|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios显示文摘Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We 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 signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The 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 method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,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. | Shilian Zheng Shichuan Chen Peihan Qi Huaji Zhou Xiaoniu Yang | 2020 | China Communications2020，17，2： | 16 |
| 2 | Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework显示文摘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 samples.In this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian domains.However,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 over-fitting.Fortunately,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 data.We present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal data.According 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 signal.We validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System(ACARS)signal dataset.Extensive experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies. | Huaji Zhou Licheng Jiao Shilian Zheng Lifeng Yang Weiguo Shen Xiaoniu Yang | 2020 | China Communications2020，17，10： | 8 |
| 3 | Progress in polymer solar cell显示文摘This review outlines current progresses in polymer solar cell. Compared to traditional silicon-based photovoltaic (PV) technology, the completely different principle of optoelectric response in the polymer cell results in a novel configuration of the device and more complicated photovoltaic generation proc-ess. The conception of bulk-heterojunction (BHJ) is introduced and its advantage in terms of mor-phology is addressed. The main aspects including the morphology of photoactive layer, which limit the efficiency and stability of polymer solar cell, are discussed in detail. The solutions to boosting up both the efficiency and stability (lifetime) of the polymer solar cell are highlighted at the end of this review. | LI LiGui LU GuangHao YANG XiaoNiu ZHOU EnLe | 2007 | Chinese Science Bulletin2007，52，2： | 4 |
| 4 | Primary User Adversarial Attacks on Deep Learning-Based Spectrum Sensing and the Defense Method显示文摘The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been verified.In this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing model.PUAA adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing model.We design three PUAA methods in black box scenario.In or-der to defend against PUAA,we propose a defense method based on autoencoder named DeepFilter.We 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 defense.Extensive experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of DeepFilter.Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model.In 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. | Shilian Zheng Linhui Ye Xuanye Wang Jinyin Chen Huaji Zhou Caiyi Lou Zhijin Zhao Xiaoniu Yang | 2021 | China Communications2021，18，12： | 3 |
| 5 | Performance analysis of three multi-radio access control policies in heterogeneous wireless networks显示文摘Access control policy in wireless networks has a signifcant impact on QoS satisfaction and resource utilization efciency.The design of access control policy in heterogeneous wireless networks(HWNs)becomes more challenging especially for the heterogeneous multiple access protocols of each radio network.In this paper,a Markov model is proposed to analyze the performance of three access control policies for HWNs.The frst policy is the optimal radio access technology(O-RAT)selection,where the incoming trafc always tries to access one network with the maximum service rate before admission.The second policy intends to allocate the same data to all networks.And the trafc will leave the system if it is accomplished frst by one of these networks,which is formulated as the aggregated multi-radio access(A-MRA)technology.The third policy is named the parallel multi-radio access(P-MRA)transmission,in which the incoming trafc is split into diferent networks.The trafc is served with the sum of the service rates provided by overall networks.Numerical and simulate results show the efectiveness of our analytical framework and the performance gain of the three access control policies.As illustrated with some representative results,the P-MRA policy shows superior performance gain to the other two policies independent on the specifc parameters of the diferent multiple access protocols due to the multiplexing gain. | ZHENG Jie LI JianDong LIU Qin SHI Hua YANG XiaoNiu | 2013 | Science China(Information Sciences)2013，56，12： | 2 |
| 6 | Joint subcarrier,code,and power allocation for parallel multi-radio access in heterogeneous wireless networks显示文摘In the heterogeneous wireless networks,it has been proved that the joint spectrum and power allocation can achieve network diversity gains for parallel multi-radio access in theory.This article aims to develop an effective and practical algorithm of joint subcarrier,code,and power allocation for parallel multiradio access of the downlink in heterogeneous wireless networks(e.g.,CDMA and OFDMA).Firstly,we propose a unified framework to formulate the subcarrier,code,and power allocation as an optimization problem.Secondly,we propose a resource element(subcarrier and code)scheme based on the threshold type.Simulation results show that the proposed scheme outperforms the existing algorithm for considered wireless scenarios. | ZHENG Jie LI JianDong SHI Hua LIU Qin YANG XiaoNiu | 2014 | Science China(Information Sciences)2014，57，8： | 2 |
| 7 | The modeling and analysis for autonomous navigation system based on tightly coupled GPS/1NS显示文摘 | WANG Lijun YANG Xiaoniu ZHAO Huichang | 2006 | International Conference on Microwave and Millimeter Wave Technology2006，，： | 1 |
| 8 | Exploiting transmission opportunities in heterogeneous wireless networks:a transmission power saving perspective显示文摘Exploiting transmission opportunities in dimensions of user equipments(UEs),radio access networks(RANs),and radio resource units(RRUs),in heterogeneous wireless networks(HWNs),we focus on a new perspective of saving the uplink transmission power,so as to lengthen the battery life time of UEs.Moreover,to ensure the quality of service for each UE,we guarantee each a minimal transmission rate.To achieve the above HWN control objective,we have to make an efcient matching among UEs,RANs,and RRUs,which is formulated as a binary linear optimization problem.Due to its NP-hard complexity,we develop a rate-power efciency based HWN control algorithm with low computational complexity.We verify the performance of our HWN control scheme through simulation.Extensive simulation results demonstrate the advantage of our HWN control method in saving the uplink transmission power and guaranteeing the UE transmission rate requirement. | JU HongHao LI JianDong LONG Yan YANG XiaoNiu | 2014 | Science China(Information Sciences)2014，57，2： | 1 |
| 9 | Few-shot electromagnetic signal classification:A data union augmentation method显示文摘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. | Huaji ZHOU Jing BAI Yiran WANG Licheng JIAO Shilian ZHENG Weiguo SHEN Jie XU Xiaoniu YANG | 2022 | Chinese Journal of Aeronautics2022，35，9： | 1 |
| 10 | On Minimizing Delay with Probabilistic Splitting of Traffic Flow in Heterogeneous Wireless Networks显示文摘In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is derived in a probabilistic manner.The basic idea can be understood via treating the integrated heterogeneous wireless networks as different coupled and parallel queuing systems.The integrated network performance can approach that of one queue with maximal the multiplexing gain.For the purpose of illustrating the effectively of our proposed model,the Cellular/WLAN interworking is exploited.To minimize the average delay,a heuristic search algorithm is used to get the optimal probability of splitting traffic flow.Further,a Markov process is applied to evaluate the performance of the proposed scheme and compare with that of selecting the best network to access in terms of packet mean delay and blocking probability.Numerical results illustrate our proposed framework is effective and the flow splitting transmission can obtain more performance gain in heterogeneous wireless networks. | ZHENG Jie LI Jiandong LIU Qin SHI Hua YANG Xiaoniu | 2014 | China Communications2014，11，12： | 1 |
| 11 | Toward High-Performance Polymer So- lar Cells: The Important of Morphology Control 显示文摘 | Xiaoniu Yang Joachim hoos | 2007 | Macromole- cules2007，40，5： | 1 |
| 12 | Distributed Con- sensus Algorithms for Decision Fusion Based Cooperative Spectrum Sensing in Cognitive Radio显示文摘 | Zheng Shilian Yang Xiaoniu Lou Caiyi | 2011 | Communications & Information Technologies2011，42，3： | 1 |
| 13 | Distributed Con- sensus Algorithms for Decision Fusion Based Cooperative Spectrum Sensing in Cognitive Radio显示文摘 | Zheng Shilian Yang Xiaoniu Lou Caiyi | 2011 | Communications & Information Technologies2011，42，3： | 1 |
| 14 | Development and studies on polima cytoplasmic male sterity'three lines'in Brassica napus显示文摘 | Fu Tingdong Yang Guangshen Yang xiaoniu | 1990 | Plant Breeding1990，104，： | 1 |
| 15 | Non-isothermal Crystallization Kinetics: Poly(ethylene terephthalate)- poly(ethylene oxide) Segmented Copolymer and Poly(ethylene oxide) Homopolymer显示文摘 | Xiaohua Kong Xiaoniu Yang Gao Li | | European Polymer Journal0，37，9： | 1 |
| 16 | Morphology and Performance of Poly(2-methoxy-5-(20-ethyl- h exyloxy)-p-p he nyle n evi nylene) (M E H-PPV): (6,6)-Phenyl-C61 - butyric Acid Methyl Ester (PCBM) Based Polymer Solar Cells显示文摘聚合物太阳能电池基于合成电影被制作 poly (2-methoxy-5-( 2-ethyl-hexyloxy )-1,4-phenylenevinylene)( MEH-PPV ):有 1:3 , 1:4 和 1:5 的重量混合比率的 fullerene 衍生物( 6,6 ) -phenyl-C61-butyric 酸甲基酉旨( PCBM ),从氯仿( CF )纺纱涂, chlorobenzene ( CB ),并且 o-dichlorobenzene ( ODCB )答案分别地。Photoinduced 电流和设备的力量变换效率(PCE ) 在溶剂上显示出依赖。太阳能电池在 1:5 有最高的 PCE 混合比率。传播电子显微镜学(TEM ) 形态学表明在 MEH-PPV 有一些虚空: PCBM 电影。空数字从 CF 与溶剂减少到 CB 和 ODCB。我们发现虚空被原子力量显微镜学被 TEM 和电影底部方面形态学学习通过电子断层摄影术技术在这些电影的底部定位。费用搬运人运输效率和收集效率应该由于虚空极大地减少,并且这部电影有越多虚空,效率越度减少。从 CF 准备的太阳能电池的 PCE 比从 CB 和 ODCB 准备的太阳能电池的低。MEH-PPV 的空现象: PCBM 基于太阳能电池和方法调查空位置提供一条试验性的证据和研究脑力与高效制作聚合物太阳能电池。 | Leijing Liu Svetlana van Bavel Shanpeng Wen Xiaoniu Yang Joachim Loos | 2013 | Chinese Journal of Chemistry2013，31，6： | 1 |
| 17 | Nonisothermal Crystallization Kinetics:Poly(ethylene terephthalate)- poly(ethylene oxide) Segmented Copolyrner and Poly(ethy- lene oxide) Homopolymer显示文摘 | Xiaohua Kong Xiaoniu Yang Gao Li | 2001 | European Polymer Journal2001，37，9： | 1 |
| 18 | Blind source separation algorithm for communication complex signals in communication reconnaissance显示文摘Most blind source separation algorithms are only applicable to real signals,while in communication reconnaissance processed signals are complex.To solve this problem,a blind source separation algorithm for communication complex signals is deduced,which is obtained by adopting the Kullback-Leibler divergence to measure the signals’independence.On the other hand,the performance of natural gradient is better than that of stochastic gradient,thus the natural gradient of the cost function is used to optimize the algorithm.According to the conclusion that the signal’s mixing matrix after whitening is orthogonal,we deduce the iterative algorithm by constraining the separating matrix to an orthogonal matrix.Simulation results show that this algorithm can efficiently separate the source signals even in noise circumstances. | Weihong FU Xiaoniu YANG Nai’an LIU Xingwen ZENG | 2008 | Frontiers of Electrical and Electronic Engineering in China2008，3，3： | 0 |
| 19 | Contrastive Clustering for Unsupervised Recognition of Interference Signals显示文摘Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning,many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However,there is no unsupervised interference signals recognition algorithm at present.In this paper,an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering(DDCC)is proposed.Specifically,in the first phase,four data augmentation strategies for interference signals are used in data-augmentation-based(DA-based)contrastive learning.In the second phase,the original dataset’s k-nearest neighbor set(KNNset)is designed in double dimensions contrastive learning.In addition,a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies;the feature dimensional contrastive learning in the second phase can improve the clustering purity;the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular,the clustering purity of our method is above 92%,SCAN’s is 81%,and the other three methods’are below 71%when jammingnoise-ratio(JNR)is−5 dB.In addition,our method is close to the supervised learning algorithm. | Xiangwei Chen Zhijin Zhao Xueyi Ye Shilian Zheng Caiyi Lou Xiaoniu Yang | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 20 | Incremental Learning of Radio Modulation Classification Based on Sample Recall显示文摘Radio modulation classification has always been an important technology in the field of communications.The difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old tasks.In this paper,we propose a sample memory and recall framework for incremental learning of radio modulation classification.For data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for memorizing.We compare the performance of our proposed method with three baselines through a large number of simulation experiments.Results show that our method achieves far higher classification accuracy than finetuning method and feature extraction method.Furthermore,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting. | Yan Zhao Shichuan Chen Tao Chen Weiguo Shen Shilian Zheng Zhijin Zhao Xiaoniu Yang | 2023 | China Communications2023，20，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员