Random Access Control in NB-IoT With Model-Based Reinforcement Learning

By /

Project publication · IEEE Internet of Things Journal

Summary

The article proposes model-based reinforcement learning for random access control in NB-IoT, aimed at improving NPRACH resource allocation.

Resultados de control de acceso aleatorio NB-IoT con aprendizaje por refuerzo basado en modelo
Figure from the original article. Source: IEEE Internet of Things Journal, reproduced without modification under CC BY-NC-ND 4.0.

Authors: Juan J. Alcaraz, Juan-Carlos Sanchez-Aarnoutse, Alejandro-Santos Martinez-Sala, Francisco-Javier Gonzalez-Castaño

Journal: IEEE Internet of Things Journal

DOI: 10.1109/JIOT.2024.3499854

Funding: PID2023-148214OB-C21.

UPCT Repository: http://hdl.handle.net/10317/19795

Bibliometric indicators

JCR quartileQ1
Impact factor8.9
Google Scholar citations2
JCR area
Telecommunications
JCR year
2024
Scholar query
21 May 2026

Main contribution

The technique adjusts access parameters and coverage thresholds to balance success rate, congestion and available resources. The use of a model improves early-stage learning efficiency compared with purely empirical approaches.

Link with BoND1

Although NB-IoT is a cellular technology, massive access is a common problem across many IoT systems. For BoND1, the article contributes knowledge on adaptive resource control, a relevant element for designing scalable connectivity in networks with many devices.

DOI UPCT Repository
Scroll al inicio