High resolution parameter estimation for millimeter wave propagation in dynamic scenarios

未来的第五代移动的无线电系统将使用毫米波频率范围内的频率，带宽高达几GHz。为了实现合理的链路预算，需要非常大的（“大规模”）天线阵列。这些不可避免地具有高度的方向性，必须以适应性的方式进行调整。对于预测和评估这样的系统的性能，没有可用的信道模型。这主要是因为缺乏合适的信道测量，该信道测量将允许在沿着移动的站的轨迹的动态场景中以必要的分辨率来确定发射机和接收机侧上的电磁波的传播方向、它们的传播时间、多普勒频移和极化取向。因此，该项目的目的是研究基于最大似然原理的高分辨率参数估计器，以解决特定天线阵列架构背景下的这一问题。参数估计器的数据模型考虑到方向、飞行时间和多普勒不再以可因子分解的形式存在的事实。这克服了传统的窄带假设，并需要在时域中的阵列，这涉及到新的设计范例的天线元件和它们的安排方面的直径和天线间距的一致的描述。为了应对高时变场景的特殊挑战，必须通过贝叶斯滤波器跟踪传播路径。为此，我们采用了雷达多目标跟踪方法，这些方法适用于本文提出的估计任务，因此，我们期望具有诸如降低计算复杂度、提高精度和消除模糊度等优点，这可能是由于特殊的阵列设计和不可因式分解的数据模型。这将伴随着一个实用的方法，为适应模型的顺序，评估的“生命周期”的传播路径和他们的持续跟踪的情况下，短期shadowing.In这方面，新的方法将被开发，这将允许新的见解信道建模。这些包括用于描述波传播的镜面反射和漫射（不可分辨）分量的模型、它们的偏振建模以及在维度方向、时间和多普勒中的动态关系。最后，我们的目标是统计描述的集群，这不能确定传统的方法的空间和时间分辨率，但需要信道建模。

Future mobile radio systems of the 5th generation will use frequencies in the millimeter wave frequency range with bandwidths up to several GHz. To achieve a reasonable link budget, very large ("massive") antenna arrays are required. These inevitably have a high directivity, which must be adjusted in an adaptive way. For the prediction and evaluation of the performance of such systems, there are no channel models available. This is mainly because of the lack of suitable channel measurements, which would allow determining the propagation directions of the electromagnetic waves on the transmitter and receiver side, their propagation time, Doppler shift, and polarization orientation with the necessary resolution in dynamic scenarios along the trajectory of the mobile station. The aim of this project is therefore research towards a high-resolution parameter estimator based upon to the maximum likelihood principle, which solves this problem in the context of a specific antenna array architecture. The data model of the parameter estimator takes into account the fact that directions, time-of-flight and Doppler do no longer exist in a factorizable form. This overcomes the conventional narrow-band assumption and requires a consistent description of the array in the time domain, which involves new design paradigms for the antenna elements and their arrangement with regard to the diameter and antenna spacing. In order to meet the special challenges of highly time-variant scenarios, the propagation paths have to be tracked by Bayesian filters. For this purpose, methods of multi-target tracking in Radar are used, which are adapted for the estimation task presented here.As a result, we expect advantages, such as reduced computational complexity, higher accuracy, and resolutions of ambiguities, which may result from the special array design and the non-factorizable data model. This will be accompanied by a practical approach for the adaptation of the model order, the evaluation of the "lifetime" of propagation paths and their sustained tracking in case of short-term shadowing.In this context, novel methods will be developed, which will allow new insights for channel modeling. These include models for the description of specular and diffuse (non-resolvable) components of wave propagation, their polarimetric modeling and the dynamic relationship in the dimensions direction, time and Doppler. Finally, we aim at statistical descriptions of clusters, which cannot be determined by conventional methods of spatial and temporal resolution, but are required for channel modeling.