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.

翻译：本文提出了一种编码器-解码器神经架构（称为Channelformer），用于在下行链路场景中实现正交频分复用（OFDM）波形的改进信道估计。在解码器处理输入特征之前，采用自注意力机制对输入特征进行输入预编码。具体而言，我们在编码器中实现多头注意力机制，并分别将残差卷积神经架构作为解码器。我们还采用定制的权重级剪枝，通过微调过程精简训练后的神经网络，显著降低计算复杂度，从而实现低复杂度和低延迟的解决方案。与完整的Channelformer相比，该方法在保持几乎相同性能的同时，参数可减少多达70%。我们还基于第五代（5G）新空口（NR）配置，提出了一种适用于现代通信系统的高效在线训练方法，该方法仅需接收端可用的信息即可进行在线训练。基于工业标准信道模型的仿真结果表明，与其他候选神经网络信道估计方法相比，基于注意力的解决方案展现出更优的估计性能。