发表机构
Songshan Laboratory; Information Engineering University, PLA Cyberspace Force（松山湖实验室; 信息工程大学，解放军网络空间部队）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种仅利用接收信号功率样本的盲IRS波束成形方法，通过构建最小二乘问题估计信道相位差，获得最优IRS配置，在量化电平数大于2时优于CSM方法且复杂度相同。
AI 中文摘要
本文提出了一种新颖的盲波束成形策略，用于在智能反射面（IRS）辅助下增强信号，该方法仅使用接收信号功率的样本。与现有的基于条件采样均值（CSM）的方法不同，后者仅依赖于比较CSM值，所提出的算法通过构建最小二乘问题，充分利用了采样数据中的信息。这使得能够估计IRS反射信道与直达信道之间的相位差，最终得出最优的IRS相位配置。仿真结果验证了，通过充分利用收集到的数据，所提出的方案优于基于CSM的方法，尤其是在IRS相位量化电平数大于2时，同时具有相同的计算复杂度。
英文摘要
This paper proposes a novel blind beamforming strategy for signal enhancement aided by an intelligent reflecting surface (IRS), using only samples of the received signal power. Unlike existing conditional sampling mean (CSM) based approaches, which rely solely on comparing the CSM values, the proposed algorithm fully exploits the information in the sampled data by constructing a least squares problem. This enables the estimation of the phase difference between the IRS-reflected channel and the direct channel, ultimately yielding the optimal IRS phase configuration. The simulation results verify that, by fully leveraging the collected data, the proposed scheme outperforms the CSM-based method, especially when the number of IRS phase quantization levels is larger than 2, while incurring the same level of computational complexity.
Comments6 pages, 4 figures, accepted by 2026 IEEE/CIC International Conference on Communications in China (ICCC)
