论文标题
毫米波车辆网络中的联合初始访问和定位：混合模型/数据驱动方法
Joint Initial Access and Localization in Millimeter Wave Vehicular Networks: a Hybrid Model/Data Driven Approach
论文作者
论文摘要
当考虑混合MMWAVE MIMO系统时，高分辨率压缩通道估计为车辆定位提供了信息。但是，当需要高精度定位时，复杂性和记忆要求可能会成为瓶颈。另一个挑战是需要路径订单信息，以应用通道路径参数与车辆，RSU和散射器位置之间的适当几何关系。在本文中，我们提出了基于多维正交匹配的追踪的出发角度和到达时间差的低复杂性通道估计策略。我们还设计了一个深层神经网络，该网络可以预测通道路径的顺序，因此仅将LOS和一阶反射用于本地化。通过射线追踪产生的逼真的车辆通道获得的仿真结果表明，可以为50％的用户获得子米精度，而无需求助于完美的同步假设或不可行的全数字高分辨率MIMO架构。
High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered. Complexity and memory requirements can, however, become a bottleneck when high accuracy localization is required. An additional challenge is the need of path order information to apply the appropriate geometric relationships between the channel path parameters and the vehicle, RSU and scatterers position. In this paper, we propose a low complexity channel estimation strategy of the angle of departure and time difference of arrival based on multidimensional orthogonal matching pursuit. We also design a deep neural network that predicts the order of the channel paths so only the LoS and first order reflections are used for localization. Simulation results obtained with realistic vehicular channels generated by ray tracing show that sub-meter accuracy can be achieved for 50% of the users, without resorting to perfect synchronization assumptions or unfeasible all-digital high resolution MIMO architectures.
