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25th International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Tonchev, Krasimir Neshov, Nikolay Ivanov, Antoni Manolova, Agata Poulkov, Vladimir Tech Univ Sofia Fac Telecommun Sofia Bulgaria

ISBN: (纸本)9781665473187

Recognition of the radio signal's modulating scheme is becoming increasingly important in civil and military applications. It can potentially alleviate the electromagnetic signal congestion in 5G networks by utilization of dynamic spectrum access or perform friend/foe identification in electronic military warfare as well as to support the detection of cyber-security related attacks. The recent advances in graph-convolutional networks (GCN) reveal a potential for usage in applications such as automatic modulation classification (AMC). Considering the structure of the modulated signal in time and frequency, this work proposes GCN architecture for AMC in various signal to noise (SNR) levels. The experimental results reveal that such approach delivers comparable results to other approaches published in the literature.

关键词： automatic modulation classification automatic modulation recognition graph-convolutional neural networks graph transformer deep learning radio signal recognition

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Performance Analysis of Different Signal Representations and Optimizers for CNN Based automatic modulation classification

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WIRELESS PERSONAL COMMUNICATIONS 2024年第4期139卷 2503-2528页

作者： Chahil, Sardar Tamoor Hussain Zakwan, Muhammad Khan, Khurram Fazil, Adnan Air Univ Inst Avion & Aeronaut Dept Avion Engn E 9 Islamabad 44000 Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Topi Pakistan

automatic modulation classification (AMC) plays a crucial role in non-cooperative communication systems by identifying modulation types of received signals without prior information. Recently, Convolutional Neural Networks (CNN) based AMC techniques have shown great promise in achieving high classification accuracy for multiple modulation schemes. In this regard, researchers have used different input signal representations and optimizers for training CNN models. This paper investigates the effectiveness of using constellation diagrams and spectrograms as input representations along with various optimizers for CNN based AMC using SqueezeNet model. The optimizers assessed include Stochastic Gradient Descent with Momentum (SGDM), Adaptive Moment Estimation (ADAM), and Root Mean Square Propagation (RMSprop). Eleven modulation schemes were analyzed, and classification performance was measured using accuracy, F1 score, and specificity. The best results were obtained with constellation diagrams for RMSprop optimizer, achieving an accuracy of 99.55%, an F1 score of 99.54%, and a specificity of 99.95% at 20 dB SNR. The ADAM optimizer with constellation diagrams followed closely, yielding 96.81% accuracy, 96.82% F1 score, and 99.63% specificity. These configurations also provided the best results over wide range of SNRs from - 20 to 20 dB. Additionally, a comparative analysis with previous approach utilizing IQ sequential signal frames demonstrated a significant improvement in classification accuracy with the mentioned configurations. These findings highlight the effectiveness of specific input types and optimizers in enhancing classification performance for CNN-based AMC applications.

关键词： automatic modulation classification Convolutional neural networks Communications Constellation diagrams Spectrograms Deep learning

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SigDA: A Superimposed Domain Adaptation Framework for automatic modulation classification

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 13159-13172页

作者： Wang, Shuang Xing, Hantong Wang, Chenxu Zhou, Huaji Hou, Biao Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Natl Key Lab Electromagnet Space Secur Jiaxing 314033 Peoples R China

Due to the uncertainty of non-cooperative communication channels, the received signals often contain various impairment factors, leading to a significant decline in the performance of existing deep learning (DL)-based automatic modulation classification (AMC) models. Several preliminary works utilize domain adaptation (DA) to alleviate this issue, however, they are constrained by singular domain difference factor, whereas in practice, these factors often manifest cumulatively. Therefore, this paper introduce a more realistic task named superimposed DA, where multiple domain difference factors are overlaid, reflecting the cumulative nature of them. We propose the SigDA as a solution framework, which adopts adversarial training to align the data distribution in different domains. Two technical modules, Multi-task based Masked Signal Feature Extractor (M2SFE) and Signal Feature Pyramid Aggregation (SFPA), are innovatively designed in SigDA. M2SFE utilizes mask and reconstruction task to enhance feature extraction and achieves discriminative feature selection through the design of feature mapping layers, while SFPA can solve the problem of inconsistent signal length in superimposed DA and can aggregate the features of signals into the same dimension. We consider and superimpose various typical signal domain difference factors, comprehensive experiments demonstrate that the proposed framework can achieve significant performance improvement in various communication channels.

关键词： Feature extraction Task analysis modulation Adaptation models Convolution Data models Wireless communication automatic modulation classification domain adaptation multi-task learning adversarial training

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Binarized ResNet: Enabling Robust automatic modulation classification at the Resource-Constrained Edge

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第5期10卷 1913-1927页

作者： Shankar, Nitin Priyadarshini Sadhukhan, Deepsayan Nayak, Nancy Tholeti, Thulasi Kalyani, Sheetal Indian Inst Technol Madras Dept Elect Engn Chennai 600036 India Northeastern Univ Inst Experiential Al Boston MA 02115 USA

Recently, Deep Neural Networks (DNNs) have been used extensively for automatic modulation classification (AMC). Due to their high complexity, DNNs are typically unsuitable for deployment at resource-constrained edge networks. They are also vulnerable to adversarial attacks, which is a significant security concern. This work proposes a Rotated Binary Large ResNet (RBLResNet) for AMC that can be deployed at the edge network because of its low complexity. The performance gap between the RBLResNet and existing architectures with floating-point weights and activations can be closed by two proposed ensemble methods: (i) Multilevel classification (MC) and (ii) bagging multiple RBLResNets. The MC method achieves an accuracy of 93.39% at 10dB over all the 24 modulation classes of the Deepsig dataset. This performance is comparable to state-of-the-art performances, with 4.75 times lower memory and 1214 times lower computation. Furthermore, RBLResNet exhibits high adversarial robustness compared to existing DNN models. The proposed MC method employing RBLResNets demonstrates a notable adversarial accuracy of 87.25% across a diverse spectrum of Signal-to-Noise Ratios (SNRs), outperforming existing methods and well-established defense mechanisms to the best of our knowledge. Low memory, low computation, and the highest adversarial robustness make it a better choice for robust AMC in low-power edge devices.

关键词： modulation Feature extraction Robustness Iron Computational modeling Bagging Training Deep learning wireless communication automatic modulation classification binary neural network ensemble bagging computation and memory efficiency

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STARNet: An Efficient Spatiotemporal Feature Sharing Reconstructing Network for automatic modulation classification

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第10期23卷 13300-13312页

作者： Zhang, Xiangli Wang, Zishuo Wang, Xuesong Luo, Tianze Xiao, Yong Fang, Bin Xiao, Fei Luo, Dapeng China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Chinese Construct Third Engn Bur Intelligent Technol Co Ltd Wuhan 430070 Peoples R China

automatic modulation classification (AMC) is a crucial task in the field of wireless communication, allowing for the identification of the modulation scheme of a received radio signal without prior knowledge of the communication system. Recently, AMC approaches based on Deep Learning (DL) have achieved outstanding results. However, the majority of current DL-based AMC methods face challenges in achieving high recognition accuracy while remaining computationally efficient. Some researchers have designed autoencoder-based models to generate low-dimensional temporal feature embeddings of the radio signal, thereby reducing the number of model parameters while maintaining high performance in recognizing modulation formats. However, when further improving AMC performance via learning low-dimensional spatial-temporal feature representations, traditional autoencoder models require both a convolutional decoder and an LSTM decoder to reconstruct temporal and spatial features separately, which unavoidably raises the model parameters. In this paper, we propose a spatiotemporal feature sharing reconstructing network (STARNet) to simultaneously extract low-dimensional spatial and temporal feature representations of radio signals using a single autoencoder structure, thereby reducing the number of model parameters and improving AMC performance. Additionally, we construct a Hybrid Attentive Ghost (HA-Ghost) to automatically extract discriminative radio signal spatial information according to signal reconstruction performance. Extensive experiments on benchmark datasets demonstrate that the proposed STARNet achieves an average modulation classification accuracy of 63.64%, outperforming previous state-of-the-art models. Despite extracting more types of features, STARNet has only 14,860 parameters, which is smaller than existing spatiotemporal autoencoder-based methods.

关键词： Feature extraction modulation Spatiotemporal phenomena Computational modeling Convolution Wireless communication Decoding automatic modulation classification spatiotemporal feature representation autoencoder network attentive ghost sharing decoder deep learning

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Data and Knowledge Dual-Driven automatic modulation classification for 6G Wireless Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第5期23卷 4228-4242页

作者： Ding, Rui Zhou, Fuhui Wu, Qihui Dong, Chao Han, Zhu Dobre, Octavia A. Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 330031 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea Mem Univ Fac Engn & Appl Sci St John NF A1B 3X5 Canada

automatic modulation classification (AMC) is of crucial importance in the sixth generation wireless communication networks. Deep learning (DL)-based AMC schemes have attracted extensive attention due to their superior accuracy compared with the conventional methods. However, a pure data-driven DL method relies on a large amount of labeled training samples and the classification accuracy is poor, especially in the low signal-to-noise ratio (SNR). In order to tackle this problem, two data-and-knowledge dual-driven AMC schemes are designed. A novel data and semantic knowledge driven AMC scheme is proposed by exploiting the semantic attribute information of different modulations. Moreover, a prior knowledge driven multi-task learning visual model is established to improve the classification performance in low SNR. Furthermore, another novel data and multi-domain knowledge joint driven AMC scheme is proposed by using the semantic attribute knowledge and the prior knowledge based multi-task learning visual model. Extensive simulation results demonstrate that our proposed data-and-knowledge dual-driven AMC schemes achieve the best performance compared with the benchmark schemes in terms of classification accuracy. Moreover, it is shown that the expert knowledge spawns for AMC accuracy improvement and a decrease in the required number of training samples.

关键词： modulation Feature extraction Training Signal to noise ratio Wireless communication Semantics Computational modeling automatic modulation classification data-and-knowledge dual-driven attribute knowledge deep learning low signal-to-noise ratio

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Reliable automatic modulation classification via Grayscale Spectral Quotient Constellation Matrix and Deep Learning Models

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2025年第3期19卷 583-594页

作者： He, Jiashuo Chen, Yuting Ying, Shanchuan Chang, Shuo Huang, Sai Feng, Zhiyong Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Beijing Mech & Elect Overall Design Dept Beijing 100097 Peoples R China

automatic modulation classification (AMC) is one of the crucial technologies for designing an intelligent and efficient transceiver for future wireless communications. However, the channel interferences can cause instability in traditional signal representations, such as inphase and quadrature (I/Q) sequence, and constellations, leading to poor generalization and significant classification performance degradation in new channel environments. Retraining the classifier to achieve robust and effective performance in such cases requires a large number of re-collected samples and consumes vast computational resources, which makes it costly and difficult to apply in practice. To solve this problem, we propose the grayscale spectral quotient constellation matrix (GSQCM)-based AMC methods using deep learning (DL) in orthogonal frequency division multiplexing (OFDM) systems, which do not require retraining the classifier or performing equalization even for the unseen channel cases. Specifically, we first propose a novel method, named bidirectional and multi-step spectral cyclic division (BMSSCD), to generate the channel-robust spectral quotient signals in a length-extension manner. Then, we convert these generated signals into dimension-specific GSQCMs. Finally, the GSQCMs are used as the input to train our classifiers based on several classical DL models, such as AlexNet, VGGNet, GoogLeNet, and ResNet. It is noted that all of the DL-based classifiers are trained under additive white Gaussian noise (AWGN) channel but tested under Rician and Rayleigh multipath fading channels. Extensive simulations show that (i) the novel signal representation, i.e., GSQCM, is well suited as network input for the DL-based AMC methods to train the reliable classifiers, avoiding the model overfitting on the dataset collected under a specific channel condition, (ii) the proposed GSQCM-DL methods exhibit strong generalization, achieving robust and superior performance in comparison to some existi

关键词： modulation Signal representation OFDM Training Rician channels Convolutional neural networks Feature extraction Computational modeling AWGN channels Testing automatic modulation classification convolutional neural network generalization Rician multipath fading channel spectral quotient constellation

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Frequency-Constrained Iterative Adversarial Attacks for automatic modulation classification

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IEEE COMMUNICATIONS LETTERS 2024年第12期28卷 2734-2738页

作者： Chen, Yigong Qiao, Xiaoqiang Zhang, Jiang Zhang, Tao Du, Yihang Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Natl Univ Def Technol Res Inst 63 Nanjing 210007 Peoples R China

Although adversarial attacks present a significant threat to intelligent models based on deep learning (DL) for automatic modulation classification (AMC). However, the existing works for electromagnetic signal adversarial attacks introduce high frequency components in the frequency domain, which causes spectral mismatch and glitch problems, degrading the attack success rate after transmission through a band-limited channel. In this letter, we propose a frequency-constrained iterative adversarial attacks (FCIAA) algorithm which can suppress the high frequency components and optimize adversarial perturbations during the iterative process to alleviate such problems. The experiments using qualitative and quantitative indicators demonstrate that the proposed algorithm can effectively constrain out-of-band perturbation energy, which improves both the time and frequency domain concealment quality of the adversarial signals and enhances adversarial attacks effect.

关键词： Perturbation methods Low-pass filters Filtering algorithms Frequency-domain analysis modulation classification algorithms Wireless communication Bandwidth Vectors Transmitters automatic modulation classification adversarial attacks low-pass filter wireless security deep learning

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Imbalanced Domain Adaptation for automatic modulation classification

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第11期13卷 3172-3176页

作者： Mei, Luyang Wang, Shuang Xing, Hantong Wang, Chenxu Xu, Yi Zhou, Huaji Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China China Elect Technol Grp Corp Sci & Technol Commun Informat Secur Control Lab Jiaxing 314033 Peoples R China

In non-cooperative communication, complex channel conditions can cause data distribution shift (DDS), which can significantly degrade the performance of existing automatic modulation classification (AMC) models. Initial attempts have explored the use of unsupervised domain adaptation (UDA) to mitigate this issue. However, the considerations for these works are still limited as they assume that the label distribution is domain invariant. In real-world scenarios, significant variations in the usage frequencies of different modulation types often result in imbalanced data volumes and inconsistent label distributions. This challenge, known as label distribution shift (LDS), poses a substantial challenge for cross-domain alignment. To address this limitation, this letter considers AMC in imbalanced domain adaptation (IDA) scenarios, which handle the DDS and LDS simultaneously. We propose a novel method called Pseudo label-based Imbalanced Alignment (PIA). Specifically, our PIA leverages pseudo labels in the target domain to achieve implicit and explicit conditional feature alignment, through re-weighting self-training and centroid feature alignment, respectively. We construct various IDA scenarios for experiments using custom and public datasets, and the results prove the effectiveness and superiority of PIA.

关键词： modulation Training Feature extraction Adaptation models Rician channels Signal to noise ratio Research and development automatic modulation classification imbalanced domain adaptation label distribution shift conditional feature alignment

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A Transformer and Convolution-Based Learning Framework for automatic modulation classification

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IEEE COMMUNICATIONS LETTERS 2024年第6期28卷 1392-1396页

作者： Ma, Wenxuan Cai, Zhuoran Wang, Chuan Yantai Univ Sch Phys & Elect Informat Yantai 264000 Peoples R China

automatic modulation classification (AMC) is a typical pattern classification task that is an intermediate process between signal detection and demodulation. Deep learning methods used in AMC, such as convolutional neural network (CNN) have their shortcomings. We propose a new parallel CNN transformer network (PCTNet), which not only possesses the advantages of transformer to capture long-range dependencies, but also utilizes the advantages of CNN to extract local information. PCTNet is a parallel design of CNN and transformer, with a delivery mechanism in the middle. Extensive simulation results show that our proposed PCTNet can achieve superior classification performance than traditional deep models.

关键词： automatic modulation classification signal processing cognitive radio deep learning convolutional network

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