In this paper,a massive multiple input multiple output(MIMO)channel measurement campaign with two setups is conducted in an indoor lobby environment.In the first setup,two types of 256-element virtual uniform rectangu...In this paper,a massive multiple input multiple output(MIMO)channel measurement campaign with two setups is conducted in an indoor lobby environment.In the first setup,two types of 256-element virtual uniform rectangular arrays(URAs),i.e.,the 4×64 virtual URA and the 64×4 virtual URA are used.The carrier frequency is 11 GHz;in the second setup,measurements are performed at 4,6,11,13,15,18 GHz at two different user locations.The channel characterization is presented by investigating the typical channel parameters,including average power delay profile(APDP),K factor,root mean square(RMS)delay spread,and coherence bandwidth.Moreover,the channel characteristics in angular domain are investigated by applying the space-alternating generalized expectation-maximization(SAGE)algorithm.The extracted multipath components(MPCs)are preliminarily clustered by visual inspection,and related to the interacting objects(IOs)in physical environment.Multipath structures at multiple frequency bands are examined.Direction spread of departure is estimated to evaluate the directional dispersion at the base station(BS)side.The results in this paper can help to reveal the propagation mechanisms in massive MIMO channels,and provide a foundation for the design and application of the practical massive MIMO system.展开更多
In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival ...In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival (DOA) tracking algorithms. On the other hand, although the DOA estimation methods based on the Maximum Likelihood (ML) principle have higher resolution than the beamforming and the subspace based methods, prohibitively heavy computation limits their practical applications. This letter first proposes a new suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algo- rithms, and then proposes a Kalman filtering based tracking algorithm to model the dynamic property of directional changes for mobile terminals in such a way that the association between the estimates made at different time points is maintained. At each stage during tracking process, the current suboptimal estimates of DOA are treated as measurements, predicted and updated via a Kalman state equation, hence adaptive tracking of moving MS can be carried out without the need to perform unduly heavy computations. Computer simulation results show that this proposed algorithm has better per- formance of DOA estimation and tracking of MS than the conventional ML or subspace based algo- rithms in terms of accuracy and robustness.展开更多
为了克服正则化理论的全变分图像盲复原模型中出现的运行效率低、效果不好等问题,提出一种基于交替方向乘子法的盲复原迭代算法。该算法通过交替迭代的方式,将复原图像与点扩散函数交替估计,同时不必更新惩罚项从而提高了运行速度和复...为了克服正则化理论的全变分图像盲复原模型中出现的运行效率低、效果不好等问题,提出一种基于交替方向乘子法的盲复原迭代算法。该算法通过交替迭代的方式,将复原图像与点扩散函数交替估计,同时不必更新惩罚项从而提高了运行速度和复原的质量。计算同时加入了对点扩散函数的归一化和阈值约束条件以及对图像的正定性条件。数值试验中,对不同模糊类型的图像进行了盲复原处理,并与已有的其他盲复原方法进行了比较。从主观评价能够发现,提出的算法能够改进图像的质量,提高其分辨率;通过客观指标比较,峰值信噪比(peak signal to noise ratio,PSNR)最大能够提高1.2 d B,结构相似度(structural similarity index,SSIM)最大提高1%,计算时间最大节约一半左右。展开更多
基金supported in part by the National Key Research and Development Program of China under Grant 2016YFE0200900 and 2018YFF0212103in part by NSFC under Grant 61725101, 61771037, 6181101396, and U1834210+4 种基金in part by the Beijing Natural Science Foundation under Grant 4182047 and L172020in part by the Fundamental research funds for the central universities under Grant 2017RC031 and Grant 2018JBM301in part by the Major projects of Beijing Municipal Science and Technology Commission under Grant Z181100003218010in part by the State Key Lab of Rail Traffic Control and Safety under Grant 2017JBM332, RCS2018ZZ007, and Grant RCS2018ZT014in part by the Teaching Reform Project under Grant 134496522
文摘In this paper,a massive multiple input multiple output(MIMO)channel measurement campaign with two setups is conducted in an indoor lobby environment.In the first setup,two types of 256-element virtual uniform rectangular arrays(URAs),i.e.,the 4×64 virtual URA and the 64×4 virtual URA are used.The carrier frequency is 11 GHz;in the second setup,measurements are performed at 4,6,11,13,15,18 GHz at two different user locations.The channel characterization is presented by investigating the typical channel parameters,including average power delay profile(APDP),K factor,root mean square(RMS)delay spread,and coherence bandwidth.Moreover,the channel characteristics in angular domain are investigated by applying the space-alternating generalized expectation-maximization(SAGE)algorithm.The extracted multipath components(MPCs)are preliminarily clustered by visual inspection,and related to the interacting objects(IOs)in physical environment.Multipath structures at multiple frequency bands are examined.Direction spread of departure is estimated to evaluate the directional dispersion at the base station(BS)side.The results in this paper can help to reveal the propagation mechanisms in massive MIMO channels,and provide a foundation for the design and application of the practical massive MIMO system.
文摘In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival (DOA) tracking algorithms. On the other hand, although the DOA estimation methods based on the Maximum Likelihood (ML) principle have higher resolution than the beamforming and the subspace based methods, prohibitively heavy computation limits their practical applications. This letter first proposes a new suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algo- rithms, and then proposes a Kalman filtering based tracking algorithm to model the dynamic property of directional changes for mobile terminals in such a way that the association between the estimates made at different time points is maintained. At each stage during tracking process, the current suboptimal estimates of DOA are treated as measurements, predicted and updated via a Kalman state equation, hence adaptive tracking of moving MS can be carried out without the need to perform unduly heavy computations. Computer simulation results show that this proposed algorithm has better per- formance of DOA estimation and tracking of MS than the conventional ML or subspace based algo- rithms in terms of accuracy and robustness.
文摘为了克服正则化理论的全变分图像盲复原模型中出现的运行效率低、效果不好等问题,提出一种基于交替方向乘子法的盲复原迭代算法。该算法通过交替迭代的方式,将复原图像与点扩散函数交替估计,同时不必更新惩罚项从而提高了运行速度和复原的质量。计算同时加入了对点扩散函数的归一化和阈值约束条件以及对图像的正定性条件。数值试验中,对不同模糊类型的图像进行了盲复原处理,并与已有的其他盲复原方法进行了比较。从主观评价能够发现,提出的算法能够改进图像的质量,提高其分辨率;通过客观指标比较,峰值信噪比(peak signal to noise ratio,PSNR)最大能够提高1.2 d B,结构相似度(structural similarity index,SSIM)最大提高1%,计算时间最大节约一半左右。
