The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing powermeasurement-based IRS reflection designs in wideband channels.

翻译：智能反射面（IRS）的无源且频率平坦的反射特性，以及高维的IRS反射信道，对高效的信道估计提出了重大挑战，尤其是在具有显著多径信道延迟扩展的宽带通信系统中。为应对这些挑战，我们提出了一种新颖的神经网络（NN）赋能框架，用于宽带正交频分复用（OFDM）系统中的IRS信道自相关矩阵估计。该框架仅依赖于现有宽带通信系统中用户端易于获取的参考信号接收功率（RSRP）测量值，无需额外的导频传输。基于信道自相关矩阵的估计结果，我们优化了IRS的无源反射配置，以最大化OFDM系统中所有子载波上的用户平均接收信噪比（SNR）。数值仿真结果表明，所提算法在宽带信道中显著优于现有的基于功率测量的IRS反射设计方案。