一种大规模MIMO系统信道反馈算法

针对在大规模多输入多输出（Multiple-Input Multiple-Output，MIMO）系统中，信道状态信息（Channel State Information，CSI）反馈量过大以及反馈的CSI过时的问题，提出一种基于自回归（Autoregressive，AR）模型和主成分分析（Principal Component Analysis，PCA）方法的反馈算法。接收端进行信道估计获得CSI后，先利用AR模型预测出反馈所需时间之后的CSI，在此基础上计算压缩矩阵，然后利用PCA方法对预测的CSI进行压缩,再反馈给基站，最后基站端对接收到的CSI进行重构。从理论分析和仿真结果可以看出，该算法可以在降低反馈量的同时提高系统容量和信道恢复的准确性。

A Channel Feedback Algorithm for Massive MIMO Systems

Aiming at the problem that the channel state information (CSI) feedback is too large and the feedback CSI is outdated in a massive multiple-input multiple-output (MIMO) system, a feedback algorithm based on autoregressive (AR) model and principal component analysis (PCA) method is proposed. After receiving the CSI, the receiver uses the AR model to predict the CSI after the time required for the feedback. On the basis of this, the compression matrix is calculated, and then the predicted CSI is compressed by the PCA method, and then fed back to the base station. The received CSI is reconstructed. From the theoretical analysis and simulation results can be seen that the algorithm can reduce the amount of feedback while improving the system capacity and channel recovery accuracy.