摘要
为了能够给高频用户提供较为可靠的空闲频段,需要对高频段频谱占用的情况进行预测。本文提出一种基于自适应Volterra滤波理论的预测方法,通过对频谱占用因子时间序列进行状态空间重构,并采用递推形式的自适应递归最小二乘算法实时调整Volterra模型的核系数。实际数据处理结果表明,该预测方法能够很好地跟踪频谱占用因子的非线性变化,具有预测误差小,训练计算复杂度低等优点。
It is necessary to predict the spectrum occupancy so as to provide high-frequency users with reliably available band. This paper proposes a prediction method based on adaptive Volterra filter theory,which reconstructs the congestion time series based on State-space Reconstruction theory and modifies the kernel coefficients of Volterra model in real-time with RLS algorithm. Experiments are implemented based on real measurements and the results demonstrate that Volterra prediction method can effectively capture nonlinear variations of congestion,as well as it has the advantages of a small forecasting error and a low computational complexity in the training process.
出处
《信号处理》
CSCD
北大核心
2015年第9期1159-1164,共6页
Journal of Signal Processing
基金
国家自然科学基金(61201304)
航天支撑技术基金(2013-HT-HGD-H22)
哈尔滨市科技创新人才专项资金(2013RFQXJ097)资助
