基于支持向量机的高速铁路通信系统信道预测算法  

Support vector machine for channel prediction in high-speed railway communication systems

作　　者：董志翔 赵宜升[1] 黄锦锦[1] 陈梦嘉 陈忠辉[1] Dong Zhixiang;Zhao Yisheng;Huang Jinjin;Chen Mengjia;Chen Zhonghui(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350116,China)

机构地区：[1]福州大学物理与信息工程学院,福建福州350116

出　　处：《电子技术应用》2018年第4期117-121,共5页Application of Electronic Technique

基　　金：国家自然科学基金(U1405251);福建省自然科学基金(2015-J05122;2015J01250)

摘　　要：针对高速铁路通信系统,研究快速时变信道预测问题。通过引入支持向量机(SVM)模型,提出一种信道预测算法。通过求解二次优化问题,得到SVM的预测最优超平面,并通过循环迭代实现多步预测。为了进一步提高预测准确度,采用遗传算法(GA)对SVM模型的惩罚系数和高斯核宽度进行优化。仿真结果表明,与传统的自回归(AR)以及单一的SVM预测模型相比,所提出的同时考虑SVM和GA(SVM-GA)的预测模型具有较低的预测误差。此外,当考虑噪声对预测性能影响时,SVM-GA预测模型在归一化均方误差性能方面也优于AR和SVM模型。In this paper,the problem of fast time-varying channel prediction is investigated in high-speed railway communication systems.A channel prediction algorithm is proposed based on a Support Vector Machine(SVM)model.By solving a quadratic optimization problem,the optimal hyperplane for prediction of the SVM is obtained.Moreover,the multi-step prediction is realized through cyclic iteration.In order to further improve the prediction accuracy,the penalty coefficient and Gaussian kernel width of the SVM model are optimized by a Genetic Algorithm(GA).Simulation results show that the proposed prediction model based on both the SVM and the GA(SVM-GA)has lower prediction error than traditional Autoregressive(AR)and single SVM prediction models.In addition,when the effect of the noise on prediction performance is considered,the SVM-GA prediction model is superior to the AR and the SVM models in terms of normalized mean squared error.

关 键 词：信道预测 支持向量机 遗传算法 高速铁路通信 

分 类 号：TN911.23[电子电信—通信与信息系统]