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In this paper, we propose an innovative learning-based channel predictionscheme so as to achieve higher prediction accuracy and reduce the requirementsof huge amounts and strict sequential format of channel data. Inspired by theidea of the neural ordinary differential equation (Neural ODE), we first provethat the channel prediction problem can be modeled as an ODE problem with aknown initial value by analyzing the physical process of electromagnetic wavepropagation within a mobile environment. Then, we design a novelphysics-inspired spatial channel gradient network (SCGnet), which representsthe derivative process of channel varying as a special neural network and canobtain the gradients at any relative displacement needed for the ODE solving.With the SCGnet, the static channel at any location served by the base stationis accurately inferred through consecutive propagation and integration.Finally, we design an efficient recurrent positioning algorithm based on someprior knowledge of user mobility to obtain the velocity vector and propose anapproximate Doppler compensation method to make up the instantaneousangular-delay domain channel. Only discrete historical channel data is neededfor the training, whereas only a few fresh channel measurements are needed forthe prediction, which ensures the scheme's practicability. Comprehensiveevaluations show that the proposed scheme is most efficient in representing,learning, and predicting mobile wireless channels.

From Data-driven Learning to Physics-inspired Inferring: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE