智能反射面(Reconfigurable Intelligent Surface,RIS)有着操纵性强、能耗低、方便部署等优势,已成为6G(第六代移动通信)的关键技术。研究了关于智能反射面的信道估计算法,对于传统算法在传播时路径会因角度原因发生偏移的情况,采用了Tu...智能反射面(Reconfigurable Intelligent Surface,RIS)有着操纵性强、能耗低、方便部署等优势,已成为6G(第六代移动通信)的关键技术。研究了关于智能反射面的信道估计算法,对于传统算法在传播时路径会因角度原因发生偏移的情况,采用了Tucker分解的稀疏角度域高阶奇异值分解(High Order Singular Value Decomposition,HOSVD)信道估计算法来解决路径偏移的问题。为了验证所提出算法的鲁棒性,对比了传统的交替最小二乘法信道估计算法,可以得到不管是在用户数量、传播的路径偏移上都能取得比传统的信道估计算法更好的归一化最小均方误差(Normalized Mean Squared Error,NMSE)效果。展开更多
提出一种在欠采样条件下的经过混合结构设计的射频发射机线性化方法。该方法基于欠采样频率选择性的非线性模型来校正调制器产生的镜像干扰信号以及射频功率放大器的互调失真信号。实验结果表明LTE的70 MHz双载波信号在发射机采样速率从...提出一种在欠采样条件下的经过混合结构设计的射频发射机线性化方法。该方法基于欠采样频率选择性的非线性模型来校正调制器产生的镜像干扰信号以及射频功率放大器的互调失真信号。实验结果表明LTE的70 MHz双载波信号在发射机采样速率从491.52 Ms/s降低至122.88 Ms/s时使用该组合方法较之前方法有10 d B的归一化最小均方误差改善以及10 d B的邻道功率泄露比抑制改善。展开更多
Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards the...Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.展开更多
文摘智能反射面(Reconfigurable Intelligent Surface,RIS)有着操纵性强、能耗低、方便部署等优势,已成为6G(第六代移动通信)的关键技术。研究了关于智能反射面的信道估计算法,对于传统算法在传播时路径会因角度原因发生偏移的情况,采用了Tucker分解的稀疏角度域高阶奇异值分解(High Order Singular Value Decomposition,HOSVD)信道估计算法来解决路径偏移的问题。为了验证所提出算法的鲁棒性,对比了传统的交替最小二乘法信道估计算法,可以得到不管是在用户数量、传播的路径偏移上都能取得比传统的信道估计算法更好的归一化最小均方误差(Normalized Mean Squared Error,NMSE)效果。
文摘提出一种在欠采样条件下的经过混合结构设计的射频发射机线性化方法。该方法基于欠采样频率选择性的非线性模型来校正调制器产生的镜像干扰信号以及射频功率放大器的互调失真信号。实验结果表明LTE的70 MHz双载波信号在发射机采样速率从491.52 Ms/s降低至122.88 Ms/s时使用该组合方法较之前方法有10 d B的归一化最小均方误差改善以及10 d B的邻道功率泄露比抑制改善。
基金The School Enterprise Cooperation Project of the Department of Higher Education of the Ministry of Education(201702130029)The Project of Shandong Provincial Education Department"Research on the Flip Classroom Teaching Model Based on the Training of Applied Talents"(2017304)~~
文摘Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, 'maturing data' (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data’. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data’ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data’ phenomena.
