Channel charting is a recently proposed framework that applies dimensionality
reduction to channel state information (CSI) in wireless systems with the goal
of associating a pseudo-position to each mobile user in a low-dimensional
space: the channel chart. Channel charting summarizes the entire CSI dataset in
a self-supervised manner, which opens up a range of applications that are tied
to user location. In this article, we introduce the theoretical underpinnings
of channel charting and present an overview of recent algorithmic developments
and experimental results obtained in the field. We furthermore discuss concrete
application examples of channel charting to network- and user-related
applications, and we provide a perspective on future developments and
challenges as well as the role of channel charting in next-generation wireless
networks.

本文介绍了 “信道绘图” 框架的理论基础，以及该框架在无线通信系统中应用的算法、实验结果和具体应用实例，同时讨论了未来的发展和挑战，以及在下一代无线网络中 “信道绘图” 的作用。

无线信道图：理论，实践和应用

Wireless Channel Charting: Theory, Practice, and Applications

Accurate estimation of user location is important for many online services.
Previous neural network based methods largely ignore the hierarchical structure
among locations. In this paper, we propose a hierarchical location prediction
neural network for Twitter user geolocation. Our model first predicts the home
country for a user, then uses the country result to guide the city-level
prediction. In addition, we employ a character-aware word embedding layer to
overcome the noisy information in tweets. With the feature fusion layer, our
model can accommodate various feature combinations and achieves
state-of-the-art results over three commonly used benchmarks under different
feature settings. It not only improves the prediction accuracy but also greatly
reduces the mean error distance.

本文介绍了一种基于神经网络的层级位置预测模型，通过对用户所在国家和城市的层级结构进行考虑，并采用基于字符的词嵌入层来处理噪声数据，在不同特征组合设定下达到了最优结果，提高了预测精度并显著降低了均值误差距离。

Twitter 用户地理位置预测的分层神经网络

A Hierarchical Location Prediction Neural Network for Twitter User  Geolocation

We report on experimental results on the use of a learning-based approach to
infer the location of a mobile user of a cellular network within a cell, for a
5G-type Massive multiple input, multiple output (MIMO) system. We describe how
the sample spatial covariance matrix computed from the CSI can be used as the
input to a learning algorithm which attempts to relate it to user location. We
discuss several learning approaches, and analyze in depth the application of
extreme learning machines, for which theoretical approximate performance
benchmarks are available, to the localization problem. We validate the proposed
approach using experimental data collected on a Huawei 5G testbed, provide some
performance and robustness benchmarks, and discuss practical issues related to
the deployment of such a technique in 5G networks.

使用基于学习的方法对 5G Massive MIMO 系统下的移动用户在小区内的位置进行推测，并采用极端学习机作为学习算法并对性能和健壮性进行验证，以及探讨在 5G 网络中实际应用的相关问题。