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A Comprehensive Survey on GenAI-Enabled 6G: Technologies, Challenges, and Future Research Avenues
Sheraz, Muhammad Chuah, Teong Chee Tareen, Wajahat Ullah Khan Al-Habashna, Ala'a Saeed, Sohail Imran Ahmed, Manzoor Lee, It Ee Guizani, Mohsen
Sheraz, Muhammad
Chuah, Teong Chee
Tareen, Wajahat Ullah Khan
Al-Habashna, Ala'a
Saeed, Sohail Imran
Ahmed, Manzoor
Lee, It Ee
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Abstract
The integration of artificial intelligence (AI) in 6G demonstrates a transformative leap in redefining network efficiency, intelligence, and adaptability. However, AI largely leverages discriminative models relying on labelled and quality data, where data accessibility remains serious concern. Generative AI (GenAI) has gained traction due to its immense potential in resolving the issue of data scarcity, complexity, and incompleteness. GenAI models excel in understanding underlying data distributions, enabling them to generate synthetic data that mirrors real-world patterns. GenAI supports adaptive learning and scenario modeling, making it indispensable for addressing the unpredictability and complexity inherent in 6G networks. Since the complexity of wireless communication systems is increasing and the demand for such systems is growing, GenAI presents new ideas for enhancing network performance, increasing system efficiency, and developing intelligent decision-making capabilities. This survey paper investigates the promising role of GenAI in the evolution of 6G networks. An in-depth discussion of notable GenAI models is presented, outlining their application in enhancing key network components. Specifically, the application of GenAI in advanced technologies including reconfigurable intelligent surfaces (RIS), unmanned aerial vehicles (UAVs), digital twins (DTs), and integrated sensing and communications (ISACs) is thoroughly investigated with respect to optimize the adaptability, flexibility, and robustness of the wireless networks. Moreover, use cases of GenAI-enabled wireless networks are presented to highlight the realization of GenAI in 6G. The paper also presents the lessons learned, existing challenges, and future research directions. This paper systematically explores GenAI and its pivotal role in the development of 6G, providing a foundation for researchers to further investigate and advance GenAI-enabled 6G.
Citation
M. Sheraz et al., "A Comprehensive Survey on GenAI-Enabled 6G: Technologies, Challenges, and Future Research Avenues," in IEEE Open Journal of the Communications Society, doi: 10.1109/OJCOMS.2025.3568496
Source
IEEE Open Journal of the Communications Society
Conference
Keywords
Generative Artificial Intelligence, Unmanned Aerial Vehicles, Reconfigurable Intelligent Surfaces, Digital Twin Networks, Deep Learning, 6G, Generative Diffusion Model, Large Language Model
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Source
Publisher
IEEE