  In this paper, we devise a highly efficient machine learning-based channel
estimation for orthogonal frequency division multiplexing (OFDM) systems, in
which the training of the estimator is performed online. A simple learning
module is employed for the proposed learning-based estimator. The training
process is thus much faster and the required training data is reduced
significantly. Besides, a training data construction approach utilizing least
square (LS) estimation results is proposed so that the training data can be
collected during the data transmission. The feasibility of this novel
construction approach is verified by theoretical analysis and simulations.
Based on this construction approach, two alternative training data generation
schemes are proposed. One scheme transmits additional block pilot symbols to
create training data, while the other scheme adopts a decision-directed method
and does not require extra pilot overhead. Simulation results show the
robustness of the proposed channel estimation method. Furthermore, the proposed
method shows better adaptation to practical imperfections compared with the
conventional minimum mean-square error (MMSE) channel estimation. It
outperforms the existing machine learning-based channel estimation techniques
under varying channel conditions.
