检索结果-内蒙古大学图书馆

89th IEEE Vehicular Technology Conference (VTC Spring)

作者： Kalam, Lamia M. Theagarajan, Lakshmi N. Natl Inst Technol Karnataka Dept ECE Mangalore 575025 India Indian Inst Technol Palakkad Dept EE Palakkad 678557 Kerala India

ISBN: (纸本)9781728112176

automatic modulation classification (AMC) is the problem of identifying the modulation type of a given radio frequency (RF) signal. This operation is one of the key steps in a cognitive radio based spectrum sharing communication network. It is known that the optimal classification algorithms for AMC are computationally intensive which renders real-time implementation almost impossible. In this paper, we propose a practical AMC algorithm that employs multiple stages of clustering to identify the modulation type of the received RF signal. Here, we consider the communication signals to be modulated using the most common digital modulation types: phase shift keying (PSK) or quadrature amplitude modulation (QAM). First, we present a novel algorithm that performs multiple stages of clustering to identify the clusters present in the received data and classifies it to one of the several possible modulation types. Second, we validate our proposed algorithm through practical implementation using software defined radios (SDR). Our results show that the proposed multistage clustering based AMC algorithm works well in practical conditions.

关键词： automatic modulation classification clustering software defined radio phase shift keying quadrature amplitude modulation

来源：评论

学校读者我要写书评

暂无评论

Feature Image-Based automatic modulation classification Method Using CNN Algorithm 1

Feature Image-Based Automatic Modulation Classification Meth...

引用

1st International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Lee, Jung Ho Kim, Kwang-Yul Shin, Yoan Soongsil Univ Sch Elect Engn Seoul 06978 South Korea

ISBN: (纸本)9781538678220

In this paper, we propose a feature image-based automatic modulation classification (AMC) method to classify modulation type. The proposed method uses a convolutional neural network (CNN) which is one of deep learning algorithms for image classification. In order to classify the modulation type, various features are transformed in a two-dimensional image and this image is used as the input of the CNN. From the simulation results, we show that the proposed method improves classification performance.

关键词： automatic modulation classification cumulant deep learning convolutional neural network feature extraction

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification of digital modulation signals with stacked autoencoders

引用

DIGITAL SIGNAL PROCESSING 2017年 71卷 108-116页

作者： Ali, Afan Fan Yangyu Liu, Shu Northwestern Polytech Univ Sch Elect & Informat 127 West Youyi Rd Xian Shaanxi Peoples R China

modulation identification of the transmitted signals remain a challenging area in modern intelligent communication systems like cognitive radios. The computation of the distinct features from input data set and applying machine learning algorithms has been a well-known method in the classification of such signals. However, recently, deep neural networks, a branch of machine learning, have gained significant attention in the pattern recognition of complex data due to its superior performance. Here, we test the application of deep neural networks to the automatic modulation classification in AWGN and flat fading channel. Three training inputs were used;mainly 1) In-phase and quadrature (I-Q) constellation points, 2) the centroids of constellation points employing the fuzzy C-means algorithm to I-Q diagrams, and 3) the high-order cumulants of received samples. The unsupervised learning from these data sets was done using the sparse autoencoders and a supervised softmax classifier was employed for the classification. The designing parameters for training single and 2-layer sparse autoencoders are proposed and their performance compared with each other. The results show that a very good classification rate is achieved at a low SNR of 0 dB. This shows the potential of the deep learning model for the application of modulation classification. (C) 2017 Elsevier Inc. All rights reserved.

关键词： automatic modulation classification Pattern recognition Deep neural networks Digital modulation signals I-Q diagrams

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification Based on Deep Learning for Unmanned Aerial Vehicles

引用

SENSORS 2018年第3期18卷 924-924页

作者： Zhang, Duona Ding, Wenrui Zhang, Baochang Xie, Chunyu Li, Hongguang Liu, Chunhui Han, Jungong Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China Beihang Univ Unmanned Syst Res Inst Beijing 100083 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved;(2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment;and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

关键词： deep learning automatic modulation classification classifier fusion convolutional neural network long short-term memory

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints

引用

IEEE SIGNAL PROCESSING LETTERS 2017年第11期24卷 1626-1630页

作者： Ali, Afan Fan Yangyu Northwestern Polytech Univ Sch Elect & Informat Engn Xian 710072 Shaanxi Peoples R China

We demonstrate a novel method for the automatic modulation classification based on a deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The learning algorithm aims to constrain the negative weights, learns features that amount to a part-based representation of data, and disentangles a more meaningful hidden structure. The performance of this algorithm is tested on the fourth-order cumulants of the modulated signals. The results indicate that the autoencoder with nonnegativity constraint (ANC) improves the sparsity and minimizes the reconstruction error in comparison with the conventional sparse autoencoder. The classification accuracy of an ANC based deep network shows improved accuracy under limited signal length and fading channel.

关键词： Autoencoder automatic modulation classification cumulants deep learning networks nonnegativity constraints

来源：评论

学校读者我要写书评

暂无评论

automatic digital modulation classification using extreme learning machine with local binary pattern histogram features

引用

MEASUREMENT 2019年 145卷 214-225页

作者： Guner, Ahmet Alcin, Omer Faruk Sengur, Abdulkadir Bingol Univ Dept Elect Elect Engn TR-12000 Bingol Turkey Firat Univ Dept Elect & Elect Engn TR-23000 Elazig Turkey

Discrimination of the Local Binary Pattern (LBP) in the classification of different digital modulation types was investigated in this study. It has been shown that LBP can be used as a feature extraction method for AMC schemes. A new AMC scheme is proposed using Extreme Learning Machine (ELM) as a classifier, which has a faster learning process and better generalization performance than conventional machine learning methods. The study also investigated the stability of the proposed AMC scheme, which is affected by variation in the values of the roll-off factor, frequency and phase offset that can affect the stability and performance of the system. Through simulation, a classification accuracy of over 95% was achieved at low SNR levels such as -2 dB. It was also shown that the proposed AMC scheme is more successful under similar conditions when making comparisons to other studies. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： automatic modulation classification Extreme learning machine Local binary pattern Digital modulation

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification by Exploiting Cyclostationary Features in Wavelet Domain 23

Automatic Modulation Classification by Exploiting Cyclostati...

引用

23rd National Conference on Communications (NCC)

作者： Kumar, Sushant Bohara, Vivek Ashok Darak, Sumit J. IIIT Delhi Wirocomm Res Grp New Delhi India IIIT Delhi Dept Elect & Commun New Delhi India

ISBN: (纸本)9781509053568

In multi-standard wireless communication receivers, automatic modulation classification is critical to blindly demodulate received signal. In this paper, a new method for automatic modulation classification of any received signal has been proposed. The proposed method exploits cyclostationary features of received signal in wavelet domain. Received signal is sampled at estimated symbol rate followed by quantization operation. Next, modulation type is identified from the histogram plot of the features of the received signal. Simulation results in AWGN channel shows the superiority of the proposed approach over existing approaches.

关键词： Continuous wavelet transform cyclostationary feature detection histogram sampling quantization automatic modulation classification

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification using Recurrent Neural Networks 3

Automatic Modulation Classification using Recurrent Neural N...

引用

3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Hong, Dehua Zhang, Zilong Xu, Xiaodong Univ Sci & Technol China Chinese Acad Sci Key Lab Wireless Opt Commun Hefei Anhui Peoples R China China Elect Technol Grp Corp Res Inst 28 Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509063529

automatic modulation classification (AMC) is one of the essential technologies, and also a hard nut to crack in the field of cognitive radio (CR) and non-cooperative communication systems. In this work, we propose a novel AMC method based on the promising recurrent neural network (RNN), which is shown to have the capability to sufficiently exploit the temporal sequence characteristic of received communication signals. This method resorts to raw signals directly with limited data length, and avoids extracting signal features manually. The proposed method is compared with a convolutional neural network (CNN) based method and the result indicates the superiority of the proposed one, especially when signal-to-noise ratio (SNR) is above -4dB. Furthermore, a comparative study is presented to evaluate the availability of the other different RNN structures. And a more efficient structure is recommended based on two-layer gated recurrent unit (GRU) network Additional numerical results demonstrate that the proposed structure achieves an improved performance from 80% to 91% in terms of classification accuracy.

关键词： automatic modulation classification recurrent neural network convolutional neural network gated recurrent unit

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification using Principle Composition Analysis based Features Selection

Automatic Modulation Classification using Principle Composit...

引用

Computing Conference

作者： Ali, Afan Fan Yangyu Northwestern Polytech Univ Sch Elect & Informat Xian Shaanxi Peoples R China Northwestern Polytech Univ Fac Elect & Informat Xian Shaanxi Peoples R China

ISBN: (纸本)9781509054435

automatic modulation classification (AMC) plays an important role in both military and civilian applications. Feature based AMC is used in this paper. Principle Component Analysis (PCA) is employed to reduce dimensions of the feature vector. Two classifiers mainly k-nearest neighbor (KNN) and Support Vector Machine (SVM) are used to investigate the correct classification rate against different SNRs for test signals. Experiments are conducted using data trained at two different SNRs of 15dB and 3dB respectively. Results show that KNN classifier shows better results when data is trained at high SNRs. However, both the classifiers show almost same performance when data is trained at low SNR.

关键词： automatic modulation classification Principle Component Analysis Feature based classifier Support Vector Machine k-Nearest Neighbor

来源：评论

学校读者我要写书评

暂无评论

Unsupervised feature learning and automatic modulation classification using deep learning model

引用

PHYSICAL COMMUNICATION 2017年第Part1期25卷 75-84页

作者： Ali, Afan Fan Yangyu Northwestern Polytech Univ Sch Elect & Informat 127 West Youyi Rd Xian Shaanxi Peoples R China

Recently, deep learning has received a lot of attention in many machine learning applications for its superior classification performance in speech recognition, natural language understanding and image processing. However, it still lacks attention in automatic modulation classification (AMC) until now. Here, we introduce the application of deep learning in AMC. We propose a fully connected 2 layer feed-forward deep neural network (DNN) with layerwise unsupervised pretraining for the classification of digitally modulated signals in various channel conditions. The system uses independent autoencoders (AEs) for feature learning with multiple hidden nodes. Signal information from the received samples is extracted and preprocessed via I and Q components, and formed into training input to 1st AE layer. A probabilistic based method is employed at the output layer to detect the correct modulation signal. Simulation results show that a significant improvement can be achieved compared to the other conventional machine learning methods in the literature. Moreover, we also show that our proposed method can extract the features from cyclic-stationary data samples. A good classification accuracy was achieved, even when the proposed deep network is trained and tested at different SNRs. This shows the future potential of the deep learning model for application to AMC. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Deep learning networks automatic modulation classification Digital modulation Autoencoders

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：