Simple and Efficient Algorithm for Automatic Modulation Recognition for Analogue and Digital Signals

  • Badreldeen Ismail Dahap
  • Liao HongShu
  • Mohammed Ramadan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


In this paper we propose new analogue and digital recognition algorithm to discriminate between 15 signals (amplitude modulation (AM), frequency modulation (FM), double sideband modulation (DSB), lower sideband modulation (LSB), upper sideband modulation (USB), vestigial sideband (VSB), combined (AM–FM), carrier wave (CW), Noise, binary amplitude shift keying (ASK2), ASK4, binary phase shift keying (PSK2), PSK4, binary frequency shift keying (FSK2) and FSK4). Six key features extracted from instantaneous information (amplitude and phase) and signal spectral, are used to fulfill the requirement of this algorithm. Computer simulations for the signals of interest corrupted by band limited Gaussian noise was performed, the simulation results show that the overall recognition rate can reach 99.6 % when the signal to noise ratio (SNR) = 3 dB. This algorithm uses a lesser number of features compared with most of the existing automatic analogue and digital modulation recognition algorithms, thus leading to lower computational load.


Features Instantaneous information Modulation recognition and algorithm 



The authors would like to acknowledge Sarafadeen of UESTC for his support and contributions towards the accomplishment of this paper. This work was supported in part by the National Natural Science Foundation of China under Grant 11176005.


  1. 1.
    Azzouz EE, Nandi AK (1996) Automatic modulation recognition of communications signals. Kluwer Academic, NetherlandsCrossRefGoogle Scholar
  2. 2.
    Azzouz EE, Nandi AK (1996) Procedure for automatic recognition of analogue and digital modulations. Communications – IEEE Proceedin 143(5):259–266CrossRefGoogle Scholar
  3. 3.
    Nandi AK, Azzouz EE (1998) Algorithms for automatic modulation recognition of communication signals. IEEE T Commun 46(4):431–436CrossRefGoogle Scholar
  4. 4.
    Cheol-Sun Park, Dae Young Kim (2007) A modulation classification of analog and digital signals using neural network and support vector machine. ISNN 2007, Part III, LNCS 4493, p. 368–373Google Scholar
  5. 5.
    Jie Yang, Xumeng Wang, Hongli Wu (2009) Modified automatic modulation recognition algorithm. Wireless Communications, Networking and Mobile Computing, p. 1–4, Dec 2009.
  6. 6.
    Xudong Liu, Jinzhao Su, Wei Wu (2010) A modulation recognition method based on carrier frequency estimation and decision theory. Communications (APCC), 2010 16th Asia-Pacific conference, p. 6–11, Nov 2010Google Scholar
  7. 7.
    Chisheng Li, ShuliangXu, GuofengZha (2010) An efficient recognition algorithm between analog and digital signals at low SNR. IEEE Intelligent Computation Technology and Automation (ICICTA), (1)505–508Google Scholar
  8. 8.
    Jaspal Bagga, Neeta Tripathi (2011) Analysis of digitally modulated signals using instantaneous and stochastic features for classification. International Journal of Soft Computing and Engineering (IJSCE) 1(2) ISSN: 2231–2307Google Scholar
  9. 9.
    Dahap B, Liao H (2013) Simple and efficient algorithm for automatic digital modulation recognition. (sent to an international conference on communications signals processing and system —CSPS2013), “accepted”Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Badreldeen Ismail Dahap
    • 1
  • Liao HongShu
    • 2
  • Mohammed Ramadan
    • 1
  1. 1.Department of Electronics and Computer science, Collage of EngineeringKarary UniversityOmdurmanSudan
  2. 2.School of Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengduP. R of China

Personalised recommendations