Modulation recognition using artificial neural networks

被引:137
|
作者
Nandi, AK
Azzouz, EE
机构
关键词
artificial neural networks; analogue modulation recognition; digital modulation recognition; signal classification;
D O I
10.1016/S0165-1684(96)00165-X
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents artificial neural networks (ANNs) for the recognition of either analogue or digital modulation types. Computer simulations of different types of band-limited, modulated signals corrupted by band-limited Gaussian noise sequence have been carried out to measure the performance of the ANN approach. The threshold SNR for the recognition of either analogue or digitally modulated signals with average success rate greater than or equal to 98% is found to be about 10 dB. Comparisons of results from the ANN approaches and the decision-tree methods are presented. (C) 1997 Elsevier Science B.V.
引用
收藏
页码:165 / 175
页数:11
相关论文
共 50 条
  • [21] Recognition of overlapping targets using artificial neural networks
    Shiotani, Shigetoshi
    Fukuda, Toshio
    Shibata, Takanori
    Sasaki, Kyousuke
    Takeuchi, Naokazu
    Kinoshita, Tatsuyuki
    Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C, 1994, 60 (578): : 3476 - 3483
  • [22] MUSICAL NOTES RECOGNITION USING ARTIFICIAL NEURAL NETWORKS
    Moise, Adrian
    Constantin, Adrian
    Bucur, Gabriela
    ANNALS OF DAAAM FOR 2009 & PROCEEDINGS OF THE 20TH INTERNATIONAL DAAAM SYMPOSIUM, 2009, 20 : 1159 - 1160
  • [23] Ventilation mode recognition using artificial neural networks
    Leon, MA
    Lorini, FL
    COMPUTERS AND BIOMEDICAL RESEARCH, 1997, 30 (05): : 373 - 378
  • [24] Artificial grammar recognition using spiking neural networks
    Philip Cavaco
    Baran Çürüklü
    Karl Magnus Petersson
    BMC Neuroscience, 10 (Suppl 1)
  • [25] Thyroid disease recognition using artificial neural networks
    Chatur, P.N.
    Ghatol, A.A.
    IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India), 2000, 17 (03): : 143 - 145
  • [26] Automated recognition of VOCs using artificial neural networks
    Liu, BP
    Li, Y
    Zhang, L
    Zhang, LM
    Wang, XF
    Wang, JD
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26 (01) : 51 - 53
  • [27] Isolated speech recognition using artificial neural networks
    Polur, PD
    Zhou, RB
    Yang, J
    Adnani, F
    Hobson, RS
    PROCEEDINGS OF THE 23RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: BUILDING NEW BRIDGES AT THE FRONTIERS OF ENGINEERING AND MEDICINE, 2001, 23 : 1731 - 1734
  • [28] An Effective Speech Emotion Recognition Using Artificial Neural Networks
    Anoop, V.
    Rao, P. V.
    Aruna, S.
    INTERNATIONAL PROCEEDINGS ON ADVANCES IN SOFT COMPUTING, INTELLIGENT SYSTEMS AND APPLICATIONS, ASISA 2016, 2018, 628 : 393 - 401
  • [29] Face Detection with Expression Recognition using Artificial Neural Networks
    Owayjan, Michel
    Achkar, Roger
    Iskandar, Moussa
    2016 3RD MIDDLE EAST CONFERENCE ON BIOMEDICAL ENGINEERING (MECBME), 2016, : 115 - 119
  • [30] Face recognition system using artificial neural networks approach
    Nazeer, Shahrin Azuan
    Omar, Nazaruddin
    Khalid, Marzuki
    2007 INTERNATIONAL CONFERENCE OF SIGNAL PROCESSING, COMMUNICATIONS AND NETWORKING, VOLS 1 AND 2, 2006, : 420 - +