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

Autor:	Leonel Sousa, Gabriel Falcao, Benoit Champagne, Mohammad Amin Malekisadr, Joao Gante
Rok vydání:	2022
Předmět:	Maximum power principle Uncertainty estimation Computer science Applied Mathematics Signal Processing Monte Carlo method MIMO Extremely high frequency Inference Electrical and Electronic Engineering Algorithm Convolutional neural network Dropout (neural networks)
Zdroj:	IEEE Signal Processing Letters. 29:269-273
ISSN:	1558-2361 1070-9908
DOI:	10.1109/lsp.2021.3130504
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::39da922ec389fbb6c1fc8e79c4203727 https://doi.org/10.1109/lsp.2021.3130504 Zobrazit plný text záznamu