基于动态模式分解的移动用户信道容量预测算法

doi:10.13190/j.jbupt.2020-188

北京邮电大学学报 ›› 2021, Vol. 44 ›› Issue (4): 89-94.doi: 10.13190/j.jbupt.2020-188

基于动态模式分解的移动用户信道容量预测算法

朱军¹, 唐宝煜¹, 李凯²

1. 安徽大学电子信息工程学院, 合肥 230601;
2. 上海科技大学创意与艺术学院, 上海 201210

收稿日期:2020-09-28 发布日期:2021-10-13
作者简介:朱军(1968-),女,副教授,硕士生导师,E-mail:junzhu@ahu.edu.cn.
基金资助:
安徽省科技重大专项项目（18030901010）

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

ZHU Jun¹, TANG Bao-yu¹, LI Kai²

1. School of Electronics and Information Engineering, Anhui University, Hefei 230601, China;
2. School of Creativity and Art, Shanghai Tech University, Shanghai 201210, China

Received:2020-09-28 Published:2021-10-13

摘要/Abstract

摘要： 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.

关键词: 多输入多输出, 动态模式分解, 经验模态分解, 选择性归一化, 信道容量预测

Abstract: 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.

Key words: multiple input multiple output, dynamic mode decomposition, empirical mode decomposition, selective normalization, channel capacity prediction

中图分类号:

TN929.5

朱军, 唐宝煜, 李凯. 基于动态模式分解的移动用户信道容量预测算法[J]. 北京邮电大学学报, 2021, 44(4): 89-94.

ZHU Jun, TANG Bao-yu, LI Kai. Prediction Algorithm of Mobile User Channel Capacity Based on Dynamic Mode Decomposition[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 89-94.

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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

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