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.

本研究提出了使用多层感知机神经网络（NN）在大型室内环境中定位移动毫米波客户端的方法。通过选择多个经过自监督训练的小型 NN，并根据最佳 NN 进行切换，我们解决了定位准确性不足的问题。我们提出了两种切换方案：基于卡尔曼滤波和基于训练数据的统计分布。通过模拟分析，我们证明了我们的方法优于几何定位方案和使用单个 NN 的方法。