- -

Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC

Show full item record

Pacheco-Paramo, DF.; Tello-Oquendo, L.; Pla, V.; Martínez Bauset, J. (2019). Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC. Ad Hoc Networks. 94:1-14. https://doi.org/10.1016/j.adhoc.2019.101939

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/151100

Files in this item

Item Metadata

Title: Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC
Author: Pacheco-Paramo, Diego F. Tello-Oquendo, Luis Pla, Vicent Martínez Bauset, Jorge
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
[EN] One important issue that needs to be addressed in order to provide effective massive deployments of IoT devices is access control. In 5G cellular networks, the Access Class Barring (ACB) method aims at increasing the ...[+]
Subjects: Access control , Deep reinforcement learning , Massive machine type communications , 5G , Cellular networks
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Ad Hoc Networks. (issn: 1570-8705 )
DOI: 10.1016/j.adhoc.2019.101939
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.adhoc.2019.101939
Project ID:
UNACH/CONV.2018-ING010
MICINN/PGC2018-094151-B-I00
Thanks:
The research of D. Pacheco-Paramo was supported by Universidad Sergio Arboleda, P.t. Tecnologias para la inclusion social y la competitividad economica. 0.E.6. The research of L Tello-Oquendo was conducted under project ...[+]
Type: Artículo

recommendations

 

This item appears in the following Collection(s)

Show full item record