针对近年来战术通信用户对网络连接性能和吞吐量越来越高的需求,对美军战术通信系统和主流宽带通信波形进行了简要介绍,并以此为基础和参照,提出了一种适用于战术移动环境下的宽带通信波形的物理层设计方法,波形采用正交频分复用(Orthog...针对近年来战术通信用户对网络连接性能和吞吐量越来越高的需求,对美军战术通信系统和主流宽带通信波形进行了简要介绍,并以此为基础和参照,提出了一种适用于战术移动环境下的宽带通信波形的物理层设计方法,波形采用正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)和Turbo码等先进编码调制技术。提出了在接收端采用初始信道估计和逐符号信道估计等信号处理方法,并在AWGN信道和无线衰落信道条件下对波形的误码率进行了蒙特卡罗仿真。仿真结果表明,波形在战术移动环境下具有优良的传输性能。展开更多
In modern science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within systems.However,e...In modern science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within systems.However,empirical data contaminated by process and measurement noise remain a significant obstacle for this type of modeling.In this study,we have developed a data-driven method capable of directly uncovering linear dynamical systems from noisy data.This method combines the Kalman smoothing and sparse Bayesian learning to decouple process and measurement noise under the expectation-maximization framework,presenting an analytical method for alternate state estimation and system identification.Furthermore,the discovered model explicitly characterizes the probability distribution of process and measurement noise,as they are essential for filtering,smoothing,and stochastic control.We have successfully applied the proposed algorithm to several simulation systems.Experimental results demonstrate its potential to enable linear dynamical system discovery in practical applications where noise-free data are intractable to capture.展开更多
文摘针对近年来战术通信用户对网络连接性能和吞吐量越来越高的需求,对美军战术通信系统和主流宽带通信波形进行了简要介绍,并以此为基础和参照,提出了一种适用于战术移动环境下的宽带通信波形的物理层设计方法,波形采用正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)和Turbo码等先进编码调制技术。提出了在接收端采用初始信道估计和逐符号信道估计等信号处理方法,并在AWGN信道和无线衰落信道条件下对波形的误码率进行了蒙特卡罗仿真。仿真结果表明,波形在战术移动环境下具有优良的传输性能。
基金supported by the National Natural Science Foundation of China(Grant No.92167201).
文摘In modern science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within systems.However,empirical data contaminated by process and measurement noise remain a significant obstacle for this type of modeling.In this study,we have developed a data-driven method capable of directly uncovering linear dynamical systems from noisy data.This method combines the Kalman smoothing and sparse Bayesian learning to decouple process and measurement noise under the expectation-maximization framework,presenting an analytical method for alternate state estimation and system identification.Furthermore,the discovered model explicitly characterizes the probability distribution of process and measurement noise,as they are essential for filtering,smoothing,and stochastic control.We have successfully applied the proposed algorithm to several simulation systems.Experimental results demonstrate its potential to enable linear dynamical system discovery in practical applications where noise-free data are intractable to capture.
