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

TL;DR

This paper introduces a rank-one tensor model for IRS phase-shift feedback that significantly reduces overhead and can enhance data rates by balancing feedback reduction and performance.

Contribution

It presents a novel tensor-based factorization method for IRS phase-shift vectors, enabling efficient feedback overhead management and improved system performance.

Findings

Reduces feedback overhead significantly

Improves data rate in certain scenarios

Offers a trade-off between feedback and data rate

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.