Application of the decision trees to recognize the types of digital modulation of radio signals in cognitive systems of HF communication

Chirov, D. S.; Vynogradov, A. N.; Vorobyova, E. O.

doi:10.1109/synchroinfo.2018.8457031

Cited by 9 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML algorithms for modulation recognition mainly include decision tree [1], [2], the k-nearest neighbor [3], support vector machine [4], [5], artificial neural network [6] and some hybrid algorithms [7]- [9].…”

Section: ML and Dl For Modulation Recognitionmentioning

confidence: 99%

Deep Learning for Modulation Recognition: A Survey With a Demonstration

2020

View full text Add to dashboard Cite

In this paper, we review a variety of deep learning algorithms and models for modulation recognition and classification of wireless communication signals. Specifically, deep learning (DL) has shown overwhelming advantages in computer vision, robotics, and voice recognition. Recently, DL has been proposed to apply to wireless communications for signal detection and classification in order to better learn the active users for electromagnetic spectrum sharing purposes. Therefore, we aim to provide a survey on the most recent techniques which use DL for recognizing and classifying a wireless signal. We focus on the most widely used DL models, emphasize the advantages and limitations, and discuss the challenges as well as future directions. In addition, we also apply a DL algorithm, convolutional neural network (CNN), to demonstrate the feasibility of using CNN to recognize and classify the over-the-air wireless signals using Mathworks DL toolbox with PlutoSDR and Universal Software Radio Peripheral (USRP), respectively.

show abstract

Section: ML and Dl For Modulation Recognitionmentioning

confidence: 99%

Deep Learning for Modulation Recognition: A Survey With a Demonstration

2020

View full text Add to dashboard Cite

show abstract

“…For the classification step, support vector machine (SVM) [11], decision tree (DT) [12], and Knearest neighbor (KNN) [13] are considered the most used classical classifiers in traditional FB algorithms. The performance of the conventional FB methods is mainly affected by the extracted handcraft features and the applied classifiers; therefore, these methods have limited performance in a complex wireless communication system.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Abd-Elaziz,

Abdalla,

Elsayed

2023

Sensors

View full text Add to dashboard Cite

Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at −2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.

show abstract

“…Feature-based approaches can be divided into traditional AMC methods and deep learning methods. The features traditional AMC methods ultilized are handcraft features and the classifiers usually choose traditional machine learning methods such as support vector machine [8], decision tree [9] and K-neighbourhood algorithms [10]. The most popular handcraft features are High-Order Cumulant features [11], cyclic spectrum features [12] and Wigner Ville distribution features [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

Zhang

Yin

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

show abstract

Application of the decision trees to recognize the types of digital modulation of radio signals in cognitive systems of HF communication

Cited by 9 publications

References 6 publications

Deep Learning for Modulation Recognition: A Survey With a Demonstration

Deep Learning for Modulation Recognition: A Survey With a Demonstration

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

Contact Info

Product

Resources

About