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MIMO Channel Estimation Using Transformer-Based Generative Diffusion Models

delete2026-04-02
delete0
PRE
AI
W
Wanyu Song
J
Jun Zhang
S
Shuo Yang
Y
Yong Li
Y
Yiyang Ni
石
石瑾 (Shi Jin)
DOI：10.1109/tvt.2026.3680259delete
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摘要

摘要

En 中文
近期研究表明，人工智能（AI）在信道估计方面具有巨大潜力。然而，大多数现有的基于AI的信道估计器依赖训练信道样本，导致在分布外数据上的估计精度降低。为解决此问题，我们提出了一种针对多输入多输出（MIMO）系统的鲁棒信道估计器，称为基于Transformer的扩散模型（TDM），该模型在无监督过程下训练且独立于信道分布。我们采用Transformer结构从含噪信道矩阵中精确去噪，并在推理阶段开发了一种后验采样方法用于信道重建。实验结果表明，TDM方案具有优越性和分布外鲁棒性。
Keyword:
MIMO channel estimation
generative AI
diffusion models
attention mechanism

期刊

IEEE Transactions on Vehicular Technology 封面图
IEEE Transactions on Vehicular Technology
IF:
7.1
论文数:
1.8W
被引数:
6.6W

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N
nanjing university of posts and telecommunications
学者数:
3.8K
论文数: 1.6K
被引数: 0
A
academy of network and communications of cetc
学者数:
4
论文数: 2
被引数: 0
Jiangsu Second Normal University 封面图
Jiangsu Second Normal University
学者数:
488
论文数: 437
被引数: 357
S
Southeast University
学者数:
2.1W
论文数: 8.6K
被引数: 480
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