基于动态模式分解的移动用户信道容量预测算法

Prediction Algorithm of Mobile User Channel Capacity Based on Dynamic Mode Decomposition

在多输入多输出环境下,为了能够连续预测出移动用户的信道容量并以此合理地分配用户资源,提出了一种基于动态模式分解(DMD)的信道容量预测方法及其优化方法:基于经验模态分解的选择性归一化动态模式分解(ESN-DMD)。仿真结果表明,DMD算法只适用于预测低移速低复杂度的用户信号,ESN-DMD算法可以预测不同移速的用户信道容量。

To predict the channel capacity of mobile users and appropriately allocate user resources in multiple-input-multiple-output systems,a channel capacity prediction method based on dynamic mode decomposition(DMD)is proposed.Meanwhile,a selective normalized dynamic mode decomposition method based on empirical mode decomposition(ESN-DMD)is proposed to optimize the system..The simulation results show that the DMD algorithm is only suitable for the prediction of user signals at low moving speed and low complex,while the ESN-DMD algorithm can adapt to the prediction of channel capacity of users with different moving speeds.