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Found 7 papers, 0 papers with code
Tensor-based modeling/estimation of static channels in IRS-assisted MIMO systems
This paper proposes a tensor-based parametric modeling and estimation framework in multiple-input multiple-output (MIMO) systems assisted by intelligent reflecting surfaces (IRSs).
Tensor-Based Channel Estimation and Data-Aided Tracking in IRS-Assisted MIMO Systems
This letter proposes a model for symbol detection in the uplink of IRS-assisted networks in the presence of channel aging.
Two-Dimensional Channel Parameter Estimation for IRS-Assisted Networks
The tradeoffs between performance and complexity offered by the proposed methods are discussed and numerically assessed.
Tensor-Based High-Resolution Channel Estimation for RIS-Assisted Communications
This letter proposes a high-resolution channel estimation for reconfigurable intelligent surface (RIS)-assisted communication networks.
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.
