# Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study

arXiv: https://arxiv.org/abs/2411.08341 · HTML: https://mlsanity.com/paper/2411.08341/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 13 Nov 2024.

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

- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion](https://mlsanity.com/paper/2404.09140/) (2024)
- [TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks](https://mlsanity.com/paper/2008.09567/) (2020)
- [NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation](https://mlsanity.com/paper/2310.08543/) (2023)
- [At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence](https://mlsanity.com/paper/2402.18587/) (2024)
- [Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler](https://mlsanity.com/paper/2311.17451/) (2023)
- [From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks](https://mlsanity.com/paper/2310.18382/) (2023)
- [RFBoost: Understanding and Boosting Deep WiFi Sensing via Physical Data Augmentation](https://mlsanity.com/paper/2410.07230/) (2024)

## Built on by (most cited first, 4 of 4)

- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security](https://mlsanity.com/paper/2605.23989/) (2026)
- [Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model](https://mlsanity.com/paper/2509.15258/) (2025)

## Abstract (arXiv)

Data augmentation as a technique can mitigate data scarcity in machine learning. However, owing to fundamental differences in wireless data structures, traditional data augmentation techniques may not be suitable for wireless data. Fortunately, Generative Artificial Intelligence (GenAI) can be an effective solution to wireless data augmentation due to its excellent data generation capability. This article systematically explores the potential and effectiveness of generative data augmentation in wireless networks. We first briefly review data augmentation techniques, discuss their limitations in wireless networks, and introduce generative data augmentation, including reviewing GenAI models and their applications in data augmentation. We then explore the application prospects of generative data augmentation in wireless networks from the physical, network, and application layers, providing a generative data augmentation architecture for each application. Subsequently, we propose a general generative data augmentation framework for Wi-Fi gesture recognition. Specifically, we leverage transformer-based diffusion models to generate high-quality channel state information data. To evaluate the effectiveness of the proposed framework, we conduct a case study using the Widar 3.0 dataset, which employs a residual network model for Wi-Fi gesture recognition. Simulation results demonstrate that the proposed framework can enhance the performance of Wi-Fi gesture recognition. Finally, we discuss research directions for generative data augmentation.