6G Wireless Networks in the Generative AI Age: Overview, Techniques, and Future Trends

As the world move beyond the 5G era, the emergence of 6G promises a significant integration with innovative communication paradigms and burgeoning technology trends, actualizing previously utopian concepts alongside increased technical complexities. Analytical models offer basic frameworks, but ML and AI now outperform them in solving complex problems, either by augmenting or supplanting model-based methodologies. The predominant focus of data-driven wireless research is on discriminative AI (DAI), which necessitates extensive real-world datasets. In contrast to DAI, Generative AI (GenAI) refers to generative models (GMs) that can identify the fundamental data circulation, patterns, and characteristics of the incoming data. Given these attractive characteristics, GenAI can either substitute or augment DAI methodologies in multiple contexts. This comprehensive tutorial-survey article begins with an overview of 6G and wireless intellectual ability by delineating potential 6G applications and services. The aspects presented in this paper support the internet of things integration with 6G networks with the support of the AI as intelligent systems. This review paper concentrates on fundamental wireless research domains, encompassing network optimization, organization, and management. It examines the foundational learning principles of DAI and its methodologies, the application of DAI in wireless networks, and the utilization of GMs in 6G networks. Due to its comprehensive nature, this paper will act as a crucial reference for researchers and professionals exploring this dynamic and promising field.

@article{Murad2025,
  title     = {6G Wireless Networks in the Generative AI Age: Overview, Techniques, and Future Trends},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Sallar S. Murad and Rozin Badeel and Harth Ghassan Hamid and Reham A. Ahmed},
  doi       = {10.14569/IJACSA.2025.0161292},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161292}
}