检索结果-内蒙古大学图书馆

Deep Learning-Based automatic modulation classification With Blind OFDM Parameter Estimation

IEEE ACCESS 2021年 9卷 108305-108317页

作者： Park, Myung Chul Han, Dong Seog Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea

automatic modulation classification (AMC) is an essential factor in dynamic spectrum access to fulfill the spectrum demand of 5G wireless communications for achieving high data rate and low latency. Many deep learning (DL)-based AMC methods have achieved improved accuracy for classifying analog modulation schemes, single-carrier-based modulation schemes, and multi-carrier signals using several DL architectures such as the convolutional neural network (CNN) and long-short term memory (LSTM). However, most conventional DL-based AMC methods have confused the orthogonal frequency multiplexing division (OFDM)-based signals with different OFDM useful symbol lengths. To resolve the issue, we propose a CNN model operating on the fast Fourier transformation window bank (FWB) to extract the useful symbol length in OFDM, which represents the identification of each OFDM-based wireless communication technology. After extracting the OFDM useful symbol length, we propose a DL-based AMC system combined with FWB and in-phase and quadrature-phase signals to classify the OFDM symbol length and single-carrier modulation schemes simultaneously. Furthermore, we explore the constraints of the FWB parameters according to the length and the fast Fourier transformation (FFT) size of the OFDM signal to achieve good classification accuracy through the experiment. We constructed a dataset by generating OFDM signals of different lengths while changing the FFT size in a fixed bandwidth and selecting only quadrature amplitude modulation (QAM) schemes from RadioML2016.10a. Experimental results show the improved classification accuracy by about 30% over conventional classifiers in additive white Gaussian noise, synchronization impairments, and fading environments.

关键词： OFDM modulation Feature extraction Wireless communication Synchronization Quadrature amplitude modulation Time-domain analysis automatic modulation classification cognitive radio deep learning modulation neural networks orthogonal frequency-division multiplexing (OFDM)

来源：评论

学校读者我要写书评

暂无评论

Training Images Generation for CNN Based automatic modulation classification

引用

IEEE ACCESS 2021年 9卷 62916-62925页

作者： Zhang, Wei-Tao Cui, Dan Lou, Shun-Tian Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Xidian Univ Res Inst Adv Remote Sensing Technol Xian 710071 Peoples R China

Convolutional neural network (CNN) models have recently demonstrated impressive classification and recognition performance on image and video processing scope. In this paper, we investigate the application of CNN to identifying modulation classes for digitally modulated signals. First, the received baseband data samples of modulated signal are gathered up and transformed to generate the constellation-like training images for convolutional networks. Among the resulting training images, the proposed convolutional gray image is preferred for network training and inference because of the lower computational burden. Second, we propose to use a multiple-scale convolutional neural network (MSCNN) as the classifier. The skip-connection technique is deployed for mitigating the negative effect of vanishing gradients and overfitting during the network training process. Numerical simulations have been carried out to validate the effectiveness of the proposed scheme, the results show that the proposed scheme outperforms the traditional algorithms in terms of classification accuracy.

关键词： modulation Computational modeling Training Feature extraction Constellation diagram Load modeling Convolutional neural networks Convolutional neural network automatic modulation classification deep learning

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification: A Deep Architecture Survey

引用

IEEE ACCESS 2021年 9卷 142950-142971页

作者： Thien Huynh-The Quoc-Viet Pham Toan-Van Nguyen Thanh Thi Nguyen Ruby, Rukhsana Zeng, Ming Kim, Dong-Seong Kumoh Natl Inst Technol Dept IT Convergence Gumi 39177 Gyeongsangbuk D South Korea Pusan Natl Univ Korean Southeast Ctr 4 Ind Revolut Leader Educ Busan 46241 South Korea Utah State Univ Dept Elect & Comp Engn Logan UT 84321 USA Deakin Univ Sch Informat Technol Burwood Campus Burwood Vic Australia Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Laval Univ Dept Elect & Comp Engn Quebec City PQ G1V 0A6 Canada

automatic modulation classification (AMC), which aims to blindly identify the modulation type of an incoming signal at the receiver in wireless communication systems, is a fundamental signal processing technique in the physical layer to improve the spectrum utilization efficiency. Motivated by deep learning (DL) high-impact success in many informatics domains, including radio signal processing for communications, numerous recent AMC methods exploiting deep networks have been proposed to overcome the existing drawbacks of traditional approaches. DL is capable of learning the underlying characteristics of radio signals effectively for modulation pattern recognition, which in turn improves the modulation classification performance under the presence of channel impairments. In this work, we first provide the fundamental concepts of various architectures, such as neural networks, recurrent neural networks, long short-term memory, and convolutional neural networks as the necessary background. We then convey a comprehensive study of DL for AMC in wireless communications, where technical analysis is deliberated in the perspective of state-of-the-art deep architectures. Remarkably, several sophisticated structures and advanced designs of convolutional neural networks are investigated for different data types of sequential radio signals, spectrum images, and constellation images to deal with various channel impairments. Finally, we discuss some primary research challenges and potential future directions in the area of DL for modulation classification.

关键词： modulation Feature extraction Wireless communication Task analysis Recurrent neural networks Receivers Interference automatic modulation classification convolutional neural networks deep learning architectures deep neural networks recurrent neural networks wireless communications

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Robust automatic modulation classification for Cognitive Radio Networks

引用

IEEE ACCESS 2021年 9卷 92386-92393页

作者： Kim, Seung-Hwan Kim, Jae-Woo Nwadiugwu, Williams-Paul Kim, Dong-Seong Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi 39177 South Korea

In this paper, a novel deep learning-based robust automatic modulation classification (AMC) method is proposed for cognitive radio networks. Generally, as network input of AMC convolutional neural networks (CNNs) images or complex signals are utilized in time domain or frequency domain. In terms of the image that contains RGB(Red, Green, Blue) levels the input size may be larger than the complex signal, which represents the increase of computational complexity. In terms of the complex signal it is normally used as 2 x N size for the input, which is divided into in-phase and quadrature-phase (IQ) components. In this paper, the input size is extended as 4xN size by copying IQ components and concatenating in reverse order to improve the classification accuracy. Since the increase in the amount of computation complexity due to the extended input size, the proposed CNN archiecture is designed to reduce the size from 4 x N to 2 x N by an average pooling layer, which can enhence the classification accuracy as well. The simulation results show that the classification accuracy of the proposed model is higher than the conventional models in the almost signal-to-noise ratio (SNR) range.

关键词： automatic modulation classification deep learning model convolution neural network frame extension cognitive radio

来源：评论

学校读者我要写书评

暂无评论

automatic modulation classification Using Multi-Scale Convolutional Neural Network 31

Automatic Modulation Classification Using Multi-Scale Convol...

引用

31st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC)

作者： Chen, Hongtai Guo, Li Dong, Chao Cong, Fuze Mu, Xidong Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Beijing Peoples R China

ISBN: (纸本)9781728144900

In this paper, a multi-scale convolutional neural network-based (MSN) method is proposed for robust automatic modulation classification (AMC). The classifier directly utilizes in-phase and quadrature (I/Q) samples to identify the modulation type of received signal without any data preprocessing, thereby reducing the computational complexity. Further, the network architecture employs one-dimensional convolution (Conv1D) to extract multi-scale feature maps due to its merits of low computational complexity. Then these multi-scale feature maps are merged together by repeated multi-scale fusions, in order to improve the classification accuracy performance and the robustness to varying SNR environment. Repeated multi-scale fusions can make better use of amplitude-phase information because it can learn the local changes brought by modulation as well as the timing characteristics of the samples. Simulation results show that proposed MSN achieves classification rate of 97.38% classification accuracy at high SNR regimes for 24 different modulation types on the public well-known over-the-air (OTA) dataset. Moreover, MSN still can recognize the modulation types of received signals with the accuracy rates of about 95% under varying SNR scenarios. Compared to the methods proposed in other papers, our classifier not only shows a better performance in terms of classification accuracy, but also is the most robust in varying SNR environment.

关键词： automatic modulation classification Multi-scale feature maps Repeated multi-scale fusions Varying SNR

来源：评论

学校读者我要写书评

暂无评论

A Data Preprocessing Method for automatic modulation classification Based on CNN

引用

IEEE COMMUNICATIONS LETTERS 2021年第4期25卷 1206-1210页

作者： Zhang, Haozheng Huang, Ming Yang, Jingjing Sun, Wei Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Yunnan Peoples R China

As a backbone of deep learning models, convolutional neural networks (CNNs) are widely used in the field of automatic modulation classification. Nevertheless, we speculate that the forms of signal samples make them inefficient for direct use as a CNN input. In this letter, a novel data preprocessing method is proposed to markedly improve CNN-based automatic modulation classification. The benchmark dataset used in this research is the well-known RadioML2016.10a dataset. The experimental results show that using the proposed method gains approximately 10% accuracy improvement in a simple CNN. Furthermore, according to the form of the preprocessed data, we designed a CNN with residual blocks to reach a maximum accuracy of 93.7% when the signal-to-noise ratio is 14 dB, which outperforms state-of-the-art automatic modulation classifiers.

关键词： modulation Feature extraction Data preprocessing Neural networks Convolution Shape Digital modulation automatic modulation classification convolutional neural network (CNN) data preprocessing residual block

来源：评论

学校读者我要写书评

暂无评论

Time and phase features network model for automatic modulation classification

引用

COMPUTERS & ELECTRICAL ENGINEERING 2023年第PartA期111卷

作者： Cui, Tianshu Wang, Dong Ji, Libin Han, Jiabao Huang, Zhen Tsinghua Univ Beijing Beijing Natl Researh Ctr Informat Sci & Tehnol BNR 30 Shuangqing Rd Beijing 100190 Peoples R China Chinese Acad Sci Natl Space Sci Ctr Key Lab Elect & Informat Technol Space Syst Beijing 100190 Peoples R China

automatic modulation classification (AMC) constitutes a fundamental technology for enabling automatic demodulation in Cognitive Communication Systems (CCS). Due to the size, weight, and power (SWaP) constraints of embedded computers employed in CCS, there are limited compu-tational and memory resources. While deep neural networks possess strong feature representation and high accuracy recognition capabilities, they usually come with a high number of network parameters and high computational complexity, thereby reducing the real-time processing ability of CCS. Therefore, neural network structures intended for CCS must be lightweight and compu-tationally efficient. In this paper, we propose a high-performance and resource-friendly network model based on an analysis of the modulation mechanism of communication signals. The network extracts phase features and short-time features sequentially using directional convolutional fil-ters. Long short-term memory (LSTM) units are then used to extract long-term features, and only one fully connected layer is used for classification. Experiments with a standard dataset consisting of 11 communication modulation types demonstrate that our proposed model achieves an ac-curacy greater than 84.5%, even when the signal-to-noise ratio (SNR) is 0 dB, and the model has only 29187 parameters. On a Jetson Nano embedded platform, the model achieves a processing speed of up to 375366 in-phase and quadrature samples/s. Overall, the results suggest that our proposed approach is both lightweight and highly efficient, making it more suitable for CCS applications.

关键词： automatic modulation classification Deep learning Phase features Short -time features Long-time features Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Frequency learning attention networks based on deep learning for automatic modulation classification in wireless communication

引用

PATTERN RECOGNITION 2023年第1期137卷

作者： Zhang, Duona Lu, Yuanyao Li, Yundong Ding, Wenrui Zhang, Baochang Xiao, Jing North China Univ Technol Sch Informat Sci & Technol Beijing Peoples R China Beihang Univ Unmanned Syst Res Inst Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China

Deep neural networks have been recently applied in automatic modulation classification task and achieved remarkable success. However, Existing neural networks mainly focus on the purely data-driven architecture design, and fail to explore the hand-crafted feature mechanisms which are particularly sig-nificant for radio signal presentation in wireless communication. Inspired by digital signal processing theories, we propose frequency learning attention networks (FLANs) to analyze the radio spectral bias from frequency perspective, based on a multi-spectral attention mechanism for learning-based frequency components selection. FLANs are the general case of classical global average pooling and leverage iden-tical structures of the popular neural networks. Extensive experiments have been conducted to validate the superiority of FLANs for automatic modulation classification over a wide variety of state-of-the-art methods on RADIOML 2018.01A dataset.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Frequency learning Attention mechanism automatic modulation classification Wireless communication

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based automatic modulation classification Using Robust CNN Architecture for Cognitive Radio Networks

引用

SENSORS 2023年第23期23卷 9467-9467页

作者： Abd-Elaziz, Ola Fekry Abdalla, Mahmoud Elsayed, Rania A. Zagazig Univ Dept Elect & Commun Engn Zagazig 44519 Egypt October 6 Univ Dept Elect & Commun Engn Fac Engn 6th October City Cairo 12585 Egypt

automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at -2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.

关键词： automatic modulation classification raw IQ sequences deep learning convolutional neural network and wireless channel impairments

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised-based automatic modulation classification with domain adaptation for wireless IoT spectrum monitoring

引用

FRONTIERS IN PHYSICS 2023年 11卷

作者： Zhou, Quan Zhang, Ronghui Jing, Zexuan Jing, Xiaojun Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China

Dramatic advances in wireless networks have led to the Smart IoT, which may enable new modes of information transfer. Generally, data-driven advanced artificial intelligence (AI) techniques can be used in smart IoT networks, which prones to a lot of unexpected challenges. Enabling efficient management of spectrum resources based on cognitive radio is considered to be an effective means to address the limited spectrum resources. As a key technology in CR, automatic modulation recognition (AMR) is developing towards intelligence with deep learning as the main approach. Deep learning (DL) has been widely applied to AMR for possible improvement of recognition accuracy, while the superb performance highly depends on high-quality and well-labeled datasets. Consequently, these requirements prone to poor performance in the environments where the datasets are not well-labeled. Motivated by this, domain adaptation is considered for AMR in this paper, and a novel network architecture is proposed therein, termed semi-supervised automatic modulation recognition (SemiAMR). Specifically, source domain high SNR data are mapped by the source encoder to the classification domain and classified by the classifier, where the source data with labels enable this training process. Next, the discriminator and the target domain encoder are trained by determining whether the data is from the target or source domain. Finally, the target domain encoder and classifier are combined and are used to infer the data labels of the target domain. Experimental results state that the accuracy of proposed SemiAMR achieves 1\% to 27\% improvements when compared with classical schemes under the target domain condition where there is no corresponding labeled data and signal-to-noise ratios (SNRs) varies from -20 dB to -4 dB.

关键词： automatic modulation classification wireless IoT semi-supervised learning domain adaptation convolutional neural 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：