Low-Complexity Channel Parameter Estimation for Coprime HRIS-Assisted MIMO Systems
Abstract
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...
Description / Details
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.
Source: arXiv:2608.13436v1 - http://arxiv.org/abs/2608.13436v1 PDF: https://arxiv.org/pdf/2608.13436v1 Original Link: http://arxiv.org/abs/2608.13436v1
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Aug 14, 2026
Chemical Engineering
Engineering
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