检索结果-内蒙古大学图书馆

PASS-Net: A Pseudo Classes and Stochastic Classifiers-Based Network for Few-Shot Class-Incremental automatic modulation classification

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 17987-18003页

作者： Tan, Haoyue Zhang, Zhenxi Li, Yu Shi, Xiaoran Wang, Li Yang, Xinyao Zhou, Feng Xidian Univ Minist Educ Key Lab Elect Informat Countermeasure & Simulat Te Xian 710126 Peoples R China Honor Device Co Ltd Xian 710126 Peoples R China iFLYTEK Co Ltd Hefei 230000 Peoples R China

Recently, significant progress has been made in deep learning, which has been widely applied in automatic modulation classification (AMC) with remarkable outcomes. However, current deep learning based AMC (DL-AMC) algorithms show limitations in their ability to accommodate dynamically changing communication scenarios. With the increasing number of modulation types, most DL-AMC algorithms often need to be re-trained, making it hard to transfer previous knowledge to the new models. Also, modulation classification faces the difficulty of acquiring and annotating a large number of signals. To address these challenges, we have modeled a few-shot class-incremental AMC (FSCI-AMC) task and proposed a pseudo classes and stochastic classifiers-based network (PASS-Net) to accomplish it. Firstly, the pseudo classes are generated to reserve space for new types, enhancing the model's continuous learning capability. Additionally, stochastic classifiers ensure the reliability of generated pseudo classes. Finally, in the incremental session, both real and pseudo classes are used for modulation classification. To evaluate the proposed approach, experiments were conducted on 7 modulation types as base classes and another 7 modulation types as incremental classes. The results show superior performance in 7 sessions of 1-way 1-shot and 1-way 5-shot class-incremental experiments compared to other competitive methods.

关键词： modulation Power capacitors Training Stochastic processes Feature extraction Libraries Adaptation models automatic modulation classification class-incremental learning few-shot learning pseudo classes stochastic classifiers

来源：评论

学校读者我要写书评

暂无评论

Joint Signal Detection and automatic modulation classification via Deep Learning

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第11期23卷 17129-17142页

作者： Xing, Huijun Zhang, Xuhui Chang, Shuo Ren, Jinke Zhang, Zixun Xu, Jie Cui, Shuguang Chinese Univ Hong Kong Shenzhen Shenzhen Future Network Intelligence Inst FNii She Sch Sci & Engn SSE Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Shenzhen Guangdong Prov Key Lab Future Networks Intelligenc Shenzhen 518172 Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China

Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario, in which multiple signals with different modulation schemes coexist at different carrier frequencies. We first generate a coexisting RADIOML dataset (CRML23) to facilitate the joint design. Different from the publicly available AMC dataset, ignoring the signal detection step and containing only one signal, our synthetic dataset covers the more realistic multiple-signal coexisting scenario. Then, we present a joint framework for detection and classification (JDM) for such a multiple-signal coexisting environment, which consists of two modules for signal detection and AMC, respectively. In particular, these two modules are interconnected using a designated data structure called "proposal". Finally, we conduct extensive simulations over the newly developed dataset, which demonstrate the effectiveness of our designs. Our code and dataset are now available as open-source resources at https://***/Singingkettle/ChangShuoRadioData.

关键词： Feature extraction Signal detection Frequency modulation Time-frequency analysis Signal to noise ratio Industries Deep learning automatic modulation classification dataset design hierarchical classification head

来源：评论

学校读者我要写书评

暂无评论

A Convolutional and Transformer Based Deep Neural Network for automatic modulation classification

引用

China Communications 2023年第5期20卷 135-147页

作者： Shanchuan Ying Sai Huang Shuo Chang Zheng Yang Zhiyong Feng Ningyan Guo Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China

automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil *** this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification *** can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final *** the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential *** the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN *** the transformer module,three self-attention layers are designed for extracting global features and generating the classification *** the classifier,the final decision is made based on the maximum a posterior *** simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.

关键词： automatic modulation classification deep neural network convolutional neural network transformer

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification of Real Signals in AWGN Channel Based on Sixth-Order Cumulants

Automatic Modulation Classification of Real Signals in AWGN ...

引用

作者： Simic, M. Brno University of Technology

automatic modulation classification (AMC) represents an important integral part of modern communication systems. While novel AMC algorithms based on complex neural network structures showed significant performance improvements, in practical applications low algorithm complexity of AMC algorithms based on higher-order cumulants still make them very attractive. AMC algorithm based on sixth-order cumulants showed very good performance in this context, especially when it comes to distinguishing Binary Phase Shift Keying (BPSK) signals from complex constellations. Still, no further analysis of expected performance with other real constellations was presented for this algorithm so far. In this paper, the performance was explored in a wider context of real signals classification, by observing various Pulse Amplitude modulation (PAM) constellations, whose statistical features are presented for the first time. Their classification performance was tested via Monte – Carlo simulations, and explained through the presence of bias under conditions of strong additive white Gaussian noise channel, reported in this paper for real signals for the first time. One new approach in AMC is proposed, which ensures improvement in the classification of real signal constellations. Achieved improvement is confirmed in many Monte – Carlo experiments, where proposed new AMC scheme is tested versus the most popular standard higher-order cumulants-based algorithms.

关键词： automatic modulation classification feature-based cumulants pulse amplitude modulation real constellations AWGN

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification of radar signals utilizing X-net

引用

DIGITAL SIGNAL PROCESSING 2022年第0期123卷 103396-103396页

作者： Chen, Kuiyu Zhang, Jingyi Chen, Si Zhang, Shuning Zhao, Huichang Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

automatic modulation classification (AMC) of radar signals has long been a challenge, especially in the area of electronic reconnaissance, where collecting and labeling numerous signal samples are usually harsh and impracticable. In this article, a novel recognition network is proposed for detecting radiation signals under intense noise background with only simulation samples for model training. Owing to the X-shaped structure, the recognition network is named X-net. A residual learning convolutional denoising autoencoder (RLCDAE) and a supplementary classification network based on noise level estimation (NLE) constitute the X-net. Via pre-training of RLCDAE, the robustness against noise is enhanced. Then, a supplementary classification network further improves the recognition performance under low signalto-noise ratios (SNRs). To evaluate the method, comparative experiments with some excellent algorithms on noise immunity and recognition accuracy are conducted. The cognitive system can still recognize ten kinds of radar signals with an overall precision of 96% even when the SNR is -8 dB. Furthermore, simulation samples trained model is verified by measured data. Outstanding performance proves the effectiveness and superiority of the proposed method on cognitive radar signals under low SNRs.& nbsp;(C) 2022 Elsevier Inc. All rights reserved.

关键词： automatic modulation classification Radar signals Residual learning convolutional denoising autoencoder Supplementary classification networks Noise level estimation Measured data

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification Scheme Based on LSTM With Random Erasing and Attention Mechanism

引用

IEEE ACCESS 2020年 8卷 154290-154300页

作者： Chen, Yufan Shao, Wei Liu, Jin Yu, Lu Qian, Zuping Army Engn Univ PLA Coll Commun Engn Nanjing 210007 Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

automatic modulation classification (AMC) is a key technology of cognitive radio used in non-cooperative communication. Recently, deep learning has been applied to AMC tasks. In this paper, an AMC scheme based on deep learning is proposed, which combines random erasing and attention mechanism to achieve high classification accuracy. Firstly, we propose two data augmentation methods, random erasing at sample level and random erasing at amplitude/phase (AP) channel level. The former replaces training samples with noise information, while the latter replaces AP channel information of training samples with noise information. Erased data segments are randomly stitched to enable training data expansion. Training data of different qualities enables deep learning model to have stronger generalization capability and higher robustness. Then, we propose a single-layer Long Short-Term Memory (LSTM) model based on attention mechanism. In the first part of this model, we propose the signal embedding, which enables the input to contain modulation information more comprehensively and accurately. Then hidden state output by LSTM is input into the attention module, and weighting is applied to the hidden state to help the LSTM model capture the temporal features of modulated signals. Compared to a model without attention mechanism, this model has faster convergence speed and better classification performance. Lastly, we propose a random erasing-based test time augmentation (RE-TTA) method. Test data is randomly erased for multiple times and classification results are comprehensively evaluated, in order to further improve classification accuracy. Experimental results on dataset RML2016.10a show that classification accuracy of the proposed scheme is competitive compared with the state-of-the-art methods.

关键词： automatic modulation classification random erasing long short-term memory attention mechanism

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification in Time-Varying Channels Based on Deep Learning

引用

IEEE ACCESS 2020年 8卷 197508-197522页

作者： Zhou, Yu Lin, Tian Zhu, Yu Fudan Univ Dept Commun Sci & Engn Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China

automatic modulation classification (AMC) is an important technology in military signal reconnaissance and civilian communications such as cognitive radios. Most of the existing works focused on the AMC in additional white Gaussian noise channels, but the AMC in time-varying wireless channels is more practical and challenging. In this article, we investigate the AMC in time-varying channels by using the deep learning method for high classification accuracy. Specifically, we take the modulation constellation diagram (CD) as the key feature and propose a slotted constellation diagram (slotted-CD) scheme in order to extract the feature of the time-evolution of the CD due to channel variation. We then develop an advanced neural network for modulation classification, where the output sub-images from the slotted-CD feature extractor are first processed separately by a number of parallel convolutional neural networks and then further processed by a recurrent neural network for exploring their time relationship. Experimental results show that the proposed AMC scheme achieves higher classification accuracy in both slow and fast fading channels when compared with the traditional deep learning based AMC schemes. Such performance improvement can be clearly illustrated by visualizing the outputs of the convolutional layers of the classifier. We also show that visualization can help optimize the parameters of the AMC neural networks.

关键词： automatic modulation classification constellation diagram time-varying convolutional neural network bidirectional long short-term memory network

来源：评论

学校读者我要写书评

暂无评论

AMCRN: Few-Shot Learning for automatic modulation classification

引用

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

A Deep Learning-Based Novel Class Discovery Approach for automatic modulation classification

引用

IEEE COMMUNICATIONS LETTERS 2023年第11期27卷 3018-3022页

作者： Zhang, Rui Zhao, Yanlong Yin, Zhendong Li, Dasen Wu, Zhilu Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

The existing automatic modulation classification (AMC) methods require the training and testing datasets share a common set of modulation categories. However, the AMC model may encounter the need to discriminate novel classes in non-cooperative environments. To the best of our knowledge, the research reporting AMC in the class-disjoint environment has not been addressed yet. In this letter, a novel class discovery method is proposed for AMC leveraging the information contained in the labeled training dataset. Specifically, a 3-stage deep learning method is introduced to recognize samples of the known classes and cluster samples of novel classes. The extracted features and the pairwise similarity relationship are considered as the common knowledge between the two class-disjoint datasets and are utilized to help the construction and training of the classifier for novel classes. The simulation results validate the effectiveness and performance of the proposed method.

关键词： automatic modulation classification novel class discovery 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

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：