One main challenge for implementing intelligent reflecting surface (IRS) aided communications lies in the difficulty to obtain the channel knowledge for the base station (BS)-IRS-user cascaded links, which is needed to design high-performance IRS reflection in practice. Traditional methods for estimating IRS cascaded channels are usually based on the additional pilot signals received at the BS/users, which increase the system training overhead and also may not be compatible with the current communication protocols. To tackle this challenge, we propose in this paper a new single-layer neural network (NN)-enabled IRS channel estimation method based on only the knowledge of users' individual received signal power measurements corresponding to different IRS random training reflections, which are easily accessible in current wireless systems. To evaluate the effectiveness of the proposed channel estimation method, we design the IRS reflection for data transmission based on the estimated cascaded channels in an IRS-aided multiuser communication system. Numerical results show that the proposed IRS channel estimation and reflection design can significantly improve the minimum received signal-to-noise ratio (SNR) among all users, as compared to existing power measurement based designs.

翻译：智能反射面（IRS）辅助通信的主要挑战之一在于难以获取基站（BS）-IRS-用户级联链路的信道知识，而这是实际中设计高性能IRS反射所必需的。传统的IRS级联信道估计方法通常依赖于在基站/用户处接收的额外导频信号，这不仅增加了系统训练开销，还可能无法兼容当前通信协议。为应对这一挑战，本文提出了一种基于单层神经网络（NN）的新型IRS信道估计方法，仅需利用用户各自在不同IRS随机训练反射下对应的接收信号功率测量值——这些测量值在当前无线系统中易于获取。为评估所提信道估计方法的有效性，我们在IRS辅助多用户通信系统中基于估计的级联信道设计了数据传输的IRS反射。数值结果表明，与现有基于功率测量的方案相比，所提IRS信道估计与反射设计方案能显著提升所有用户中的最小接收信噪比（SNR）。