Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Al-Nuaimi, Dhamyaa H.; Hashim, Ivan A.; Abidin, Intan Sorfina Zainal; Salman, Laith B.; Isa, Nor Ashidi Mat

doi:10.3390/electronics8121407

Cited by 37 publications

(20 citation statements)

References 86 publications

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“…The disadvantages of a likelihood-based approach may include high computational complexity and sensitivity to impairments, such as phase and frequency offsets. The feature-based AMC, maybe comparatively more efficient [23] as it leverages robust extracted features such as an instantaneous amplitude, phase, and frequency [26], cyclostationary features [27], higher-order cumulants [28], and spectral correlation features [22]. In [23,29], the authors investigated different modulation types of recognition in a digital video broadcasting scenario based on higher-order cumulants and MLP.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom

2020

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The Quality of Service (QoS) and security of Satellite Communication (Satcom) are crucial as Satcom plays a significant role in a wide range of applications, such as direct broadcast satellite, earth observation, navigation, and government/military systems. Therefore, it is necessary to ensure that transmissions are incorruptible, particularly in the presence of challenges such as Radio Frequency Interference (RFI), which is of primary concern for the efficiency of communications. The security of a wireless communication system can be improved using a robust RFI detection method, which could, in turn, lead to an effective mitigation process. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron (MLP) neural network. The considered characteristics are signal modulation and the type of RFI. In the experiments, a real-time video stream transmitted in the direct broadcast satellite is utilized with four modulation types, namely, QPSK, 8APSK, 16APSK, and 32APSK. Moreover, it is assumed that the communication signal can be combined with one of the three significant types of interference, namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI), and Chirp Interference (CI). In addition, two robust feature selection techniques have been developed to select more informative features, which leads to improving the classification precision. Furthermore, the robustness of the trained techniques is assessed to predict unknown signals at different Signal to Noise Ratios (SNRs).

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Section: Related Workmentioning

confidence: 99%

Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom

2020

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“…In prior works, two traditional processes have been executed to identify the modulation type of the received signal [5][6][7]. They are maximum likelihood-and feature extractionbased modulation identification.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, the feature-based AMC (FB-AMC) method has less computation time and does not require prior information about the transmitter. It relies on two significant processes, namely, feature extraction and classification [7]. The higher-order statistics (HOS) features present in the time domain were considered by authors in [12,13] for MC.…”

Section: Introductionmentioning

confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

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“…All the above-mentioned methods belong to the feature-based statistical approach. It consists of extracting explicit features from the received signal, then passing them through a classification algorithm where the decision is made based on their observed values [ 14 ]. This decision making step is mostly based on the analysis of the probability distribution function of the feature vectors or the evaluation of the Euclidean distance between their prescribed and estimated values.…”

Section: Introductionmentioning

confidence: 99%

V2X Wireless Technology Identification Using Time–Frequency Analysis and Random Forest Classifier

Skiribou

Elbahhar

2021

Sensors

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Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Some existing techniques show good performance but they are either sensitive to noise level or have high computational complexity. In this paper, a machine learning algorithm is proposed for the identification of vehicular communication signals. The feature vector is made up of Instantaneous Frequency (IF) resulting from time–frequency (TF) analysis. Its dimension is then reduced using the Singular Value Decomposition (SVD) technique, before being fed into a Random Forest classifier. Simulation results show the relevance and the low complexity of IF features compared to existing cyclostationarity-based ones. Furthermore, we found that the same accuracy can be maintained regardless of the noise level. The proposed framework thus provides a more accurate, robust and less complex V2X signal identification system.

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Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Cited by 37 publications

References 86 publications

Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom

Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

V2X Wireless Technology Identification Using Time–Frequency Analysis and Random Forest Classifier

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