Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915567462514688 |
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| author | Böck, Benedikt Kasibovic, Amar Utschick, Wolfgang |
| author_facet | Böck, Benedikt Kasibovic, Amar Utschick, Wolfgang |
| contents | Standardized (link-level) channel models such as the 3GPP TDL and CDL models are frequently used to evaluate machine learning (ML)-based physical-layer methods. However, in this work, we argue that a link-level perspective incorporates limiting assumptions, causing unwanted distributional shifts or necessitating impractical online training. An additional drawback is that this perspective leads to (near-)Gaussian channel characteristics. Thus, ML-based models, trained on link-level channel data, do not outperform classical approaches for a variety of physical-layer applications. Particularly, we demonstrate the optimality of simple linear methods for channel compression, estimation, and modeling, revealing the unsuitability of link-level channel models for evaluating ML models. On the upside, adopting a scenario-level perspective offers a solution to this problem and unlocks the relative gains enabled by ML. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models Böck, Benedikt Kasibovic, Amar Utschick, Wolfgang Signal Processing Standardized (link-level) channel models such as the 3GPP TDL and CDL models are frequently used to evaluate machine learning (ML)-based physical-layer methods. However, in this work, we argue that a link-level perspective incorporates limiting assumptions, causing unwanted distributional shifts or necessitating impractical online training. An additional drawback is that this perspective leads to (near-)Gaussian channel characteristics. Thus, ML-based models, trained on link-level channel data, do not outperform classical approaches for a variety of physical-layer applications. Particularly, we demonstrate the optimality of simple linear methods for channel compression, estimation, and modeling, revealing the unsuitability of link-level channel models for evaluating ML models. On the upside, adopting a scenario-level perspective offers a solution to this problem and unlocks the relative gains enabled by ML. |
| title | Wireless Channel Modeling for Machine Learning -- A Critical View on Standardized Channel Models |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.12279 |