注意力机制CNN的毫米波大规模MIMO系统信道估计算法

在室外光线追踪通信场景下,针对毫米波大规模多输入多输出(multiple input multiple output,MI-MO)信道具有稀疏特性、系统受噪声因素影响导致信道估计精度低的问题,提出一种基于图像去噪的注意力机制卷积神经网络信道估计方法.首先,设定参数产生模拟真实环境的数据集,将所产生的信道矩阵看作二维图像...

Attention mechanism based CNN channel estimation algorithm in millimeter-wave massive MIMO system

In outdoor ray tracing communication scenarios, aiming at the problems of low channel estimation accuracy defected by its sparse characteristics and noise factors in millimeter-wave massive multiple input multiple output (MIMO), an image denoising attention mechanism based convolutional neural network channel estimation algorithm is proposed. Firstly, after constructing the data set for simulating the real environment by setting the parameters, generate channel matrix is regarded as a two-dimensional image. Then, the attention mechanism network is constructed to enhance the saliency of the noise features in the image, and the attention mechanism network is embedded in the convolutional neural network (CNN) for feature fusion. Finally, the noise extracted by the network model achieves the denoising effect and the denoised image, the estimated channel matrix is obtained. The simulation results demonstrate that the proposed attention mechanism based CNN (Attention-CNN) algorithm achieves better performance of higher channel estimation accuracy, which improved by about 1.86 dB on average, compared with least square (LS), minimum mean square error (MMSE), CNN and denoising convolutional neural network (DnCNN).