卫星S频段下行链路频谱占用建模与预测  

Modeling and prediction of time series for S-band spectrum use in satellite downlink

作　　者：刘稳 洪涛[1] 王忠[2] 张更新 LIU Wen;HONG Tao;WANG Zhong;ZHANG Gengxin(Institute of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing Jiangsu 210003,China;Unit 31006 of PLA,Beijing 100840,China)

机构地区：[1]南京邮电大学通信与信息工程学院,江苏南京210003 [2]中国人民解放军31006部队,北京100840

出　　处：《太赫兹科学与电子信息学报》2020年第6期984-991,共8页Journal of Terahertz Science and Electronic Information Technology

基　　金：国家自然科学基金项目资助(91738201,61801445)。

摘　　要：目前提出的频谱占用模型能够在时域上描述和重现基本的统计特征,如传统的地面移动通信的频谱占用/空闲周期长度可以用经典的广义帕累托(GP)分布、指数分布等分布来拟合。然而在某些复杂的如卫星链路频谱占用场景中,传统的参数估计分布无法给出良好的拟合。为此提出了用核密度估计(KDE)的方法来进行概率密度分布的拟合,在此基础上,分别采用差分整合移动平均自回归模型(ARIMA)和模糊神经网络对频谱占用模型的时间序列进行预测并进行对比。结论表明,核密度估计的使用可以更加准确地描述并再现卫星下行链路所使用S频段的占用时间序列的统计特征,而模糊神经网络的预测比ARIMA模型预测更加精确。The development of the Cognitive Radio(CR)technology has benefited from the availability of realistic and accurate spectrum occupancy models.The spectrum occupancy models proposed in the literatures so far are able to capture and reproduce the statistical characteristics of occupied time series.For example,the busy/idle-period lengths of terrestrial wireless network can be fitted by Generalized Pareto(GP)distribution,exponential distribution,etc.However,the traditional parameter estimation distribution cannot give a good fit in satellite link spectrum occupancy.In this context,a method of Kernel Density Estimation(KDE)is proposed to fit the probability density distribution.On this basis,the Auto Regressive Integrated Moving Average Model(ARIMA)and fuzzy neural network are adopted to predict and compare the time series of the spectrum occupancy models.The conclusion shows that the proposed method can describe and reproduce the statistical characteristics of the occupied time series of the S-band used in the satellite downlink more accurately;while the prediction of the fuzzy neural network is more accurate than that of the ARIMA model.

关 键 词：频谱占用模型 概率密度分布 核密度估计 差分整合移动平均自回归模型预测 模糊神经网络预测 

分 类 号：TN129[电子电信—物理电子学]