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An efficient data-driven prediction strategy for multi-antennafrequency-selective channels must operate based on a small number of pilotsymbols. This paper proposes novel channel prediction algorithms that addressthis goal by integrating transfer and meta-learning with a reduced-rankparametrization of the channel. The proposed methods optimize linear predictorsby utilizing data from previous frames, which are generally characterized bydistinct propagation characteristics, in order to enable fast training on thetime slots of the current frame. The proposed predictors rely on a novellong-short-term decomposition (LSTD) of the linear prediction model thatleverages the disaggregation of the channel into long-term space-timesignatures and fading amplitudes. We first develop predictors forsingle-antenna frequency-flat channels based on transfer/meta-learned quadraticregularization. Then, we introduce transfer and meta-learning algorithms forLSTD-based prediction models that build on equilibrium propagation (EP) andalternating least squares (ALS). Numerical results under the 3GPP 5G standardchannel model demonstrate the impact of transfer and meta-learning on reducingthe number of pilots for channel prediction, as well as the merits of theproposed LSTD parametrization.

Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition