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Reducing the Control Overhead of Intelligent Reconfigurable Surfaces Via a Tensor-Based Low-Rank Factorization Approach
We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links.
Tensor-Based Channel Estimation for RIS-Assisted Networks Operating Under Imperfections
Furthermore, the identifiability and computational complexity of the proposed algorithm are analyzed, and we study the effect of different imperfections on the channel estimation quality.
IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation
In this paper, we propose a rank-one tensor modeling approach that yields a compact representation of the optimum IRS phase-shift vector for reducing the feedback overhead.
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