Deep Learning-Based Positioning With Multi-Task Learning and Uncertainty-Based Fusion

Deep Learning-Based Positioning With Multi-Task Learning and Uncertainty-Based Fusion

Deep learning (DL) methods have been shown to improve the performance of several use cases for the fifth-generation (5G) New radio (NR) air interface. In this paper we investigate user equipment (UE) positioning using the channel state information (CSI) fingerprints between a UE and multiple base st...

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Bibliographic Details
Main Authors: Anastasios Foliadis, Mario H. Castaneda Garcia, Richard A. Stirling-Gallacher, Reiner S. Thoma
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Machine Learning in Communications and Networking
Subjects:
Deep learning
wireless positioning
late fusion
early fusion
multi-task learning
uncertainty estimation
Online Access:https://ieeexplore.ieee.org/document/10632202/
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https://ieeexplore.ieee.org/document/10632202/

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