为了满足未来物联网全频谱、全场景、全业务的组网需求,降低组网成本和人力投入,提升无线接入网的智能性,业界提出了意图驱动的6G无线接入网络(ID-RAN,intent-driven radio access network)。ID-RAN以人工智能、网络功能虚拟化、软件定...为了满足未来物联网全频谱、全场景、全业务的组网需求,降低组网成本和人力投入,提升无线接入网的智能性,业界提出了意图驱动的6G无线接入网络(ID-RAN,intent-driven radio access network)。ID-RAN以人工智能、网络功能虚拟化、软件定义网络等技术为基础,能够将用户或运营商对网络期望的业务、性能和组网"意图"转化为实际组网策略,从而实现网络的高效柔性可重构。首先概述了ID-RAN的体系架构,然后对意图转译、冲突解决、配置激活、网络性能评估与优化等关键技术进行了介绍,最后通过开放式软件定义硬件平台展示了意图驱动的6G无线组网性能。展开更多
In this paper,we consider the weighted local polynomial calibration estimation and imputation estimation of a non-parametric function when the data are right censored and the censoring indicators are missing at random...In this paper,we consider the weighted local polynomial calibration estimation and imputation estimation of a non-parametric function when the data are right censored and the censoring indicators are missing at random,and establish the asymptotic normality of these estimators.As their applications,we derive the weighted local linear calibration estimators and imputation estimations of the conditional distribution function,the conditional density function and the conditional quantile function,and investigate the asymptotic normality of these estimators.Finally,the simulation studies are conducted to illustrate the finite sample performance of the estimators.展开更多
文摘为了满足未来物联网全频谱、全场景、全业务的组网需求,降低组网成本和人力投入,提升无线接入网的智能性,业界提出了意图驱动的6G无线接入网络(ID-RAN,intent-driven radio access network)。ID-RAN以人工智能、网络功能虚拟化、软件定义网络等技术为基础,能够将用户或运营商对网络期望的业务、性能和组网"意图"转化为实际组网策略,从而实现网络的高效柔性可重构。首先概述了ID-RAN的体系架构,然后对意图转译、冲突解决、配置激活、网络性能评估与优化等关键技术进行了介绍,最后通过开放式软件定义硬件平台展示了意图驱动的6G无线组网性能。
基金supported in part by the National Social Science Foundation of China(Grant No.20BTJ049).
文摘In this paper,we consider the weighted local polynomial calibration estimation and imputation estimation of a non-parametric function when the data are right censored and the censoring indicators are missing at random,and establish the asymptotic normality of these estimators.As their applications,we derive the weighted local linear calibration estimators and imputation estimations of the conditional distribution function,the conditional density function and the conditional quantile function,and investigate the asymptotic normality of these estimators.Finally,the simulation studies are conducted to illustrate the finite sample performance of the estimators.
