We consider the localization of a mobile millimeter-wave client in a large indoor environment using multilayer perceptron neural networks (NNs). Instead of training and deploying a single deep model, we proceed by choosing among multiple tiny NNs trained in a self-supervised manner. The main challenge then becomes to determine and switch to the best NN among the available ones, as an incorrect NN will fail to localize the client. In order to upkeep the localization accuracy, we propose two switching schemes: one based on a Kalman filter, and one based on the statistical distribution of the training data. We analyze the proposed schemes via simulations, showing that our approach outperforms both geometric localization schemes and the use of a single NN.

翻译：我们考虑在大型室内环境中，利用多层感知器神经网络（NN）对移动毫米波客户端进行定位。我们不采用训练和部署单个深度模型的方式，而是选择在自监督训练下的多个微型神经网络进行切换。主要挑战在于如何决定并切换到可用的最佳神经网络，因为错误的神经网络将无法定位客户端。为了保持定位精度，我们提出了两种切换方案：一种基于卡尔曼滤波器，另一种基于训练数据的统计分布。我们通过仿真分析所提出的方案，结果表明，我们的方法优于几何定位方案和单个神经网络的使用。