admission control; deep learning; LSTM; MORL; multi-connectivity; Network slicing; non-terrestrial network (NTN); resource allocation; satellite communications; Admission-control; Deep learning; Multi objective; Multi-connectivity; Multi-objective reinforcement learning; Non-terrestrial network; Reinforcement learnings; Resources allocation; Satellite communications; Terrestrial networks; Hardware and Architecture; Computer Networks and Communications; Artificial Intelligence
Résumé :
[en] Recently, there has been a surge in the adoption of multi-orbital satellite networks for integrated service delivery. Operators are increasingly collaborating and constructing multi-layer network infrastructures to meet growing traffic demands. This paper introduces a novel service delivery model where infrastructure providers (InPs) lease out resources from non-terrestrial networks (NTNs) as slices to mobile virtual service operators (MVSOs). These MVSOs then offer the leased resources to subscribers, facilitating efficient utilization of NTN resources in the telecommunications ecosystem. The model utilizes an innovative NTN slicing architecture that incorporates multi-layer satellites, including low Earth orbit (LEO), medium Earth orbit (MEO), and geostationary orbit (GEO) constellations. It features a hybrid gateway station (HGS) tailored to the virtualization architecture specified by the 3rd Generation Partnership Project (3GPP). In this setting, we formulate a multi-objective optimization problem (MOOP) comprising two combinatorial objective functions for InPs and MVSOs, aiming to maximize revenue. The proposed algorithm addresses the joint network slicing and admission control (AC) requirements by employing techniques such as non-dominated sorting genetic algorithm II (NSGA-II), multi-objective reinforcement learning (MORL), and a heuristic approach. Our algorithm outperforms the Round Robin (RR) and Max-Min fairness approaches, achieving increases in peak revenue of 3.91% and 18.73%, respectively.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM - Signal Processing & Communications
Précision sur le type de document :
Compte rendu
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
DAZHI, Michael ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
AL-HRAISHAWI, Hayder ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust > SigCom > Team Symeon CHATZINOTAS
Shankar, Bhavani ; University of Luxembourg, Luxembourg
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Grotz, Joel ; SES, Betzdorf, Luxembourg
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Joint NTN Slicing and Admission Control for Infrastructure-as-a-Service: A Deep Learning Aided Multi-objective Optimization
Date de publication/diffusion :
16 septembre 2024
Titre du périodique :
IEEE Transactions on Cognitive Communications and Networking
eISSN :
2332-7731
Maison d'édition :
Institute of Electrical and Electronics Engineers Inc.
R-AGR-3929 - IPBG19/14016225/INSTRUCT - SES - CHATZINOTAS Symeon
Organisme subsidiant :
Fonds National de la Recherche Luxembourg
Subventionnement (détails) :
This work is financially supported by Fond National de la Recherche (FNR), under the Industrial Partnership Block Grant (IPBG) ref 14016225, project known as INSTRUCT: INtegrated Satellite \u2013 TeRrestrial Systems for Ubiquitous Beyond 5G CommunicaTions Networks.
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