Slodkowski joint spectrum is similar to Taylor joint spectrum, but it has more important meaning in theory and application. In this paper we characterize Slodkowski joint spectrum and generalize some results about ten...Slodkowski joint spectrum is similar to Taylor joint spectrum, but it has more important meaning in theory and application. In this paper we characterize Slodkowski joint spectrum and generalize some results about tensor product.展开更多
In this paper we characterize the left joint spectrum of an n-tuple T = (T1,… ,Tn) of dominant bounded linear operators on a complex Hilbert space H and the unital C-algebra C(T) generated by T1, …,Tn and Ⅰ; moreov...In this paper we characterize the left joint spectrum of an n-tuple T = (T1,… ,Tn) of dominant bounded linear operators on a complex Hilbert space H and the unital C-algebra C(T) generated by T1, …,Tn and Ⅰ; moreover, we give an application of this characterization.展开更多
In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use par...In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.展开更多
Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity an...Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.展开更多
针对机载预警雷达空时自适应处理(space-time adaptive processing,STAP)所面临的异构杂波环境,基于杂波和噪声的联合稀疏特性提出了一种直接数据域(direct data domain,D3)STAP方法。首先通过子孔径平滑技术扩充训练样本集合;然后基于...针对机载预警雷达空时自适应处理(space-time adaptive processing,STAP)所面临的异构杂波环境,基于杂波和噪声的联合稀疏特性提出了一种直接数据域(direct data domain,D3)STAP方法。首先通过子孔径平滑技术扩充训练样本集合;然后基于杂波谱二阶表征理论构造STAP功率字典矩阵、导出目标函数,并解得待检测单元信号的空时功率谱;最后根据杂波先验信息重构无孔径损失的杂波加噪声协方差矩阵。数值实验验证了所提方法的协方差矩阵估计精度高于传统的稀疏恢复D3-STAP算法,且在理想情况和存在阵列误差的情况下,所提方法皆具备更好的低速目标检测性能。展开更多
It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mecha...It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power(JSAP)resource allocation based on deep Q-learning networks(DQNs).Each UAV to UAV(U2U)link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another.The convolutional neural network,target network,and experience replay are adopted while training.The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.展开更多
文摘Slodkowski joint spectrum is similar to Taylor joint spectrum, but it has more important meaning in theory and application. In this paper we characterize Slodkowski joint spectrum and generalize some results about tensor product.
文摘In this paper we characterize the left joint spectrum of an n-tuple T = (T1,… ,Tn) of dominant bounded linear operators on a complex Hilbert space H and the unital C-algebra C(T) generated by T1, …,Tn and Ⅰ; moreover, we give an application of this characterization.
基金Project supported by the National Fundamental Research (Grant Nos.2009CB3020402,2010CB731803)the National Natural Science Foundation of China (Grant Nos.60702046,60832005,60972050,60632040)the Natural High-Technology Research and Development Program of China (Grant Nos.2007AA01Z267,2009AA01Z248,2009AA011802)
文摘In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.
基金Supported by the National Natural Science Foundation of China (No. 61102066)China Postdoctoral Science Foundation (No. 2012M511365)the Scientific Research Project of Zhejiang Provincial Education Department (No.Y201119890)
文摘Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.
文摘针对机载预警雷达空时自适应处理(space-time adaptive processing,STAP)所面临的异构杂波环境,基于杂波和噪声的联合稀疏特性提出了一种直接数据域(direct data domain,D3)STAP方法。首先通过子孔径平滑技术扩充训练样本集合;然后基于杂波谱二阶表征理论构造STAP功率字典矩阵、导出目标函数,并解得待检测单元信号的空时功率谱;最后根据杂波先验信息重构无孔径损失的杂波加噪声协方差矩阵。数值实验验证了所提方法的协方差矩阵估计精度高于传统的稀疏恢复D3-STAP算法,且在理想情况和存在阵列误差的情况下,所提方法皆具备更好的低速目标检测性能。
基金supported by the National Natural Science Foundation of China(62031017,61971221).
文摘It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power(JSAP)resource allocation based on deep Q-learning networks(DQNs).Each UAV to UAV(U2U)link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another.The convolutional neural network,target network,and experience replay are adopted while training.The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.
