MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI
MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI
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DOI：
10.1109/globecom46510.2021.9685564
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发表时间：
2021-10
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影响因子：
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通讯作者：
Katarina Vuckovic;F. Hejazi;Nazanin Rahnavard
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作者：
Katarina Vuckovic;F. Hejazi;Nazanin Rahnavard
针对大规模多输入多输出（MIMO）系统，提出了一种利用信道状态信息（CSI）的地图辅助定位方法。地图辅助定位是一种环境感知方法，其中通信系统具有关于周围环境的信息。通过将多径分量（MPC）的射频射线跟踪参数与环境地图相结合，可以实现定位。不幸的是，在现实世界的场景中，光线跟踪参数通常不是显式可用的。因此，在基站处增加了额外的复杂性以获得该信息。另一方面，CSI是通常针对任何通信信道估计的公共通信参数。在这项工作中，我们利用已经可用的CSI数据，提出了一种新的地图辅助CSI定位方法，称为MAP-CSI。我们表明，出发角度（AoD）和到达时间（ToA）可以从CSI中提取，然后与环境地图结合使用，以定位用户。我们在一个公共MIMO数据集上进行了模拟，并表明我们的方法适用于视线（LOS）和非视线（NLOS）场景。我们比较我们的方法的国家的最先进的（SoA）的方法，使用光线跟踪数据。使用MAP-CSI，我们完成了平均定位误差为1.8米的LOS和2.8米的混合（LOS和NLOS样本的组合）的情况下。另一方面，SoA射线追踪的平均误差分别为1.0 m和2.2 m，但需要明确的AoD和ToA信息来执行定位任务。
This paper presents a new map-assisted localization approach utilizing Chanel State Information (CSI) in Massive Multiple-Input Multiple-Output (MIMO) systems. Map-assisted localization is an environment-aware approach in which the communication system has information regarding the surrounding environment. By combining radio frequency ray tracing parameters of the multipath components (MPC) with the environment map, it is possible to accomplish localization. Unfortunately, in real-world scenarios, ray tracing parameters are typically not explicitly available. Thus, additional complexity is added at a base station to obtain this information. On the other hand, CSI is a common communication parameter, usually estimated for any communication channel. In this work, we leverage the already available CSI data to propose a novel map-assisted CSI localization approach, referred to as MAP-CSI. We show that Angle-of-Departure (AoD) and Time-of-Arrival (ToA) can be extracted from CSI and then be used in combination with the environment map to localize the user. We perform simulations on a public MIMO dataset and show that our method works for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We compare our method to the state-of-the-art (SoA) method that uses the ray tracing data. Using MAP-CSI, we accomplish an average localization error of 1.8 m in LOS and 2.8 m in mixed (combination of LOS and NLOS samples) scenarios. On the other hand, SoA ray tracing has an average error of 1.0 m and 2.2 m, respectively, but requires explicit AoD and ToA information to perform the localization task.