DE-aided ANMSA with edge classification and its application for 5G-NR LDPC codes

Belief propagation (BP) provides the basis for the design of 5G new radio low density parity check (5G-NR LDPC) codes, which in turn require a BP-based decoding algorithm. The min-sum algorithm (MSA), the simplification of sum-product algorithm (SPA), should hardly be regarded as a BP-based decoding...

Full description

Saved in:
Bibliographic Details
Published in:IEEE International Symposium on Broadband Multimedia Systems and Broadcasting pp. 1 - 6
Main Authors: Zhou, Ziqi, Peng, Kewu, Song, Jian, He, Zhitong
Format: Conference Proceeding
Language:English
Published: IEEE 04.08.2021
Subjects:
5G mobile communication
5G new radio
adaptive normalization min-sum algorithm
belief propagation algorithm
Broadcasting
Classification algorithms
Decoding
iterative decoding algorithm
Kullback-Leibler divergence
low-density parity check codes
multi-edge-type density evolution
Parity check codes
Training
ISSN:2155-5052
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Belief propagation (BP) provides the basis for the design of 5G new radio low density parity check (5G-NR LDPC) codes, which in turn require a BP-based decoding algorithm. The min-sum algorithm (MSA), the simplification of sum-product algorithm (SPA), should hardly be regarded as a BP-based decoding algorithm because of its minimization. To remedy this, this paper adopts the multi-edge-type density evolution (MET-DE) as the main analysis tool, and introduces the concept of Kullback-Leibler divergence. Then the least Kullback-Leibler divergence criterion is proposed to identify the optimal normalization factors. Finally, Density-Evolution-aided Adaptive Normalization Min-Sum Algorithm (DE-aided ANMSA) with edge classification is further proposed for the decoding implementation of 5G-NR LDPC codes. Simulation results demonstrate that the proposed decoding algorithm can achieve excellent performance, while the greatest advantage is that its factors could be obtained more flexibly and accurately than traditional training methods. In addition, the optimal factors for base graph 2 in 5G-NR LDPC codes are provided as well.
ISSN:2155-5052
DOI:10.1109/BMSB53066.2021.9547073