The unmanned aerial vehicle (UAV) can extend the network coverage and improve the system throughput for 5th generation (5G) communication systems; hence, it receives a lot of attention recently. This paper considers the problem of channel predictive precoding for UAV-enabled cache-assisted B5G multi-input multi-output (MIMO) systems. A novel channel precoder predictor is proposed, in which the prediction is conducted on a non-linear vector space—Grassmannian manifold. The predictor at the receiver utilizes the current and previous channel matrices to solve the precoder at the next time and then feeds it back to the transmitter for precoding. More specifically, two sub-matrices are extracted from the channel right singular matrices and modeled as two points on the Grassmannian manifold. Then, the geodesic between the two points is conducted. Unlike the conventional method in which the tangent vector at the previous point is parallel transported along the geodesic, we predict the next point by use of the geodesic equation directly. We analyze the computational complexity of the proposed method and demonstrate the superiority of the proposed method by comparing with the conventional one. Besides, we adopt a general Ricean channel model in the UAV MIMO system, where both the Kronecker model and Jake’s model are incorporated. The effects of various channel model parameters on the system performance in terms of the chordal error of channel predictor and the optimum step are thoroughly investigated.

无人机（UAV）可以扩展网络覆盖范围并提高第五代（5G）通信系统的系统吞吐量。因此，它最近受到了很多关注。本文考虑了启用无人机的缓存辅助B5G多输入多输出（MIMO）系统的信道预测预编码问题。提出了一种新颖的信道预编码器预测器，该预测器在非线性矢量空间格拉斯曼流形上进行预测。接收机处的预测器利用当前和先前的信道矩阵在下一次解算预编码器，然后将其反馈给发射机进行预编码。更具体地说，从通道右奇异矩阵中提取两个子矩阵，并将其建模为格拉斯曼流形上的两个点。然后，进行两点之间的测地线。与传统方法不同，在传统方法中，前一点的切向量沿着测地线平行传输，我们直接使用测地线方程预测下一个点。我们分析了该方法的计算复杂度，并通过与传统方法的比较证明了该方法的优越性。此外，我们在UAV MIMO系统中采用了通用的Ricean信道模型，其中结合了Kronecker模型和Jake模型。深入研究了各种信道模型参数对信道预测器的弦误差和最佳步长对系统性能的影响。我们分析了该方法的计算复杂度，并通过与传统方法的比较证明了该方法的优越性。此外，我们在UAV MIMO系统中采用了通用的Ricean信道模型，其中同时集成了Kronecker模型和Jake模型。深入研究了各种信道模型参数对系统性能的影响，包括信道预测器的弦误差和最佳步长。我们分析了该方法的计算复杂度，并通过与传统方法的比较证明了该方法的优越性。此外，我们在UAV MIMO系统中采用了通用的Ricean信道模型，其中结合了Kronecker模型和Jake模型。深入研究了各种信道模型参数对信道预测器的弦误差和最佳步长对系统性能的影响。