论文标题
注意协助CSI无线本地化
Attention Aided CSI Wireless Localization
论文作者
论文摘要
深度神经网络（DNN）已成为基于渠道状态信息（CSI）的无线本地化的流行方法。一种常见的做法是在输入中使用RAW CSI，并允许网络学习相关的频道表示形式以映射到位置信息。但是，各种作品表明，RAW CSI可能对系统障碍和环境中的微小变化非常敏感。相反，手工设计的功能可能会阻碍DNN的通道表示学习的限制。在这项工作中，我们提出了基于注意力的CSI，以进行健壮的功能学习。我们评估了在两个非平稳铁路轨道环境中的集中式和分布式大型MIMO系统中所访问的功能的性能。通过与基本DNN相比，我们的方法提供了出色的性能。
Deep neural networks (DNNs) have become a popular approach for wireless localization based on channel state information (CSI). A common practice is to use the raw CSI in the input and allow the network to learn relevant channel representations for mapping to location information. However, various works show that raw CSI can be very sensitive to system impairments and small changes in the environment. On the contrary, hand-designing features may hinder the limits of channel representation learning of the DNN. In this work, we propose attention-based CSI for robust feature learning. We evaluate the performance of attended features in centralized and distributed massive MIMO systems for ray-tracing channels in two non-stationary railway track environments. By comparison to a base DNN, our approach provides exceptional performance.
