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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.
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