Q-Learning-Based Pending Zone Adjustment for Proximity Classification

This paper presents a Q-learning-based pending zone adjustment for received signal strength indicator (RSSI)-based proximity classification (QPZA). QPZA aims to improve the accuracy of RSSI-based proximity classification by adaptively adjusting the size of the pending zone, taking into account chang...

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Detalles Bibliográficos
Autores principales: Kwon, Jung-Hyok, Lee, Sol-Bee, Kim, Eui-Jik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181729/
https://www.ncbi.nlm.nih.gov/pubmed/37177556
http://dx.doi.org/10.3390/s23094352

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181729/
https://www.ncbi.nlm.nih.gov/pubmed/37177556
http://dx.doi.org/10.3390/s23094352

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