多尺度残差注意力的高速铁路OFDM信道估计

Channel Estimation of OFDM in High-Speed Railway Based on Multi-Scale Residual Attention Network

针对高速铁路正交频分复用(OFDM)通信系统在高速移动场景下,难以准确对快时变信道状态信息进行估计的问题,提出了一种基于多尺度残差注意力网络的高速铁路OFDM信道估计方法。首先,设计多尺度信道特征提取结构,对低分辨率信道矩阵采用多尺度多维特征提取,增强了信道不同尺度信息的提取能力。然后,构建残差注意力级联深度网络进行信道特征重构映射,将局部残差反馈结合注意力机制促进深层特征的融合和利用,提升OFDM信道矩阵的重构映射能力。最后,使用子像素卷积重构生成高分辨率信道矩阵,完成信道估计。通过频域和时域信道估计测试分析表明:在低速及高速铁路场景下,该方法与其他方法相比,信道估计的精度和复杂度等客观性评价指标均优于比较算法,能够满足OFDM信道估计的要求。

In order to solve the problem that it is difficult to accurately estimate the fast time-varying channel state information in orthogonal frequency division multiplexing(OFDM)communication system of high-speed railway in high-speed mobile scene,an OFDM channel estimation method based on multi-scale residual attention network was proposed.Firstly,we design the multi-scale channel feature extraction structure.For the low-quality channel matrix,we apply the multi-scale convolution kernel to extracting the shallow multi-dimensional feature information,which can improve the extraction performance of channel feature information with different scales.Then,a multi-scale residual attention cascade depth network is constructed for channel feature reconstruction and mapping.The local residual feedback is combined with CBAM(convolutional block attention module)attention mechanism to promote the fusion and utilization of deep features and improve the reconstruction and mapping ability of OFDM channel matrix.Finally,the sub-pixel convolution reconstruction is used to generate a high-resolution channel matrix to complete the channel estimation.The analysis in both frequency domain and time domain show that the proposed channel estimation method is better than other methods in terms of accuracy and complexity of channel estimation and can satisfy the needs of OFDM channel estimation.