Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study
This addresses data scarcity issues in wireless network applications like gesture recognition, but it is incremental as it adapts existing generative AI methods to a specific domain.
The paper tackles data scarcity in wireless networks by proposing a generative AI framework for data augmentation, specifically using transformer-based diffusion models to generate channel state information data for Wi-Fi gesture recognition, which enhances recognition performance as demonstrated in a case study with the Widar 3.0 dataset.
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.