A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition

Wang, Tiange; Yang, Guangsong; Chen, Penghui; Xu, Zhenghua; Jiang, Mengxi; Ye, Qiubo

doi:10.3390/app122312052

Cited by 7 publications

(6 citation statements)

References 99 publications

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“…Moreover, the high variation of entropy values among different segments of the random vibration reflects the complex and irregular patterns in the signal. These concepts find useful applications in signal processing, including but not limited to: The Shannon entropy formula in (27), introduced by Claude Shannon in 1948 [40], is the fundamental form of entropy that is widely used in signal processing. Spectral Entropy is another type of entropy that applies Shannon entropy to the power spectrum of a signal, providing insight into its spectral characteristics.…”

Section: E Signal Entropymentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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Section: E Signal Entropymentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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“…Deep learning facilitates feature extraction and learning by constructing intricate model structures and leveraging vast quantities of data for training. Currently, numerous experts have leveraged deep learning models to address signal modulation challenges [ 14 ]. D. Xu et al presented a framework for Undetectable Universal Counterperturbations (UAPs) in AMC systems.…”

Section: Related Workmentioning

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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“…Under the assumption that the signal received by the receiver has undergone carrier synchronization, symbol timing, and matched filtering, and the channel noise is Gaussian white noise, the symbol synchronous sampling complex signal sequence obtained at the output is [32] 𝑥(𝑡) = 𝑠(𝑡) + 𝑛(𝑡) = √𝐴 ∑ µ 𝑘 √𝐸 𝑛 𝑔 𝑘 λ(t − nT) exp [𝑗(2𝜋𝑓 𝑐 + Ɵ 𝑐 ] + 𝑛(𝑡) (26) x(t) is the signal received at the receiving end, and s(t) is the signal at the transmitting end, n(t) is the zero-mean complex Gaussian white noise, 𝐸 𝑛 is signal energy.…”

Section: Feature Extraction-based Classificationmentioning

confidence: 99%

“…The network structure was composed of multiple processing modules achieving a classification accuracy can reach 97.7% in high SNR only [6] [26]. F. Shi et al [27] [26] proposed an automatic modulation recognition (AMR) method which includes a multi-scale convolution deep network for recognizing modulation types achieving an overall recognition accuracy of 98.7%.again only in high SNR unlike our proposed work where we achieve high accuracy in both high and low SNR [27] [26].…”

Section: Introductionmentioning

confidence: 96%

Radio Modulation Classification Optimization Using Combinatorial Deep Learning Technique

Elkhatib,

Kamalov,

Moussa

et al. 2024

IEEE Access

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We present an automatic signal modulation classification model using combinatorial deep learning technique. Our proposed deep learning model increase accuracy for low Signal-to-Noise Ratio (SNR) and maintain a high classification accuracy for high SNR signals. Using a hybrid deep learning model combining both ConvLSTM with Transformer-block neural networks, the proposed modulation classifier architecture can learn the signal for both low and high SNR and get better accuracy for signals with high noise. The proposed deep learning modulation classification technique achieves improved classification accuracy of 66% for low SNR signals and 93.5% at high SNR showing that our model is robust under noisy signal modulation. Thus, getting better accuracy in lower SNR signals without sacrifice accuracy for higher SNR signals. An adaptive weighted focal loss function is proposed as an optimized loss function for efficient classification which can be used to control the outliers within a class imbalance and avoid underflow issues. Our deep learning radio modulation classification model works using raw signal without the need of denoising the noisy signal. INDEX TERMS Automatic modulation classification, dynamic spectrum allocation, deep learning techniques, transformer-block convLSTM, feature-based extraction, AI-based wireless communications.

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A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition

Cited by 7 publications

References 99 publications

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Signal automatic modulation based on AMC neural network fusion

Radio Modulation Classification Optimization Using Combinatorial Deep Learning Technique

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