  In this paper, we deploy the self-attention mechanism to achieve improved
channel estimation for orthogonal frequency-division multiplexing waveforms in
the downlink. Specifically, we propose a new hybrid encoder-decoder structure
(called HA02) for the first time which exploits the attention mechanism to
focus on the most important input information. In particular, we implement a
transformer encoder block as the encoder to achieve the sparsity in the input
features and a residual neural network as the decoder respectively, inspired by
the success of the attention mechanism. Using 3GPP channel models, our
simulations show superior estimation performance compared with other candidate
neural network methods for channel estimation.
