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A amendatory LRP channel condition prediction algorithm base on Kalman Filtering

机译:一种基于卡尔曼滤波的修正LRP信道状况预测算法

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This paper presents a channel condition prediction method for channel stage changes caused by the mobile move in high speed, which base on Kalman Filtering and Auto Regressive (AR) model. We introduced the selection of the sample and the revision of Kalman after studied traditional solution of LRP algorithms. Next, we extract the data from samples, established the AR model coefficient by a training sequence as the past information, predict the channel condition by the LRP channel prediction, and introduce a decision-making, when channel states have clear changes, revision them by Kalman filter. The algorithm we advanced can avoid the repeating calculation by use LRP algorithm. We found that it obtain the good effect in channel prediction by simulation analysis .
机译:本文提出了一种基于卡尔曼滤波和自回归(AR)模型的,针对高速移动引起的信道阶段变化的信道状态预测方法。在研究了LRP算法的传统解决方案之后,我们介绍了样本的选择和Kalman的修订。接下来,我们从样本中提取数据,通过训练序列建立AR模型系数作为过去的信息,通过LRP信道预测来预测信道条件,并引入决策,当信道状态发生明显变化时,通过卡尔曼滤波器。我们提出的算法可以避免使用LRP算法进行重复计算。通过仿真分析,发现在信道预测中取得了良好的效果。

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