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In this paper, we propose a rank-one tensor modeling approach that yields acompact representation of the optimum IRS phase-shift vector for reducing thefeedback overhead. The main idea consists of factorizing the IRS phase-shiftvector as a Kronecker product of smaller vectors, namely factors. The proposedphase-shift model allows the network to trade-off between achievable data rateand feedback reduction by controling the factorization parameters. Oursimulations show that the proposed phase-shift factorization drasticallyreduces the feedback overhead, while improving the data rate in some scenarios,compared to the state-of-the-art schemes.

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