Recurrent DQN for radio fingerprinting with constrained measurements collection

In this paper, we address the problem of fingerprinting-based radio localization with a particular focus on the measurements collection part. We consider the crucial circumstance where the operator that builds the fingerprinting map by collecting measurements can only travel a limited distance. We p...

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Bibliographic Details
Published in:ICT Express
Main Authors: Nicola Novello, Andrea M. Tonello
Format: Article
Language:English
Published: Elsevier 2025-02-01
Subjects:
Radio localization
Radio fingerprinting
Indoor localization
Deep reinforcement learning
Deep learning
Fingerprinting
Online Access:http://www.sciencedirect.com/science/article/pii/S2405959524000882
Description
Summary:In this paper, we address the problem of fingerprinting-based radio localization with a particular focus on the measurements collection part. We consider the crucial circumstance where the operator that builds the fingerprinting map by collecting measurements can only travel a limited distance. We propose an iterative formulation that increases the accuracy of the position prediction task by using a recurrent deep reinforcement learning algorithm. Numerical results on a real dataset show the effectiveness of the proposed method, and the comparison with other measurement collection strategies corroborates its value.
ISSN:2405-9595

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