Reference : Efficient Resource Scheduling and Optimization for Over-Loaded LEO-Terrestrial Networks

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/10993/52989

Title :	Efficient Resource Scheduling and Optimization for Over-Loaded LEO-Terrestrial Networks
Language :	English
Author, co-author :	yuan, Yaxiong []
	Lei, Lei [Xi'an Jiaotong University]
	Vu, Thang Xuan [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
	Fowler, Scott []
	Chatzinotas, Symeon [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Publication date :	May-2022
Main document title :	Proceeding of IEEE ICC 2022
Peer reviewed :	Yes
Event name :	IEEE International Conference on Communications
Event date :	16-5-2022
Event place (city) :	Seoul
Keywords :	[en] LEO satellites ; resource allocation ; reinforcement learning
Abstract :	[en] Towards the next generation networks, low earth orbit (LEO) satellites have been considered as a promising component for beyond 5G networks. In this paper, we study downlink LEO-5G communication systems in a practical scenario, where the integrated LEO-terrestrial system is over-loaded by serving a number of terminals with high-volume traffic requests. Our goal is to optimize resource scheduling such that the amount of undelivered data and the number of unserved terminals can be minimized. Due to the inherent hardness of the formulated quadratic integer programming problem, the optimal algorithm requires unaffordable complexity. To solve the problem, we propose a near-optimal algorithm based on alternating direction method of multipliers (ADMM-HEU), which saves computational time by taking advantage of the distributed ADMM structure, and a low-complexity heuristic algorithm (LC-HEU), which is based on estimation and greedy methods. The results demonstrate the near-optimality of ADMM-HEU and the computational efficiency of LC-HEU compared to the benchmarks.
Permalink :	http://hdl.handle.net/10993/52989
FnR project :	FnR ; FNR13718904 > Symeon Chatzinotas > ASWELL > Autonomous Network Slicing For Integrated Satellite-terrestrial Transport Networks > 01/06/2020 > 31/05/2023 > 2019

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