检索结果-内蒙古大学图书馆

AMCRN: Few-Shot Learning for automatic modulation classification

IEEE COMMUNICATIONS LETTERS 2022年第3期26卷 542-546页

作者： Zhou, Quan Zhang, Ronghui Mu, Junsheng Zhang, Hongming Zhang, Fangpei Jing, Xiaojun Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing 100048 Peoples R China

Deep learning (DL) has been widely applied in automatic modulation classification (AMC), while the superb performance highly depends on high-quality datasets. Motivated by this, the AMC under few-shot conditions is considered in this letter, where a novel network architecture is proposed, namely automatic modulation classification relation network (AMCRN), and verified with the baseline methods. Experimental results state that the accuracy of proposed AMCRN exceeds 90% and 10% to 50% improvements are obtained compared with classical schemes when the signal-to-noise ratio (SNR) is greater than -2 dB.

关键词： modulation Training Feature extraction Signal to noise ratio Convolutional neural networks Convolution Kernel Few shot learning automatic modulation classification relation network deep learning

来源：评论

学校读者我要写书评

暂无评论

ConvLSTMAE: A Spatiotemporal Parallel Autoencoders for automatic modulation classification

引用

IEEE COMMUNICATIONS LETTERS 2022年第8期26卷 1804-1808页

作者： Shi Yunhao Xu Hua Jiang Lei Qi Zisen Air Force Engn Univ Informat & Nav Coll Xian 710038 Shaanxi Peoples R China

automatic modulation classification (AMC) is the key technique in both military and civilian wireless communication. However, the performance is unsatisfactory, even several deep learning-based methods are involved. Targeting its low accuracy at low SNR, high computational cost and label overdependence, we propose a novel AMC framework, where the autoencoder (AE) serves as the backbone and Convolution-AE and LSTM-AE are combined in a parallel way as temporal and spatial feature extractors. The comparisons with serval algorithms on the radioML2016.10a show that our proposed network can achieve higher classification accuracy at low SNR with a low cost. In addition, it suits the semi-supervised scenario since the dependence on labels is loosen.

关键词： Feature extraction modulation Logic gates Symbols Kernel Training Image reconstruction automatic modulation classification autoencoder convolution LSTM semi-supervised

来源：评论

学校读者我要写书评

暂无评论

RanNet: Learning Residual-Attention Structure in CNNs for automatic modulation classification

引用

IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第6期11卷 1243-1247页

作者： Huynh-The, Thien Pham, Quoc-Viet Nguyen, Toan-Van Nguyen, Thanh Thi da Costa, Daniel Benevides Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi 39177 South Korea Kumoh Natl Inst Technol Dept IT Convergence Gumi 39177 South Korea Pusan Natl Univ Korean Southeast Ctr Ind Revolut Leader Educ 4 Busan South Korea Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA Deakin Univ Sch Informat Technol Waurn Ponds Vic 3216 Australia Technol Innovat Inst AI & Telecom Res Ctr Abu Dhabi U Arab Emirates Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan

With the rapid emergence of advanced technologies for wireless communications, automatic modulation classification (AMC) has been deployed in the physical layer to blindly identify the modulation fashion of an incoming signal at the receiver and consequently improve the efficiency of spectrum utilization and management. Although recent works on AMC have adopted deep learning with convolutional neural networks (CNNs) to deal with large-confusing signal data, they have shown to be vulnerable to channel deterioration by primitive architectures. In this letter, we design a high-performance CNN architecture, namely Residual-attention Convolutional Network (RanNet), that mainly involves multiple advanced processing blocks to learn intrinsic features of combined waveform data (including in-phase, quadrature, amplitude, and phase components). Each block incorporates attention connection and skip connection in a sophisticated-designed structure to strengthen relevant features and weaken irrelevant features while preventing the network from vanishing gradient. Simulation results on the RadioML 2018.01A dataset show that RanNet is robust to different channel impairments and outperforms state-of-the-art deep networks in terms of accuracy while having a reasonable complexity.

关键词： modulation Feature extraction Convolution Wireless communication Receivers Computer architecture Simulation automatic modulation classification convolutional neural network residual-attention connection structure

来源：评论

学校读者我要写书评

暂无评论

A Deep Ensemble-Based Wireless Receiver Architecture for Mitigating Adversarial Attacks in automatic modulation classification

引用

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2022年第1期8卷 71-85页

作者： Sahay, Rajeev Brinton, Christopher G. Love, David J. Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box attack environments. We begin by evaluating the architecture uncertainty environment, where we show that adversarial attacks crafted to fool specific AMC DL architectures are not directly transferable to different DL architectures. Next, we consider the domain uncertainty environment, where we show that adversarial attacks crafted on time domain and frequency domain features to not directly transfer to the altering domain. Using these insights, we develop our Assorted Deep Ensemble (ADE) defense, which is an ensemble of deep learning architectures trained on time and frequency domain representations of received signals. Through evaluation on two wireless signal datasets under different sources of uncertainty, we demonstrate that our ADE obtains substantial improvements in AMC classification performance compared with baseline defenses across different adversarial attacks and potencies.

关键词： Adversarial attacks automatic modulation classification machine learning in communications wireless security

来源：评论

学校读者我要写书评

暂无评论

A Hierarchical classification Head Based Convolutional Gated Deep Neural Network for automatic modulation classification

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第10期21卷 8713-8728页

作者： Chang, Shuo Zhang, Ruiyun Ji, Kejia Huang, Sai Feng, Zhiyong Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China

automatic modulation classification (AMC) identifies a received signal's modulation scheme without prior knowledge of the intercepted signal, which enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), lots of neural networks are introduced into AMC. To further improve classification performance, various complementary cues including in-phase/quadrature (I/Q), amplitude/phase (A/P), constellation, and other formats are used together to enhance the discrimination of the DL model, where only outputs of the last layer are used. In this paper, we find that different layers' outputs in the DL model are also complementary to each other. As a result, a hierarchical classification head based convolutional gated deep neural network (HCGDNN) is proposed by utilizing different layers' output, which only uses the I/Q cue. The proposed HCGDNN consists of three groups of convolutional neural networks (CNN) blocks, two groups of bidirectional gated recurrent units (BiGRU), and a hierarchical classification head. Compared to the long short-term memory (LSTM), the BiGRU has a smaller computational complexity and also releases the gradient dispersion and explosion in the training phase. With the help of the hierarchical classification head, three groups of modulation predictions are made for a received I/Q signal. After that, a novel nonlinear optimization fusion method is derived to generate fusion weights to fuse different groups, then a final classification decision is made. Compared to AMC methods using various cues, the proposed HCGDNN only uses I/Q cue and has low computational overhead. Numerical results suggest that the newly developed HCGDNN achieves superior performance on the public *** help other researchers, the source code will be uploaded to the github as long as the paper is published.

关键词： Feature extraction modulation Convolutional neural networks Task analysis Wireless communication Convolution Computational modeling automatic modulation classification hierarchical classification head prediction fusion nonlinear optimization

来源：评论

学校读者我要写书评

暂无评论

Neural Network Based automatic modulation classification with Online Training

Neural Network Based Automatic Modulation Classification wit...

引用

IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)

作者： Zhang, Shuo Yakopcic, Chris Taha, Tarek M. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (纸本)9798350335675

The use of wireless communication has been growing significantly. Given the limited radio spectrum available, it is important to use modulation techniques to squeeze the most information in the least amount of spectrum. To improve the throughput and reliability of communication, automatic modulation classification (AMC) has become an essential topic in the research of Cognitive Radio (CR). AMC is a technique to identify the modulation scheme of the received radio signals without any transmission parameters. The state-of-the-art approach is to use a pre-trained classifier to determine the modulation in use. Unfortunately, all forms of distortions and interference are impossible to predict, and thus these approaches fail when new distortions or interference appear that were not previously learned. In this work, we developed a technique where the transmitter periodically sends out a known ordered sequence of signals of all the modulations to be used by the receiver. The receiver knows the transmission sequence and thus knows how to label these received signals containing each required modulation. We used an over-the-air (OTA) radio transmission between software-defined radio (SDR) devices, and the collected dictionary dataset was used to train a neural network at the receiver. Our results show that training the network in real-time while the radio is in use can enable significantly better performance than any of the existing approaches.

关键词： automatic modulation classification software-defined radio deep neural networks online training

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification for MASK, MPSK, and MQAM Signals Based on Hierarchical Self-Organizing Map

引用

SENSORS 2022年第17期22卷 6449-6449页

作者： Li, Zerun Wang, Qinglin Zhu, Yufei Xing, Zuocheng Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

automatic modulation classification (AMC) plays a fundamental role in common communication systems. Existing clustering models typically handle fewer modulation types with lower classification accuracies and more computational resources. This paper proposes a hierarchical self-organizing map (SOM) based on a feature space composed of high-order cumulants (HOC) and amplitude moment features. This SOM with two stacked layers can identify intrinsic differences among samples in the feature space without the need to set thresholds. This model can roughly cluster the multiple amplitude-shift keying (MASK), multiple phase-shift keying (MPSK), and multiple quadrature amplitude keying (MQAM) samples in the root layer and then finely distinguish the samples with different orders in the leaf layers. We creatively implement a discrete transformation method based on modified activation functions. This method causes MQAM samples to cluster in the leaf layer with more distinct boundaries between clusters and higher classification accuracies. The simulation results demonstrate the superior performance of the proposed hierarchical SOM on AMC problems when compared with other clustering models. Our proposed method can manage more categories of modulation signals and obtain higher classification accuracies while using fewer computational resources.

关键词： automatic modulation classification hierarchical self-organizing map high-order cumulants amplitude moments clustering models

来源：评论

学校读者我要写书评

暂无评论

A Novel automatic modulation classification Scheme Based on Multi-Scale Networks

引用

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2022年第1期8卷 97-110页

作者： Zhang, Hao Zhou, Fuhui Wu, Qihui Wu, Wei Hu, Rose Qingyang Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum Nanjing 210000 Peoples R China Nanjing Univ Posts & Telecommun Coll Commun & Informat Engn Nanjing 210003 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum Minist Ind & Informat Technol Nanjing 210000 Peoples R China Utah State Univ Dept Elect & Comp Engn Logan UT 84322 USA

automatic modulation classification enables intelligent communications and it is of crucial importance in today's and future wireless communication networks. Although many automatic modulation classification schemes have been proposed, they cannot tackle the intra-class diversity problem caused by the dynamic changes of the wireless communication environment. In order to overcome this problem, inspired by face recognition, a novel automatic modulation classification scheme is proposed by using the multi-scale network in this paper. Moreover, a novel loss function that combines the center loss and the cross entropy loss is exploited to learn both discriminative and separable features in order to further improve the classification performance. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy. The influence of the network parameters and the loss function with the two-stage training strategy on the classification accuracy of our proposed scheme are investigated.

关键词： automatic modulation classification deep learning discriminative features center loss

来源：评论

学校读者我要写书评

暂无评论

3D convolutional neural networks based automatic modulation classification in the presence of channel noise

引用

IET COMMUNICATIONS 2022年第5期16卷 497-509页

作者： Khan, Rahim Yang, Qiang Ullah, Inam Rehman, Ateeq Ur Bin Tufail, Ahsan Noor, Alam Rehman, Abdul Cengiz, Korhan Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Hohai Univ HHU Coll Internet Things IoT Engn Changzhou Campus Changzhou Jiangsu Peoples R China Govt Coll Univ Dept Elect Engn Lahore Pakistan Politecn Porto ISEP CISTER Res Ctr Porto Portugal COMSATS Univ Islamabad Dept Elect & Comp Engn Sahiwal Campus Sahiwal Pakistan Kyungpook Natl Univ Dept Comp Sci & Engn Daegu South Korea Univ Cent Punjab Fac Media & Commun Studies Lahore Pakistan Trakya Univ Dept Elect Elect Engn Edirne Turkey

automatic modulation classification is a task that is essentially required in many intelligent communication systems such as fibre-optic, next-generation 5G or 6G systems, cognitive radio as well as multimedia internet-of-things networks etc. Deep learning (DL) is a representation learning method that takes raw data and finds representations for different tasks such as classification and detection. DL techniques like Convolutional Neural Networks (CNNs) have a strong potential to process and analyse large chunks of data. In this work, we considered the problem of multiclass (eight classes) classification of modulated signals, which are, Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16 and 64 Quadrature Amplitude modulation corrupted by Additive White Gaussian Noise, Rician and Rayleigh fading channels using 3D-CNN architectures in both frequency and spatial domains while deploying three approaches for data augmentation, which are, random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross-validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10-fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10-fold CV without augmentation in the frequency domain and we found learning in the spatial domain to be better than learning in the frequency domain.

关键词： next generation networks intelligent communication systems modulated signals 64 Quadrature Amplitude modulation data augmentation quadrature amplitude modulation channel noise additive white Gaussian noise channels random shift deep learning quadrature phase shift keying learning (artificial intelligence) 6G mobile communication next generation 6G systems statistical analysis AWGN channels Rayleigh fading channels Quadrature Phase Shift Keying representation learning method Rician fading channels spatial domains Rayleigh channels convolutional neural nets 3D convolutional neural networks 3D-CNN architectures cross-validation based hyperparameter selection statistical approach signal classification automatic modulation classification Binary Phase Shift Keying 5G mobile communication spatial domain 16 Quadrature Amplitude modulation random zoomed in-out technique random weak Gaussian blurring augmentation techniques frequency domain cognitive radio multiclass classification telecommunication computing next generation 5G systems

来源：评论

学校读者我要写书评

暂无评论

MoDANet: Multi-Task Deep Network for Joint automatic modulation classification and Direction of Arrival Estimation

引用

IEEE COMMUNICATIONS LETTERS 2022年第2期26卷 335-339页

作者： Doan, Van-Sang Huynh-The, Thien Hoang, Van-Phuc Nguyen, Duy-Thai Naval Acad Fac Commun & Radar Nha Trang 650000 Vietnam Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi Si 39177 Gyeongsangbuk D South Korea Le Quy Don Tech Univ Inst Syst Integrat Hanoi 11300 Vietnam Acad Mil Sci & Technol Inst Elect Hanoi 11300 Vietnam

In this letter, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the effect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation.

关键词： Direction-of-arrival estimation modulation Estimation Antenna arrays Task analysis Neurons Convolution automatic modulation classification direction of arrival estimation deep neural network MIMO system

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：