检索结果-内蒙古大学图书馆

Time and Space Complexity Reduction of KFDA-based LTE modulation classification

International Journal of Sensors, Wireless Communications and Control 2023年第2期13卷 117-129页

作者： Kadoun, Iyad Bizaki, Hossein Khaleghi Department of Electrical and Electronic Engineering Malek Ashtar University of Technology Tehran Iran

Background: Kernel Fisher discriminant analysis (KFDA) is a nonlinear discrimination technique for improving automatic modulation classification (AMC) accuracy. Our study showed that the higher-order cumulants (HOCs) of the Long-term evolution (LTE) modulation types are nonline-arly separable, so the KFDA technique is a good solution for its modulation classification problem. Still, research papers showed that the KFDA suffers from high time and space computational com-plexity. Some studies concentrated on reducing the KFDA time complexity while preserving the AMC performance accuracy by finding faster calculation techniques, but unfortunately, they couldn't reduce the space complexity. Objective: This study aims to reduce the time and space computational complexity of the KFDA algorithm while preserving the AMC performance accuracy. Methods: Two new time and space complexity reduction algorithms have been proposed. The first algorithm is the most discriminative dataset points (MDDP) algorithm, while the second is the k-nearest neighbors-based clustering (KNN-C) algorithm. Results: The simulation results show that these algorithms could reduce the time and space complexi-ties, but their complexity reduction is a function of signal-to-noise ratio (SNR) values. On the other hand, the KNN-C-based KFDA algorithm has less complexity than the MDDP-based KFDA algo-rithm. Conclusion: The time and space computation complexity of the KFDA could be effectively reduced using MDDP and KNN-C algorithms;as a result, its calculation became much faster and had less storage size. © 2023 Bentham Science Publishers.

关键词： automatic modulation classification higher-order cumulants (HOCs) kernel fisher discriminant analysis long-term evolution MDDP algorithm signal-to-noise ratio

来源：评论

学校读者我要写书评

暂无评论

Stealthy Adversarial Attacks Against Automated modulation classification in Cognitive Radio

Stealthy Adversarial Attacks Against Automated Modulation Cl...

引用

IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)

作者： Fernando, Praveen Wei-Kocsis, Jin Purdue Univ Dept Comp & Informat Technol W Lafayette IN 47907 USA

ISBN: (纸本)9798350335675

In cognitive radio systems, wireless spectrum sensing plays a crucial role in identifying the state of the wireless environment, which leads to the effective utilization of scarce spectral resources for various application fields. As a critical part of wireless spectrum sensing, automatic modulation classification (AMC) is used to identify the modulation types of the received signals automatically. With the advances in sensing and computing technologies, artificial intelligence (AI), especially deep learning (DL), has been widely applied to enhance the effectiveness and timely response of AMC. However, in recent years, increasing evidence shows that carefully-crafted adversarial noise can mislead learning models, which raises concerns about the trustworthiness of DL solutions in AMC. Therefore, it is crucial to sufficiently mitigate the adversarial perturbations in DL-powered AMC. To realize a successful mitigation strategy, it can be beneficial to first adopt an adversarial mindset and formulate threat models of practical perturbations in DL-powered AMC. However, there is still limited work focusing on generating practical adversarial attacks in DL-powered AMC that has complex domain knowledge. In this paper, we propose an innovative and stealthy adversarial attack method that can practically compromise the DL-based decision-making of AMCs. In the performance evaluation section, different scenarios will be considered to illustrate the effectiveness of our proposed adversarial attack method in achieving a high success rate and sufficient stealthiness in AMC.

关键词： automatic modulation classification Adversarial Attack Deep Learning Cognitive Radio

来源：评论

学校读者我要写书评

暂无评论

Signal modulation classification Based on the Transformer Network

引用

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2022年第3期8卷 1348-1357页

作者： Cai, Jingjing Gan, Fengming Cao, Xianghai Liu, Wei Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD S Yorkshire England

In this work, the Transformer Network (TRN) is applied to the automatic modulation classification (AMC) problem for the first time. Different from the other deep networks, the TRN can incorporate the global information of each sample sequence and exploit the information that is semantically relevant for classification. In order to illustrate the performance of the proposed model, it is compared with four other deep models and two traditional methods. Simulation results show that the proposed one has a higher classification accuracy especially at low signal to noise ratios (SNRs), and the number of training parameters of the proposed model is less than those of the other deep models, which makes it more suitable for practical applications.

关键词： automatic modulation classification transformer network deep learning

来源：评论

学校读者我要写书评

暂无评论

Exploiting ResNeXt with Convolutional Shortcut for Signal modulation classification at Low SNRs

Exploiting ResNeXt with Convolutional Shortcut for Signal Mo...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Gu, Yuanyuan Zhou, Xin Univ Chinese Acad Sci Inst Software Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665488679

Most of the current ResNeXt-based AMC models only use identity mappings as skip connections, which cannot significantly enhance the representation abilities of the network. To address this problem, we proposed an improved ResNeXt-based AMC network (CSCA-ResNeXt) in this paper. Combining convolutional shortcuts with identity skip connections avoids network degradation and fully extracts the shallow and deep features. We also introduce the CA (Coordinate Attention) to highlight important features. It not only captures cross-channel information, but also extracts positional information and long-range correlations of signal sequences. In addition, the use of GAP (Global Average Pooling) classification enhances the association between feature maps and modulation modes, improves generalization of the network and reduces the number of parameters. CSCA-ResNeXt achieves state-of-the-art classification and generalization performance on the RML2016.10a and RML2016.10b datasets, with the highest classification accuracy of 94.45% and 95.18%, respectively. Moreover, the recognition accuracy at low SNRs of -20 to 0 dB is 39.84% and 42.49%, surpassing MCLDNN by 1.89% and 1.29% respectively. Furthermore, CSCA-ResNeXt reduces the misclassification between QAM16 and QAM64.

关键词： automatic modulation classification ResNeXt convolutional shortcut coordinate attention

来源：评论

学校读者我要写书评

暂无评论

Measurement-based modulation classification in Unlicensed Millimeter-Wave Bands

Measurement-based Modulation Classification in Unlicensed Mi...

引用

IEEE Wireless Communications and Networking Conference (WCNC)

作者： Sumen, Gizem Gorcin, Ali Qaraqe, Khalid A. TUBITAK Informat & Informat Secur Res Ctr BILGEM HISAR Lab Kocaeli Turkiye Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkiye Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkiye Texas A&M Univ Qatar Dept Elect & Comp Engn Doha Qatar

ISBN: (纸本)9781665491228

automatic modulation classification (AMC) facilitates adaptive modulation schemes, leading to the minimization of pilot signals, thus affecting spectral efficiency and reducing the power consumption in wireless communications systems. Since high-frequency heterogeneous and adaptive networks are established as future projections, AMC will also play a critical role in the millimeter-wave (mmWave) band communications. This study proposes multi-channel convolutional long short-term deep neural network (MCLDNN) model for AMC in mmWave bands. The performance of the proposed method is evaluated under real conditions based on a measurement campaign. 802.11ad signals are utilized for the measurements in 57.24 GHz to 59.40 GHz band. The classification performance of the proposed model is compared with that of well-known deep-learning methods, i.e., convolutional neural network and convolutional long short-term deep neural network. The measurement results imply the robustness of the proposed method to real-life conditions and its superiority against contemporary networks, especially in low signal-to-noise ratio (SNR) region.

关键词： automatic modulation classification unlicensed millimeter-wave convolutional neural network deep learning

来源：评论

学校读者我要写书评

暂无评论

modulation Signal classification Algorithm Based on Denoising Residual Convolutional Neural Network

引用

IEEE ACCESS 2022年 10卷 121733-121740页

作者： Guo, Yecai Wang, Xue Univ Informat Sci & Technol Dept Elect & Informat Engn Nanjing 210044 Peoples R China Wuxi Univ Dept Elect Informat Engn Wuxi 214105 Peoples R China

Traditional denoising algorithms are easy to lose signal details, resulting in low recognition accuracy of modulated signals. A modulation signal classification algorithm based on denoising residual Convolutional Neural Network (DRCNet) is proposed. DRCNet inserts a soft threshold function as a nonlinear transformation layer into the deep architecture to build a soft threshold learning network (STLNet). STLNet obtains an appropriate threshold according to the signal-to-noise ratio of the input signal samples, which is used to remove useless noise features, thereby effectively denoising the signal. This paper builds a serial network (RCTLNet) connected by Convolutional Neural Networks and Long-Short-Term Memory Networks. RCTLNet uses the convolutional neural network to extract the spatial features of the signal samples, and uses the long and short-term memory network to extract the temporal features of the signal samples. And then realize the classification and identification of the modulated signal. Experiments show that the recognition accuracy of the proposed modulated signal classification model is 92%, which is 7% higher than that of the Convolutional Long-Short Term Memory Network (CNN-LSTM).

关键词： automatic modulation classification convolutional neural network denoising network long-short term memory network

来源：评论

学校读者我要写书评

暂无评论

UniQGAN: Unified Generative Adversarial Networks for Augmented modulation classification

引用

IEEE COMMUNICATIONS LETTERS 2022年第2期26卷 355-358页

作者： Lee, Insup Lee, Wonjun Korea Univ Sch Cybersecur Network & Secur Res Lab Seoul 02841 South Korea

Deep learning has been widely applied to automatic modulation classification (AMC), and there have been many studies on data augmentation techniques using deep generative models to improve performance. However, existing solutions need to train different models independently for each SNR, which leads to undeniable overhead. This letter presents UniQGAN, Unified Generative Adversarial Networks for IQ constellations of various SNRs, requiring a single model training. The proposed method introduces multi-conditions embedding and multi-domains classification to leverage both conditions, i.e., modulation type and SNR. Experimental results show that UniQGAN effectively improves the AMC performance, while the training time is reduced.

关键词： Signal to noise ratio Generative adversarial networks modulation Training Data models Training data Constellation diagram automatic modulation classification generative adversarial networks single model training IQ~constellations

来源：评论

学校读者我要写书评

暂无评论

Maximum Likelihood Distillation for Robust modulation classification 48

Maximum Likelihood Distillation for Robust Modulation Classi...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Maroto, Javier Bovet, Gérôme Frossard, Pascal Epfl Switzerland Armasuisse Science & Technology Cyber-Defence Campus Switzerland

ISBN: (纸本)9781728163277

Deep Neural Networks are being extensively used in communication systems and automatic modulation classification (AMC) in particular. However, they are very susceptible to small adversarial perturbations that are carefully crafted to change the network decision. In this work, we build on knowledge distillation ideas and adversarial training in order to build more robust AMC systems. We first outline the importance of the quality of the training data in terms of accuracy and robustness of the model. We then propose to use the Maximum Likelihood function, which could solve the AMC problem in offline settings, to generate better training labels. Those labels teach the model to be uncertain in challenging conditions, which permits to increase the accuracy, as well as the robustness of the model when combined with adversarial training. Interestingly, we observe that this increase in performance transfers to online settings, where the Maximum Likelihood function cannot be used in practice. Overall, this work highlights the potential of learning to be uncertain in difficult scenarios, compared to directly removing label noise. © 2023 IEEE.

关键词： automatic modulation classification Knowledge Distillation Maximum Likelihood Neural Networks Robustness

来源：评论

学校读者我要写书评

暂无评论

High-Efficiency modulation classification With Temporal-Frequency Analysis Based on Multi-channel Filter Bank

High-Efficiency Modulation Classification With Temporal-Freq...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dai, Yifan Gao, Xin Jing, K.E. Tian, Bin School of Telecommunications Engineering Xididan University Xi'an China No.8511 Research Institute of CASIC Nanjing China National Time Service Center Chinese Academy of Sciences Xi'an China

ISBN: (纸本)9798350368741

Efficiency is now a key challenge in automatic modulation classification (AMC), particularly in resource-constrained environments like mobile devices in 6G networks. This paper presents a framework based on the filter-bank channelizer (FBNet) for AMC, which gracefully strikes a balance between accuracy, complexity, and speed. By channelizing signals into simpler sub-band sequences, FBNet captures dependencies in both temporal and frequency dimensions, making modulation features more distinct. Therefore, we use a compact architecture with the custom-designed dilated convolution blocks (DCB) and the adaptive channel aggregation (ACA) module as the backbones. Experimental results demonstrate that, under the same resource constraints, our model consistently outperforms the leading models in accuracy. Further evaluation conducted on the Jetson Nano, an edge platform with computing power similar to a mobile device, reveals a 42.4% advantage in inference speed for our model, underscoring that FBNet holds great potential for deployment in resource-constrained systems. © 2025 IEEE.

关键词： automatic modulation classification deep learning filter bank time series analysis

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based modulation classification in Radio Access Network

Deep Learning Based Modulation Classification in Radio Acces...

引用

2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Guo, Hongqing Peng, Shengliang Li, Tongyun Crisp, Michael Penty, Richard College of Information Science and Engineering Huaqiao University Xiamen361021 China Uppsala University Division of Signals and Systems Department of Electrical Engineering Uppsala75 103 Sweden University of Cambridge Centre for Photonic Systems Electrical Division Department of Engineering 9JJ Thomson Avenue CambridgeCB3 0FA United Kingdom

ISBN: (纸本)9798350370218

The growing demand for traffic shaping and manipulation for efficient last-mile coverage has driven extensive research using emerging artificial intelligence to overcome the capacity hurdle in next-generation wireless systems. In the radio access network (RAN), modulation format can be critical information to determine the channel condition and implement resource allocation and data compression so as to improve the quality of service and spectral efficiency of data transmission. This paper proposes a novel deep learning-based automatic modulation classification algorithm that trains neural networks as classifiers to determine the modulation format of signals transmitted in the RAN. To conquer the problem of limited training data with the received dataset, the proposed algorithm exploits a signal generation method that generates massive simulated data for training instead of relying on the received signal stream. To eliminate the issue of inconsistent data distribution between the training and inferring stages, data augmentation is applied to increase the diversity of training data and enrich the training dataset. The experiment results demonstrate that the proposed algorithm accurately identifies the modulation formats of signals received from the RAN. © 2023 IEEE.

关键词： automatic modulation classification deep learning fronthaul Radio access network

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：