检索结果-内蒙古大学图书馆

A Novel Sparse Classifier for automatic modulation classification using Cyclostationary Features

WIRELESS PERSONAL COMMUNICATIONS 2017年第3期96卷 4895-4917页

作者： Satija, Udit Ramkumar, Barathram Manikandan, M. Sabarimalai Indian Inst Technol Sch Elect Sci Bhubaneswar Orissa India

automatic modulation classification plays a key role in cognitive radio for recognizing the modulation scheme. In this paper, we propose a new classifier based on sparse signal decomposition using an overcomplete composite dictionary (constructed using cyclostationary coefficients) for the classification of modulation format of primary user or to identify noise. The basic principle of the classifier is to classify the received signal modulation format based on reconstructed sparse coefficients after solving norm minimization using the overcomplete dictionary. Then, relative energies of reconstructed sparse coefficients are compared for recognition of modulation format of the received signal. It is a promising candidate for the cognitive radio due to its robust classification ability. The performance of the proposed classifier is compared with other well known classifiers available in literature. Results show the superiority of the proposed classifier over other classifiers.

关键词： automatic modulation classification Cognitive radio Sparse signal decomposition

来源：评论

学校读者我要写书评

暂无评论

Robust automatic modulation classification Under Varying Noise Conditions

引用

IEEE ACCESS 2017年 5卷 19733-19741页

作者： Wu, Zhilu Zhou, Siyang Yin, Zhendong Ma, Bo Yang, Zhutian Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China China Acad Space Technol Beijing 100081 Peoples R China

automatic modulation classification (AMC) plays a key role in non-cooperative communication systems. Feature-based (FB) methods have been widely studied in particular. Most existing FB methods are deployed at a fixed SNR level, and the pre-trained classifiers may no longer be effective when the SNR level changes. The classifiers may also need to be re-trained to be suitable for the varying channel environment. To address these problems, a robust AMC method under varying noise conditions is proposed in this paper. The method attempts to select noise-insensitive features from a large feature set to ensure that the trained classifiers will be robust to SNR variations. First, a feature set consisting of 25 types of features is extracted, and 4 features that are insensitive to noise are chosen through a feature selection method based on rough set theory. The generalizability of an SVM classifier trained on the 4 chosen features is evaluated based on numerical results. The classification accuracy remains reasonable when the SNR varies between 5 and 20 dB, indicating that the proposed method can be deployed under varying noise conditions.

关键词： automatic modulation classification feature extraction feature selection noise robustness

来源：评论

学校读者我要写书评

暂无评论

k-Sparse Autoencoder-Based automatic modulation classification With Low Complexity

引用

IEEE COMMUNICATIONS LETTERS 2017年第10期21卷 2162-2165页

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

How to reduce complexity of the practical automatic modulation classification systems is a very active research area. Moreover, keeping the classification accuracy to a near optimal level is an added challenge. Recently, three new classifiers have been proposed with reduced complexity, mainly: linear support vector machine classifier, approximate maximum likelihood classifier, and backpropogation neural networks classifier. However, these methods include the sorting process of the features z to form an ordered vector (z) over right arrow employing Klog(K) comparison operations. Here, we propose a k-sparse autoencoder-based classifer, with unsorted input data features and called it unsorted deep neural network (UDNN). Thus, we strive to omit the Klog(K) comparison operations. The results obtained using the UDNN classifier show improved performance when compared with the above three methods. Moreover, using k highest hidden units to reconstruct input data further reduces the overall complexity of the AMC system.

关键词： automatic modulation classification deep neural network k-sparse autoencoders

来源：评论

学校读者我要写书评

暂无评论

Low Complexity automatic modulation classification Based on Order-Statistics

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2017年第1期16卷 400-411页

作者： Han, Lubing Gao, Feifei Li, Zan Dobre, Octavia A. Tsinghua Univ Dept Automat State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Mem Univ Dept Elect & Comp Engn St John NF A1B 3X9 Canada

In this paper, we propose three automatic modulation classification classifiers based on order-statistics and reduced order-statistics, where the order-statistics are the random variables sorted by ascending order and the reduced order-statistics represent a subset of the original order-statistics. Specifically, the linear support vector machine classifier applies the linear combination of the order-statistics of the received signals, while the approximate maximum likelihood and the backpropagation neural networks (BPNNs) classifier resort to the reduced order-statistics to decrease the computational complexity. Moreover, BPNN is applicable for modulation classification both in known and unknown channel scenarios. It is shown that in the known channel scenario, the proposed classifiers provide a good tradeoff between performance and computational complexity, while in the unknown channel scenario, the proposed BPNN classifier outperforms the expectation maximization classifier in terms of both classification performance and computational complexity. Simulations results are provided to evaluate the proposed classifiers.

关键词： automatic modulation classification order-statistics machine learning backpropagation neural networks linear support vector machine

来源：评论

学校读者我要写书评

暂无评论

Blind automatic modulation classification in Multipath Fading Channels 22

Blind Automatic Modulation Classification in Multipath Fadin...

引用

2017 22nd International Conference on Digital Signal Processing (DSP)

作者： Gouldieff, Vincent Palicot, Jacques Daumont, Steredenn IETR Cent Supelec Ave Boulaie CS 47601 F-35576 Cesson Sevigne France Zodiac Data Syst 2 Rue Caen F-14740 Bretteville Lorgueilleus France

ISBN: (纸本)9781538618950

automatic modulation classification (AMC) has been deeply studied in the context of Cognitive Radio (CR) where it represents a required step between signal detection and demodulation. Several blind methods were proposed in the case of Additive White Gaussian Noise (AWGN) channels. In this context, Analytical Mth-Power nonlinear Transformation (AMPT) was especially studied to tackle the blind AMC of digital signals. Throughout this article, we extend the study of the AMPT-based classifier to the case of imperfectly estimated multipath fading channels. To this end, the theoretical references used for the classification take the physical channel into account. Extensive simulations show the effectiveness of the proposed method compared to the existing literature.

关键词： automatic modulation classification Mth-Power nonlinear Transformation Blind Detection Multipath channel

来源：评论

学校读者我要写书评

暂无评论

Immunity of automatic modulation classification algotithms against inaccurate estimation of signal parameters 9

Immunity of Automatic Modulation Classification algotithms a...

引用

Communication and Information Technologies (KIT)

作者： Kosmowski, Krzysztof Prawdzik, Krzysztof Baranowski, Grzegorz Mil Commun Inst Radio Commun & Elect Warfare Dept Zegrze Poland

ISBN: (纸本)9788080405502

automatic modulation classification lays important role in the systems of electromagnetic spectrum monitoring. However performance of the algorithms is depreciated due to inaccurate estimation of signal parameters. The problem of unknown parameters is well documented in the literature however, authors usually propose improved algorithms in order to maximize probability of correct classification when one or more parameters are unknown. In this paper the aim is different. Immunity of selected algorithms against inaccurate preprocessing is investigated in order to assess how precise estimation of signals parameters should be.

关键词： automatic modulation classification ALRT cumulants

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification in Practical Wireless Channels

Automatic Modulation Classification in Practical Wireless Ch...

引用

7th International Conference on Information and Communication Technology Convergence (ICTC) - Towards Smarter Hyper-Connected World

作者： Kim, Sung-Jin Yoon, Dongweon Hanyang Univ 222 Wangsimni Ro Seoul South Korea

ISBN: (纸本)9781509013258

Flexible spectrum utilization becomes one of the major agendas in the next generation wireless communications. A core technology to efficiently adjust spectrum is automatic modulation classification (AMC) which recently emerges in various future wireless research including military communications, cognitive radio and high-throughput wireless. AMC is essential for capturing over-the-air information, estimating a remained spectral resource and improving spectral efficiency in the corresponding wireless services. We consider support vector machine (SVM) for AMC in practical wireless channels, which includes typical impairments such as frequency offsets and multipath fading. On the top of concatenated sorted symbols (CSS), we propose to include a new process and a new training procedure so that the classification performance is significantly improved from the conventional CSS-SVM approach in practical wireless channels.

关键词： automatic modulation classification Machine learning Support vector machine

来源：评论

学校读者我要写书评

暂无评论

On classifiers for blind feature-based automatic modulation classification over multiple-input-multiple-output channels

引用

IET COMMUNICATIONS 2016年第7期10卷 790-795页

作者： Kharbech, Sofiane Dayoub, Iyad Zwingelstein-Colin, Marie Simon, Eric Pierre Higher Inst Technol Studies Gabes Dept Commun & Informat Technol STIC Gabes 6011 Tunisia Univ Valenciennes & Hainaut Cambresis IEMN DOAE Lab UMR CNRS 8520 F-59313 Valenciennes France Univ Lille 1 TELICE Lab IEMN UMR CNRS 8520 F-59100 Lille France

modulation recognition is crucial for a good environmental awareness required by cognitive radio systems. In this study, the authors design and compare models of four among the most commonly used classifiers for feature-based automatic modulation classification (FB-AMC) algorithms. Classifiers whose models will be designed are classification tree, K-nearest neighbours, artificial neural networks (ANNs), and support vector machines. In this study, they apply some statistical pattern recognition techniques in the context of blind FB-AMC over multiple-input-multiple-output channels. Comparison criteria are classification accuracy and computational complexity. To improve the impartiality of this comparison, each classifier is optimally deployed by selecting its optimal model with respect to their context. Model selection for the classifiers is done using the k-fold cross-validation' model validation technique. The comparison study, within the considered context, shows that ANN classifiers have the best performance/complexity tradeoff.

关键词： cognitive radio wireless channels MIMO communication statistical analysis blind feature classifiers automatic modulation classification multiple-input-multiple-output channels modulation recognition environmental awareness cognitive radio systems feature based automatic modulation classification algorithms FB-AMC algorithms artificial neural networks K-nearest neighbours support vector machines statistical pattern recognition techniques computational complexity optimal model

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification for Low SNR Digital Signal in Frequency-Selective Fading Environments

引用

WIRELESS PERSONAL COMMUNICATIONS 2015年第3期84卷 1891-1906页

作者： Wallayt, Waqas Younis, Muhammad S. Imran, Muhammad Shoaib, Muhammad Guizani, Mohsen Natl Univ Sci & Technol Islamabad 44000 Pakistan King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Qatar Univ Doha 2713 Qatar

In this research, a classifier is proposed for automatic modulation classification of some common modulation schemes, i.e., BPSK, QPSK, 8-PSK and 16-QAM. Our proposed classifier considers multipath fading effects on the received signal in a non-Gaussian noise environment. automatic modulation classification is very challenging in real-world scenarios due to fading effects and additive Gaussian mixture noise on modulation schemes. Most of the available modulation classifiers do not consider the fading effects which results in degradation of classification in a blind channel environment. In our work, the channel is supposed to be suffering from excessive additive Gaussian mixture noise and frequency selective fading resulting in low signal SNR. The estimation of the unknown channel along with noise parameters is performed using ECM algorithm and then used in maximum-likelihood classifier for the classification of modulation schemes. Simulation results are presented that show 2 dB improvement in performance than classifier which considers only Gaussian noise in the received signal.

关键词： automatic modulation classification Expectation conditional maximization algorithm Maximum likelihood classification Feature based classification Gaussian mixture model Blind channel estimation Frequency selective fading

来源：评论

学校读者我要写书评

暂无评论

Deep Neural Network-based automatic modulation classification Technique

Deep Neural Network-based Automatic Modulation Classificatio...

引用

7th International Conference on Information and Communication Technology Convergence (ICTC) - Towards Smarter Hyper-Connected World

作者： Kim, Byeoungdo Kim, Jaekyum Chae, Hyunmin Yoon, Dongweon Choi, Jun Won Hanyang Univ Dept Elect Engn Seoul South Korea

ISBN: (纸本)9781509013258

Deep neural network (DNN) has recently received much attention due to its superior performance in classifying data with complex structure. In this paper, we investigate application of DNN technique to automatic classification of modulation classes for digitally modulated signals. First, we select twenty one statistical features which exhibit good separation in empirical distributions for all modulation formats considered (i.e., BPSK, QPSK, 8PSK, 16QAM, and 64QAM). These features are extracted from the received signal samples and used as the input to the fully connected DNN with three hidden layer. The training data containing 25,000 feature vectors is generated by the computer simulation under both additive Gaussian white noise (AWGN) and Rician fading channels. Our test results show that the proposed method brings dramatic performance improvement over the existing classifier especially for high Doppler fading channels.

关键词： Deep neural network automatic modulation classification digital modulations fading channels

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：