  An efficient data-driven prediction strategy for multi-antenna
frequency-selective channels must operate based on a small number of pilot
symbols. This paper proposes novel channel prediction algorithms that address
this goal by integrating transfer and meta-learning with a reduced-rank
parametrization of the channel. The proposed methods optimize linear predictors
by utilizing data from previous frames, which are generally characterized by
distinct propagation characteristics, in order to enable fast training on the
time slots of the current frame. The proposed predictors rely on a novel
long-short-term decomposition (LSTD) of the linear prediction model that
leverages the disaggregation of the channel into long-term space-time
signatures and fading amplitudes. We first develop predictors for
single-antenna frequency-flat channels based on transfer/meta-learned quadratic
regularization. Then, we introduce transfer and meta-learning algorithms for
LSTD-based prediction models that build on equilibrium propagation (EP) and
alternating least squares (ALS). Numerical results under the 3GPP 5G standard
channel model demonstrate the impact of transfer and meta-learning on reducing
the number of pilots for channel prediction, as well as the merits of the
proposed LSTD parametrization.
