The DOI matches its Crossref record.
Algorithms for automatic modulation recognition of communication signals
Abstract
This paper introduces two algorithms for analog and digital modulations recognition. The first algorithm utilizes the decision-theoretic approach in which a set of decision criteria for identifying different types of modulations is developed. In the second algorithm the artificial neural network (ANN) is used as a new approach for the modulation recognition process. Computer simulations of different types of band-limited analog and digitally modulated signals corrupted by band-limited Gaussian noise sequences have been carried out to measure the performance of the developed algorithms. In the decision-theoretic algorithm it is found that the overall success rate is over 94% at the signal-to-noise ratio (SNR) of 15 dB, while in the ANN algorithm the overall success rate is over 96% at the SNR of 15 dB.
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“This task has been accomplished for years through the approach of expert feature engineering and either analytic decision trees (single trees are widely used in practice) or trained discrimination methods operating on a compact feature space, such as support vector machines, random forests, or small feedforward NNs [53].”
From An Introduction to Deep Learning for the Physical Layer (2017) · cites this paper as [53]
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Research works printed but not linked: 15
- 10.1016/0165-1684(95)00099-2 · DOI
- 10.1109/26.380199 · DOI
- 10.1016/0165-1684(84)90063-X · DOI
- 10.1016/0165-1684(85)90144-6 · DOI
- 10.1016/0165-1684(91)90012-8 · DOI
- 10.1007/978-1-4757-2469-1 · DOI
- nagy, 1994, analysis of a method for classification of analogue modulated radio signals, Proc EUSIPCO 94, ii, 1015
- 10.1016/0165-1684(89)90093-5 · DOI
- 10.1016/0165-1684(95)00083-P · DOI
- 10.1016/0165-1684(94)00128-M · DOI
- 10.1049/ip-f-2.1990.0066 · DOI
- 10.1109/26.58753 · DOI
- fabrizi, 1986, receiver recognition of analogue modulation types, IERE Conf Radio Receiver and Associated Systems, 135
- callaghan, 1985, sampling and algorithms aid modulation recognition, Microwaves RF, 24, 117–119, 121
- 10.1109/26.141456 · DOI
Cited by
Sorted by citations
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Metadata
- Authors
- Nandi, A. K., & Azzouz, E. E.
- Affiliations
- University of Strathclyde (GB)
- ORCID
- Nandi, A.K.
- Venue
- IEEE Transactions on Communications
- Published
- 1998-04
- Volume
- 46
- Issue
- 4
- Pages
- 431-436
- ISSN
- 0090-6778 (ISSN-L)
- OpenAlex
- W2130014182
- Cited by
- 703
- References
- 0
- Citations by year
- 2026: 20 · 2025: 22 · 2024: 44 · 2023: 31 · 2022: 41 · 2021: 48 · 2020: 33 · 2019: 39 · 2018: 26 · 2017: 19 · 2016: 23 · 2015: 25 · 2014: 23 · 2013: 31 · 2012: 33
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Communications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Signal Processing Techniques
- Keywords
- automatic modulation classification, digital modulation, analog modulation, artificial neural networks, band-limited signals, Gaussian noise, signal-to-noise ratio, modulation type identification
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