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Diffusion Model Empowered Data Augmentation for automatic modulation recognition

IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第4期14卷 1224-1228页

作者： Li, Mingkun Wang, Pengyu Dong, Yuhan Wang, Zhaocheng Tsinghua Shenzhen Int Grad Sch Inst Data & Informat Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

automatic modulation recognition (AMR), which distinguishes the modulation type of wireless signals, is crucial for spectrum sensing and signal analysis, providing valuable insights into surrounding environment. Recently, deep learning (DL) has been increasingly applied in AMR due to its robust feature extraction and generalization capability. To obtain substantial high-quality data for DL training, diffusion model is utilized to augment DL training samples and facilitate the robust AMR. Considering the training difficulty of diffusion model without high signal-to-noise ratio (SNR) data, we propose a SNR-adaptive training methodology that enables effective training on insufficient dataset with low SNR. Additionally, multiple sampling method is provided to significantly increase the number of generated samples for the diffusion model, leading to the enhanced classification performance. Simulation results show that our proposed methodology could improve the recognition accuracy effectively.

关键词： Training Noise Diffusion models Signal to noise ratio modulation Feature extraction Estimation Data augmentation Wireless communication Simulation Diffusion model automatic modulation recognition data augmentation deep learning

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A Dual-Branch Network With Feature Assistance for automatic modulation recognition

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 701-705页

作者： Feng, Yuhang Duan, Ruifeng Li, Shurui Cheng, Peng Liu, Wanchun Beijing Forestry Univ Sch Informat Sci & Technol Beijing 100083 Peoples R China Beijing Forestry Univ Sch Artificial Intelligence Beijing 100083 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia Univ Sydney Sydney NSW 2006 Australia Univ Sydney Sch Elect Enginnering & Comp Sci Camperdown NSW 2308 Australia

automatic modulation recognition (AMR) is a critical technology in wireless communications, aiming to achieve high recognition accuracy with low complexity in increasingly intricate electromagnetic environments. To tackle this challenge, in this paper, we propose a dual-branch convolution cascaded transformer network with feature assistance, termed DCTFANet. To enhance the differentiation between samples, we employ the gramian angular field (GAF) to capture potential temporal correlations between each data point. Subsequently, both I/Q sequences and GAF data are input into the model for joint signal feature extraction. The network backbone is constructed using multiple improved depthwise separable convolution (DSC) blocks, which significantly reduce computational complexity. Moreover, the backbone depth is flexibly adjustable to fully exploit local features of different data types. Finally, feature transition and the transformer encoder are used to reduce parameters and extract global feature. Experimental results on RML2016.10b show that the proposed method achieves higher recognition accuracy compared to several state-of-the-art methods, especially at low signal-to-noise ratios (SNRs), with an increase of at least 10.80% at -20 dB.

关键词： Feature extraction Transformers modulation Correlation Convolution Accuracy Encoding Data mining Australia Training automatic modulation recognition gramian angular field depthwise separable convolution transformer dual-branch network

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A lightweight cross-scale feature fusion enhanced multi-level recurrent convolutional neural network for automatic modulation recognition

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DIGITAL SIGNAL PROCESSING 2025年 158卷

作者： Zhang, Ning Wang, Deqiang Wang, Ling Shandong Univ Tradit Chinese Med Sch Intelligence & Informat Engn Jinan 250355 Shandong Peoples R China Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Shandong Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211100 Jiangshu Peoples R China

automatic modulation recognition (AMR) is a technique for automatically determining the modulation type of signals, which is essential to intelligent wireless communication. Recently, deep learning (DL) techniques have significantly facilitated the advancement of AMR methods. While DL models have made remarkable improvements on recognition accuracy for AMR, their implementations on embedded and edge devices are limited by their high computational complexity and huge number of parameters. Lightweight DL-based AMR models are therefore being explored gradually, but the lightweight is often obtained at the expense of accuracy. In this paper, we present a cross-scale feature fusion enhanced multi-level recurrent convolutional neural network, and demonstrate its advantages in both saving network parameters and improving recognition accuracy. A simplified recurrent convolutional layer (SRCL) is proposed to extract spatial context information without increasing network parameters. A cross-scale attention enhanced feature fusion layer (AEFF) is developed to emphasize the effective learning of both important local details and global key connections. A multi-level lightweight feature extractor is designed which utilizes multiple SRCLs in parallel to extract features at various levels, and utilizes the AEFF to integrate the extracted multi-scale features with an emphasis of local and global significance. Taking unpreprocessed in-phase and quadrature components of communication signals as input, the proposed model achieves improved recognition accuracy using reduced number of parameters on the benchmark datasets RadioML 2016.10a, 2016.10b and 2016.04c compared with several state-of-the-art DL models for AMR.

关键词： automatic modulation recognition Recurrent convolutional neural network Feature fusion Attention mechanism Multi-scale multi-branch convolutional neural network Lightweight deep learning model

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Hybrid Deep Photonic Spiking Neural Network for automatic modulation recognition

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第6期43卷 2672-2680页

作者： Zhang, Yahui Huang, Zhiquan Xiang, Shuiying Guo, Xingxing Zhang, Wu Tan, Qinggui Han, Genquan Hao, Yue Xidian Univ State Key Lab Integrated Serv Networks Xidian 710071 Peoples R China Xidian Univ Sch Microelect State Key Discipline Lab Wide Bandgap Semicond Tec Xian 710071 Peoples R China China Acad Space Technol CAST Xian Xian 100081 Peoples R China

Spiking neural networks (SNNs) possess remarkable capabilities in processing spatial and temporal information. Photonic SNNs, combining the advantages of high-bandwidth and low-latency of photonics, are highly-efficient, high-speed neural processors. However, training a deep photonic SNN, or even a deep SNN, for many complex tasks is challenging. In this work, we propose a hybrid deep photonic SNN (HDPSNN) for the recognition of automatic modulation recognition (AMR). The proposed HDPSNN integrates existing convolutional and long short-term memory layers for feature extraction, SNNs for recognition, and photonic SNN for accelerating the recognition process. In the HDPSNN, a two-layers photonic SNN is demonstrated experimentally using vertical-cavity surface-emitting lasers neurons and Fabry-P & eacute;rot laser with an embedded saturable absorber neuron. For the hybrid deep SNN (HDSNN), numerical results show that the maximum accuracy reaches 89.59% with the signal-to-noise ratios (SNRs) of +18 dB. Additionally, the inference accuracy reaches 80% for 100 randomly selected samples at SNR of 0 dB. For the HDPSNN, experimental results indicate AMR accuracy of 70% for the inference of the same 100 randomly selected samples. Thus, the results shown that the proposed HDPSNN can achieve AMR with minor accuracy loss. These findings suggest a method to combine deep neural network, SNN and photonics to achieve complex tasks in optical SNN. It is significant to pursue ultra-low delay and solutions for complex tasks, design neural structures of photonic SNNs, and realize hardware-algorithm collaborative computing.

关键词： Neurons Photonics Vertical cavity surface emitting lasers Feature extraction Optical attenuators Accuracy Training modulation Spiking neural networks Optical modulation automatic modulation recognition photonic spiking neural networks VCSELs

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An Efficient Model for Few-Shot automatic modulation recognition Based on Supervised Contrastive Learning

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 3533-3538页

作者： Kong, Weisi Jiao, Xun Xu, Yuhua Zhang, Bolin Yang, Qinghai Xidian Univ Sch Telecommun Engn State Key Lab ISN Xian 710071 Peoples R China Acad Mil Sci Syst Engn Res Inst Beijing 100000 Peoples R China

The application of deep learning (DL) has improved the reliability and intelligence of automatic modulation recognition (AMR). However, real-world scenarios often involve a limited number of signal samples. Furthermore, most existing DL-based AMR models improve performance at the cost of computational complexity. Therefore, we propose an efficient contrastive multi-stage sparse attention network (CMSSAN) model for few-shot AMR without auxiliary datasets. Specifically, supervised contrastive learning is utilized to enhance the feature representation of the signal, and a joint loss with dynamic weights is constructed to balance the representation and classification tasks. In addition, a lightweight MSSAN encoder is proposed to enhance the recognition performance with lower computations and parameters. Simulation experiments are conducted on the ablation experiment and hyperparameter analysis of the proposed model, and the superiority of the model is verified on several datasets.

关键词： Feature extraction Convolution Transformers Training Vectors Computational modeling Accuracy modulation Encoding Kernel automatic modulation recognition Few-shot Lightweight Contrastive Learning Transformer network

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Enhancing automatic modulation recognition Through Robust Global Feature Extraction

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 4192-4207页

作者： Qu, Yunpeng Lu, Zhilin Zeng, Rui Wang, Jintao Wang, Jian Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Lab High Technol Beijing 100084 Peoples R China

automatic modulation recognition (AMR) plays a crucial role in wireless communication systems. Deep learning AMR strategies have achieved tremendous success in recent years. Modulated signals exhibit long temporal dependencies, and extracting global features is crucial in identifying modulation schemes. Traditionally, human experts analyze patterns in constellation diagrams to classify modulation schemes. Classical convolutional-based networks, due to their limited receptive fields, excel at extracting local features but struggle to capture global relationships. To address this limitation, we introduce a novel hybrid deep framework named TLDNN, which incorporates the architectures of the transformer and long short-term memory (LSTM). We utilize the self-attention mechanism of the transformer to model the global correlations in signal sequences while employing LSTM to enhance the capture of temporal dependencies. To mitigate the impact like RF fingerprint features and channel characteristics on model generalization, we propose data augmentation strategies known as segment substitution (SS) to enhance the model's robustness to modulation-related features. Experimental results on widely-used datasets demonstrate that our method achieves state-of-the-art performance and exhibits significant advantages in terms of complexity. Our framework serves as a foundational backbone that can be extended to different datasets and applied to both mobile and static scenarios. We have verified the effectiveness of our augmentation approach in enhancing the generalization, particularly in few-shot scenarios.

关键词： Feature extraction modulation Long short term memory Transformers Data mining Wireless communication Fingerprint recognition Radio frequency Data augmentation Constellation diagram automatic modulation recognition transformer LSTM deep learning data augmentation

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SemiAMR: Semi-Supervised automatic modulation recognition With Corrected Pseudo-Label and Consistency Regularization

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第1期10卷 107-121页

作者： Guo, Yuanpu Zhong, Dan Sun, Haixin Jiang, Zhiye Ye, Linmei Deng, Zhenmiao Liu, Hui Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Beijing Inst Space Long March Vehicle Natl Key Lab Sci & Technol Test Phys & Numer Math Beijing 100076 Peoples R China State Radio Regulat China Fujian Monitoring Stn Xiamen 350003 Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China

In modern communication systems, automatic modulation recognition (AMR) has broad application prospects but a large amount of training data is usually required. Due to the high cost of adding annotations to signals, it is of great significance to study AMR under the condition of few labeled samples. In this paper, an end-to-end semi-supervised radio signal recognition algorithm is proposed. In view of the poor anti-noise performance of the original IQ data, short-time Fourier transform (STFT) is introduced to mine the stable modulation characteristics. In order to overcome the noise problem caused by pseudo-labels, a mixed sample data augmentation (MSDA) method PFMix is proposed, and the consistency constraint of the model disturbance based on mean teacher (MT) framework and data disturbance based on PFMix is designed. Meanwhile, in order to provide pseudo-labels with high confidence, entropy regularization is constructed by taking the prediction variance of MT framework as correction factor. The experimental results using the open source RML2016.10A dataset show the superior performance of the proposed method. The average recognition accuracy with 1% labeled data is 88.82% at 0 dB, which is a significant improvement compared with state of the art in semi-supervised AMR.

关键词： modulation Feature extraction Data models Training Task analysis Entropy Correlation automatic modulation recognition semi-supervised learning consistency constraint entropy regularization time-frequency transform

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Improved automatic modulation recognition using deep learning with additive attention

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RESULTS IN ENGINEERING 2025年 26卷

作者： El-Haryqy, Noureddine Kharbouche, Anass Ouamna, Hamza Madini, Zhour Zouine, Younes Ibn Tofail Univ Natl Sch Appl Sci ENSA Dept Elect & Telecommun Lab Adv Syst Engn ISA Kenitra 14000 Morocco

automatic modulation recognition (AMR) is a critical task in modern communication systems, enabling applications such as cognitive radio, spectrum monitoring, and IoT networks. This paper proposes ICRNNA, a novel deep learning model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long ShortTerm Memory (BiLSTM) networks, and an attention mechanism to achieve state-of-the-art performance in AMR tasks. The proposed model is evaluated on the RadioML2016.10a and RadioML2016.10b datasets, demonstrating superior accuracy, computational efficiency, and robustness, particularly in low Signal-toNoise Ratio (SNR) environments. Through extensive ablation studies, we highlight the contributions of each component, showing that the combination of CNNs, BiLSTMs, and attention mechanisms significantly enhances performance. Comparative experiments against state-of-the-art models, including ResNet, MCLDNN, and CNNBiLSTM-DNN, reveal that ICRNNA achieves the highest accuracy (63.24% on RadioML2016.10a and 65.39% on RadioML2016.10b) and outperforms baseline models in computational efficiency, with only 48.42 MFLOPs and 0.79 million parameters. The results underscore the model's suitability for real-time applications in dynamic and noisy environments. This work advances the field of AMR by providing a robust, efficient, and high-performing solution for modern communication systems.

关键词： automatic modulation recognition Bidirectional long short-term memory networks Convolutional neural networks Deep learning Enhanced attention mechanism Signal-to-noise ratio

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Contrastive Language-Signal Prediction for automatic modulation recognition

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第11期13卷 3242-3246页

作者： Qu, Yunpeng Lu, Zhilin Hui, Bingyu Wang, Jintao Wang, Jian Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Lab High Technol Beijing 100084 Peoples R China

automatic modulation recognition (AMR) enables intelligent communication and is a critical component of wireless communication systems. Deep learning-based AMR approaches have made significant strides in recent years. These approaches involve inputting signals in the form of images or embeddings into a network, which maps them into high-dimensional feature vectors for subsequent classification. However, radio frequency (RF) signals exhibit significant differences within the same class due to noise or wireless channels. Performing classification based on high-dimensional features may be challenging in capturing robust discriminative features, thereby compromising the model's generalization ability. To address this limitation, we introduce a novel framework named CLASP, which incorporates language models through contrastive learning, coupling AMR with human language priors to extract robust discriminative features between different categories. Additionally, we treat the prediction of SNR levels as a subtask to acquire auxiliary priors that represent the impact of noise. Extensive results on widely-used datasets demonstrate that CLASP achieves state-of-the-art (SOTA) performance compared to other baselines. As a framework, CLASP exhibits universality and demonstrates superior performance compared to the linear-probe approach across different backbones.

关键词： Signal to noise ratio modulation Feature extraction Contrastive learning Binary phase shift keying Noise RF signals automatic modulation recognition contrastive learning language model deep learning

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ClST: A Convolutional Transformer Framework for automatic modulation recognition by Knowledge Distillation

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第7期23卷 8013-8028页

作者： Hou, Dongbin Li, Lixin Lin, Wensheng Liang, Junli Han, Zhu Northwestern Polytech Univ Sch Elect & Informat Xian 710065 Shaanxi Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710065 Shaanxi Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710065 Peoples R China

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments and large-scale DL models are critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution-linked signal transformer (ClST) and a novel knowledge distillation method named signal knowledge distillation (SKD). The ClST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel attention mechanism named parallel spatial-channel attention (PSCA) mechanism and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The SKD is a knowledge distillation method to effectively reduce the parameters and complexity of neural networks. We train two lightweight neural networks using the SKD algorithm, KD-CNN and KD-MobileNet, to meet the demand that neural networks can be used on miniaturized devices. The simulation results demonstrate that the ClST outperforms advanced neural networks on all datasets. Moreover, both KD-CNN and KD-MobileNet obtain higher recognition accuracy with less network complexity, which is very beneficial for the deployment of AMR on miniaturized communication devices.

关键词： Transformers Convolution Convolutional neural networks Training Neural networks modulation Complexity theory automatic modulation recognition few-shot learning transformer knowledge distillation

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