User localization and tracking in the upcoming generation of wireless
networks have the potential to be revolutionized by technologies such as the
Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches
rely on assumptions about relatively dominant Line-of-Sight (LoS) paths, or
require pilot transmission sequences whose length is comparable to the number
of DMA elements, thus, leading to limited effectiveness and considerable
measurement overheads in blocked LoS and dynamic multipath environments. In
this paper, we present a two-stage machine-learning-based approach for user
tracking, specifically designed for non-LoS multipath settings. A newly
proposed attention-based Neural Network (NN) is first trained to map noisy
channel responses to potential user positions, regardless of user mobility
patterns. This architecture constitutes a modification of the prominent vision
transformer, specifically modified for extracting information from
high-dimensional frequency response signals. As a second stage, the NN's
predictions for the past user positions are passed through a learnable
autoregressive model to exploit the time-correlated channel information and
obtain the final position predictions. The channel estimation procedure
leverages a DMA receive architecture with partially-connected radio frequency
chains, which results to reduced numbers of pilots. The numerical evaluation
over an outdoor ray-tracing scenario illustrates that despite LoS blockage,
this methodology is capable of achieving high position accuracy across various
multipath settings.

在本文中，我们提出了一种两阶段的基于机器学习的方法，专门针对非直射多径环境进行用户跟踪；首先，我们使用新提出的基于注意力机制的神经网络将嘈杂的信道响应映射到用户的可能位置；接着，通过一个可学习的自回归模型，利用时间相关的信道信息对过去的用户位置进行预测，借助动态智能表面天线的接收架构以及减少了导频数量，该方法在室外射线追踪场景中的数值评估结果表明，尽管直射路径被阻断，该方法在各种多径环境中均能实现较高的定位准确性。