Integrated sensing and communication (ISAC), and intelligent reflecting
surface (IRS) are envisioned as revolutionary technologies to enhance spectral
and energy efficiencies for next wireless system generations. For the first
time, this paper focuses on the channel estimation problem in an IRS-assisted
ISAC system. This problem is challenging due to the lack of signal processing
capacity in passive IRS, as well as the presence of mutual interference between
sensing and communication (SAC) signals in ISAC systems. A three-stage approach
is proposed to decouple the estimation problem into sub-ones, including the
estimation of the direct SAC channels in the first stage, reflected
communication channel in the second stage, and reflected sensing channel in the
third stage. The proposed three-stage approach is based on a deep-learning
framework, which involves two different convolutional neural network (CNN)
architectures to estimate the channels at the full-duplex ISAC base station.
Furthermore, two types of input-output pairs to train the CNNs are carefully
designed, which affect the estimation performance under various signal-to-noise
ratio conditions and system parameters. Simulation results validate the
superiority of the proposed estimation approach compared to the least-squares
baseline scheme, and its computational complexity is also analyzed.

本文首次聚焦在辅助智能反射表面的综合感知与通信系统中的信道估计问题，提出了基于深度学习框架的三阶段方法，其中包括直接感知与通信信道、反射通信信道、以及反射感知信道的估计，该方法在全双工综合感知与通信基站的卷积神经网络架构上进行建模，设计了两种类型的输入输出对来训练卷积神经网络，模拟结果验证了该估计方法相对于最小二乘基准方案的优越性，并且分析了其计算复杂性。