论文标题
使用MMWave射线示踪模拟与深神网络进行本地化
Localization with Deep Neural Networks using mmWave Ray Tracing Simulations
论文作者
论文摘要
世界正在朝着更快的数据转换迈进，用户是初步要求的更有效的定位。这项工作研究了将深度学习技术用于无线定位，考虑到毫米波（MMWAVE）和SUB-6 GHz频率。学习新的神经网络模型的能力使本地化过程更加容易，更快。在这项研究中，深层神经网络（DNN）用于在两个静态方案中定位用户设备（UE）。我们提出了两种不同的方法来训练神经网络，一种使用通道参数（功能），另一种使用通道响应向量，并使用初步计算机模拟比较其性能。我们观察到，考虑到所有用户都有固定数量的多径组件（MPC），该方法依赖于MPC的数量，因此我们观察到前者的定位精度高。另一方面，后一种方法与MPC无关，但与第一种方法相比，其性能相对较差。
The world is moving towards faster data transformation with more efficient localization of a user being the preliminary requirement. This work investigates the use of a deep learning technique for wireless localization, considering both millimeter-wave (mmWave) and sub-6 GHz frequencies. The capability of learning a new neural network model makes the localization process easier and faster. In this study, a Deep Neural Network (DNN) was used to localize User Equipment (UE) in two static scenarios. We propose two different methods to train a neural network, one using channel parameters (features) and another using a channel response vector and compare their performances using preliminary computer simulations. We observe that the former approach produces high localization accuracy considering that all of the users have a fixed number of multipath components (MPCs), this method is reliant on the number of MPCs. On the other hand, the latter approach is independent of the MPCs, but it performs relatively poorly compared to the first approach.
