论文标题
在OFDM系统中进行联合通道估计和信号检测的深度学习
Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems
论文作者
论文摘要
在本文中，我们通过探索无线褪色通道的时间和频率相关性，提出了一种基于深度学习的新型基于深度学习的方法，用于正交频施加多路复用（OFDM）系统中的联合通道估计和信号检测方法。具体而言，通道估计网络（CENET）旨在替代试验估算方案中常规的插值程序。然后，基于CENET的结果，通道条件恢复网络（CCRNET）旨在恢复发射信号。实验结果表明，与常规估计和检测方法相比，CENET和CCRNET具有出色的性能。此外，这两个网络均显示出参数机会的变化具有鲁棒性，这使它们吸引了实际实施。
In this paper, we propose a novel deep learning based approach for joint channel estimation and signal detection in orthogonal frequency division multiplexing (OFDM) systems by exploring the time and frequency correlation of wireless fading channels. Specifically, a Channel Estimation Network (CENet) is designed to replace the conventional interpolation procedure in pilot-aided estimation scheme. Then, based on the outcome of the CENet, a Channel Conditioned Recovery Network (CCRNet) is designed to recover the transmit signal. Experimental results demonstrate that CENet and CCRNet achieve superior performance compared with conventional estimation and detection methods. In addition, both networks are shown to be robust to the variation of parameter chances, which makes them appealing for practical implementation.
