  In this paper, we propose an encoder-decoder neural architecture (called
Channelformer) to achieve improved channel estimation for orthogonal
frequency-division multiplexing (OFDM) waveforms in downlink scenarios. The
self-attention mechanism is employed to achieve input precoding for the input
features before processing them in the decoder. In particular, we implement
multi-head attention in the encoder and a residual convolutional neural
architecture as the decoder, respectively. We also employ a customized
weight-level pruning to slim the trained neural network with a fine-tuning
process, which reduces the computational complexity significantly to realize a
low complexity and low latency solution. This enables reductions of up to 70\%
in the parameters, while maintaining an almost identical performance compared
with the complete Channelformer. We also propose an effective online training
method based on the fifth generation (5G) new radio (NR) configuration for the
modern communication systems, which only needs the available information at the
receiver for online training. Using industrial standard channel models, the
simulations of attention-based solutions show superior estimation performance
compared with other candidate neural network methods for channel estimation.
