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In this paper, we propose adaptive channel estimation for orthogonal frequency division multiplexing (OFDM) in fast time-varying (TV) channels. A basis expansion model (BEM) approach is used to capture the time variation of the channel within each OFDM block, and to reduce the estimator dimensionality. Capitalizing on the BEM structure and on a frequency domain training, two adaptive approaches are proposed, based on Kalman filtering and recursive least squares (LS) methods, which exploit the time correlation of the channel between successive blocks and do not require any a-priori knowledge of the channel statistics. Simulation results show that, compared to classical least squares and statistically-aided linear minimum mean squared error (LMMSE) approaches, the two proposed techniques effectively estimate the channel, adapt fast to its non stationary changes, thus enabling efficient TV channel …