# Channel Modeling for FR3 Upper Mid-band via Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2404.17069 · HTML: https://mlsanity.com/paper/2404.17069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [Millimeter Wave Wireless Assisted Robot Navigation with Link State Classification](https://mlsanity.com/paper/2110.14789/) (2021)
- [Millimeter Wave Channel Modeling via Generative Neural Networks](https://mlsanity.com/paper/2008.11006/) (2020)
- [Cellular Wireless Networks in the Upper Mid-Band](https://mlsanity.com/paper/2309.03038/) (2023)
- [Multi-Frequency Channel Modeling for Millimeter Wave and THz Wireless Communication via Generative Adversarial Networks](https://mlsanity.com/paper/2212.11858/) (2022)
- [Multi-Frequency Multi-Scenario Millimeter Wave MIMO Channel Measurements and Modeling for B5G Wireless Communication Systems](https://mlsanity.com/paper/2007.13973/) (2020)
- [Parametrization and Estimation of High-Rank Line-of-Sight MIMO Channels with Reflected Paths](https://mlsanity.com/paper/2205.05640/) (2022)

## Built on by (most cited first, 1 of 1)

- [Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility](https://mlsanity.com/paper/2606.20098/) (2026)

## Abstract (arXiv)

The upper mid-band (FR3) has been recently attracting interest for new generation of mobile networks, as it provides a promising balance between spectrum availability and coverage, which are inherent limitations of the sub 6GHz and millimeter wave bands, respectively. In order to efficiently design and optimize the network, channel modeling plays a key role since FR3 systems are expected to operate at multiple frequency bands. Data-driven methods, especially generative adversarial networks (GANs), can capture the intricate relationships among data samples, and provide an appropriate tool for FR3 channel modeling. In this work, we present the architecture, link state model, and path generative network of GAN-based FR3 channel modeling. The comparison of our model greatly matches the ray-tracing simulated data.