[en] Low Earth Orbit (LEO) satellite communication is crucial for developing sixth-generation (6G) networks. The integration of massive multiple-input multiple-output (MIMO) technology is being actively researched to enhance the performance of LEO satellite communication systems. However, the limited power resources of LEO satellites pose significant challenges to improving energy efficiency (EE) under power-constrained conditions. Typical optimization-based methods often lack real-time adaptability and computational efficiency. This paper proposes innovative solutions to address the challenges of precoding in massive MIMO LEO satellite communications. Specifically, we introduce a deep unfolding of the Dinkelbach algorithm and the weighted minimum mean square error (WMMSE) approach to achieve enhanced EE. This transformation of iterative optimization procedures into a graph neural network (GNN) leads to faster convergence and improved computational efficiency. Furthermore, we apply the Taylor expansion method to approximate matrix inversion within the GNN framework. Numerical experiments demonstrate the superiority of our proposed method in terms of complexity and robustness, achieving significant improvements over other state-of-the-art methods.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Zhou, Huibin; Southeast University, National Mobile Communications Research Laboratory, Nanjing, China ; Purple Mountain Laboratories, Nanjing, China
Gong, Xinrui; Purple Mountain Laboratories, Nanjing, China
Tsinos, Christos G.; National and Kapodistrian University of Athens, Department of Digital Industry Technologies, Athens, Greece
You, Li; Purple Mountain Laboratories, Nanjing, China
Gao, Xiqi; Southeast University, National Mobile Communications Research Laboratory, Nanjing, China ; Purple Mountain Laboratories, Nanjing, China
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PI Ottersten
External co-authors :
yes
Language :
English
Title :
GNN-Enabled Deep Unfolding for Precoding in Massive MIMO LEO Satellite Communications
Publication date :
2025
Event name :
2025 IEEE Wireless Communications and Networking Conference (WCNC)
Event place :
Milan, Italy
Event date :
24-03-2025 => 27-03-2025
Audience :
International
Main work title :
2025 IEEE Wireless Communications and Networking Conference, WCNC 2025
Publisher :
Institute of Electrical and Electronics Engineers Inc.
This work was supported by the National Natural Science Foundation of China for Outstanding Young Scholars under Grant 62322104, the Jiangsu Province Major Science and Technology Project under Grant BG2024005, the Natural Science Foundation of Jiangsu Province under Grant BK20231415, and the Fundamental Research Funds for the Central Universities under Grant 2242022k60007.
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