Location awareness is an essential feature to support various mobile services, and cooperative positioning with channel state information (CSI) in millimeter wave multiple-input multiple-output (MIMO) networks is promising. Meanwhile, strong non-line of sight (NLoS) effects in outdoor scenarios severely reduce the model-based localization accuracy, and existing fingerprint-based methods have a critical requirement for labeled data and lack scalability. To overcome these shortcomings, we propose a hybrid data-and-model driven cooperative localization scheme using uplink wideband MIMO CSI at multiple base stations (BS) as measurements. First, we design a data-driven line of sight (LoS) inference module, realized by a transformer with an attention mechanism, to estimate the statistics of LoS arrival times. Each BS is equipped with a module, and the counterpart LoS inferences are simultaneously and distributedly performed. Second, we locate the mobile user by a model-driven approximate maximum likelihood time difference of arrival algorithm with the estimated statistics. Experiment results in urban scenarios show that the proposed localization scheme is extensible and also robust to varying number of channel paths and combination of BS measurements. Considering a small training dataset, the proposed scheme significantly outperforms the data- and model-driven baselines in NLoS scenarios, in terms of positioning accuracy.

中文翻译：

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在毫米波 MIMO 通信中利用数据驱动的视距推断进行 TDoA 定位

位置感知是支持各种移动服务的基本功能，毫米波多输入多输出（MIMO）网络中与信道状态信息（CSI）的协作定位前景广阔。同时，室外场景中强烈的非视距（NLoS）效应严重降低了基于模型的定位精度，并且现有的基于指纹的方法对标记数据有严格要求且缺乏可扩展性。为了克服这些缺点，我们提出了一种混合数据和模型驱动的协作定位方案，使用多个基站 (BS) 上行链路宽带 MIMO CSI 作为测量。首先，我们设计了一个数据驱动的视线（LoS）推理模块，由具有注意机制的变压器实现，以估计LoS到达时间的统计数据。每个BS配备一个模块，同时分布式地进行对应的LoS推断。其次，我们通过模型驱动的近似最大似然到达时间差算法和估计的统计数据来定位移动用户。城市场景中的实验结果表明，所提出的定位方案是可扩展的，并且对于不同数量的信道路径和基站测量组合也具有鲁棒性。考虑到训练数据集较小，所提出的方案在定位精度方面明显优于 NLoS 场景中数据和模型驱动的基线。