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arXiv · 2205.12024

IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation

Bruno Sokal·Paulo R. B. Gomes·André L. F. de Almeida·Behrooz Makki·Gabor Fodor
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Abstract

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. The main idea consists of factorizing the IRS phase-shift vector as a Kronecker product of smaller vectors, namely factors. The proposed phase-shift model allows the network to trade-off between achievable data rate and feedback reduction by controling the factorization parameters. Our simulations show that the proposed phase-shift factorization drastically reduces the feedback overhead, while improving the data rate in some scenarios, compared to the state-of-the-art schemes.

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Bruno Sokal, Paulo R. B. Gomes, André L. F. de Almeida, Behrooz Makki, Gabor Fodor. 2022-05-24. IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation. https://arxiv.org/abs/2205.12024

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