论文标题
在OFDM系统中基于深度学习的渠道估计的试验模式设计
Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems
论文作者
论文摘要
在本文中，我们提出了基于深度学习的下行链路试验设计方案（DL）基于正交频段多路复用（OFDM）系统中的基于深度学习的估计（DL）。具体而言，在拟议的方案中，一种名为Concrete AutoCododer（Concreteae）的功能选择方法用于找到用于飞行员传输的最有用的位置。该自动编码器由一个混凝土层作为编码器和多层感知器（MLP）作为解码器组成。在训练过程中，混凝土层选择了最有用的试点位置，解码器重建了通道的近似估计。最终，对ChannelNet进行了培训，以旨在重建理想通道响应的混凝土的输出。估计错误结果表明，此方法的表现优于先前呈现的通道网，其性能与最小平方误差（MMSE）相当。
In this paper, we present a downlink pilot design scheme for Deep Learning (DL) based channel estimation (ChannelNet) in orthogonal frequency-division multiplexing (OFDM) systems. Specifically, in the proposed scheme, a feature selection method named Concrete Autoencoder (ConcreteAE) is used to find the most informative locations for pilot transmission. This autoencoder consists of a concrete layer as the encoder and a multilayer perceptron (MLP) as the decoder. During the training, the concrete layer selects the most informative pilot locations, and the decoder reconstructs an approximate estimation of the channel. Eventually, the ChannelNet is trained on the output of the ConcreteAE aiming to reconstruct the ideal channel response. The estimation error results show that this approach outperforms the previously presented ChannelNet with a uniformly distributed pilot pattern, and its performance is comparable to the minimum mean square error (MMSE).
