Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility
Link to paper (to be updated): [TBC]
Authors: Sina Beyraghi, Masoud Sadeghian, Firdous Bin Ismail, Angel Lozano, Paul Almasan, and Giovanni Geraci
Contact: Sina Beyraghi (mohammadsina.beyraghi@telefonica.com)
TBC.
GenAI_Channel_Modeling/
├── cDDIM_and_cFMM/ ← Generative model training & inference
├── Channel_Sionna_RT_Github/ ← Dataset generation with Sionna RT ray tracing
├── CRNet_Github/ ← Downstream task: CSI compression
├── DLGF_Github/ ← Downstream task: beam alignment
└── Effective_Rank_Github/ ← Evaluation metric: effective rank analysis
cDDIM_and_cFMM/ — Generative Models
Training and inference scripts for the two generative models proposed in the paper:
- cDDIM — conditional Denoising Diffusion Implicit Model
- cFMM — conditional Flow Matching Model
Both models support LoS-only and mixed LoS+NLoS propagation scenarios. cDDIM must be trained before cFMM to ensure consistent train/val/test dataset splits.
Channel_Sionna_RT_Github/ — Dataset Generation
Scripts to generate large-scale MIMO channel datasets from 3D radio environments using NVIDIA Sionna RT. Produces .npz files containing OFDM channel tensors and optional ray-tracing parameters (AoA, AoD, delays, CIR). These are the datasets used to train the generative models.
CRNet_Github/ — Downstream Task: CSI Compression
Extends CRNet to study how synthetically generated channels (from cDDIM, cFMM, or 3GPP stochastic models) can augment limited real channel measurements for CSI feedback compression training. Sweeps over the number of real training samples to quantify the benefit of synthetic augmentation.
DLGF_Github/ — Downstream Task: Beam Alignment
Extends the DL-GF grid-free beam alignment framework to support ray-tracing, 3GPP stochastic, and generative-model (cDDIM/cFMM) channel datasets. Includes combined pretraining-on-synthetic + finetuning-on-real training strategies for UPA antenna configurations.
Effective_Rank_Github/ — Evaluation: Effective Rank
Tools for computing and visualising the effective rank of UPA MIMO channels in beamspace, and comparing generative model outputs against a ground-truth reference using the Wasserstein-1 distance.
Open a terminal and execute the following commands to download the github repository.
git clone https://github.com/Telefonica-Scientific-Research/GenAI_Channel_Modeling
cd GenAI_Channel_Modeling - Code repository: GenAI_Channel_Modeling
- Datasets: GenAI_Channel_Modeling_Datasets
@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}
}