  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.
