Uncertainty Estimation via Monte Carlo Dropout in CNN-Based mmWave MIMO Localization

Recently, there has been much interest in the use of convolutional neural networks (CNN) for mobile user localization in massive multiple-input multiple-output (MIMO) systems operating at millimeter wave (mmWave) frequencies. However, current CNN-based approaches cannot predict the confidence interv...

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Bibliographic Details
Published in:IEEE signal processing letters 2022, Vol.29, p.269-273
Main Authors: Sadr, Mohammad Amin Maleki, Gante, Joao, Champagne, Benoit, Falcao, Gabriel, Sousa, Leonel
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
CNN
Confidence intervals
Convolutional neural networks
Deep learning
Delays
Estimation
fingerprint localization
Localization
Location awareness
massive MIMO
Maximum power
Millimeter waves
mmWave communications
Neural networks
Ray tracing
Training
Uncertainty
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Summary:Recently, there has been much interest in the use of convolutional neural networks (CNN) for mobile user localization in massive multiple-input multiple-output (MIMO) systems operating at millimeter wave (mmWave) frequencies. However, current CNN-based approaches cannot predict the confidence interval bounds for the localization accuracy. While the Bayesian neural network (BNN) method can be employed to estimate the model uncertainty, it entails a high computational cost. In this letter, the Monte Carlo (MC) dropout based method is proposed as a low-complexity approximation to BNN inference for capturing the uncertainty in a CNN-based mmWave MIMO outdoor localization system, without sacrificing accuracy. The proposed method is evaluated by means of simulations using a ray-tracing model of urban propagation at 28GHz. Results show that the localization uncertainty region can be properly determined and that their shape depends on the maximum power received at the user.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2021.3130504