检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification using convolutional neural network and support vector machine

引用

DIGITAL SIGNAL PROCESSING 2025年 164卷

作者： Nasir, Shanza Sheikh, Shahzad Amin Malik, Fahad Mumtaz NUST Coll Elect & Mech Engn Dept Elect Engn Rawalpindi 45200 Pakistan

automatic modulation classification (AMC) assumes paramount significance in modern wireless communication networks, facilitating rapid signal processing, interference reduction, and resource allocation with utmost efficiency. In this research paper, the incorporation of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) techniques for automatic modulation classification are examined by employing a variety of cutting-edge deep learning frameworks, including ResNet, Inception-ResNet, MobileNet, DenseNet, and AlexNet. For training and evaluations, HISARMOD2019 is employed, which is a highly regarded benchmark dataset across the field. The method proposed involves a multi-phased process, from preprocessing and feature extraction to training the CNN and SVM classifiers with adapted raw data. Through the application of innovative deep learning strategies, the key features and nuances present in diverse modulation approaches can be automatically identified. Harnessing the unique capacity of SVM algorithms to detect minute differences within the data allows them to categorize even the most complex instances with remarkable precision. Exhaustive testing is conducted to gauge the performance of the suggested methodology. The performance of each architecture across diverse SNR levels is assessed, evaluating its accuracy, resilience to noise, and speed. The study indicates that fusing CNN and SVM leads to more accurate modulation identification. Integrating deep learning features with SVM-based classification leads to impressive performance across modulation schemes. The findings of this research can help steer the selection of suitable models to heighten the accuracy and efficiency of automatic modulation classification, leading to enhanced resource management and improved communication network performance. In future work, the proposed model should be tested under real-world conditions, where signal imperfections like resampling, synchronization issues, and no

关键词： automatic modulation classification Convolutional neural network Machine learning Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Contrastive Learner for automatic modulation classification

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Designing novel deep learning based frameworks for performance improvement of automatic modulation classification

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Islam, Soyabul Maswood, Mirza Mohd Shahriar Khulna Univ Engn & Technol Dept Elect & Commun Engn Khulna 9203 Bangladesh

Efficiently recognizing modulated signals has grown more crucial as communication technologies advances. Unfortunately, obstacles including a large number of data and inaccurate identification provide a barrier to advancement in this field. automatic modulation classification (AMC) based on deep learning (DL) has been suggested as a productive way to get good classification performance. However, under different noise situations, particularly at low signal-to-noise ratios (SNR), the majority of existing DL-AMC algorithms have limited generalization capabilities. In this paper, we developed three AMC models: a Bidirectional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM-GRU), a hybrid Convolutional Neural Network (BiLSTM-GRU-CNN) for spatiotemporal features, and Bidirectional LSTM-GRU-CNN-Attention (AMC-BLGCA) with multihead attention. Our models can be implemented on the receiver end of a transmission system as modulation classifiers, which are capable of differentiating modulated schemes in various scenarios. According to the experiment, the AMC-BLGCA has the best overall accuracy on both datasets at 62.54% and 65.00% on RadioML2016.10a and RadioML2016.10b dataset respectively. The BiLSTM-GRU model takes second place with much fewer parameters and a very close accuracy score to the AMC-BLGCA model. The parallel hybridBiLSTM-GRU-CNN model has the lowest performance among the proposed methods, but still outperforms the recent AMC models. Our proposed methods are very lightweight and easily implementable to edge devices as they use fewer parameters and the need for computation resources is very low. The performance is 3% to 9% better than the current state-of-the-art (SOTA) AMC methods.

关键词： automatic modulation classification Convolutional neural network Bidirectional recurrent neural network Multihead attention Signal classification

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：