论文标题
使用深度学习中C-V2X中的频道估计
Channel Estimation in C-V2X using Deep Learning
论文作者
论文摘要
通道估计是当前OFDM系统中的中心分量之一，旨在通过使用Pilot符号来计算CSI来消除符号间干扰，并在整个时间频率网格中插值。它也是PHY研究最多的领域之一，LS和MMSE是两种最常用的方法。在这项工作中，我们研究了基于CNN的深神经网络体系结构的性能，以在3GPP Rel.14 CV2X技术中使用的车辆环境中的渠道估计。为此，我们将所提出的DL架构的性能与C-V2X当前使用的旧版LS频道估计值进行了比较。初步调查证明，提出的DL体系结构优于传统CV2X频道估计方法，尤其是在高移动速度下
Channel estimation forms one of the central component in current OFDM systems that aims to eliminate the inter-symbol interference by calculating the CSI using the pilot symbols and interpolating them across the entire time-frequency grid. It is also one of the most researched field in the PHY with LS and MMSE being the two most used methods. In this work, we investigate the performance of deep neural network architecture based on CNN for channel estimation in vehicular environments used in 3GPP Rel.14 CV2X technology. To this end, we compare the performance of the proposed DL architectures to the legacy LS channel estimation currently employed in C-V2X. Initial investigations prove that the proposed DL architecture outperform the legacy CV2X channel estimation methods especially at high mobile speeds
