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
Abstract :
[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.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM - Signal Processing & Communications
Precision for document type :
Review article
Disciplines :
Electrical & electronics engineering
Author, co-author :
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
External co-authors :
yes
Language :
English
Title :
Joint NTN Slicing and Admission Control for Infrastructure-as-a-Service: A Deep Learning Aided Multi-objective Optimization
Publication date :
16 September 2024
Journal title :
IEEE Transactions on Cognitive Communications and Networking
eISSN :
2332-7731
Publisher :
Institute of Electrical and Electronics Engineers Inc.
R-AGR-3929 - IPBG19/14016225/INSTRUCT - SES - CHATZINOTAS Symeon
Funders :
Fonds National de la Recherche Luxembourg
Funding text :
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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