Channel prediction is critical to address the channel aging issue in mobile scenarios. Existing channel prediction techniques are mainly designed for discrete channel prediction, which can only predict the future channel in a fixed time slot per frame, while the other intra-frame channels are usually recovered by interpolation. However, these approaches suffer from a serious interpolation loss, especially for mobile millimeter wave communications. To solve this challenging problem, we propose a tensor neural ordinary differential equation (TN-ODE) based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels. Specifically, inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning, we first utilize the neural ODE model to predict future continuous-time channels. To improve the channel prediction accuracy and reduce computational complexity, we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low dimensional learnable transform. Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.

翻译：信道预测对于解决移动场景中的信道老化问题至关重要。现有信道预测技术主要针对离散信道预测设计，仅能在每帧固定时隙预测未来信道，而帧内其他信道通常通过插值恢复。然而，这些方法存在严重的插值损失问题，尤其在移动毫米波通信中更为突出。为解决这一挑战性问题，本文提出一种基于张量神经常微分方程（TN-ODE）的连续时间信道预测方案，实现帧内信道的直接预测。具体而言，受机器学习领域最新提出的连续映射模型神经常微分方程的启发，我们首先利用神经ODE模型预测未来连续时间信道。为提升信道预测精度并降低计算复杂度，进而提出TN-ODE方案，通过低维可学习变换学习高维信道的结构特性。仿真结果表明，所提方案能够实现比现有方案更高的帧内信道预测精度。