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The emerging fifth generation（5G） wireless communication system raises new requirements on spectral efficiency and energy efficiency. A massive multiple-input multiple-output（MIMO） system, equipped with tens or even hundreds of antennas, is capable of providing significant improvements to spectral efficiency,energy efficiency, and robustness of the system. For the design, performance evaluation, and optimization of massive MIMO wireless communication systems, realistic channel models are indispensable. This article provides an overview of the latest developments in massive MIMO channel measurements and models. Also, we compare channel characteristics of four latest massive MIMO channel models, such as receiver spatial correlation functions and channel capacities. In addition, future challenges and research directions for massive MIMO channel measurements and modeling are identified.

Quantitative precipitation estimation （QPE） plays an important role in meteorological and hydrological applications.Ground-based telemetered rain gauges are widely used to collect precipitation measurements.Spatial interpolation methods are commonly employed to estimate precipitation fields covering non-observed locations.Kriging is a simple and popular geostatistical interpolation method,but it has two known problems：uncertainty underestimation and violation of assumptions.This paper tackles these problems and seeks an optimal spatial interpolation for QPE in order to enhance spatial interpolation through appropriately assessing prediction uncertainty and fulfilling the required assumptions.To this end,several methods are tested：transformation,detrending,multiple spatial correlation functions,and Bayesian kriging.In particular,we focus on a short-term and time-specific rather than a long-term and event-specific analysis.This paper analyzes a stratiform rain event with an embedded convection linked to the passing monsoon front on the 23 August 2012.Data from a total of 100 automatic weather stations are used,and the rainfall intensities are calculated from the difference of 15 minute accumulated rainfall observed every 1 minute.The one-hour average rainfall intensity is then calculated to minimize the measurement random error.Cross-validation is carried out for evaluating the interpolation methods at regional and local levels.As a result,transformation is found to play an important role in improving spatial interpolation and uncertainty assessment,and Bayesian methods generally outperform traditional ones in terms of the criteria.

利用InSAR变形监测结果进行形变机理反演时,由于InSAR获取的数据点众多,且往往含有较多的误差乃至粗差点,严重制约了反演计算的效率和可靠性。为此,本文提出顾及InSAR变形监测数据的物理空间相关性来设立协方差函数,并依据协方差函数确定四叉树象限分解阈值和最大象限大小的自适应四叉树分解InSAR数据压缩算法。本算法能够在尽可能保留形变信号特征细节信息的同时,极大地降低InSAR数据量。论文以西安地区地面沉降InSAR形变监测结果为例进行了试验分析,验证了该算法的有效性。结果表明,该方法能够在不损失形变信号特征的同时,有效地实现InSAR数据压缩和噪声消除的目的。