AI 中文总结
针对互素HRIS辅助MIMO系统，本文提出低复杂度信道参数估计方案，结合Khatri-Rao与Kronecker分解、空间平滑及Root-MUSIC，实现稳健估计，性能鲁棒。
AI 中文摘要
本文提出了一种用于混合可重构智能表面（HRIS）辅助的上行MIMO系统的参数估计方案。为降低硬件复杂度，该HRIS采用少量按稀疏互素几何排列的有源元件进行局部感知，其余元件被动反射信号。这种同时感知与反射的设计，相比全被动架构显著提升了精度。该方法利用Khatri-Rao与Kronecker分解，在基站处有效解耦级联信道；此外，空间平滑技术解决了互素阵列的秩亏问题，可通过Root-MUSIC实现稳健的角度提取。仿真结果表明，该方案在各类场景下均具备极强的估计性能鲁棒性。
英文摘要
This paper proposes a parameter estimation scheme for uplink MIMO systems assisted by a Hybrid Reconfigurable Intelligent Surface (HRIS). To reduce hardware complexity, the HRIS employs a small number of active elements arranged in a sparse coprime geometry for local sensing, while the remaining elements passively reflect signals. This simultaneous sensing and reflection significantly improves accuracy over fully passive architectures. The method leverages Khatri-Rao and Kronecker factorizations to efficiently decouple the cascaded channel at the base station. Furthermore, spatial smoothing resolves the coprime array rank deficiency, enabling robust angular extraction via Root-MUSIC. Simulations demonstrate highly resilient estimation performance across scenarios.
