Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information. In this paper, we address the problem of uplink wideband channel estimation for IRS aided multiuser multiple-input single-output (MISO) systems with hybrid architectures. Combining the structure of model driven and data driven deep learning approaches, a hybrid driven learning architecture is devised for joint estimation and learning the properties of the channels. For a passive IRS aided system, we propose a residual learned approximate message passing as a model driven network. A denoising and attention network in the data driven network is used to jointly learn spatial and frequency features. Furthermore, we design a flexible hybrid driven network in a hybrid passive and active IRS aided system. Specifically, the depthwise separable convolution is applied to the data driven network, leading to less network complexity and fewer parameters at the IRS side. Numerical results indicate that in both systems, the proposed hybrid driven channel estimation methods significantly outperform existing deep learning-based schemes and effectively reduce the pilot overhead by about 60% in IRS aided systems.

翻译：智能反射面（IRS）已被提出用于毫米波（mmWave）和太赫兹（THz）系统，以同时实现覆盖与容量增强，而混合预编码器、合并器及IRS的设计通常依赖于信道状态信息。本文针对采用混合架构的IRS辅助多用户多输入单输出（MISO）系统，研究了上行宽带信道估计问题。通过融合模型驱动与数据驱动深度学习方法的架构，设计了一种混合驱动学习框架，用于联合估计并学习信道特性。针对无源IRS辅助系统，我们提出了一种残差学习近似消息传递网络作为模型驱动网络，并在数据驱动网络中利用去噪与注意力网络联合学习空间与频率特征。此外，针对无源与有源混合IRS辅助系统，我们设计了一种灵活混合驱动网络，其中在数据驱动网络中应用深度可分离卷积，从而降低IRS侧的网络复杂度与参数量。数值结果表明，在两种系统中，所提出的混合驱动信道估计方法显著优于现有基于深度学习的方案，并在IRS辅助系统中有效将导频开销降低约60%。