Internet of Things; low earth orbit satellite; massive multiple-input multiple-output; random access; Data-detection; Delay; Low earth orbit satellites; Massive multiple-input multiple-output; Multiple inputs; Multiple outputs; Random access; Satellite constellations; Signal processing algorithms; Computer Networks and Communications; Electrical and Electronic Engineering; Index Terms- Internet of Things
Abstract :
[en] Low earth orbit (LEO) satellite constellation-enabled communication networks are expected to be an important part of many Internet of Things (IoT) deployments due to their unique advantage of providing seamless global coverage. In this paper, we investigate the random access problem in massive multiple-input multiple-output-based LEO satellite systems, where the multi-satellite cooperative processing mechanism is considered. Specifically, at edge satellite nodes, we conceive a training sequence padded multi-carrier system to overcome the issue of imperfect synchronization, where the training sequence is utilized to detect the devices' activity and estimate their channels. Considering the inherent sparsity of terrestrial-satellite links and the sporadic traffic feature of IoT terminals, we utilize the orthogonal approximate message passing-multiple measurement vector algorithm to estimate the delay coefficients and user terminal activity. To further utilize the structure of the receive array, a two-dimensional estimation of signal parameters via rotational invariance technique is performed for enhancing channel estimation. Finally, at the central server node, we propose a majority voting scheme to enhance activity detection by aggregating backhaul information from multiple satellites. Moreover, multi-satellite cooperative linear data detection and multi-satellite cooperative Bayesian dequantization data detection are proposed to cope with perfect and quantized backhaul, respectively. Simulation results verify the effectiveness of our proposed schemes in terms of channel estimation, activity detection, and data detection for quasi-synchronous random access in satellite systems.
Precision for document type :
Review article
Disciplines :
Computer science
Author, co-author :
Ying, Keke ; Beijing Institute of Technology, School of Information and Electronics, Beijing, China
Gao, Zhen ; Miit Key Laboratory of Complex-Field Intelligent Sensing, Beijing Institute of Technology, Beijing, China ; Beijing Institute of Technology (Jiaxing), Yangtze Delta Region Academy, Jiaxing, China ; Beijing Institute of Technology, Advanced Technology Research Institute, Jinan, China
Chen, Sheng ; University of Southampton, School of Electronics and Computer Science, Southampton, United Kingdom
Zhou, Mingyu ; University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg City, Luxembourg
Zheng, Dezhi; Miit Key Laboratory of Complex-Field Intelligent Sensing, Beijing Institute of Technology, Beijing, China ; Beijing Institute of Technology (Jiaxing), Yangtze Delta Region Academy, Jiaxing, China ; Beijing Institute of Technology, Advanced Technology Research Institute, Jinan, China
CHATZINOTAS, Symeon ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Baicells Technologies Company Ltd., Beijing, China ; Princeton University, Department of Electrical and Computer Engineering, Princeton, United States
Ottersten, Bjorn ; Baicells Technologies Company Ltd., Beijing, China ; Princeton University, Department of Electrical and Computer Engineering, Princeton, United States
Poor, H. Vincent ; Beijing Institute of Technology, School of Information and Electronics, Beijing, China
External co-authors :
yes
Language :
English
Title :
Quasi-Synchronous Random Access for Massive MIMO-Based LEO Satellite Constellations
Publication date :
June 2023
Journal title :
IEEE Journal on Selected Areas In Communications
ISSN :
0733-8716
Publisher :
Institute of Electrical and Electronics Engineers Inc.
The work of Zhen Gao was supported in part by the Natural Science Foundation of China (NSFC) under Grant 62071044 and Grant U2001210, in part by the Shandong Province Natural Science Foundation under Grant ZR2022YQ62, and in part by the Beijing Nova Program. The work of H. Vincent Poor was supported by the National Science Foundation of United States under Grant CNS-2128448.
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