channel estimation,convergence,convolutional codes,iterative methods,least mean squares methods,probability,signal processing,turbo codes,DE analysis,belief propagation,biased convolutional codes,biased turbo codes,channel decoding,conventional unbiased CC,convergence property,decoding scheme,decoding threshold,density evolution,high SNR regime,iterative LMMSE channel estimation,iterative channel estimation,linear minimum mean-squared error channel estimation,occurrence probability,parallel concatenation,parity bit,performance degradation,pilot-based schemes,probabilistic bias,signal-to-noise ratio regime,transmitted symbols,Biased convolutional codes,belief propagation,density evolution,iterative decoding,linear minimum mean-squared error (LMMSE) channel estimation
Concatenated error correction code,Sequential decoding,Convolutional code,Low-density parity-check code,Computer science,Turbo code,Serial concatenated convolutional codes,Electronic engineering,Decoding methods,List decoding
Journal
Volume
Issue
ISSN
61
7
0090-6778
Citations 
PageRank 
References 
3
0.39
20
Authors
3
Name
Order
Citations
PageRank
Keigo Takeuchi19312.11
R. Muller21206124.92
Mikko Vehkapera3926.45