发表机构
Songshan Laboratory; PLA Strategic Support Force Information Engineering University（松山湖实验室; 中国人民解放军战略支援部队信息工程大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对IRS辅助通信中CSI获取困难的问题，提出结合PPI算法与CSM方法的盲干扰抑制策略，仅依赖接收信号功率配置IRS，理论分析与仿真验证其能将干扰降至噪声水平，显著提升SINR。
AI 中文摘要
智能反射面（IRS）在无线通信系统中用于抑制干扰的应用近来引起了大量研究关注。现有的大多数方法依赖于完全或部分信道状态信息（CSI）来配置IRS。然而，在IRS辅助系统中获取准确的CSI涉及相当大的导频开销，并引入不可忽略的延迟。在强干扰条件下，这一问题进一步加剧，因为干扰源通常是非协作的，使得CSI获取更加困难。因此，现有的依赖CSI的干扰抑制方法在实践中难以部署。为解决这些限制，我们提出了一种新颖的盲干扰抑制策略，该策略结合了比例相位反转（PPI）算法与条件样本均值（CSM）方法。所提方法仅利用接收信号功率来确定IRS配置，无需任何先验CSI。我们通过推导所提方案的理论性能进行了全面的性能评估，并通过数值仿真进行了验证。此外，在不同参数设置下的仿真结果表明，所提盲干扰抑制方案将干扰功率降低至噪声水平，从而实现了显著的信号与干扰加噪声比（SINR）提升，并优于现有基准方案。
英文摘要
The application of intelligent reflecting surfaces (IRSs) to suppress interference in wireless communication systems has recently attracted significant research attention. Most existing approaches rely on complete or partial channel state information (CSI) to configure the IRS. However, acquiring accurate CSI in IRS-assisted systems involves considerable pilot overhead and introduces non-negligible delays. This issue is further exacerbated under strong interference conditions, where interfering sources are typically non-cooperative, making CSI acquisition even more challenging. As a result, existing CSI-dependent interference suppression methods become difficult to deploy in practice. To address these limitations, we propose a novel blind interference suppression strategy that combines a proportional phase-inversion (PPI) algorithm with the conditional sample mean (CSM) method. The proposed approach determines the IRS configuration using only the received signal power, without requiring any prior CSI. We conduct a comprehensive performance evaluation by deriving the theoretical performance of the proposed scheme, which is subsequently verified through numerical simulations. Furthermore, simulation results across various parameter settings demonstrate that the proposed blind interference suppression scheme reduces the interference power to the level of noise, thereby achieving a marked signal-to-interference-plus-noise ratio (SINR) improvement and outperforms existing benchmark schemes.
Comments13 pages, 13 figures, accepted by IEEE Internet of Things Journal
