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

School of Electronic and Information Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1975-1979
Number of pages	5
ISBN (Electronic)	9781728147437
DOIs	https://doi.org/10.1109/ICCC47050.2019.9064425
State	Published - Dec 2019
Event	5th IEEE International Conference on Computer and Communications, ICCC 2019 - Chengdu, China Duration: 6 Dec 2019 → 9 Dec 2019

Publication series

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

Conference

Conference	5th IEEE International Conference on Computer and Communications, ICCC 2019
Country/Territory	China
City	Chengdu
Period	6/12/19 → 9/12/19

Keywords

convolutional neural network
demodulation
impulsive noise
minimum shift keying

Access to Document

10.1109/ICCC47050.2019.9064425

Cite this

Tan, Q., & Zhao, L. (2019). MSK Demodulator and Impulsive Noise Depression Based on Convolutional Neural Network with Gated Layers. In 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019 (pp. 1975-1979). Article 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. pp. 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. in 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., pp. 1975-1979, 5th IEEE International Conference on Computer and Communications, ICCC 2019, Chengdu, China, 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. p. 1975-1979 9064425 (2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In 2019 IEEE 5th International Conference on Computer and Communications, ICCC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 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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