Alternating optimization (AO); graph neural networks (GNNs); integrated satellite-terrestrial networks; linear processing; throughput maximization; Air grounds; Alternating optimizations; Graph neural networks; Integrated networks; Integrated satellite-terrestrial network; Linear processing; Optimisations; Resource management; Satellite broadcasting; Satellite-terrestrial network; Space-air-ground integrated network; Throughput maximization; Uplink; Signal Processing; Information Systems; Hardware and Architecture; Computer Science Applications; Computer Networks and Communications; Internet of Things; Throughput; Space-air-ground integrated networks; Optimization
Abstract :
[en] Both space and ground communications have been proven effective solutions under different perspectives in Internet of Things (IoT) networks. This article investigates multiple-access scenarios, where plenty of IoT users are cooperatively served by a satellite in space and access points (APs) on the ground. Available users in each coherence interval are split into scheduled and unscheduled subsets to optimize limited radio resources. We compute the uplink ergodic throughput of each scheduled user under imperfect channel state information (CSI) and nonorthogonal pilot signals. As maximum-radio combining is deployed locally at the ground gateway and the APs, the uplink ergodic throughput is obtained in a closed-form expression. The analytical results explicitly unveil the effects of channel conditions and pilot contamination on each scheduled user. By maximizing the sum throughput, the system can simultaneously determine scheduled users and perform power allocation based on either a model-based approach with alternating optimization or a learning-based approach with the graph neural network. Numerical results manifest that integrated satellite-terrestrial cell-free massive multiple-input-multiple-output systems can significantly improve the sum ergodic throughput over coherence intervals. The integrated systems can schedule the vast majority of users; some might be out of service due to the limited power budget.
Disciplines :
Computer science
Author, co-author :
Van Chien, Trinh ; Hanoi University of Science and Technology, School of Information and Communication Technology, Hanoi, Viet Nam
An Le, Ha; Seoul National University, Department of Electrical and Computer Engineering, Seoul, South Korea
Hai Tung, Ta; Hanoi University of Science and Technology, School of Information and Communication Technology, Hanoi, Viet Nam
Ngo, Hien Quoc ; Queen's University Belfast, School of Electronics, Electrical Engineering and Computer Science, Belfast, United Kingdom
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
External co-authors :
yes
Language :
English
Title :
Joint Power Allocation and User Scheduling in Integrated Satellite-Terrestrial Cell-Free Massive MIMO IoT Systems
Publication date :
15 October 2024
Journal title :
IEEE Internet of Things Journal
eISSN :
2327-4662
Publisher :
Institute of Electrical and Electronics Engineers Inc.
Hanoi University of Science and Technology U.K. Research and Innovation Future Leaders Fellowships Department for the Economy Northern Ireland under the U.S.–Ireland Research and Development Partnership Programme Luxembourg National Research Fund (FNR) in the frameworks of the FNR-CORE Project “MegaLEO: Self-Organised Lower Earth Orbit Mega-Constellations” Leveraging AI to Empower the Next Generation of Satellite Communications
Funding text :
This research is funded by Hanoi University of Science and Technology (HUST) under Project number T2022-TT-001 for Trinh Van Chien. The work of Hien Quoc Ngo was supported in part by the U.K. Research and Innovation Future Leaders Fellowships under Grant MR/X010635/1, and in part by the Department for the Economy Northern Ireland under the U.S.-Ireland Research and Development Partnership Programme. The work of Symeon Chatzinotas was supported by the Luxembourg National Research Fund (FNR) in the frameworks of the FNR-CORE Project \"MegaLEO: Self-Organised Lower Earth Orbit Mega-Constellations\"under Grant C20/IS/14767486, and Leveraging AI to Empower the Next Generation of Satellite Communications (SmartSpace) under Grant C21/IS/16193290.Trinh Van Chien and Ta Hai Tung are with the School of Information and Communication Technology (SoICT), Hanoi University of Science and Technology (HUST), 100000 Hanoi, Vietnam (email: chientv@soict.hust.edu.vn, tungth@soict.hust.edu.vn). Ha An Le is with the Department of Electrical and Computer Engineering, Seoul National University, Korea (email: 25251225@snu.ac.kr). Hien Quoc Ngo is with the School of Electronics, Electrical Engineering and Computer Science, Queen\u2019s University Belfast, Belfast BT7 1NN, United Kingdom (email: hien.ngo@qub.ac.uk). Symeon Chatzinotas is with the Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg (email: symeon.chatzinotas@uni.lu). This research is funded by Hanoi University of Science and Technology (HUST) under project number T2022-TT-001 for Trinh Van Chien. The work of H. Q. Ngo was supported by the U.K. Research and Innovation Future Leaders Fellowships under Grant MR/X010635/1, and a research grant from the Department for the Economy Northern Ireland under the US-Ireland R&D Partnership Programme. The work of S. Chatzinotas was supported by the Luxembourg National Research Fund (FNR) in the frameworks of the FNR-CORE Project \u201CMegaLEO: Self-Organised Lower Earth Orbit Mega-Constellations\u201D under Grant C20/IS/14767486, Leveraging AI to Empower the Next Generation of Satellite Communications (SmartSpace) under Grant C21/IS/16193290, and Leveraging AI to Empower the Next Generation of Satellite Communications (SmartSpace) under Grant C21/IS/16193290.
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