Automatic Modulation Recognition in Wireless Multi-carrier Wireless Systems with Cepstral Features

Keshk, Mohamed; El-Naby, Mohammed Abd; Al‐Makhlasawy, Rasha M.; El-Khobby, Heba A.; Hamouda, Walaa; Elnaby, Mustafa M. Abd; El-Rabaie, El-Sayed M.; Dessouky, Moawad I.; Alshebeili, Saleh A.; El‐Samie, Fathi E. Abd

doi:10.1007/s11277-014-2183-3

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…Six key features are extracted from instantaneous amplitude, phase and frequency, etc., as the characteristics of modulation recognition [16,17], including:…”

Section: Resultsmentioning

confidence: 99%

Modulation recognition of communication signals based on SCHKS-SSVM

Zhang

Chen

Sun

2017

Journal of Systems Engineering and Electronics

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A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB. Xiaolin Zhang was born in 1971. He is an engineering doctor of information and communication engineering. He graduated from the College of Information and Communication Engineering in 1995 and received a Ph.D. degree from Harbin Engineering University, Harbin, China, in 2007. Now, he is an associate professor, a master graduate student tutor in Harbin Engineering University. He has published a number of academic papers, of which 10 pieces of EI retrieval. He successively presided over complete postdoctoral fund in China and postdoctoral fund in Heilongjiang Province, as a technical director successively participated in national, provincial and lateral projects. He is the member of China Communication Association. His research interests are communication signal detection and recognition, and broadband digital communication system. Jian Chen was born in 1990. In 2014, he enrolled in Harbin Engineering University with a major of information and communication engineering. He is pursuing his engineering master's degree in Harbin Engineering University, now. Especially in the direct sequence spread spectrum communication, he has a very deep research experience. He has published two international EI conference papers. His research interests are spread spectrum communication, cognitive precision communication electronic warfare, communication electronic countermeasure, and non-cooperative wireless communication detection. Zhiguo Sun was born in 1977. He was an engineering doctor of information and communication engineering. He received a Ph.D. degree from Harbin Engineering University, Harbin, China, in 2005. Now, he is an associate professor, a doctor/master graduate student tutor in Harbin Engineering University. His research interests are cognitive precision communication electronic warfare, non-cooperative wireless communication detection, high efficiency electromagnetic interference guidance, and multi-dimensional feature covert communication technology. He has participat...

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“…Six key features are extracted from instantaneous amplitude, phase and frequency, etc., as the characteristics of modulation recognition [16,17], including:…”

Section: Resultsmentioning

confidence: 99%

Modulation recognition of communication signals based on SCHKS-SSVM

Zhang

Chen

Sun

2017

Journal of Systems Engineering and Electronics

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“…From the results obtained, our method outperforms all these modulation schemes. Keshk et al [26] Table 2 compares the proposed method with [32] for BPSK, QPSK, and 16QAM modulation schemes. Accuracy of classifiers based on Moment, Cumulant, GP-KNN, and EM-ML changes with carrier phase offset as given in [33] while the developed algorithm is independent to such phase offsets.…”

Section: Comparison With Other Classifiersmentioning

confidence: 99%

“…The commercially available system for blind signal modulation detection is a black box system with no scope of further modification. Many papers with FPGA implementation based on wavelet [25], ANN [26], and conventional features [27,28] are given in the literature, but only a few have been designed for realtime operation and provides little information about implementation. With the evolution of SDR, implementation of a radio receiver can be easily achieved through LabVIEW coding.…”

Section: Introductionmentioning

confidence: 99%

Implementation of modulation classifier over software defined radio

Jajoo¹,

Kumar²,

Kumar

et al. 2020

IET Communications

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“…Several techniques depend on investigating the nature of the modulation itself to extract features, and by observing this nature and using an appropriate classification rule, the classification is correctly done. 3 The features include higher order statistics (HOS) and cumulants (e.g., Abu-Romoh et al 4 ), time domain features (e.g., Nandi and Azzouz 5 ), cyclo-stationarity, 6 cepstral features, 7,8 and several other useful features that are generally selected in an ad-hoc manner. The classifier applied on the extracted features has also an important role in achieving a high accuracy of the FB-AMC method.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for wireless modulation classification based on discrete wavelet transform

Al‐Makhlasawy

Ghanem

Kassem

et al. 2021

Int J Communication

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SummaryIn the presence of noise in communication systems, constellation diagram points are scattered to the extent that may make the modulation classification a difficult task. With the plethora of applications of machine and deep learning, several communication systems have adopted machine and deep learning to solve some classical detection and classification problems. Casting the modulation order detection as a pattern classification of the constellation images opens the door for application of mature machine learning and image processing tools to solve the classification problem, efficiently. This paper presents a system based on a wavelet‐aided convolutional neural network (CNN) classifier to efficiently detect the modulation type and order in the presence of noise. The proposed system depends on a pretrained CNN setup, which is trained with a set of constellation diagrams for each modulation scheme and used after that for testing. In addition, discrete wavelet transform (DWT) is investigated to generate representative patterns from constellation diagrams to be used for the training and testing tasks as well. The wavelet approximation images and their corresponding wavelet sub‐bands across all predefined scales are used in the dataset. Several pretrained networks including AlexNet, VGG‐16, and VGG‐19 are used as classifiers for the modulation type from the DWTs for different constellation diagrams. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different signal‐to‐noise ratios (SNRs).

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Automatic Modulation Recognition in Wireless Multi-carrier Wireless Systems with Cepstral Features

Cited by 19 publications

References 9 publications

Modulation recognition of communication signals based on SCHKS-SSVM

Modulation recognition of communication signals based on SCHKS-SSVM

Implementation of modulation classifier over software defined radio

Deep learning for wireless modulation classification based on discrete wavelet transform

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