基于混沌时间序列建模的频谱状态持续时长预测  

Prediction of Spectrum State Duration Based on Chaotic Time Series Modelling

作　　者：张茜 刘光斌[1] 郭金库 余志勇[1] 

机构地区：[1]第二炮兵工程大学,西安710025

出　　处：《电子与信息学报》2015年第4期868-873,共6页Journal of Electronics & Information Technology

基　　金：国家自然科学基金青年科学基金(61201120)资助课题

摘　　要：为提高频谱利用率,该文利用非线性动力学理论对频谱状态持续时长序列进行建模并预测。以实际采集的频谱数据作为研究对象,采用指向导数法对该时长序列进行非一致延长时间相空间重构,利用基于尺度的Lyapunov指数判定其混沌特性。以基于Davidon-Fletcher-Powell方法的二阶Volterra预测模型(DFPSOVF)为基础,提出一种基于限域拟牛顿方法的Volterra自适应滤波器系数调整模型,并将该模型应用于具有混沌特性的短时频谱状态持续时长预测,通过自适应剔除对预测贡献小的滤波器系数,降低预测模型的复杂度。实验结果表明该算法在保证预测精度的同时降低运算复杂度。In order to enhance the spectrum utilization, this paper uses the nonlinear dynamics theory for modeling and prediction of spectrum state duration. Firstly, the real spectrum state duration is investigated. Then, this study uses the directional derivative to accomplish the state-space reconstruction of the spectrum time series with the non-uniform time delays. Finally, the Scale-Dependent Lyapunov Exponent（SDLE） is used to determine the characteristics of chaos. Based on the Davidon-Fletcher-Powell-based Second Order of Volterra Filter（DFPSOVF） method, a novel Volterra model with adaptive coefficient adjusting using Limited storage Broyden-FletcherGoldfarb-Shanno quasi-Newton（L-BFGS） method is proposed. Furthermore, the proposed model is applied to predict the short-term spectrum with chaotic characteristics. To reduce the complexity of this new model, the useless filter coefficients are eliminated adaptively. The numerical simulations show that the new algorithm can reduce the complexity and guarantee prediction accuracy.

关 键 词：频谱感知 频谱预测 混沌 限域拟牛顿方法 

分 类 号：TN92[电子电信—通信与信息系统]