论文标题
使用神经网络解决方案的低复杂性通道估计
Low Complexity Channel estimation with Neural Network Solutions
论文作者
论文摘要
渠道估计的机器学习研究，尤其是用于无线通信的神经网络解决方案，引起了当前的重大兴趣。这是因为传统方法无法满足高速通信的当前需求。在本文中，我们在下行链路场景中部署了一般残差卷积神经网络，以实现正交频分多路复用（OFDM）信号的通道估计。我们的方法还部署了一个简单的插值层，以替换其他网络中用于降低计算成本的转置卷积层。提出的方法更容易适应不同的试点模式和数据包大小。与其他深度学习方法进行通道估计相比，我们对3GPP渠道模型的结果表明，我们的方法改善了平方误差性能。
Research on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest. This is because conventional methods cannot meet the present demands of the high speed communication. In the paper, we deploy a general residual convolutional neural network to achieve channel estimation for the orthogonal frequency-division multiplexing (OFDM) signals in a downlink scenario. Our method also deploys a simple interpolation layer to replace the transposed convolutional layer used in other networks to reduce the computation cost. The proposed method is more easily adapted to different pilot patterns and packet sizes. Compared with other deep learning methods for channel estimation, our results for 3GPP channel models suggest improved mean squared error performance for our approach.
