Modulation classification in the presence of adjacent channel interference using convolutional neural networks

Al‐Makhlasawy, Rasha M.; Hefnawy, Alaa A.; Elnaby, Mustafa M. Abd; El‐Samie, Fathi E. Abd

doi:10.1002/dac.4295

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…Set-up of the 5G communication recently in various papers like [28] where the objective is actually the modulation classification using CNN in presence of wireless interference and where constellation diagrams are used as in input to the CNN in a similar way to this paper. Then, the objective of [28] is complementary to the objective in this paper.…”

Section: A Workflowmentioning

confidence: 99%

Wireless Interference Identification With Convolutional Neural Networks Based on the FPGA Implementation of the LTE Cell-Specific Reference Signal (CRS)

Baldini,

Bonavitacola,

Chareau

2024

IEEE Trans. Cogn. Commun. Netw.

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Wireless Interference Identification (WII) is an important function in the context of non-cooperative spectrum coexistence management problems because interference may cause loss or degradation of service. Deep Learning (DL) has been recently introduced in spectrum coexistence problems and more specifically in WII where it has demonstrated a superior performance to shallow machine learning algorithms. This paper proposes an advancement in literature by exploiting the existing function in modern cellular networks systems for channel estimation to implement WII. In particular, a channel estimator function based on the LTE Cell Specific Reference Signal (CRS) was implemented in Field Programmable Gate Array (FPGA) by the authors and it was used to generate channel estimates, which are given as an input to a DL algorithm. This study applies Convolutional Neural Networks (CNN) on three different data sets for WII, where the victim is a LTE-plus communication system with 40 MHz bandwidth and the interferences are 1) LTE with 20 MHz bandwidth and FDD modulation, 2) LTE with 20 MHz bandwidth and TDD modulation and 3) WiFi (802.11g). This paper describes the FPGA channel estimator implementation and it performs an extensive analysis of the impact of the parameters of the proposed approach and the CNN architecture. The results show that the proposed approach outperforms other approaches based on DL and constellation diagrams or shallow machine learning algorithms.

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Section: A Workflowmentioning

confidence: 99%

Wireless Interference Identification With Convolutional Neural Networks Based on the FPGA Implementation of the LTE Cell-Specific Reference Signal (CRS)

Baldini,

Bonavitacola,

Chareau

2024

IEEE Trans. Cogn. Commun. Netw.

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“…SVM occupies a dominant position in the field of machine learning due to its complete theoretical basis and good practical effects. Modulation recognizers based on the SVM model are emerging endlessly [9,10]. The essence is to slightly improve the SVM model based on the extracted communication signal feature vector, such as improving the multiclassification strategy of SVM, improving the kernel function of SVM, and improving the penalty term of SVM [11,12].…”

Section: Introductionmentioning

confidence: 99%

Communication Signal Modulation Mechanism Based on Artificial Feature Engineering Deep Neural Network Modulation Identifier

Shi

2021

Wireless Communications and Mobile Computing

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Based on the characteristics of time domain and frequency domain recognition theory, a recognition scheme is designed to complete the modulation identification of communication signals including 16 analog and digital modulations, involving 10 different eigenvalues in total. In the in-class recognition of FSK signal, feature extraction in frequency domain is carried out, and a statistical algorithm of spectral peak number is proposed. This paper presents a method to calculate the rotation degree of constellation image. By calculating the rotation degree and modifying the clustering radius, the recognition rate of QAM signal is improved significantly. Another commonly used method for calculating the rotation of constellations is based on Radon transform. Compared with the proposed algorithm, the proposed algorithm has lower computational complexity and higher accuracy under certain SNR conditions. In the modulation discriminator of the deep neural network, the spectral features and cumulative features are extracted as inputs, the modified linear elements are used as neuron activation functions, and the cross-entropy is used as loss functions. In the modulation recognitor of deep neural network, deep neural network and cyclic neural network are constructed for modulation recognition of communication signals. The neural network automatic modulation recognizer is implemented on CPU and GPU, which verifies the recognition accuracy of communication signal modulation recognizer based on neural network. The experimental results show that the communication signal modulation recognizer based on artificial neural network has good classification accuracy in both the training set and the test set.

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“…Different pretrained networks, namely, AlexNet, ResNetv4, VGG-16, and GoogLeNet-v2 have been used for AMC. In Al-Makhlasawy et al, 23 a DL-based modulation classification scheme depending on a CNN has been applied to classify modulation in the presence of adjacent channel interference (ACI) using pretrained models.…”

Section: Introductionmentioning

confidence: 99%

“…Casting the AMC problem as an image classification problem does not only allow the use of pretrained models on images, but also enables the application of a useful preprocessing step that may further facilitate the feature extraction from the constellation diagrams. For example, in Al-Makhlasawy et al, 23 Radon transforms (RTs) of constellation diagrams have been used with singular value decomposition (SVD) for optical modulation classification.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we implement pretrained CNN models on wavelet-decomposed constellation diagrams. Different from the work in Al-Makhlasawy et al 23 that considers only a CNN network, we use the wavelet transform to reduce the constellation diagram dimesionality, and hence facilitate the feature extraction process. The work in this paper is also different from that in Eltaieb et al 24 that applies the powerful, yet more time-consuming RT.…”

Section: Introductionmentioning

confidence: 99%

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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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Modulation classification in the presence of adjacent channel interference using convolutional neural networks

Cited by 11 publications

References 14 publications

Wireless Interference Identification With Convolutional Neural Networks Based on the FPGA Implementation of the LTE Cell-Specific Reference Signal (CRS)

Wireless Interference Identification With Convolutional Neural Networks Based on the FPGA Implementation of the LTE Cell-Specific Reference Signal (CRS)

Communication Signal Modulation Mechanism Based on Artificial Feature Engineering Deep Neural Network Modulation Identifier

Deep learning for wireless modulation classification based on discrete wavelet transform

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