Automatic Modulation Recognition in OFDM Systems using Cepstral Analysis and Support Vector Machines

Al‐Makhlasawy, Rasha M.

doi:10.4172/2167-0919.1000105

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…In the paper [ 82 ], a cepstral algorithm for MC is proposed with adaptive modulation in OFDM systems. The expert domain features of the received signal are extracted using Mel frequency cepstral coefficients (MFCCs), and the modulation formats and their order are classified using a multi-layer feed-forward neural network.…”

Section: Artificial Intelligence-based Approach To MCmentioning

confidence: 99%

“…Various MC algorithms for the OFDM systems were carried out in [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ]. The algorithms for multiple-input multiple-output and OFDM (MIMO-OFDM) systems based on deep neural network (DNN) and Gibbs sampling are investigated in [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar

Majhi

Gui

et al. 2022

Sensors

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Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

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Section: Artificial Intelligence-based Approach To MCmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar

Majhi

Gui

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…• Other types of feature extraction There are several other types of features to be extracted, such as constellation shape, 37 zero-crossing rate, 38 Mel frequency cepstral coefficients (MFCCs), 91 phase features, 39 fuzzy-logic-based features, 40 Hellinger distance 41 , Euclidian distance, 42 and entropy. 43…”

Section: S-transformmentioning

confidence: 99%

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

Abdel‐Moneim

El‐Shafai

Abdel-Salam

et al. 2021

Int J Communication

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Automatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency-selective multipath fading, and time-varying channels in real-world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood-based (LB) and feature-based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal-to-noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC. K E Y W O R D S automatic modulation classification (AMC), deep learning (DL), feature-based (FB) methods, likelihood-based (LB) methods | INTRODUCTIONAutomatic modulation classification (AMC) is important in wireless communication systems used in military and civilian applications to enhance the efficiency of the spectrum utilization, redue the overhead, and resolve the shortage problems. Unfortunately, the restricted spectrum resources barely satisfy the ever-increasing demand for 5G 1,2 and Internet of Things (IoT) networks. 3 The AMC can be used for better management of the available spectrum. A simple block diagram of a communication system based on AMC is presented in Figure 1. 4 The AMC architecture contains two steps: signal preprocessing and a proper algorithm for classification. The preprocessing tasks involve reduction of noise, carrier frequency estimation, symbol period estimation, equalization, and signal power evaluation. On the other hand, the AMC methods comprise traditional methods including decisiontheoretic methods and feature-based methods 4,5 along with advanced methods 6 as shown in Figure 2.

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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 OFDM Systems using Cepstral Analysis and Support Vector Machines

Cited by 8 publications

References 12 publications

A Survey of Blind Modulation Classification Techniques for OFDM Signals

A Survey of Blind Modulation Classification Techniques for OFDM Signals

A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

Deep learning for wireless modulation classification based on discrete wavelet transform

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