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Channel prediction is critical to address the channel aging issue in mobilescenarios. Existing channel prediction techniques are mainly designed fordiscrete channel prediction, which can only predict the future channel in afixed time slot per frame, while the other intra-frame channels are usuallyrecovered by interpolation. However, these approaches suffer from a seriousinterpolation loss, especially for mobile millimeter wave communications. Tosolve this challenging problem, we propose a tensor neural ordinarydifferential equation (TN-ODE) based continuous-time channel prediction schemeto realize the direct prediction of intra-frame channels. Specifically,inspired by the recently developed continuous mapping model named neural ODE inthe field of machine learning, we first utilize the neural ODE model to predictfuture continuous-time channels. To improve the channel prediction accuracy andreduce computational complexity, we then propose the TN-ODE scheme to learn thestructural characteristics of the high-dimensional channel by low dimensionallearnable transform. Simulation results show that the proposed scheme is ableto achieve higher intra-frame channel prediction accuracy than existingschemes.

Continuous-Time Channel Prediction Based on Tensor Neural Ordinary Differential Equation