{"ID":2895839,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.08974","arxiv_id":"2507.08974","title":"Domain Adaptation-Enabled Realistic Map-Based Channel Estimation for MIMO-OFDM","abstract":"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.","short_abstract":"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 d...","url_abs":"https://arxiv.org/abs/2507.08974","url_pdf":"https://arxiv.org/pdf/2507.08974v1","authors":"[\"Thien Hieu Hoang\",\"Tri Nhu Do\",\"Georges Kaddoum\"]","published":"2025-07-11T19:05:26Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"eess.SY\"]","methods":"[]","has_code":false}