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Frequency Domain Goodness of Fit Test Based Spectrum Sensing Method with Dynamically Varying Noise

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China Communications 2020年第12期17卷 172-179页

作者： Rui Gao peihan qi Zhenghua Zhang School of Information Engineering Yangzhou University88 South Daxue RoadYangzhou 225009China State Key Laboratory of Integrated Service Networks Xidian UniversityNo.2 South Taibai RoadXi'an 710071China

In cognitive radio networks,spectrum sensing under circumstances of dynamically varying noise and lacking prior information is a key challenge to the conventional spectrum sensing *** the necessary information is rather difficult to obtain practically,most existing spectrum sensing methods are fettered in *** by these,in this paper,a Frequency domain Goodness of Fit Test(FGoF)based spectrum sensing method is *** FGoF makes full use of underlying information in Guard-Bands and the advantages of GoF test works for any *** and simulated results show that the FGoF is a robust spectrum sensing method in cognitive radio with the inherent advantages of invulnerability to dynamically varying noise.

关键词： spectrum sensing goodness of fit test dynamically varying noise noise uncertainty

Effective bias reduction methods for passive source localization using TDOA and GROA

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Science China(Information Sciences) 2013年第7期56卷 97-108页

作者： HAO BenJian LI Zan qi peihan GUAN Lei State Key Laboratory of Integrated Services Networks Xidian University

For passive source localization based on both TDOA and GROA, this paper proposes two bias reduction methods for the well-known Weighted-Least-Squares (WLS) estimator. We first derive the passive source localization bias from the two-step algebraic closed-form solution. This bias is found to be considerably larger than the Maximum Likelihood Estimator (MLE) and limits the WLS estimator's practical applications. In this paper, We develop two methods to reduce the bias. The first one called Bias-Subtraction-Method (BSM) directly subtracts the expected bias from the solution of the WLS estimator, and the second one called Bias-Reduction-Method (BRM) imposes a constraint to the equation error formulation to improve the source location estimate. The noise covariance matrix must be known exactly in calculating the expected bias in BSM, and we only need to know the structure of it in BRM. For far-field sources localization when the noise is Gaussian and not too large, both of the two proposed methods can reduce the localization bias effectively and achieve the Cramér-Rao Lower Bound (CRLB) performance very well, and the BRM almost has the same performance as the MLE estimator. Simulations corroborate the performance of the two proposed methods.

关键词： source localization bias reduction Time Differences of Arrival (TDOA) Gain Ratios of Arrival (GROA) Cramér-Rao Lower Bound (CRLB)

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

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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

Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM

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China Communications 2018年第12期15卷 55-63页

作者： Faquan Yang Ling Yang Dong Wang peihan qi Haiyan Wang 无 School of Electrical and Information Engineering Foshan UniversityFoshan 528000China Xinhua College of Sun Yat-sen University Guanzuo 523133China State Key Laboratory of Integrated Service Networks Xidian UniversityXian 710071China

For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.

关键词： clustering algorithm feature extraction grading algorithm support vector machine modulation recognition

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High-Speed Railway 2024年第2期2卷 101-109页

作者： Jiaxu Guo Ding Ding peihan Yang qi Zou Yaping Huang School of Computer Science and Technology Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Big Data&Artificial Intelligence in Transportation Ministry of EducationBeijing 100044China

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining ***,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale *** the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUqi-Miner),to enable the effective mining of railway fault *** algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent ***,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the *** experimental results on the real fault dataset show that RHUqi-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

关键词： High utility Quantitative Frequent pattern mining Related degree pruning Fixed pattern length

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新型抗噪声不确定度谱分段对消频谱感知算法

西安电子科技大学学报 2013年第6期40卷 19-24页

作者：齐佩汉司江勃李赞高锐西安电子科技大学综合业务网理论及关键技术国家重点实验室陕西西安710071

针对噪声不确定度严重影响能量检测算法频谱感知性能的问题,提出了一种基于功率谱分段对消的频谱感知算法.该算法利用被监测信号周期图估计功率谱谱线互不相关的性质,以频带内一些谱线强度和与剩余谱线强度和的比值作为检验统计量,可实... 详细信息

针对噪声不确定度严重影响能量检测算法频谱感知性能的问题,提出了一种基于功率谱分段对消的频谱感知算法.该算法利用被监测信号周期图估计功率谱谱线互不相关的性质,以频带内一些谱线强度和与剩余谱线强度和的比值作为检验统计量,可实时鲁棒地感知监测频段的占用情况.理论分析和仿真结果表明,该算法可以在较宽的信噪比范围内获得较低的虚警概率和较高的检测概率,能有效克服噪声不确定度对检测性能的影响,判决门限不随次级用户周围环境噪声电平的变化而改变,适用于复杂电磁环境下的频谱监测和认知无线电系统.

关键词：认知无线电频谱感知功率谱密度噪声不确定度

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低信噪比下基于双检测长度的频谱感知算法

西安电子科技大学学报 2014年第3期41卷 88-94页

作者：高锐李赞齐佩汉关磊刘进西安电子科技大学综合业务网理论及关键技术国家重点实验室陕西西安710071

针对现有认知网络中低信噪比条件下频谱感知性能差、速度慢的问题,提出了一种基于双检测长度的感知算法.该算法先进行一次短数据长度能量检测,当第1次判决主用户不存在时,再进行第2次长数据长度能量检测,并作出最终的感知判决.从理论上... 详细信息

针对现有认知网络中低信噪比条件下频谱感知性能差、速度慢的问题,提出了一种基于双检测长度的感知算法.该算法先进行一次短数据长度能量检测,当第1次判决主用户不存在时,再进行第2次长数据长度能量检测,并作出最终的感知判决.从理论上推导了平均样本数、检测性能、算法复杂度等重要性质.基于matlab的仿真结果验证了理论推导的正确性,表明所提算法在低信噪比条件下,在感知性能较传统能量检测算法有所提升的同时,具有更少的平均样本数和更低算法复杂度的特点,并有望应用于未来实际认知网络中.

关键词：认知无线电频谱感知能量检测双检测长度

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一种双重序贯检测的协作频谱感知方法

西安交通大学学报 2014年第4期48卷 102-108页

作者：高锐李赞司江勃齐佩汉潘蕾西安电子科技大学综合业务网国家重点实验室西安710071

针对现有认知传感网在低信噪比条件下感知性能差、感知时间长、协作开销大的问题,提出了一种双重序贯检测的协作频谱感知(CSDS)方法。该方法首先在各传感器节点将数据进行分段处理,以各分段的能量作为一个样本点进行序贯检测,当各节点... 详细信息

针对现有认知传感网在低信噪比条件下感知性能差、感知时间长、协作开销大的问题,提出了一种双重序贯检测的协作频谱感知(CSDS)方法。该方法首先在各传感器节点将数据进行分段处理,以各分段的能量作为一个样本点进行序贯检测,当各节点的对数似然比和大于门限时向融合中心发送协作信息,然后在融合中心再进行序贯检测直到作出判决。理论推导给出了平均样本数、平均感知时间等性能参数的精确表达式。采用Matlab的仿真结果表明:CSDS方法在低信噪比条件下,在保持感知性能的同时,所需平均样本数和平均感知时间低于能量检测的40%,且协作开销较现有协作序贯检测方法显著减少。

关键词：认知传感网协作感知低信噪比序贯检测

异构联邦双向知识蒸馏传递半监督调制类型识别

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通信学报 2023年第11期44卷 183-200页

作者：齐佩汉丁渊磊尹凯徐佳波李赞西安电子科技大学通信工程学院陕西西安710071

新一代移动通信系统的规模化部署和快速发展支撑巨量多样物联网设备的广泛应用。然而物联网设备的分布式应用导致的私有数据迥异和本地处理模型差别,严重制约全局智能模型的聚合能力。因此,针对认知物联网中智能调制类型识别面临的数据... 详细信息

新一代移动通信系统的规模化部署和快速发展支撑巨量多样物联网设备的广泛应用。然而物联网设备的分布式应用导致的私有数据迥异和本地处理模型差别,严重制约全局智能模型的聚合能力。因此,针对认知物联网中智能调制类型识别面临的数据异质、模型异构和标记不足等问题,提出异构联邦双向知识蒸馏传递半监督调制类型识别算法,通过变分自编码器在云端生成公开伪数据集支持上行全局知识蒸馏,自适应共享至本地辅助下行异质知识蒸馏,并在蒸馏过程中内嵌半监督算法。仿真结果表明,在通信信号处理领域中,所提算法的有效性和适用性优于现有的联邦学习算法。

关键词：联邦半监督学习模型异构变分自编码器知识蒸馏