MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers

Qiuyue Tan; Ling Zhao

doi:10.1109/ICCC47050.2019.9064425

MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers

Qiuyue Tan
, Ling Zhao

电子信息工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

4 引用（Scopus）

摘要

In this paper, a novel demodulation scheme is proposed to demodulate minimum shift keying (MSK) signals and depress the impulsive noise. The combination of convolutional neural network (CNN) and GatedNet is able to achieve both demodulation and impulsive noise depression, which distinguishes the proposed scheme from others. Furthermore, considering the features of demodulation, the use and structure of GatedNet is redesigned in this demodulation scheme. The simulation results demonstrate that this scheme can improve demodulation performance about 2dB under impulsive noise, compared with the demodulation based on coherent sequence detection and an impulsive based branch metric, whose performance can closely approach the performance of maximum likelihood algorithm.

源语言	英语
主期刊名	2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019
出版商	Institute of Electrical and Electronics Engineers Inc.
页	1975-1979
页数	5
ISBN（电子版）	9781728147437
DOI	https://doi.org/10.1109/ICCC47050.2019.9064425
出版状态	已出版 - 12月 2019
活动	5th IEEE International Conference on Computer and Communications, ICCC 2019 - Chengdu, 中国期限: 6 12月 2019 → 9 12月 2019

出版系列

姓名	2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019

会议

会议	5th IEEE International Conference on Computer and Communications, ICCC 2019
国家/地区	中国
市	Chengdu
时期	6/12/19 → 9/12/19

访问文件

10.1109/ICCC47050.2019.9064425

其它文件与链接

链接到 Scopus 的出版物

引用此

Tan, Q., & Zhao, L. (2019). MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers. 在 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019 (页码 1975-1979). 文章 9064425 (2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCC47050.2019.9064425

Tan, Qiuyue ; Zhao, Ling. / MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers. 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 1975-1979 (2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019).

@inproceedings{048c0ab835874aa894bde891f012ab7a,

title = "MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers",

abstract = "In this paper, a novel demodulation scheme is proposed to demodulate minimum shift keying (MSK) signals and depress the impulsive noise. The combination of convolutional neural network (CNN) and GatedNet is able to achieve both demodulation and impulsive noise depression, which distinguishes the proposed scheme from others. Furthermore, considering the features of demodulation, the use and structure of GatedNet is redesigned in this demodulation scheme. The simulation results demonstrate that this scheme can improve demodulation performance about 2dB under impulsive noise, compared with the demodulation based on coherent sequence detection and an impulsive based branch metric, whose performance can closely approach the performance of maximum likelihood algorithm.",

keywords = "convolutional neural network, demodulation, impulsive noise, minimum shift keying",

author = "Qiuyue Tan and Ling Zhao",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 5th IEEE International Conference on Computer and Communications, ICCC 2019 ; Conference date: 06-12-2019 Through 09-12-2019",

year = "2019",

month = dec,

doi = "10.1109/ICCC47050.2019.9064425",

language = "英语",

series = "2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1975--1979",

booktitle = "2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019",

address = "美国",

}

Tan, Q & Zhao, L 2019, MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers. 在 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019., 9064425, 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019, Institute of Electrical and Electronics Engineers Inc., 页码 1975-1979, 5th IEEE International Conference on Computer and Communications, ICCC 2019, Chengdu, 中国, 6/12/19. https://doi.org/10.1109/ICCC47050.2019.9064425

MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers. / Tan, Qiuyue; Zhao, Ling.
2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 1975-1979 9064425 (2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers

AU - Tan, Qiuyue

AU - Zhao, Ling

PY - 2019/12

Y1 - 2019/12

N2 - In this paper, a novel demodulation scheme is proposed to demodulate minimum shift keying (MSK) signals and depress the impulsive noise. The combination of convolutional neural network (CNN) and GatedNet is able to achieve both demodulation and impulsive noise depression, which distinguishes the proposed scheme from others. Furthermore, considering the features of demodulation, the use and structure of GatedNet is redesigned in this demodulation scheme. The simulation results demonstrate that this scheme can improve demodulation performance about 2dB under impulsive noise, compared with the demodulation based on coherent sequence detection and an impulsive based branch metric, whose performance can closely approach the performance of maximum likelihood algorithm.

AB - In this paper, a novel demodulation scheme is proposed to demodulate minimum shift keying (MSK) signals and depress the impulsive noise. The combination of convolutional neural network (CNN) and GatedNet is able to achieve both demodulation and impulsive noise depression, which distinguishes the proposed scheme from others. Furthermore, considering the features of demodulation, the use and structure of GatedNet is redesigned in this demodulation scheme. The simulation results demonstrate that this scheme can improve demodulation performance about 2dB under impulsive noise, compared with the demodulation based on coherent sequence detection and an impulsive based branch metric, whose performance can closely approach the performance of maximum likelihood algorithm.

KW - convolutional neural network

KW - demodulation

KW - impulsive noise

KW - minimum shift keying

UR - https://www.scopus.com/pages/publications/85084046959

U2 - 10.1109/ICCC47050.2019.9064425

DO - 10.1109/ICCC47050.2019.9064425

M3 - 会议稿件

AN - SCOPUS:85084046959

T3 - 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019

SP - 1975

EP - 1979

BT - 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Computer and Communications, ICCC 2019

Y2 - 6 December 2019 through 9 December 2019

ER -

Tan Q, Zhao L. MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers. 在 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 页码 1975-1979. 9064425. (2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019). doi: 10.1109/ICCC47050.2019.9064425

MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers

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