In practical massive multiple-input multiple-output (MIMO) systems, the precoding matrix is often obtained from the eigenvectors of channel matrices and is challenging to update in time due to finite computation resources at the base station, especially in mobile scenarios. In order to reduce the precoding complexity while enhancing the spectral efficiency (SE), a novel precoding matrix prediction method based on the eigenvector prediction (EGVP) is proposed. The basic idea is to decompose the periodic uplink channel eigenvector samples into a linear combination of the channel state information (CSI) and channel weights. We further prove that the channel weights can be interpolated by an exponential model corresponding to the Doppler characteristics of the CSI. A fast matrix pencil prediction (FMPP) method is also devised to predict the CSI. We also prove that our scheme achieves asymptotically error-free precoder prediction with a distinct complexity advantage. Simulation results show that under the perfect non-delayed CSI, the proposed EGVP method reduces floating point operations by 80\% without losing SE performance compared to the traditional full-time precoding scheme. In more realistic cases with CSI delays, the proposed EGVP-FMPP scheme has clear SE performance gains compared to the precoding scheme widely used in current communication systems.

翻译：在实际大规模多输入多输出（MIMO）系统中，预编码矩阵通常从信道矩阵的特征向量中获取，由于基站计算资源有限，尤其在移动场景下难以及时更新。为在提升频谱效率（SE）的同时降低预编码复杂度，本文提出一种基于特征向量预测（EGVP）的新型预编码矩阵预测方法。其核心思想是将周期性上行链路信道特征向量样本分解为信道状态信息（CSI）与信道权重的线性组合。我们进一步证明信道权重可通过与CSI多普勒特性对应的指数模型进行插值。同时设计了快速矩阵铅笔预测（FMPP）方法用于预测CSI。我们还证明了该方案能以显著复杂度优势实现渐近无误差的预编码器预测。仿真结果表明：在理想无延迟CSI条件下，与传统全时预编码方案相比，所提EGVP方法在保持SE性能不变的同时将浮点运算量降低80%。在存在CSI延迟的更实际场景中，所提EGVP-FMPP方案相较于当前通信系统中广泛使用的预编码方案具有明显的SE性能增益。