Deep Reinforcement Learning for Backhaul Link Selection for Network Slices in IAB Networks

António J. Morgado, Firooz B. Saghezchi*, Pablo Fondo-Ferreiro, Felipe Gil-Castineira, Maria Papaioannou, Kostas Ramantas, Jonathan Rodriguez

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddCyfraniad i gynhadleddadolygiad gan gymheiriaid


Integrated Access and Backhaul (IAB) has been recently proposed by 3GPP to enable network operators to deploy fifth generation (5G) mobile networks with reduced costs. In this paper, we propose to use IAB to build a dynamic wireless backhaul network capable to provide additional capacity to those Base Stations (BS) experiencing congestion momentarily. As the mobile traffic demand varies across time and space, and the number of slice combinations deployed in a BS can be prohibitively high, we propose to use Deep Reinforcement Learning (DRL) to select, from a set of candidate BSs, the one that can provide backhaul capacity for each of the slices deployed in a congested BS. Our results show that a Double Deep Q-Network (DDQN) agent using a fully connected neural network and the Rectified Linear Unit (ReLU) activation function with only one hidden layer is capable to perform the BS selection task successfully, without any failure during the test phase, after being trained for around 20 episodes.

Iaith wreiddiolSaesneg
TeitlGLOBECOM 2023 - 2023 IEEE Global Communications Conference
CyhoeddwrInstitute of Electrical and Electronics Engineers Inc.
Nifer y tudalennau6
ISBN (Electronig)9798350310900
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 26 Chwef 2024
Digwyddiad2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaisia
Hyd: 4 Rhag 20238 Rhag 2023

Cyfres gyhoeddiadau

EnwProceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Argraffiad)2334-0983
ISSN (Electronig)2576-6813


Cynhadledd2023 IEEE Global Communications Conference, GLOBECOM 2023
DinasKuala Lumpur

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