DESIGN OF AN LDPC DECODING ALGORITHM BASED ON NEURAL NETWORK APPROACHES
Section: SBORNIK TRUDOV KONFERENCII
Authors:
2.
National Research University of Electronic Technology
(kafedra TKS)
student from 01.01.2020 to 01.01.2025
Russian Federation
student from 01.01.2020 to 01.01.2025
Russian Federation
Type:
Сonference article
Pages:
from 36
to 41
Published:
15.09.2025
Subject area:
UDC 621.391
Language:
Russian
Keywords:
LDPC codes, neural networks, iterative decoding, Min-Sum, Sum-Product, gradient descent
Funding:
"The work was carried out at NRU MIET in the frame of government university support program “Priority-2030”."
Abstract:
In conditions of growing requirements to wireless communication channels, and also active introduction of neural network technologies the algorithm of decoding LDPC code, applying neural network solution for updating of coefficients of check and variable nodes is offered. When comparing BER and BLER values, the proposed algorithm demonstrates an advantage over traditional methods.
In conditions of growing requirements to wireless communication channels, and also active introduction of neural network technologies the algorithm of decoding LDPC code, applying neural network solution for updating of coefficients of check and variable nodes is offered. When comparing BER and BLER values, the proposed algorithm demonstrates an advantage over traditional methods.
Keywords:
LDPC codes, neural networks, iterative decoding, Min-Sum, Sum-Product, gradient descent
LDPC codes, neural networks, iterative decoding, Min-Sum, Sum-Product, gradient descent
1. Gallager R. Teoriya informacii i nadezhnaya svyaz'. – M.: Sovetskoe radio, 1974. – 720 c.
2. 3GPP TS 38.212. NR; Multiplexing and Channel Coding 3rd Generation Partnership Project; Technical Specification Group Radio Access Network.
3. Lin S., Costello D. J. Error control coding. – Lebanon, IN: Prentice hall, 2001. – Vol. 2. – №. 4.
4. Moon T.K. Error correction coding. Mathematical Methods and Algorithms. – Wiley-Interscience, 2005. – 800 pp. DOI: https://doi.org/10.1002/0471739219
5. Vapnik V. N. An Overview of Statistical Learning Theory. – IEEE Transactions on neural networks, 1999. – Vol. 10. No. 5. – pp. 988–999. DOI: https://doi.org/10.1109/72.788640
