论文标题
基于注意的无线通道估计的神经网络
Attention Based Neural Networks for Wireless Channel Estimation
论文作者
论文摘要
在本文中，我们部署了自我注意的机制，以实现下行链路中正交频划线波形的改进通道估计。具体而言，我们首次提出了一种新的混合编码器结构（称为HA02），该结构利用了注意机制专注于最重要的输入信息。特别是，我们将一个变压器编码器块作为编码器，以分别获得输入特征中的稀疏性和残留神经网络作为解码器，受到注意机制成功的启发。使用3GPP通道模型，与其他候选神经网络方法相比，我们的模拟显示出较高的估计性能。
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.
