Optimization of Iterative Decoding for Polar Product Codes

To improve the error-correction performance of polar product codes, this paper designs an optimization criterion for the weighted factors (WFs) in the iterative decoding. Firstly, a weighted mean square error criterion is proposed to optimize the WFs. Secondly, this criterion for optimizing the WFs is extended to polar and single parity-check turbo product code (P&SPC-TPC) and polar turbo product code (Polar-TPC). The optimized WFs obtained according to this criterion are superior to those obtained from Monte Carlo simulations. The two optimized iterative decoders of TPCs, P&SPC-TPC and Polar-TPC, can obtain noticeable performance gains compared with the original product decoders.

Authors

Institutions

Publication Details

Published
2024-08-21
DOI
https://doi.org/10.1109/apwcs61586.2024.10679318
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of Iterative Decoding for Polar Product Codes

Yimeng Liu, Yangmin Li, Yue Xu, Ming Jiang
Algorithms and Data Compression
article

Optimization of Iterative Decoding for Polar Product Codes

Yimeng Liu, Yangmin Li, Yue Xu, Ming Jiang
article en

Abstract

To improve the error-correction performance of polar product codes, this paper designs an optimization criterion for the weighted factors (WFs) in the iterative decoding. Firstly, a weighted mean square error criterion is proposed to optimize the WFs. Secondly, this criterion for optimizing the WFs is extended to polar and single parity-check turbo product code (P&SPC-TPC) and polar turbo product code (Polar-TPC). The optimized WFs obtained according to this criterion are superior to those obtained from Monte Carlo simulations. The two optimized iterative decoders of TPCs, P&SPC-TPC and Polar-TPC, can obtain noticeable performance gains compared with the original product decoders.

Vol. 07
Purple Mountain Laboratories (CN), Southeast University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 86%
Algorithms and Data Compression
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.