论文标题
基于学习的渠道估计和双噪声补偿在双重选择的通道中
Learning based Channel Estimation and Phase Noise Compensation in Doubly-Selective Channels
论文作者
论文摘要
在这封信中，我们提出了一种基于学习的信道估计方案，用于在双重频率褪色通道中存在相位噪声的情况下，用于正交频施加多路复用（OFDM）系统。二维（2D）卷积神经网络（CNN）用于有效训练和跟踪频率和时域的通道变化。拟议的网络基于TF网格中稀疏的飞行员的整个时间频率（TF）网格学习和估算通道系数。为了使网络与相位噪声（PN）损伤保持稳健，这是一种新颖的训练方案，在使用将训练数据随机旋转之前，然后使用将其送入网络。此外，使用估计的通道系数，设计了一个简单有效的PN估计和补偿方案。数值结果表明，在存在相噪声的情况下，提出的网络和PN补偿方案实现了强大的OFDM性能。
In this letter, we propose a learning based channel estimation scheme for orthogonal frequency division multiplexing (OFDM) systems in the presence of phase noise in doubly-selective fading channels. Two-dimensional (2D) convolutional neural networks (CNNs) are employed for effective training and tracking of channel variation in both frequency as well as time domain. The proposed network learns and estimates the channel coefficients in the entire time-frequency (TF) grid based on pilots sparsely populated in the TF grid. In order to make the network robust to phase noise (PN) impairment, a novel training scheme where the training data is rotated by random phases before being fed to the network is employed. Further, using the estimated channel coefficients, a simple and effective PN estimation and compensation scheme is devised. Numerical results demonstrate that the proposed network and PN compensation scheme achieve robust OFDM performance in the presence of phase noise.
