AI 中文总结
针对动态NTN系统高信道估计挑战，提出基于RMT尖峰协方差模型的半盲信道估计框架，仿真显示其性能优于传统估计器。
AI 中文摘要
半盲信道估计在大规模无线系统中能在导频开销与估计精度间实现理想权衡，但在非地面网络（NTN）这类高动态环境中，快速时变信道与高系统维度会因采样噪声严重降低传统基于协方差估计器的性能，可靠信道获取极具挑战。本文针对NTN系统中的多用户上行链路，提出一种鲁棒半盲信道估计框架，引入最优正则化最小二乘公式以平衡基于训练的信息与盲子空间结构。通过在随机矩阵理论（RMT）框架内利用尖峰协方差模型，推导了所得信道均方误差的闭式表征，并获得最优正则化参数的解析易处理设计。该估计器计算高效，尤其适用于高维场景。基于现实第三代合作伙伴计划（3GPP）NTN信道模型的仿真结果显示，其相较传统半盲及基于训练的估计器，性能有显著提升。
英文摘要
Semi-blind channel estimation offers an attractive tradeoff between pilot overhead and estimation accuracy in large-scale wireless systems. However, reliable channel acquisition becomes particularly challenging in highly dynamic environments such as non-terrestrial networks (NTNs), where rapidly varying channels and high system dimensionality significantly degrade the performance of conventional covariance-based estimators due to sampling noise. In this paper, we propose a robust semi-blind channel estimation framework for multi-user uplink systems operating in NTN systems. The proposed approach introduces an optimally regularized least-squares formulation that balances training-based information and blind subspace structure. By exploiting the spiked covariance model within a random matrix theory (RMT) framework, we derive a closed-form characterization of the resulting channel mean-squared error and obtain an analytically tractable design of the optimal regularization parameter. The resulting estimator is computationally efficient and particularly well suited to high-dimensional regimes. Simulation results under realistic Third Generation Partnership Project (3GPP) NTN channel models demonstrate substantial performance improvements over conventional semi-blind and training-based estimators.
