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.
Cite
@article{arxiv.2205.12024,
title = {IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation},
author = {Bruno Sokal and Paulo R. B. Gomes and André L. F. de Almeida and Behrooz Makki and Gabor Fodor},
journal= {arXiv preprint arXiv:2205.12024},
year = {2022}
}