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automatic modulation classification scheme for next-generation cellular networks using optimized adaptive multi-scale dual attention network

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2025年第3期18卷 1-18页

作者： Dinesh, G. Priya, W. Deva Shirley, C. P. Vignesh, T. SRM Inst Sci & Technol Dept Computat Intelligence Sch Comp Chennai Tamil Nadu India Saveetha Inst Med & Tech Sci SIMATS Univ Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Karunya Inst Technol & Sci Dept Comp Sci & Engn Coimbatore Tamil Nadu India Panimalar Engn Coll Dept Comp Sci & Business Syst Chennai Tamil Nadu India

In the automatic modulation classification (AMC) is a major role on the appropriate detection of suspicious and unnecessary signals actions to perform complete safe communication in next-generation cellular networks. Traditional AMC schemes often struggle with the complexity and variability inherent in modern communication environments. This paper proposes a novel automatic modulation classification method for Next-Generation Cellular Networks using Optimized Adaptive Multi-Scale Dual Attention Network (AMC-NGCN-AMSDAN). Initially, the input signal data are taken from HisarMod2019.1 dataset. Coherence Shock Filtering (CSF) is used to maintain various kinds of modulation systems and tunes the modulation data range. Then the spectral features are extracted by Multi-level Haar Wavelet Features Fusion Network (MHWFN). After that, Adaptive Multi-Scale Dual Attention Network (AMSDAN) is used to categorize the modulation schemes, like Analog, FSK, PAM, PSK, and QAM. Finally, the Gazelle Optimization Algorithm (GOA) is proposed to optimize the AMSDAN weight parameter. The AMC-NGCN-AMSDAN method attains 22.75%, 25.52%, 27.22% higher accuracy and 22.25%, 27.22%, 22.32% lesser computational time compared to the existing models, like Artificial intelligence-driven real-time AMC for next-generation cellular networks (AIDRT-AMC-NGCN), Robust AMC utilizing Convolutional Deep Neural Network with Scalogram Information (AMC-CDNN-SI), and Deep Learning-dependent Robust AMC for Next-Generation Networks (DL-AMC-NGN) respectively.

关键词： automatic modulation classification Adaptive multi-scale dual attention network Coherence shock filtering Gazelle optimization algorithm

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Development of Enriched ROA With Dilated Hybrid Network for automatic modulation classification Framework in CRNs

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第5期38卷

作者： Rao, N. Venkateswara Krishna, B. T. JNTU Kakinada Dept ECE Kakinada Andhra Pradesh India

automatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning-aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1-score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs.

关键词： adaptive and dilated hybrid network automatic modulation classification cognitive radio networks enriched remora optimization algorithm gated recurrent unit temporal convolution network

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A Novel and Effective Model for automatic modulation classification Prediction Based on Multi-BIGRU, Multi-Encoder, and Hyper-Cross

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IEEE ACCESS 2025年 13卷 20260-20277页

作者： Lin, Jianzheng China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

automatic modulation classification (AMC) is a pivotal technology in various communication systems. In recent years, deep learning (DL) has been widely applied in AMC methods due to its powerful feature extraction capabilities. However, currently proposed AMC methods still have room for improvement in classification performance. To further enhance the prediction accuracy of AMC, we propose a new model called MMH-AMC: automatic modulation classification with Multi-BIGRU, Multi-Encoder, and Hyber-Cross. Multi-BIGRU employs a multi-layer bidirectional GRU architecture to extract deep distribution patterns of AP data from both directions simultaneously. Multi-Encoder adopts a multi-layer encoder architecture with a core of Multi-Head Attention, aiming to extract deep-level IQ data distributions. To unify the feature distributions outputted by Multi-Encoder and Hyber-Cross modules, we designed and utilized the Hyber-Cross module. To validate the model's performance, we compared six different deep learning models and achieved the best performance in various scenarios.

关键词： Feature extraction modulation Deep learning Data models Data mining Convolutional neural networks Computational modeling Training Signal to noise ratio Data augmentation automatic modulation classification multi-BIGRU multi-encoder hyber-cross

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Adversarial Attack and Reliable Defense Based on Frequency Domain Feature Enhancement for automatic modulation classification

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 3731-3744页

作者： Meng, Yongchao Qi, Peihan Zheng, Shilian Cai, Zihao Zhou, Xiaoyu Jiang, Tao Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Innovat Studio Academician Yang Natl Key Lab Electromagnet Space Secur Jiaxing 314033 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China

Deep neural networks (DNNs) greatly enable the task of automatic modulation classification (AMC) by virtue of their powerful feature extraction capability. However, extensive research has shown that DNNs are highly vulnerable to adversarial attacks, which can lead them to confidently output incorrect results with high confidence scores. Existing adversarial attack methods often focus solely on temporal characteristics of signals while neglecting frequency domain information, resulting in adversarial examples with poor transferability and inadequate performance in the closed-box scenario. An adversarial attack method based on frequency domain feature enhanced and integral gradient (FEIG) for AMC task is proposed in this paper. The approach utilizes techniques such as translation interpolation and Inverse Fast Fourier Transform to enhance the frequency domain information of original examples, thereby constructing enhanced baseline examples. Subsequently, these generated enhanced baseline examples are used as new inputs for gradient integration to obtain adversarial examples. Compared to traditional methods, the generated adversarial examples exhibit stronger transferability. Furthermore, in order to improve the defense performance of the model, an enhanced hybrid adversarial training (EH-AT) framework is proposed in this paper. The original clean example and the adversarial example generated by the proposed attack method are trained with joint loss constraints, which greatly enhances the robustness of the model. Experimental results demonstrate the effectiveness of the FEIG attack method and the EH-AT framework.

关键词： Feature extraction modulation Training Robustness Frequency-domain analysis Perturbation methods Accuracy Receivers Computational modeling Wireless communication automatic modulation classification adversarial attack frequency domain feature transferability adversarial training

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A Contrastive Learner for automatic modulation classification

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第4期24卷 3575-3589页

作者： Du, Mingyang Pan, Jifei Bi, Daping Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China

The use of supervised deep neural network (DNN) for automatic modulation classification, offering an end-to-end diagram, has gained significant attention in military and civilian field, e.g., spectrum monitoring, specific emitter identification and cognitive radio. However, this approach suffers from issues such as generalization error and spurious correlations. In an effort to capture and extract more abstract and useful concepts that can enhance performance on downstream tasks, one of the promising representation learning methods, known as "contrastive learning", has achieved notable success in computer vision and natural language processing. This approach maximizes the similarities between different views of the same data example in the latent space to learn useful features. In this paper, we propose a contrastive-based objective for improving the transferability performance on the lower signal-to-noise ratios (SNR) dataset. Compared to the previous denoised-based methods used for classifying noisy signal data, we eliminate the constraints of pairwise input. This means that our model can leverage arbitrary combination of noisy and clean signal examples within same category. Additionally, the introduction of the noise level estimation enhances the robustness to the uncertain noise conditions. Simulation results on both the synthetic radar signal dataset and public communication signal dataset demonstrate that the proposed method exhibits minimal generalization error and showcases promising performance on signal data with different noise type.

关键词： Noise measurement Signal to noise ratio Feature extraction modulation Data models Contrastive learning Training Time-frequency analysis Robustness Accuracy automatic modulation classification contrastive learning noise corruption self-supervised

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A Novel Knowledge Graph Driven automatic modulation classification Framework for 6G Wireless Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第3期24卷 2373-2388页

作者： Li, Yike Zhou, Fuhui Yuan, Lu Wu, Qihui Al-Dhahir, Naofal Wong, Kai-Kit Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Artificial Intelligence Nanjing 210000 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Nanjing 210000 Peoples R China Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA UCL Dept Elect & Elect Engn London WC1E 6BT England

automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth-generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driven schemes have achieved high accuracy in AMC. However, most of these schemes focus on generating additional prior knowledge of unknown signals, which needs more computation cost in the inference phase. To solve these problems, we propose for the first time a modulation knowledge graph (MKG), and a novel knowledge graph (KG) driven AMC (KGAMC) framework by training the networks under the guidance of MKG domain knowledge. To achieve the best performance by exploiting KGAMC, a KG-driven multi-time-scale network (KG-MTSNet) is proposed to extract the MKG knowledge and the scale and frequency features of the sampled signals. Moreover, to utilize the knowledge, a designed feature aggregation loss is implemented to improve the signal feature presentation obtained by the data-driven model. Simulation results demonstrate that KGAMC significantly boosts the performances of data-driven models, and the KG-MTSNet achieves a superior classification performance compared to other benchmarks. Furthermore, the effectiveness of KGAMC is demonstrated in terms of the interpretability of the feature extraction and the sample shortage situation.

关键词： Feature extraction Wireless communication modulation Knowledge engineering Computational modeling Training Accuracy 6G mobile communication Knowledge graphs Data models automatic modulation classification data-and-knowledge dual-driven knowledge graph feature aggregation

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Discriminative estimated cumulants-based automatic modulation classification over multipath fading channels

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TELECOMMUNICATION SYSTEMS 2025年第2期88卷 1-23页

作者： Kadoun, Iyad Bizaki, Hossein Khaleghi Malek Ashtar Univ Technol Dept Elect & Comp Engn Tehran Iran

automatic modulation classification (AMC) is an essential task in intelligent receivers. AMC over multipath fading channels have two problems: The first problem is that the Higher-order moment (HOM)-based normalized channel coefficients estimator is not valid for some types of digital modulations. The second problem is about poor classification accuracy. This study addresses the aforementioned challenges through a multi-pronged approach. Firstly, it introduces a novel HOM-based normalized channel coefficient estimator applicable to a broad spectrum of digital modulation schemes. Secondly, it derives mathematical expressions for the estimated normalized HOMs and Higher-order Cumulants (HOCs) of the transmitted signal. Finally, the research employs feature selection algorithms to identify the most discriminatory estimated HOCs for Adaptive modulation and Coding (AMC). Simulation results demonstrate that the classification accuracy using estimated Higher-order Cumulants (HOCs) for M-ary Phase Shift Keying (MPSK) and M-ary Quadrature Amplitude modulation (MQAM) schemes shows significant improvement compared to previous studies. Perfect classification (100% accuracy) is achieved for the 3-tap multipath channel at Signal-to-Noise Ratio (SNR) values exceeding 6 dB, and for the 4-tap multipath channel at SNR values above 7 dB.

关键词： automatic modulation classification Discrimination function Higher-order cumulants Multipath fading channel Feature selection algorithms

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PSRNet: Few-Shot automatic modulation classification Under Potential Domain Differences

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 371-384页

作者： Xing, Hantong Wang, Shuang Wang, Jiacheng Mei, Luyang Xu, Yi Zhou, Huaji Xu, Hua Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Natl Key Lab Electromagnet Space Secur Jiaxing 314033 Peoples R China Air Force Engn Univ Informat & Nav Coll Xian 710077 Peoples R China

Learning from a limited number of samples in automatic modulation classification (AMC) has garnered considerable attention. However, existing few-shot AMC works solely focus on single-domain conditions where the training and testing data share the same data distribution, which overlook the potential domain differences. In practice, the complex and variable communication channels, along with different radio frequency (RF) devices, may result in significant data distribution differences, which can be defined as cross-domain conditions. The neglect of such cross-domain conditions may leads to a significant decline in the performance of existing few-shot AMC models. To consider a more general situation, this paper unifies single-domain and cross-domain few-shot AMC into one task, named SaC-FSL. We propose the Paired Samples Relationship Network (PSRNet) as a solution. PSRNet does not require additional network structure design for domain shifts. It distinguishes categories by learning the relationships between sample pairs rather than directly learning the features of samples. To achieve this, we randomly pair the samples to construct different relationships between different classes and domains, and learn these relationships through classification task. Extensive experiments conducted on multiple datasets have demonstrated the superiority of our PSRNet, which can achieve considerable improvements in both single-domain and cross-domain conditions.

关键词： automatic modulation classification few-shot learning cross-domain feature relationship learning automatic modulation classification few-shot learning cross-domain feature relationship learning

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A Novel Training Strategy for Deep Learning Model Compression Applied to automatic modulation classification

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 477-492页

作者： Goldbarg, Mateus A. Balza, Micael Silva, Sergio N. Silva, Lucileide M. D. Fernandes, Marcelo A. C. Univ Fed Rio Grande do Norte InovAI Lab nPITI IMD BR-59078970 Natal Brazil Univ Fed Rio Grande do Norte Leading Adv Technol Ctr Excellence nPITI IMD BR-59078970 Natal Brazil Fed Inst Educ Sci & Technol Rio Grande Norte BR-59200000 Paraiso Santa Cruz Brazil Univ Fed Rio Grande do Norte Dept Comp & Automat Engn BR-59078970 Natal Brazil

Deep learning techniques, such as deep neural networks (DNNs), have proven highly effective in addressing various automatic modulation classification challenges. However, their computational demands pose a significant hurdle for real-time modulation detection. To tackle this issue, a novel training strategy is proposed in this study. This strategy aims to minimize both pruning and quantization losses during the training of compressed models, thereby reducing the computational complexity of DNNs. The effectiveness of this approach was demonstrated through experiments involving the classification of 24 modulations: OOK, ASK4, ASK8, BPSK, QPSK, PSK8, PSK16, PSK32, APSK16, APSK32, APSK64, APSK128, QAM16, QAM32, QAM64, QAM128, QAM256, AM SSB WC, AM SSB SC, AM DSB WC, AM DSB SC, FM, GMSK and OQPS. Remarkably, the results showed a substantial reduction in both DNN weights and operations, while maintaining a high level of classification accuracy. By streamlining the computational demands of deep learning models, this strategy opens up new possibilities for real-time modulation detection applications, particularly in scenarios where computational resources are limited. This research represents a significant advancement towards the practical deployment of deep learning in modulation classification systems, paving the way for enhanced efficiency and performance in wireless communication technologies.

关键词： Accuracy modulation Signal to noise ratio Quantization (signal) Deep learning Computational modeling Adaptation models Real-time systems Iterative methods Binary phase shift keying automatic modulation classification aware compression pruning quantization computational complexity

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automatic modulation classification using modulation fingerprint extraction

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Journal of Systems Engineering and Electronics 2021年第4期32卷 799-810页

作者： NOROLAHI Jafar AZMI Paeiz AHMADI Farzaneh Electrical and Engineering Department Tarbiat Modares UniversityTehran 14115Iran

An automatic method for classifying frequency shift keying(FSK),minimum shift keying(MSK),phase shift keying(PSK),quadrature amplitude modulation(QAM),and orthogonal frequency division multiplexing(OFDM)is proposed by simultaneously using normality test,spectral analysis,and geometrical characteristics of in-phase-quadrature(I-Q)constellation *** the extracted features are unique for each modulation,they can be considered as a fingerprint of each *** show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio(SNR)and success *** example,the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%.Another advantage of the proposed method is its wide SNR range;such that the probability of classification for 16-QAM at SNR=3 dB is almost *** proposed method also provides a database for geometrical features of I-Q constellation *** comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal,the processing gain of 4 dB is *** can be mentioned about the preference of the proposed algorithm are low complexity,low SNR,wide range of modulation set,and enhanced recognition at higher-order modulations.

关键词： automatic modulation classification in-phase-quadrature(I-Q)constellation diagram spectral analysis feature based modulation classification

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