Wireless modulation classification based on Radon transform and convolutional neural networks

Ghanem, Hanan S.; Al‐Makhlasawy, Rasha M.; El‐Shafai, Walid; Elsabrouty, Maha; Hamed, Hesham F. A.; Salama, Gerges M.; El‐Samie, Fathi E. Abd

doi:10.1007/s12652-021-03650-7

Cited by 10 publications

(8 citation statements)

References 24 publications

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“…First, we compare our method with three different state-of-the-art models of deep neural networks as examples. The models we examine are ResNet [ 21 ], DenseNet [ 22 ], LSTM [ 10 ], FLANs [ 8 ] and VGG [ 23 ]. We aim to compare our model to the neural networks in two manners.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Dgani,

Cohen

2024

Sensors

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This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier’s performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks’ end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.

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Section: Simulation and Resultsmentioning

confidence: 99%

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Dgani,

Cohen

2024

Sensors

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“…Long Short-Term Memory (LSTM) is a special architecture of RNN proposed by Sepp Hochreiter and Jürgen Schmidhuber in 1997 [ 22 ]. LSTM can be used to effectively solve the problems of gradient disappear and gradient explosion that traditional RNN encounters when learning long sequences.…”

Section: Design Of Signal Amc Model With Neural Network Fusionmentioning

confidence: 99%

Signal automatic modulation based on AMC neural network fusion

Yin,

Diao

2024

PLoS ONE

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With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further improve the communication quality and system processing efficiency, this study combines two different neural network algorithms to optimize the traditional signal automatic modulation classification method. In this paper, the basic technology involved in the communication process, including automatic signal modulation technology and signal classification technology, is discussed. Then, combining parallel convolution and simple cyclic unit network, three different connection paths of automatic signal modulation classification model are constructed. The performance test results show that the classification model can achieve a stable training and verification state when the two networks are connected. After 20 and 29 iterations, the loss values are 0.13 and 0.18, respectively. In addition, when the signal-to-noise ratio (SNR) is 25dB, the classification accuracy of parallel convolutional neural network and simple cyclic unit network model is as high as 0.99. Finally, the classification models of parallel convolutional neural networks and simple cyclic unit networks have stable correct classification probabilities when Doppler shift conditions are introduced as interference in practical application environment. In summary, the neural network fusion classification model designed can significantly improve the shortcomings of traditional automatic modulation classification methods, and further improve the classification accuracy of modulated signals.

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“…Researchers in [26] utilized CNN to identify various wireless signals based on their modulation. Constellation diagrams were generated for each signal category, and then used for training and testing several pre-trained CNN-based models, including AlexNet, VGG-16, and VGG-19.…”

Section: ) Dl-based Algorithmsmentioning

confidence: 99%

Wireless Technology Identification Employing Dynamic Mode Decomposition Modeling

Elsebaay

Refai

2023

IEEE Access

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Significant growth in broadband wireless services, as well as ever-increasing demand on the spectrum caused by the Internet of Things (IoT) have overstretched limited available spectrum space for wireless services. Heterogeneous wireless networks (HetNets)-wherein multiple wireless technologies (e.g., Wi-Fi, Bluetooth, Zigbee, LTE, and GSM) coexist and share spectrum-are a promising solution for enhancing spectrum sharing. An essential element in developing coexistence protocols is correctly identifying wireless technologies anticipated to share spectrum and to shift users between available wireless technologies in an effort to optimize spectrum usage and minimize interference. For the coexistence research reported in this paper, we analyzed the performance of our developed novel algorithm based on dynamic mode decomposition (DMD) mathematical modeling to identify and differentiate among various wireless technologies. More specifically, our technique identified GSM and LTE signals in the cellular domain, IEEE802.11n, ac, and ax in the Wi-Fi domain, as well as Bluetooth and Zigbee. The proposed DMD-based technique identifies the time domain signature of a signal by capturing embedded periodic features transmitted within the signal. Performance and accuracy were tested and validated using an experimental dataset collected for various time series, and raw-power measurements of the targeted technologies. Results showed that the developed DMD-based algorithm can differentiate and classify individual and coexisting wireless signals with high accuracy -greater than 90% for most cases. Furthermore, only a short time-less than one second-is required for identifying a signal and enabling implementation in real-time practical networks. The advantage of the developed technique over comparable techniques is lower complexity (i.e., shorter processing and training time, no channel estimation, no time/frequency synchronization, and no need for long observation-time intervals).

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Wireless modulation classification based on Radon transform and convolutional neural networks

Cited by 10 publications

References 24 publications

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Signal automatic modulation based on AMC neural network fusion

Wireless Technology Identification Employing Dynamic Mode Decomposition Modeling

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