This work proposes a novel channel estimator based on diffusion models (DMs),
one of the currently top-rated generative models. Contrary to related works
utilizing generative priors, a lightweight convolutional neural network (CNN)
with positional embedding of the signal-to-noise ratio (SNR) information is
designed by learning the channel distribution in the sparse angular domain.
Combined with an estimation strategy that avoids stochastic resampling and
truncates reverse diffusion steps that account for lower SNR than the given
pilot observation, the resulting DM estimator has both low complexity and
memory overhead. Numerical results exhibit better performance than
state-of-the-art channel estimators utilizing generative priors.

基于扩散模型的信道估计器结合了轻量级卷积神经网络和位置嵌入技术，通过学习稀疏角度域中的信噪比信息，避免了随机重采样和截断逆扩散步骤来提高估计性能。与利用生成先验的最新信道估计器相比，该方法具有更低的复杂性和内存开销。

基于扩散的低复杂度 MIMO 信道估计的生成先验模型

Diffusion-based Generative Prior for Low-Complexity MIMO Channel  Estimation

In this paper, we propose a data-driven deep learning (DL) approach to
jointly design the pilot signals and channel estimator for wideband massive
multiple-input multiple-output (MIMO) systems. By exploiting the angular-domain
compressibility of massive MIMO channels, the conceived DL framework can
reliably reconstruct the high-dimensional channels from the under-determined
measurements. Specifically, we design an end-to-end deep neural network (DNN)
architecture composed of dimensionality reduction network and reconstruction
network to respectively mimic the pilot signals and channel estimator, which
can be acquired by data-driven deep learning. For the dimensionality reduction
network, we design a fully-connected layer by compressing the high-dimensional
massive MIMO channel vector as input to low-dimensional received measurements,
where the weights are regarded as the pilot signals. For the reconstruction
network, we design a fully-connected layer followed by multiple cascaded
convolutional layers, which will reconstruct the high-dimensional channel as
the output. By defining the mean square error between input and output as loss
function, we leverage Adam algorithm to train the end-to-end DNN aforementioned
with extensive channel samples. In this way, both the pilot signals and channel
estimator can be simultaneously obtained. The simulation results demonstrate
that the superiority of the proposed solution over state-of-the-art compressive
sensing approaches.

本文提出了一种基于数据驱动的深度学习方法，用于联合设计宽带大规模多输入多输出系统的导频信号和信道估计器。通过利用大规模 MIMO 信道的角度域压缩性，该方法可以从欠定的测量中可靠地重构高维信道，模拟结果表明，该方法优于最先进的压缩感知方法。

基于数据驱动的深度学习设计海量 MIMO 飞行员和信道估计器

Data-Driven Deep Learning to Design Pilot and Channel Estimator For  Massive MIMO

The research about deep learning application for physical layer has been
received much attention in recent years. In this paper, we propose a Deep
Learning (DL) based channel estimator under time varying Rayleigh fading
channel. We build up, train and test the channel estimator using Neural Network
(NN). The proposed DL-based estimator can dynamically track the channel status
without any prior knowledge about the channel model and statistic
characteristics. The simulation results show the proposed NN estimator has
better Mean Square Error (MSE) performance compared with the traditional
algorithms and some other DL-based architectures. Furthermore, the proposed
DL-based estimator also shows its robustness with the different pilot
densities.

本研究提出了一种基于深度学习和神经网络的物理层信道估计器，可以在没有先验知识的情况下动态追踪信道状态，并且与传统算法和其他深度学习架构相比，具有更好的均方误差性能和鲁棒性。