检索结果-南通市图书馆

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Communications 2020年第2期17卷 138-148页

作者： Shilian Zheng Shichuan Chen Peihan Qi huaji zhou Xiaoniu Yang Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China State Key Laboratory of Integrated Service Networks Xidian UniversityXi’an 710071China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of ChinaXidian UniversityXi’an 710071China

Spectrum sensing is a key technology for cognitive *** present spectrum sensing as a classification problem and propose a sensing method based on deep learning *** normalize the received signal power to overcome the effects of noise power *** train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new *** also use transfer learning strategies to improve the performance for real-world *** experiments are conducted to evaluate the performance of this *** simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based *** addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new ***,the real-world signal detection experiment results show that the detection performance can be further improved by transfer ***,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.

关键词： spectrum sensing deep learning convolutional neural network cognitive radio spectrum management

Deep radio signal clustering with interpretability analysis based on saliency map

在线全文

学校读者我要写书评

暂无评论

Digital Communications and Networks 2024年第5期10卷 1448-1458页

作者： huaji zhou Jing Bai Yiran Wang Junjie Ren Xiaoniu Yang Licheng Jiao School of Artificial Intelligence Xidian University Science and Technology on Communication Information Security Control Laboratory

With the development of information technology,radio communication technology has made rapid *** radio signals that have appeared in space are difficult to classify without manually *** radio signal clustering methods have recently become an urgent need for this ***,the high complexity of deep learning makes it difficult to understand the decision results of the clustering models,making it essential to conduct interpretable *** paper proposed a combined loss function for unsupervised clustering based on *** combined loss function includes reconstruction loss and deep clustering *** clustering loss is added based on reconstruction loss,which makes similar deep features converge more in feature *** addition,a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency *** experiments have been conducted on a modulated signal dataset,and the results indicate the superior performance of our proposed method over other clustering *** particular,for the simulated dataset containing six modulation modes,when the SNR is 20dB,the clustering accuracy of the proposed method is greater than 78%.The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.

关键词： Unsupervised radio signal clustering Autoencoder Clustering features visualization Deep learning interpretability

Deep Spectrum Prediction in High Frequency Communication Based on Temporal-Spectral Residual Network

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

China Communications 2018年第9期15卷 25-34页

作者： Ling Yu Jin Chen Yuming Zhang huaji zhou Jiachen Sun College of Communications Engineering Army Engineering University of PLA Nanjing 210000 China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033 China

High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.

关键词： HF communication deep learning spectrum prediction temporal-spectral residual network

Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework

在线全文

学校读者我要写书评

暂无评论

China Communications 2020年第10期17卷 157-169页

作者： huaji zhou Licheng Jiao Shilian Zheng Lifeng Yang Weiguo Shen Xiaoniu Yang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi'an 710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

Generative adversarial network(GAN)has achieved great success in many fields such as computer vision,speech processing,and natural language processing,because of its powerful capabilities for generating realistic *** this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian ***,in many specific scenarios,we can’t obtain enough labeled data,which cause failure of deep learning methods because they are easy to fall into ***,semi-supervised learning(SSL)can leverage the large amount of unlabeled data to enhance the classification performance of classifiers,especially in scenarios with limited amount of labeled *** present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal *** to the characteristics of the electromagnetic signal,we propose a weighted loss function,leading to an effective classifier to realize the end-to-end classification of the electromagnetic *** validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System(ACARS)signal *** experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.

关键词： generative adversarial network semi-supervised learning electromagnetic signal classification end-to-end classification weighted loss function

Few-shot electromagnetic signal classification:A data union augmentation method

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2022年第9期35卷 49-57页

作者： huaji zhou Jing BAI Yiran WANG Licheng JIAO Shilian ZHENG Weiguo SHEN Jie XU Xiaoniu YANG School of Artificial Intelligence Xidian UniversityXidian UniversityXi'an 710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

Deep learning has been fully verified and accepted in the field of electromagnetic signal classification. However, in many specific scenarios, such as radio resource management for aircraft communications, labeled data are difficult to obtain, which makes the best deep learning methods at present seem almost powerless, because these methods need a large amount of labeled data for training. When the training dataset is small, it is highly possible to fall into overfitting, which causes performance degradation of the deep neural network. For few-shot electromagnetic signal classification, data augmentation is one of the most intuitive countermeasures. In this work, a generative adversarial network based on the data augmentation method is proposed to achieve better classification performance for electromagnetic signals. Based on the similarity principle, a screening mechanism is established to obtain high-quality generated signals. Then, a data union augmentation algorithm is designed by introducing spatiotemporally flipped shapes of the signal. To verify the effectiveness of the proposed data augmentation algorithm, experiments are conducted on the RADIOML 2016.04C dataset and real-world ACARS dataset. The experimental results show that the proposed method significantly improves the performance of few-shot electromagnetic signal classification.

关键词： Data union augmentation Electromagnetic signal classification Few-shot Generative adversarial network Screening mechanism

Primary User Adversarial Attacks on Deep Learning-Based Spectrum Sensing and the Defense Method

在线全文

学校读者我要写书评

暂无评论

China Communications 2021年第12期18卷 94-107页

作者： Shilian Zheng Linhui Ye Xuanye Wang Jinyin Chen huaji zhou Caiyi Lou Zhijin Zhao Xiaoniu Yang Science and Technology on Communication Information Security Control Laboratory Jiaxing314000China College of Information Engineering Zhejiang University of TechnologyHangzhou310012China Institute of Cyberspace Security Zhejiang University of TechnologyHangzhou310012China School of Communication Engineering Hangzhou Dianzi UniversityHangzhou310018China

The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been *** this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing *** adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing *** design three PUAA methods in black box *** or-der to defend against PUAA,we propose a defense method based on autoencoder named *** apply the long short-term memory network and the convolutional neural network together to DeepFilter,so that it can extract the temporal and local features of the input signal at the same time to achieve effective *** experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of *** show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing *** addition,the experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model.

关键词： spectrum sensing cognitive radio deep learning adversarial attack autoencoder defense

面向通信信号调制识别的半监督生成对抗网络框架

在线全文

学校读者我要写书评

暂无评论

国防科技大学学报 2023年第6期45卷 78-83页

作者：周华吉徐杰郑仕链沈伟国王巍楼财义电磁空间安全全国重点实验室浙江嘉兴314033 西安电子科技大学人工智能学院陕西西安710071

针对只有少量标签数据的弱监督条件下现有调制信号识别模型准确率较低的问题,提出基于生成对抗网络的半监督学习框架。该方法通过对通信信号进行冗余空域变换,使其在适应生成对抗网络模型的同时保留丰富的信号相邻特征;通过梯度惩罚Wass... 详细信息

针对只有少量标签数据的弱监督条件下现有调制信号识别模型准确率较低的问题,提出基于生成对抗网络的半监督学习框架。该方法通过对通信信号进行冗余空域变换,使其在适应生成对抗网络模型的同时保留丰富的信号相邻特征;通过梯度惩罚Wasserstein生成对抗网络的引入,构建适宜电磁信号处理的半监督学习框架,实现对无标签信号样本的有效利用。为了验证所提算法的有效性,在RADIOML 2016.04C数据集上进行测试。实验结果表明,该方法在半监督条件下能训练出高效的分类器,获得优异的调制识别结果。

关键词：生成对抗网络半监督学习框架通信信号调制识别

无人机群通信网络态势感知研究现状与发展趋势

在线全文

学校读者我要写书评

暂无评论

航空兵器 2019年第4期26卷 16-22页

作者：廖方圆周华吉李京华崔如松丁国如中国电子科技集团有限公司北京100846 陆军工程大学南京210007 93534部队天津301700

无人机群作战被认为是颠覆未来战场的新型作战力量,通信网络是牵引着无人机群正常运行的"风筝线",是连通无人机群的"神经脉络",无人机群通信网络态势(离散状态、全局形势以及演化趋势)信息的高效可靠获取是突破"电磁迷雾"并取得未来战... 详细信息

无人机群作战被认为是颠覆未来战场的新型作战力量,通信网络是牵引着无人机群正常运行的"风筝线",是连通无人机群的"神经脉络",无人机群通信网络态势(离散状态、全局形势以及演化趋势)信息的高效可靠获取是突破"电磁迷雾"并取得未来战场制信息权的基础和关键。本文从战场网络态势感知、典型无人机群作战项目、无人机群通信网络态势感知研究动态三个方面综述了国内外相关研究现状,分析了当前研究存在的不足,展望了未来发展趋势。

关键词：无人机群通信网络态势感知频谱感知网络结构态势预测

在线全文

学校读者我要写书评

暂无评论

社会网络中基于主题的影响力最大化算法

计算机应用研究 2016年第12期33卷 3611-3614页

作者：朱玉婷李雷施化吉周从华施磊磊徐慧江苏大学计算机科学与通信工程学院江苏镇江212013 大全集团江苏扬中212211

为了解决现有的影响力最大化研究没有充分考虑主题对影响力节点挖掘的影响而导致特定主题下节点集合的影响范围不大这一问题,提出了一种社会网络中基于主题的影响力最大化算法TIM。该算法首先根据主题敏感阈值对初始节点集进行预处理,... 详细信息

为了解决现有的影响力最大化研究没有充分考虑主题对影响力节点挖掘的影响而导致特定主题下节点集合的影响范围不大这一问题,提出了一种社会网络中基于主题的影响力最大化算法TIM。该算法首先根据主题敏感阈值对初始节点集进行预处理,剔除干扰节点,再在新的节点集合上分两个阶段进行节点挖掘。第一阶段挖掘主题权威性大的节点,第二阶段挖掘主题影响增量最大的节点,最后综合两个阶段的节点作为结果集并进行实验验证。实验结果表明,相比其他算法,TIM算法挖掘的节点集合在特定主题下的影响范围更大,时间复杂度更低。

关键词：社会网络影响力最大化主题节点挖掘节点集