The Role of Artificial Intelligence in the Architectural Transformation of Modern Wireless Networks: From Fifth-Generation (5G) Systems to AI-Native Sixth-Generation (6G) Networks
DOI:
https://doi.org/10.65405/yvtf8520Keywords:
Artificial Intelligence, 5G, 6G, AI-Native Networks, AI-RAN, Edge Intelligence, Wireless Communications, Cognitive Networks, Cybersecurity, Network Automation.Abstract
The wireless communications domain is undergoing a profound architectural transformation that extends beyond conventional performance enhancement and capacity expansion toward redefining how networks are designed, operated, and managed. In this context, artificial intelligence (AI) has emerged as a foundational enabler of this transformation, shifting modern wireless systems from static, rule-based operational paradigms toward cognitive, adaptive, predictive, and self-decision-making infrastructures [1]–[5].
This paper investigates the structural role of AI in reshaping contemporary wireless communication networks, with a particular focus on the transition from AI-assisted fifth-generation (5G) systems to AI-native sixth-generation (6G) networks. The study examines key technical pillars including intelligent spectrum management, edge intelligence, AI-driven beamforming, resource orchestration and network slicing, and AI-based cybersecurity, while also highlighting the growing convergence among communications, sensing, and intelligent computing [4]–[12].
The paper further argues that 5G-Advanced represents a strategic transitional stage in which AI capabilities are increasingly incorporated into standardized and operational network functions, especially within radio access, model lifecycle management, and intelligent automation. This evolution reflects a broader shift from “AI-enhanced networks” to “networks designed around native intelligence”. The study concludes that 6G should not be viewed merely as a faster successor to 5G, but rather as a fundamental paradigm shift toward self-learning, self-evolving, and semantically aware communication infrastructures capable of supporting future applications such as digital twins, physical AI, and semantic communications.
Downloads
References
[1] W. Saad, M. Bennis, and M. Chen, “A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems,” IEEE Network, vol. 34, no. 3, pp. 134–142, 2020.
[2] H. Tataria, M. Shafi, A. F. Molisch, M. Dohler, H. Sjöland, and F. Tufvesson, “6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, 2021.
[3] X. You et al., “Towards 6G Wireless Communication Networks: Vision, Enabling Technologies, and New Paradigm Shifts,” Science China Information Sciences, vol. 64, no. 1, pp. 1–74, 2021.
[4] Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, “Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3072–3108, 2019.
[5] M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019.
[6] M. Z. Chowdhury et al., “6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions,” IEEE Open Journal of the Communications Society, vol. 1, pp. 957–975, 2020.
[7] N. H. Mahmood et al., “Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 1071–1110, 2022.
[8] J. Zhao, Y. Liu, K. K. Chai, Y. Chen, and Z. Han, “AI-Driven Resource Allocation for Beyond 5G and 6G Wireless Networks: A Survey,” IEEE Access, vol. 10, 2022.
[9] S. Zhang, M. Feizarefi, and A. F. Mirzaei, “AI-Native Integrated Sensing and Communications for Self-Organizing Wireless Networks: Architectures, Learning Paradigms, and System-Level Design,” 2025.
[10] Z. Liu et al., “Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G,” 2025.
[11] Sowan, E., Elbuaishi, A., & Areir, M. (2026). Enhancing Face Recognition Performance: A Comparative Evaluation of ArcFace and FaceNet using Image Preprocessing and ROC–EER Metrics. Al-Farooq Journal of Sciences, 2(3), 973-990.
[12] E. C. Strinati et al., “Towards Distributed and Intelligent Integrated Sensing and Communications for 6G Networks,” 2024.
[13] 3GPP, “AI/ML for NG-RAN and 5G-Advanced Towards 6G,” Technical Report, 2026.
[14] 3GPP, “Engineering Intelligence: Shaping AI/ML Management for the 5G System,” Technical Report, 2025.
[15] 3GPP, “Enhancements for Artificial Intelligence (AI) / Machine Learning (ML) for NG-RAN (Release 19),” Work Item Description, 2025.
[16] AI-RAN Alliance, “AI-RAN Alliance Whitepaper,” 2024.
[16] BenHusein, A. H., Shakrum, F. M., & Elgdiri, E. M. (2026). A Comprehensive Performance Evaluation of a Wi-Fi-Based Wireless Sensor Network Using Raspberry Pi and ESP8266 Under Multi-Rate Traffic Conditions. Al-Farooq Journal of Sciences, 2(3), 816-818.
