  Predicting fading channels is a classical problem with a vast array of
applications, including as an enabler of artificial intelligence (AI)-based
proactive resource allocation for cellular networks. Under the assumption that
the fading channel follows a stationary complex Gaussian process, as for
Rayleigh and Rician fading models, the optimal predictor is linear, and it can
be directly computed from the Doppler spectrum via standard linear minimum mean
squared error (LMMSE) estimation. However, in practice, the Doppler spectrum is
unknown, and the predictor has only access to a limited time series of
estimated channels. This paper proposes to leverage meta-learning in order to
mitigate the requirements in terms of training data for channel fading
prediction. Specifically, it first develops an offline low-complexity solution
based on linear filtering via a meta-trained quadratic regularization. Then, an
online method is proposed based on gradient descent and equilibrium propagation
(EP). Numerical results demonstrate the advantages of the proposed approach,
showing its capacity to approach the genie-aided LMMSE solution with a small
number of training data points.
