Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 75 days
Avg accept to publ: 48 days
APC: 300 EUR


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


TRAFFIC STATS

2,485,606 unique visits
989,853 downloads
Since November 1, 2009



Robots online now
bingbot
SemanticScholar


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Application of the Voltage Control Technique and MPPT of Stand-alone PV System with Storage, HIVZIEFENDIC, J., VUIC, L., LALE, S., SARIC, M.
Issue 1/2022

AbstractPlus






LATEST NEWS

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

Read More »


    
 

  4/2019 - 8

Research on Influencing Factors of Digital Signal Modulation Recognition

WANG, J. See more information about WANG, J. on SCOPUS See more information about WANG, J. on IEEExplore See more information about WANG, J. on Web of Science, DU, H. See more information about DU, H. on SCOPUS See more information about DU, H. on SCOPUS See more information about DU, H. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (550 KB) | Citation | Downloads: 890 | Views: 1,813

Author keywords
pattern recognition, digital modulation, higher order statistics, multiple signal classification, machine learning

References keywords
modulation(21), recognition(13), order(12), communications(12), signal(9), digital(8), cumulants(8), classification(8), automatic(7), signals(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-11-30
Volume 19, Issue 4, Year 2019, On page(s): 65 - 72
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.04008
Web of Science Accession Number: 000500274700007
SCOPUS ID: 85077254689

Abstract
Quick view
Full text preview
In the real environment, modulation recognition has low classification recognition rate under low SNR and is affected by many factors such as symbol rate, frequency offset and adjacent channel crosstalk. Based on the combination of high-order cumulants and instantaneous features, this paper firstly analyzes the performance of modulation signal recognition in Gaussian environment. Then through the experimental verification, symbol rate, frequency offset, adjacent channel crosstalk has an impact on the accuracy of modulation recognition. The experimental results show that the ratio of symbol rate and sampling rate has a significant impact on the recognition results, while frequency offset and adjacent channel crosstalk have little impact on the recognition rate.


References | Cited By  «-- Click to see who has cited this paper

[1] T. Liu, Y. Guan, Y. Lin, "Research on modulation recognition with ensemble learning," EURASIP Journal on Wireless Communications and Networking, vol. 179, no. 1, 2017.
[CrossRef] [Web of Science Times Cited 39]


[2] L. Zhang, "Research on modulation recognition algorithm based on high-order cumulant," Journal of Information Engineering University, vol. 18, no. 4, pp. 403-408, 2017.
[CrossRef]


[3] J. J. Guo, H. D. Yin, L. Jiang, "Recognition of digital modulation signals via high-order cumulants," Communications Technology, vol. 47, no. 11, pp. 1255-1260, 2014.
[CrossRef]


[4] A. Swami and B. Sadler, "Modulation classification via hierarchical agglomerative cluster analysis," In First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications, pp. 141-144, 1997.
[CrossRef] [Web of Science Times Cited 12]


[5] C. M. Spooner, "On the utility of sixth-order cyclic cumulants for RF signal classification," In Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 890-897, 2001.
[CrossRef] [Web of Science Times Cited 75]


[6] O. A. Dobre, Y. Barness, and W. Su, "Higher-order cyclic cumulants for high order modulation classification," In IEEE Military Communications Conference, vol. 1, pp. 112-117, 2003.
[CrossRef]


[7] H. Wu, M. Saquib, and Z. Yun, "Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels," IEEE Transactions on Wireless Communications, vol. 7, no. 8, pp. 3098-3105, 2008.
[CrossRef] [Web of Science Times Cited 217]


[8] L. Liu and J. Xu, "A Novel Modulation Classification Method Based on High Order Cumulants," In 2006 International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1-5, 2006.
[CrossRef]


[9] Y. Q. Zhang , "Digital Signal Modulation Recognition Based on Higher Order Cumulants," Information & Communications, vol. 2, pp. 27-30, 2016.

[10] Li Y., Guo X. G., Zhao X., "Study on Modulation Recognition Based on Higher-Order Cumulants," Journal of Southwest University of Science and Technology (Natural Science Edition), vol. 33, no. 3, pp. 64-68, 2018.

[11] Hu X., Liu H. L., Zhang J. C., "Algorithm of Modulation Identification Based on Higher Order Cumulant and Instantaneous Characteristics," World Science and Technology Research and Development, vol. 33, no. 6, pp. 1002-1005, 2011.
[CrossRef]


[12] Yuan L.F., Ning S.G., He Y.G, Lyu M., Lu J., "Modulation recognition method based on high-order cumulant feature learning," Systems Engineering and Electronics, vol. 41, no. 9, pp. 2122-2131.
[CrossRef]


[13] W. Juanping, H. Yingzheng, Z. Jinmei, and W. Huakui, "Automatic Modulation Recognition of Digital Communication Signals," presented at the international conference on pervasive computing, 2010, pp. 590-593.
[CrossRef]


[14] W. Xie, S. Hu, C. Yu, P. Zhu, X. Peng, and J. Ouyang, "Deep Learning in Digital Modulation Recognition Using High Order Cumulants," IEEE Access, vol. 7, pp. 63760-63766, 2019.
[CrossRef] [Web of Science Times Cited 57]


[15] E. E. Azzouz and A. K. Nandi, "Automatic identification of digital modulation types," Signal Processing, vol. 47, no. 1, pp. 55-69, 1995.
[CrossRef] [Web of Science Times Cited 167]


[16] A. K. Nandi and E. E. Azzouz, "Algorithms for automatic modulation recognition of communication signals," IEEE Transactions on Communications, vol. 46, no. 4, pp. 431-436, 1998.
[CrossRef] [Web of Science Times Cited 406]


[17] G. P. Zhang, "Neural networks for classification: a survey," systems man and cybernetics, vol. 30, no. 4, pp. 451-462, 2000.
[CrossRef] [Web of Science Times Cited 1036]


[18] A. E. Shermeh and R. Ghazalian, "Recognition of communication signal types using genetic algorithm and support vector machines based on the higher order statistics," Digital Signal Processing, vol. 20, no. 6, pp. 1748-1757, 2010.
[CrossRef] [Web of Science Times Cited 22]


[19] Flohberger, M., Gappmair, W., & Koudelka, O., "Modulation classifier for signals used in satellite communications," In 2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop, pp. 198-202, 2010.
[CrossRef] [Web of Science Times Cited 10]


[20] A. Swami and B. M. Sadler, "Hierarchical digital modulation classification using cumulants," IEEE Transactions on Communications, vol. 48, no. 3, pp. 416-429, 2000.
[CrossRef] [Web of Science Times Cited 724]


[21] A. K. Nandi and E. E. Azzouz, "Automatic analogue modulation recognition," Signal Processing, vol. 46, no. 2, pp. 211-222, 1995.
[CrossRef] [Web of Science Times Cited 86]


[22] H. U. Youqiang, J. Liu, and X. Tan, "Digital modulation recognition based on instantaneous information," The Journal of China Universities of Posts and Telecommunications, vol. 17, no. 3, pp. 52-90, 2010.
[CrossRef]


[23] L. I. Qiuna, "Modulation Classification Method of MQAM Signals Based on Amplitude Statistical Moment," Computer Simulation, 2009.
[CrossRef]


[24] Y. Ettefagh, M. H. Moghaddam, and S. Eghbalian, "An adaptive neural network approach for automatic modulation recognition," presented at the conference on information sciences and systems, 2017, pp. 1-5.
[CrossRef]


[25] S. K. Murthy, "Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey," Data Mining and Knowledge Discovery, vol. 2, no. 4, pp. 345-389, 1998.
[CrossRef] [Web of Science Times Cited 589]


[26] Maimon, O., Rokach, L., "Decision trees," Data mining and knowledge discovery handbook. Springer, Boston, MA, 2005, pp. 165-192.
[CrossRef] [Web of Science Times Cited 362]




References Weight

Web of Science® Citations for all references: 3,802 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 141 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-17 12:32 in 136 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy