Authors: Abdulkadir Celik, Ahmed M. Eltawil

Published on: February 02, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2402.18587

Summary

• What is new: Generative AI (GenAI) is emerging as a critical tool in the wireless domain, offering solutions where data is scarce or complex, contrasting the traditional reliance on discriminative AI (DAI) and large datasets.
• Why this is important: The challenge in wireless research of acquiring vast, comprehensive real-world datasets is costly and complex, making it difficult to apply DAI effectively.
• What the research proposes: Employing GenAI and generative models (GMs) to understand and generate data distributions, supplementing or replacing DAI in the wireless domain for enhanced performance and innovation.
• Results: Through an extensive review of around 120 technical papers, GenAI is shown to significantly impact core wireless research areas and 6G network innovations, proving its utility and effectiveness across various applications.

Technical Details

Technological frameworks used: Generative AI frameworks including generative adversarial networks, variational autoencoders, flow-based and diffusion-based GMs, generative transformers, large language models.

Models used: GMs applied to applications such as network traffic analytics, cross-layer network security, and digital twins.

Data used: Real-world and simulated wireless domain data to train and evaluate GenAI models.

Potential Impact

Telecommunications, network providers, IoT service companies, mobile edge computing vendors, and businesses employing large-scale wireless networks could significantly benefit or need to adapt due to GenAI advancements.

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