Statistical CSI-Based Distributed Precoding for Multi-Satellite Cooperative Transmission

This paper studies the distributed precoding design for multi-satellite massive MIMO transmission. We first conduct a detailed analysis of the transceiver process, examining the effects of delay and Doppler compensation errors and emphasizing the nearly independent nature of inter-satellite interference. Based on the derived signal model, an approximate expected sum rate maximization problem is formulated, incorporating statistical channel state information and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation cannot hold equivalence in the considered scenario. To address this, we propose a modified WMMSE formulation leveraging channel covariance matrix decomposition. By exploiting channel characteristics, a low-complexity decomposition method is then developed, accompanied by an efficient algorithm. Simulation results validate the effectiveness and robustness of the proposed method in some practical simulated scenarios.

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Publication Details

Published
2025-10-19
DOI
https://doi.org/10.1109/vtc2025-fall65116.2025.11310514
Primary Topic
Satellite Communication Systems
Type
article
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Statistical CSI-Based Distributed Precoding for Multi-Satellite Cooperative Transmission

WenJin Wang, Yafei Wang, Konstantinos Ntontin, Symeon Chatzinotas et al.
Satellite Communication Systems
article

Statistical CSI-Based Distributed Precoding for Multi-Satellite Cooperative Transmission

WenJin Wang, Yafei Wang, Konstantinos Ntontin, Symeon Chatzinotas, Vu Nguyen HA, Björn Ottersten
article en

Abstract

This paper studies the distributed precoding design for multi-satellite massive MIMO transmission. We first conduct a detailed analysis of the transceiver process, examining the effects of delay and Doppler compensation errors and emphasizing the nearly independent nature of inter-satellite interference. Based on the derived signal model, an approximate expected sum rate maximization problem is formulated, incorporating statistical channel state information and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation cannot hold equivalence in the considered scenario. To address this, we propose a modified WMMSE formulation leveraging channel covariance matrix decomposition. By exploiting channel characteristics, a low-complexity decomposition method is then developed, accompanied by an efficient algorithm. Simulation results validate the effectiveness and robustness of the proposed method in some practical simulated scenarios.

University of Luxembourg (LU), Southeast University (CN)
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Openalex Percentile: Top 19%
Satellite Communication Systems
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