  Obtaining accurate channel state information (CSI) is crucial and challenging
for multiple-input multiple-output (MIMO) wireless communication systems.
Conventional channel estimation method cannot guarantee the accuracy of mobile
CSI while requires high signaling overhead. Through exploring the intrinsic
correlation among a set of historical CSI instances randomly obtained in a
certain communication environment, channel prediction can significantly
increase CSI accuracy and save signaling overhead. In this paper, we propose a
novel channel prediction method based on ordinary differential equation
(ODE)-recurrent neural network (RNN) for accurate and flexible mobile MIMO
channel prediction. Differing from existing works using sequential network
structures for exploring the numerical correlation between observed data, our
proposed method tries to represent the implicit physics process of path
responses changing by specially designed continuous learning network with ODE
structure. Due to the targeted design of learning network, our proposed method
fits the mathematics feature of CSI data better and enjoy higher network
interpretability. Experimental results show that the proposed learning approach
outperforms existing methods, especially for long time interval of the CSI
sequence and large channel measurement error.
