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arXiv 2505.08038eess.SP
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基于统计信道状态信息的分布式预编码设计用于OFDM协作多卫星系统

Statistical CSI-Based Distributed Precoding Design for OFDM-Cooperative Multi-Satellite Systems

National Mobile Communications Research Laboratory, Southeast University(东南大学国家移动通信重点实验室);Purple Mountain Laboratories(紫金山实验室);University of Luxembourg(卢森堡大学);Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg(卢森堡大学安全、可靠与信任跨学科中心);Shanghai Satellite Network Research Institute Co., Ltd.(上海卫星网络研究院有限公司);Shanghai Key Laboratory of Satellite Network(上海市卫星网络重点实验室);State Key Laboratory of Satellite Network(卫星网络国家重点实验室)

Yafei Wang, Vu Nguyen Ha, Konstantinos Ntontin, Hong Yan, Wenjin Wang, Symeon Chatzinotas, Björn Ottersten

AI总结:

本文提出了一种基于统计信道状态信息的分布式预编码设计方法,通过引入等变深度学习框架提升多卫星系统传输性能。

摘要:

本文研究了多卫星大规模MIMO传输的分布式预编码设计。我们首先对收发机模型进行了详细分析,在其中引入了时延和多普勒预补偿以确保相干传输。在此分析中,我们考察了预补偿误差对传输模型的影响,强调了星间干扰的近独立性,并最终推导出接收信号模型。基于此信号模型,我们提出了一个近似预期速率最大化问题,该问题考虑了统计信道状态信息(sCSI)和补偿误差。不同于传统方法将此类问题转换为加权最小均方误差(WMMSE)最小化,我们证明这种转换在所考虑的场景中无法保持等价性。为此,我们引入了基于通道协方差矩阵分解的WMMSE(CDWMMSE)公式。利用通道特性,我们开发了一种低复杂度的分解方法并提出了一种优化算法。为进一步降低计算复杂度,我们引入了一种模型驱动的可扩展深度学习(DL)方法,该方法利用了从sCSI到最优闭式解中未知变量的映射的等变性,通过新颖的密集Transformer网络和缩放不变损失函数设计来提升性能。仿真结果验证了所提方法在某些实际场景中的有效性与鲁棒性。我们还展示了DL方法可以适应用户和卫星数量变化的动态环境。

英文摘要:

This paper investigates the design of distributed precoding for multi-satellite massive MIMO transmissions. We first conduct a detailed analysis of the transceiver model, in which delay and Doppler precompensation is introduced to ensure coherent transmission. In this analysis, we examine the impact of precompensation errors on the transmission model, emphasize the near-independence of inter-satellite interference, and ultimately derive the received signal model. Based on such signal model, we formulate an approximate expected rate maximization problem that considers both statistical channel state information (sCSI) and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation fails to maintain equivalence in the considered scenario. To address this, we introduce an equivalent covariance decomposition-based WMMSE (CDWMMSE) formulation derived based on channel covariance matrix decomposition. Taking advantage of the channel characteristics, we develop a low-complexity decomposition method and propose an optimization algorithm. To further reduce computational complexity, we introduce a model-driven scalable deep learning (DL) approach that leverages the equivariance of the mapping from sCSI to the unknown variables in the optimal closed-form solution, enhancing performance through novel dense Transformer network and scaling-invariant loss function design. Simulation results validate the effectiveness and robustness of the proposed method in some practical scenarios. We also demonstrate that the DL approach can adapt to dynamic settings with varying numbers of users and satellites.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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