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Indoor Millimeter Wave Localization Using Multiple Self-Supervised Tiny Neural Networks
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DOI：10.1109/LCOMM.2024.3376150.png)
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摘要
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We consider the localization of a mobile mmw 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 is then 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 preserve the localization accuracy, we propose two switching schemes: one based on the innovation measured by 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.
Keyword:
Location awareness
Kalman filters
Artificial neural networks
Training
Millimeter wave communication
Computational modeling
Covariance matrices
Millimeter waves
localization
neural networks (NN)
Kalman filters (KFs)
statistical distribution
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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引用论文
Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments将毫米波网络扩展到密集部署和动态环境.png)
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