论文标题
IRS相移反馈的开销模型基于排名一张量近似
IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation
论文作者
论文摘要
在本文中，我们提出了一种排名一的张量建模方法，该方法产生了最佳IRS相移矢量的紧凑表示，以减少反馈开销。主要思想包括将IRS相移矢量分解为较小矢量的Kronecker产品，即因素。提出的相移模型允许网络通过控制分解参数来折衷可实现的数据速率和降低反馈。我们的模拟表明，与最先进的方案相比，提出的相移分解大大降低了反馈开销，同时在某些情况下提高了数据速率。
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.
