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.

本论文提出了一种基于模型推导和数据驱动深度学习相结合的混合驱动学习体系结构，用于联合估计和学习通道特性，在智能反射平面辅助的多用户多输入单输出系统中进行上行宽带通道估计，采用残余学习的近似消息传递作为模型驱动网络，数据驱动网络中使用降噪和注意力网络来共同学习空间和频率特征。数值结果表明，所提出的混合驱动信道估计方法在智能反射平面辅助系统中显著优于现有的基于深度学习的方案，并在多层数字设计中实现了网络复杂度的降低。

智能反射面辅助毫米波通信信道估计中的混合驱动学习

Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting  Surface Aided Millimeter Wave Communications

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems
to achieve both array and multiplexing gain. The design of the hybrid precoders
and combiners, though, is usually based on knowledge of the channel. Prior work
on mmWave channel estimation with hybrid architectures focused on narrowband
channels. Since mmWave systems will be wideband with frequency selectivity, it
is vital to develop channel estimation solutions for hybrid architectures based
wideband mmWave systems. In this paper, we develop a sparse formulation and
compressed sensing based solutions for the wideband mmWave channel estimation
problem for hybrid architectures. First, we leverage the sparse structure of
the frequency selective mmWave channels and formulate the channel estimation
problem as a sparse recovery in both time and frequency domains. Then, we
propose explicit channel estimation techniques for purely time or frequency
domains and for combined time/frequency domains. Our solutions are suitable for
both SC-FDE and OFDM systems. Simulation results show that the proposed
solutions achieve good channel estimation quality, while requiring small
training overhead. Leveraging the hybrid architecture at the transceivers gives
further improvement in estimation error performance and achievable rates.

本文利用稀疏建模和压缩感知提出了一种广域毫米波信道估计方法，能够应用于基于混合模拟 / 数字前向器和组合时间 / 频率域系统。仿真实验表明，在利用收发机混合结构的情况下，该方案在保证通道估计质量的情况下，具有更好的性能和更小的训练开销。