Low Complexity LDPC Decoder Based on Deep Learning

Computer & Telecommunication

Current Issue | Archive | Adv Search

Hubei Key Laboratory of Intelligent Wireless Communication

Download:

PDF(0KB)
Export: BibTeX | EndNote (RIS)

Abstract In the research of channel decoding combined with deep learning technology, the problem of dimension limitation has al- ways been the focus of researchers. Since the deep neural network is storage intensive, the channel decoder of deep neural network usually needs much more computation consumption and memory than the conventional belief-propagation (BP) decoding. In order to alleviate this problem, an improved neural network decoder for LDPC code is proposed. According to the weight parameter value distribution in the deep neural network channel decoder, weight parameters are selectively added to a new neural network decoder. By limiting the number of training parameters, the scale of the deep neural network channel decoder is reduced. And our algorithm gets a large decoding gain than BP decoding.

Key words： deep learning channel decoding LDPC cod

Published: 10 March 2020

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	YANG Zhen-lin

Cite this article:

YANG Zhen-lin. Low Complexity LDPC Decoder Based on Deep Learning. Computer & Telecommunication, 2020, 1(3): 62-65.

URL:

https://www.computertelecom.com.cn/EN/ OR https://www.computertelecom.com.cn/EN/Y2020/V1/I3/62

[1]	LI Chun-hui WANG Xiao-ying ZHANG Qing-jie LIU Han-zhuo LIANG Jia-ye GAO Ning-kang. DDoS Attack Detection Method Based on Multi-scale Convolutional Neural Network [J]. 电脑与电信, 2024, 1(6): 35-.
[2]	WANG Jin WANG Rui. Facial Expression Recognition Algorithm Based on Multi-scale Feature Deep Learning[J]. 电脑与电信, 2024, 1(5): 75-.
[3]	NIE Cheng WANG Jie. A Review of Research and Development in Data Analysis Methods [J]. 电脑与电信, 2024, 1(4): 200-25.
[4]	XIN Bo-fu. Aerial Vehicle Detection in Low Light Environment Based on Yolov5[J]. 电脑与电信, 2024, 1(1): 78-83.
[5]	SU Cui-wen CHAI Guo-qiang. Real-time Facial Expression Recognition Based on Facial Feature Detection [J]. 电脑与电信, 2023, 1(1-2): 17-21.
[6]	HE Zong-xi JIANG Ming-zhong XIE Ming-xia PANG Jia-bao CHEN Qiu-yan HU Yi-bo. Design of Driver Fatigue Recognition Algorithm Based on YOLOv8 and Face Key Points Detection[J]. 电脑与电信, 2023, 1(11): 1-6.
[7]	ZHANG Miao-miao CHAI Guo-qiang YU Hai-le XU Hao-xuan. Facial Feature Test and Fatigue Driving Warning Based on Deep Learning [J]. 电脑与电信, 2022, 1(12): 1-.
[8]	XU Ming-yuan YANG Yan-hong. Design of Baggage Detection System Based on Deep Learning[J]. 电脑与电信, 2021, 1(4): 36-40.
[9]	LIN Long. Model-based Robust RecognitionAlgorithm for Deep Learning Communication Signals[J]. 电脑与电信, 2021, 1(1): 20-22.
[10]	YAN Lei. Study on the Deep Learning Diagnosis and Intervention of Online Courses for Higher Vocational College Enrollment Students under LearningAnalysis Technology[J]. 电脑与电信, 2020, 1(6): 30-33.
[11]	. Time Window Setting on PredictionAccuracy of Rock Pressure in Fully Mechanized Working Face[J]. 电脑与电信, 2020, 1(4): 14-18.
[12]	ZHANG Gang , CHEN Jia-lian, SONG Jian, GUO Jun-qi, ZHOU Chen-rui. Chinese Landscape Painting Automated Generation Model Based on Generative Adversarial Networks[J]. 电脑与电信, 2020, 1(3): 1-.
[13]	GENG Yi-wen. Research on Recommendation System Based on StackedAutoencoder[J]. 电脑与电信, 2020, 1(11): 65-70.
[14]	HUANG Ling-zhen. Development in the Research on Steganalysis[J]. 电脑与电信, 2018, 1(1-2): 79-81.
[15]	LinWeisheng. The Application of Deep Learning Technologies in Data Analysis of Information System[J]. 电脑与电信, 2017, 1(6): 51-53.

Viewed

Full text

Abstract

Cited

Shared

Discussed