IRS辅助无线系统中的盲干扰抑制：一种统计信道比估计方法
Blind Interference Suppression in IRS-Aided Wireless Systems: A Statistical Channel Ratio Estimation Approach
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中文总结 AI 辅助
本文提出一种无需CSI的盲干扰抑制框架，通过估计IRS反射路径与直接干扰链路的信道比，并设计低复杂度相位优化算法，将强干扰抑制到接近无干扰上界，适用于竞争频谱环境。
中文摘要 AI 辅助
本文研究了在智能反射面（IRS）辅助的无线链路中，在没有任何信道状态信息（CSI）或干扰源协作的情况下抑制非协作干扰的问题。我们提出了一种完全盲的框架，仅依赖于接收信号功率测量。一个关键洞察是，消除聚合干扰信道仅需要IRS反射路径与直接干扰链路之间的复数比，而非绝对CSI。我们开发了一种新颖的估计算法，仅利用在随机IRS配置下收集的功率样本即可获得这些信道比的无偏估计。理论上，我们证明了当离散相位级数$K\geq 3$时，无偏估计是可行的，并建立了相位偏移和幅度比估计的克拉美-罗下界（CRLBs），从而提供了设计指导。基于估计的比值，我们提出了两种低复杂度的IRS相位优化算法：一种一次性贪心方法和一种迭代变体，后者可减轻弱反射元件引起的误差传播。仿真表明，所提出的方案可以将强干扰抑制到接近无干扰上界的几dB以内，为竞争频谱环境中的鲁棒无线通信提供了一种实用的、无需CSI的解决方案。
英文摘要
This paper addresses the problem of suppressing non-cooperative interference in intelligent reflecting surface (IRS)-aided wireless links without any channel state information (CSI) or cooperation from the interferer. We propose a fully blind framework that relies solely on received signal power measurements. A key insight is that nulling the aggregate interference channel requires only the complex ratios between the IRS-reflected paths and the direct interference link, rather than absolute CSI. We develop a novel estimation algorithm that obtains unbiased estimates of these channel ratios using only power samples collected under random IRS configurations. Theoretically, we prove that unbiased estimation is feasible when the number of discrete phase levels $K\geq 3$, and establish the Cramer-Rao lower bounds (CRLBs) for both the phase offset and amplitude ratio estimates, thus providing design guidance. Based on the estimated ratios, we propose two low-complexity IRS phase optimization algorithms: a one-shot greedy method and an iterative variant that mitigates error propagation from weakly reflecting elements. Simulations demonstrate that the proposed schemes can suppress strong interference to within a few dB of the interference-free upper bound, offering a practical, CSI-free solution for robust wireless communications in contested spectral environments.
发表机构
- Songshan Laboratory（嵩山实验室）
- Information Engineering University（信息工程大学）
机构由 AI 辅助整理，请以论文原文为准。
