Maximum Likelihood Distillation for Robust Modulation Classification

Maroto, Javier; Bovet, Gérôme; Frossard, Pascal

doi:10.1109/icassp49357.2023.10096156

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Many scholars have conducted a lot of research on this technology. After decades of development, radio signal modulation technologies are mainly divided into two categories: those that use the decision theory modulation method based on maximum likelihood (LB) [8][9][10] and those that use the pattern recognition method based on feature extraction (FB) [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Research on Communication Signal Modulation Recognition Based on a CCLDNN

He,

Zeng

2024

Electronics

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In this paper, a new automatic modulation recognition (AMR) method named CCLDNN (complex-valued convolution long short-term memory deep neural network) is proposed. It is designed to significantly improve the recognition accuracy of modulation modes in low signal-to-noise ratio (SNR) environments. The model integrates the advantages of existing mainstream neural networks. The phase and amplitude information of complex signals is effectively captured through a complex module in the input layer. The Squeeze-and-Excitation (SE) attention mechanism, Bi-LSTM layer, and deep convolutional layer are introduced in the feature extraction layer to gradually enhance feature expression. Among these, the introduction of LSTM enables the model to capture the sequence dependence of signals, and the application of the SE attention mechanism further improves the model’s ability to focus on key features. Tests using the RadioML2016.10a dataset show that the model performs well at multiple SNR levels, achieving an average recognition accuracy of more than 80% over an SNR range of 0 dB to 18 dB. However, under the condition of a low SNR from −20 dB to −2 dB, the model still maintains a high recognition ability. The advanced CCLDNN method shows great deep learning potential in solving practical communication problems.

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Section: Introductionmentioning

confidence: 99%

Research on Communication Signal Modulation Recognition Based on a CCLDNN

He,

Zeng

2024

Electronics

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An ultra lightweight neural network for automatic modulation classification in drone communications

Wang,

Fang,

Fan

et al. 2024

Sci Rep

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Unmanned aerial vehicle (UAV)-assisted communication based on automatic modulation classification (AMC) technology is considered an effective solution to improve the transmission efficiency of wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment. However, many existing deep learning (DL)-based AMC methods cannot be directly applied to UAV platform with limited computing power and storage space, because of the contradiction between accuracy and efficiency. This paper mainly studies the lightweight of DL-based AMC networks to improve adaptability in resource-constrained scenarios. To address this challenge, we propose an ultra-lightweight neural network (ULNN). This network incorporates a lightweight convolutional structure, attention mechanism, and cross-channel feature fusion technique. Additionally, we introduce data augmentation (DA) based on signal phase offsets during the model training process, aimed at improving the model’s generalization ability and preventing overfitting. Through experimental validation on the public dataset RML2016.10 A, the ULNN we proposed achieves an average precision of 62.83% with only 8815 parameters and reaches a peak classification accuracy of 92.11% at SNR = 10 dB. The experimental results show that ULNN can achieve high recognition accuracy while keeping the model lightweight, and is suitable for UAV platform with limited resources.

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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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Maximum Likelihood Distillation for Robust Modulation Classification

Cited by 3 publications

References 21 publications

Research on Communication Signal Modulation Recognition Based on a CCLDNN

Research on Communication Signal Modulation Recognition Based on a CCLDNN

An ultra lightweight neural network for automatic modulation classification in drone communications

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

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