An Iterative Decoding Algorithm for Channels with Additive Linear Dynamical Noise

Tadashi WADAYAMA

An Iterative Decoding Algorithm for Channels with Additive Linear Dynamical Noise

Tadashi WADAYAMA

Full Text Views

0

Cite this

Summary :

In this paper, an iterative decoding algorithm for channels with additive linear dynamical noise is presented. The proposed algorithm is based on the tightly coupled two inference algorithms: the sum-product algorithm which infers the information symbols of an low density parity check (LDPC) code and the Kalman smoothing algorithm which infers the channel states. The linear dynamical noise are the noise generated from a linear dynamical system. We often encounter such noise (i.e., additive colored noise) in practical communication and storage systems. The conventional iterative decoding algorithms such as the sum-product algorithm cannot derive full potential of turbo codes nor LDPC codes over such a channel because the conventional algorithms are designed under the independence assumption on the noise. Several simulations have been performed to assess the performance of the proposed algorithm. From the simulation results, it can be concluded that the Kalman smoothing algorithm deserves to be implemented in a decoder when the linear dynamical part of the linear dynamical noise is dominant rather than the white Gaussian noise part. In such a case, the performance of the proposed algorithm is far superior to that of the conventional algorithm.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.10 pp.2452-2460

Publication Date: 2003/10/01

Publicized

Online ISSN

DOI

Type of Manuscript: Special Section PAPER (Special Section on Information Theory and Its Applications)

Category: Coding Theory

Cite this

Copy

Tadashi WADAYAMA, "An Iterative Decoding Algorithm for Channels with Additive Linear Dynamical Noise" in IEICE TRANSACTIONS on Fundamentals, vol. E86-A, no. 10, pp. 2452-2460, October 2003, doi: .
Abstract: In this paper, an iterative decoding algorithm for channels with additive linear dynamical noise is presented. The proposed algorithm is based on the tightly coupled two inference algorithms: the sum-product algorithm which infers the information symbols of an low density parity check (LDPC) code and the Kalman smoothing algorithm which infers the channel states. The linear dynamical noise are the noise generated from a linear dynamical system. We often encounter such noise (i.e., additive colored noise) in practical communication and storage systems. The conventional iterative decoding algorithms such as the sum-product algorithm cannot derive full potential of turbo codes nor LDPC codes over such a channel because the conventional algorithms are designed under the independence assumption on the noise. Several simulations have been performed to assess the performance of the proposed algorithm. From the simulation results, it can be concluded that the Kalman smoothing algorithm deserves to be implemented in a decoder when the linear dynamical part of the linear dynamical noise is dominant rather than the white Gaussian noise part. In such a case, the performance of the proposed algorithm is far superior to that of the conventional algorithm.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_10_2452/_p

Copy

@ARTICLE{e86-a_10_2452,
author={Tadashi WADAYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Iterative Decoding Algorithm for Channels with Additive Linear Dynamical Noise},
year={2003},
volume={E86-A},
number={10},
pages={2452-2460},
abstract={In this paper, an iterative decoding algorithm for channels with additive linear dynamical noise is presented. The proposed algorithm is based on the tightly coupled two inference algorithms: the sum-product algorithm which infers the information symbols of an low density parity check (LDPC) code and the Kalman smoothing algorithm which infers the channel states. The linear dynamical noise are the noise generated from a linear dynamical system. We often encounter such noise (i.e., additive colored noise) in practical communication and storage systems. The conventional iterative decoding algorithms such as the sum-product algorithm cannot derive full potential of turbo codes nor LDPC codes over such a channel because the conventional algorithms are designed under the independence assumption on the noise. Several simulations have been performed to assess the performance of the proposed algorithm. From the simulation results, it can be concluded that the Kalman smoothing algorithm deserves to be implemented in a decoder when the linear dynamical part of the linear dynamical noise is dominant rather than the white Gaussian noise part. In such a case, the performance of the proposed algorithm is far superior to that of the conventional algorithm.},
keywords={},
doi={},
ISSN={},
month={October},}

Copy

TY - JOUR
TI - An Iterative Decoding Algorithm for Channels with Additive Linear Dynamical Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2452
EP - 2460
AU - Tadashi WADAYAMA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2003
AB - In this paper, an iterative decoding algorithm for channels with additive linear dynamical noise is presented. The proposed algorithm is based on the tightly coupled two inference algorithms: the sum-product algorithm which infers the information symbols of an low density parity check (LDPC) code and the Kalman smoothing algorithm which infers the channel states. The linear dynamical noise are the noise generated from a linear dynamical system. We often encounter such noise (i.e., additive colored noise) in practical communication and storage systems. The conventional iterative decoding algorithms such as the sum-product algorithm cannot derive full potential of turbo codes nor LDPC codes over such a channel because the conventional algorithms are designed under the independence assumption on the noise. Several simulations have been performed to assess the performance of the proposed algorithm. From the simulation results, it can be concluded that the Kalman smoothing algorithm deserves to be implemented in a decoder when the linear dynamical part of the linear dynamical noise is dominant rather than the white Gaussian noise part. In such a case, the performance of the proposed algorithm is far superior to that of the conventional algorithm.
ER -