While massive multiple-input multiple-output (MIMO) has achieved tremendous success in both theory and practice, it faces a crisis of sharp performance degradation in moderate or high-mobility scenarios (e.g., 30 km/h), due to the breach of uplink-downlink channel duality. Such a “curse of mobility” has spurred the research on channel prediction in high-mobility scenarios. Instead of predicting channel response matrix in the space-frequency domain, we investigate it in the angle-delay domain by utilizing the high angle-delay resolution of wideband massive MIMO systems. Specifically, we study the general angle-delay domain channel characterization and obtain that: 1) the correlations between the angle-delay domain channel response matrix (ADCRM) elements are decoupled significantly; 2) when the number of antennas and bandwidth are limited, the decoupling is insufficient and residual correlations between the neighboring ADCRM elements exist. Then focusing on the ADCRM, we propose two channel prediction methods: a spatio-temporal autoregressive (ST-AR) model-driven unsupervised-learning method and a deep learning (DL) based data-driven supervised-learning method. While the model-driven method provides a principled way for channel prediction, the data-driven method is generalizable to various channel scenarios. In particular, ST-AR exploits the residual spatio-temporal correlations of the channel element with its most neighboring elements, and DL realizes element-wise angle-delay domain channel prediction utilizing a complex-valued neural network (CVNN). Simulation results under the 3GPP non-line-of-sight (NLOS) scenarios indicate that, compared to the state-of-the-art Prony-based angular-delay domain (PAD) prediction method, both the proposed ST-AR and the CVNN-based channel prediction methods can enhance the channel prediction accuracy.

中文翻译：

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高移动性大规模 MIMO 中的信道预测：从时空自回归到深度学习

虽然大规模多输入多输出 (MIMO) 在理论和实践上都取得了巨大成功，但由于违反了上下行信道二重性。这种“移动性诅咒”激发了对高移动性场景中信道预测的研究。我们不是在空频域中预测信道响应矩阵，而是利用宽带大规模 MIMO 系统的高角度延迟分辨率在角度延迟域中对其进行研究。具体而言，我们研究了一般的角延迟域信道特征，并得出：1）角延迟域信道响应矩阵（ADCCRM）元素之间的相关性显着解耦；2）当天线数量和带宽有限时，解耦不充分，相邻 ADCRM 元素之间存在残差相关性。然后专注于 ADCRM，我们提出了两种通道预测方法：时空自回归 (ST-AR) 模型驱动的无监督学习方法和基于深度学习 (DL) 的数据驱动的监督学习方法。虽然模型驱动的方法为信道预测提供了一种原则性的方法，但数据驱动的方法可推广到各种信道场景。特别是，ST-AR 利用了信道元素与其最相邻元素的残余时空相关性，而 DL 利用复值神经网络 (CVNN) 实现了逐元素角度延迟域信道预测。3GPP 非视距 (NLOS) 场景下的仿真结果表明，