Research into a least multipath based wireless local positioning technique for massive MIMO systems in extreme multipath conditions

在未来的无线通信网络(5G、6G等)中，大规模的MIMO系统提高了可实现的数据速率。由于可实现的数据速率取决于空间记录信息(空间分集)的独立性，因此大型天线阵列是有利的。在这个项目中，大规模MIMO系统的空间分集被用来实现精确的室内定位，即使在极端的多径条件下也是如此。虽然UWB系统试图通过增加带宽来隔离直接路径和多径传播，但在带宽有限的定位系统中，视线(LOS)的冲激响应和多径传播存在重叠。因此，仅基于对单个路径的评估的LOS识别是不可能的。因此，在本项目中，利用海量MIMO系统的空间分集来分离所有接收路径的公共LOS与叠加多径传播中的LOS，这与天线距离的增加无关。为此，开发了最小多径度量来取代通常建立的最小二乘度量。这里的目的是估计多径传播，将其从信号中去除，从而显著提高定位精度。为此，在测量评估期间，每个天线的接收信道被单个假设的多径信道叠加。虽然每个接收器的视线路径直接取决于发射机的位置，但朝向所有接收器的多径传播是不同的，并且是稀疏的，因为它们是由环境中的很少反射引起的。因此，在正确的发射机位置，剩余的接收信号可以仅用很少的进一步多径传播来描述，而在错误地假设的发射机位置，LOS路径必须使用附加的多径传播来描述。根据压缩感知理论，可以在正确的发射机位置用一个小的L1范数来估计多径传播。因此，通过同时最小搜索发射机位置和相应的多径传播来执行最小多径位置估计。由于对接收通道的相对相位信息的评估提高了发射机位置的分辨率，现在可以将LOS路径从多径传播中分离出来。反之亦然，同时从评估的相位中减去多径传播的影响，从而提高了定位精度。对于新的本地化概念同样重要的是，将研究等效校准概念。为此，将再次使用建议的最小多径度量。新的校准和定位概念将通过使用独特的大规模MIMO阵列进行测量来验证，该阵列已经在LHFT获得。

In future wireless communication networks (5G, 6G, etc.), massive MIMO systems increase the achievable data rate. Since the achievable data rate depends on the independence of the spatially recorded information (spatial diversity), large antenna arrays are favorable. In this project, the spatial diversity of Massive MIMO systems is used to enable exact indoor localization even under extreme multipath conditions. While UWB systems attempt to isolate the direct path from the multipath propagation by increasing the bandwidth, the impulse responses of the line of sight (LOS) and the multipath propagation overlap in localization systems with limited bandwidth. Therefore, a LOS identification, which is solely based on the evaluation of a single path, is not possible. Hence, in this project the spatial diversity of Massive MIMO systems is used to separate the common LOS of all receive paths from the superimposed multipath propagation, which decorrelates with increasing antenna distance. For this purpose, a least multipath metric is developed to replace the commonly established least squares metric. Here, the aim is to estimate the multipath propagation, remove it from the signal, and thereby, drastically improve the localization accuracy. For this purpose, the receive channel of each antenna is superimposed by an individual hypothetical multipath channel during the measurement evaluation. While the LOS path at each receiver directly depends on the transmitter’s position, the multipath propagation towards all receivers differ and are sparse, because they are caused by few reflections at the environment. Hence, at the correct transmitter position, the remaining receive signal can be described with only little further multipath propagation, whereas at an incorrectly assumed transmitter position, the LOS path has to be described using additional multipath propagation. According to the compressed sensing theory, the multipath propagation can then be estimated with a small L1 norm at the correct transmitter position. Hence, the least multipath position estimation is performed by a simultaneous minimum search of both the transmitter position and the corresponding multipath propagation. Since the evaluation of the receive channels’ relative phase information is increasing the resolution of the transmitter position, the LOS paths can now be separated from the multipath propagation. Vice versa, the effect of the multipath propagation is simultaneously subtracted from the evaluated phases and hence, the localization accuracy increases. Equally important to the novel localization concept, an equivalent calibration concept will be investigated. For this purpose, again the proposed least multipath metric will be used. The novel calibration and localization concept will be validated by measurements using a unique massive MIMO array, which is already available at the LHFT.