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

Created by
  • Haebom

Author

Jinbo Wen, Jiawen Kang, Dusit Niyato, Yang Zhang, Jiacheng Wang, Biplab Sikdar, Ping Zhang

Outline

This paper points out that existing data augmentation techniques are ineffective due to structural differences in wireless data, and systematically explores the potential and effectiveness of wireless data augmentation using generative artificial intelligence (GenAI). We begin by briefly reviewing existing data augmentation techniques and their limitations, followed by introducing the GenAI model and its applications in data augmentation. We explore the potential applications of generative data augmentation at the physical, network, and application layers, and present generative data augmentation architectures for each application. Specifically, we propose a general generative data augmentation framework for Wi-Fi gesture recognition, generating high-quality channel state information data using a transformer-based diffusion model. We evaluate the effectiveness of the proposed framework through a case study using the Widar 3.0 dataset and discuss future research directions.

Takeaways, Limitations

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Takeaways:
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We propose that GenAI-based generative data augmentation is an effective solution to address the wireless data shortage problem.
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It demonstrates the applicability of generative data augmentation at the physical, network, and application layers of wireless networks.
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We propose a concrete generative data augmentation framework for Wi-Fi gesture recognition and experimentally verify its performance improvement.
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Limitations:
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The proposed framework is specialized for Wi-Fi gesture recognition, and further research is needed to determine its generalizability to other wireless network applications.
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Performance may be affected by the size and diversity of the dataset used.
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Further evaluation and improvement of the quality and realism of the generated data is needed.
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Further research is needed on the applicability and generalization performance to various wireless network environments.
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