Abstract added.

README.md

@@ -2,13 +2,15 @@
 license: mit
 ---
 
-# Dataset Card — GenAI Channel Modeling Datasets
+# Dataset Card — Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility
 
-Ray-traced site-specific MIMO channel datasets used in the paper:
+#### Link to paper (to be updated): [[TBC](TBC)]
+#### Authors: Sina Beyraghi, Masoud Sadeghian, Firdous Bin Ismail, Angel Lozano, Paul Almasan, and Giovanni Geraci
 
-> **Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility**  
-> Sina Beyraghi, Masoud Sadeghian, Firdous Bin Ismail, Angel Lozano, Paul Almasan, and Giovanni Geraci  
-> [[arXiv:2510.10190](https://arxiv.org/abs/2510.10190)]
+Contact: Sina Beyraghi (<mohammadsina.beyraghi@telefonica.com>), Paul Almasan 
+
+## Abstract
+This paper explores the use of generative models to synthesize high-quality, site-specific multiple-input multiple-output (MIMO) channel data, addressing the high cost of the extensive measurement campaigns required to acquire real-world data for AI-native wireless networks. Two location-conditioned generative paradigms are compared: a conditional denoising diffusion implicit model (cDDIM), and a conditional flow matching model (cFMM). Both these models generate MIMO channel matrices conditioned on user coordinates, to preserve the spatial structure of the deployment site. The approaches are evaluated across three dimensions: statistical fidelity (including beam consistency and effective rank), generation efficiency, and utility in downstream tasks such as channel-state information compression and beam alignment. Results across diverse propagation scenarios (28 GHz and 3.5 GHz, both line-of-sight and non-line-of-sight) demonstrate that both models accurately capture site-specific characteristics, even when trained on scarce ground-truth data. Notably, cFMM achieves a quality comparable to cDDIM with roughly an order of magnitude less inference time. Augmenting scarce site-specific datasets with these synthetic channels yields hefty performance gains in downstream physical layer tasks compared to using scarce data alone or stochastic channels.
 
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 ## Citation
 
-```bibtex
-@article{beyraghi2025sitespecific,
-  title   = {Site-Specific MIMO Channel Generation via Diffusion and Flow Matching:
-             Fidelity, Efficiency, and Downstream Utility},
-  author  = {Beyraghi, Sina and Sadeghian, Masoud and Bin Ismail, Firdous and
-             Lozano, Angel and Almasan, Paul and Geraci, Giovanni},
-  journal = {arXiv preprint arXiv:2510.10190},
-  year    = {2025}
-}
-```
+TBC.