标题： 基于域适应的MIMO-OFDM现实地图信道估计 显示英文标题

标题： Domain Adaptation-Enabled Realistic Map-Based Channel Estimation for MIMO-OFDM

摘要： 准确的信道估计对于提高无线通信中的信号处理性能至关重要。 然而，传统的基于模型的方法在动态环境中经常遇到困难。 同样，替代的机器学习方法由于信道特性的变化，在不同数据集之间通常缺乏泛化能力。 为了解决这个问题，在本研究中，我们提出了一种新的领域自适应方法，以弥合准静态信道模型（QSCM）和基于地图的信道模型（MBCM）之间的差距。 具体来说，我们首先提出了一种考虑实际信道仿真的信道估计流程来训练我们的基础模型。 然后，我们提出了领域自适应方法来解决估计问题。 使用基于仿真的训练来减少在实际无线环境中有效应用所需的数据量，我们发现所提出的策略即使在有限的真实信道信息下也能实现稳健的模型性能。 显示英文摘要

摘要： Accurate channel estimation is crucial for the improvement of signal processing performance in wireless communications. However, traditional model-based methods frequently experience difficulties in dynamic environments. Similarly, alternative machine-learning approaches typically lack generalization across different datasets due to variations in channel characteristics. To address this issue, in this study, we propose a novel domain adaptation approach to bridge the gap between the quasi-static channel model (QSCM) and the map-based channel model (MBCM). Specifically, we first proposed a channel estimation pipeline that takes into account realistic channel simulation to train our foundation model. Then, we proposed domain adaptation methods to address the estimation problem. Using simulation-based training to reduce data requirements for effective application in practical wireless environments, we find that the proposed strategy enables robust model performance, even with limited true channel information.