Automatic Modulation Classification in Impulsive Noise: Hyperbolic-Tangent Cyclic Spectrum and Multibranch Attention Shuffle Network

Ma, Jitong; Hu, Mutian; Wang, Tianyu; Yang, Zhengyan; Wan, Liangtian; Qiu, Tianshuang

doi:10.1109/tim.2023.3244798

Cited by 16 publications

(2 citation statements)

References 39 publications

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“…Signal classification tasks play an important role in various application scenarios, such as communication signal classification (Ma et al 2023;Hao et al 2023), medical detection (Malik et al 2021;Li et al 2020b), sound recognition (Zhang et al 2021c;Tzinis et al 2020), and so on. With the advancements in artificial intelligence technologies like deep learning, signal classification methods have become more precise and intelligent.…”

Section: Introductionmentioning

confidence: 99%

Harnessing the Power of SVD: An SVA Module for Enhanced Signal Classification

Zhai,

Yang,

et al. 2024

AAAI

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Deep learning methods have achieved outstanding performance in various signal tasks. However, due to degraded signals in real electromagnetic environment, it is crucial to seek methods that can improve the representation of signal features. In this paper, a Singular Value decomposition-based Attention, SVA is proposed to explore structure of signal data for adaptively enhancing intrinsic feature. Using a deep neural network as a base model, SVA performs feature semantic subspace learning through a decomposition layer and combines it with an attention layer to achieve adaptive enhancement of signal features. Moreover, we consider the gradient explosion problem brought by SVA and optimize SVA to improve the stability of training. Extensive experimental results demon-strate that applying SVA to a generalized classification model can significantly improve its ability in representations, making its recognition performance competitive with, or even better than, the state-of-the-art task-specific models.

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Section: Introductionmentioning

confidence: 99%

Harnessing the Power of SVD: An SVA Module for Enhanced Signal Classification

Zhai,

Yang,

et al. 2024

AAAI

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“…It has been around since the 1980s [6,7] but gained more popularity after the publication of the Cognitive Radio idea. Today, the modern AMC literature is mainly focused on deep learning and neural networks [8][9][10]. However, the classic AMC algorithms are split between two methods [11].…”

Section: Introductionmentioning

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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