3D Channel Tracking for UAV-Satellite Communications in Space-Air-Ground Integrated Networks

The space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. Howev...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2020-12, Vol.38 (12), p.2810-2823
Main Authors: Yu, Jiadong, Liu, Xiaolan, Gao, Yue, Shen, Xuemin
Format: Article
Language:English
Subjects:
5G mobile communication
Algorithms
Analytical models
Channel tracking
Communications systems
Correlation analysis
Logistics
Low earth orbits
Markov chains
massive antenna array
Mathematical model
Message passing
Optimization
Satellite communications
Satellite tracking
Space communications
Statistical analysis
Three dimensional models
Two dimensional models
UAV-satellite communication
Unmanned aerial vehicles
Wide area networks
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Summary:The space-air-ground integrated network (SAGIN) aims to provide seamless wide-area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. However, it is a key challenge to track dynamic channel information due to the low earth orbit (LEO) satellite orbiting and three-dimensional (3D) UAV trajectory. In this paper, we explore the 3D channel tracking for a Ka-band UAV-satellite communication system. We firstly propose a statistical dynamic channel model called 3D two-dimensional Markov model (3D-2D-MM) for the UAV-satellite communication system by exploiting the probabilistic insight relationship of both hidden value vector and joint hidden support vector. Specifically, for the joint hidden support vector, we consider a more realistic 3D support vector in both azimuth and elevation direction. Moreover, the spatial sparsity structure and the time-varying probabilistic relationship between degree patterns named the spatial and temporal correlation, respectively, are studied for each direction. Furthermore, we derive a novel 3D dynamic turbo approximate message passing (3D-DTAMP) algorithm to recursively track the dynamic channel with the 3D-2D-MM priors. Numerical results show that our proposed algorithm achieves superior channel tracking performance to the state-of-the-art algorithms with lower pilot overhead and comparable complexity.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2020.3005490