Volume 17, Number 5

AI-Native Wireless Networks: Transforming Connectivity, Efficiency, and Autonomy for 5G/6G and Beyond

  Authors

Akheel Mohammed ¹, Zubair Ahmed Mohammed ², Naveed Uddin Mohammed ², Shravan Kumar Gunda ³, Mohammed Azmath Ansari ⁴ and Mohd Abdul Raheem ⁵, ¹ University of the Cumberlands, USA, ² Lindsey Wilson College, USA, ³ Northwestern Polytechnic University, USA, ⁴ Concordia University, USA, ⁵ State University of New York Institute of Technology, USA

  Abstract

The doubling in mobile devices and services has introduced unprecedented challenges for next-generation wireless and mobile networks, especially as the industry moves toward 5G and 6G architectures. Conventional, rule-based network management paradigms fail to tackle challenges like scalability, latency, spectrum and energy efficiency, and dynamic resource allocation in today's complicated, heterogeneous environments. Artificial Intelligence (AI) is transforming this landscape by providing adaptive, data-driven solutions at every layer of the network. With machine learning, deep learning, and reinforcement learning, AI allows for traffic forecasting, real-time resource utilization optimization, mobility expectation, anomaly detection, and energy efficiency. These technologies, such as AI deployment at the edge and core, support self-organizing networks, low-latency response, and improved Quality of Service (QoS) and user experience. Key advantages are enhanced throughput, lower latency, and better spectral usage, particularly with deep reinforcement and federated learning techniques. However, challenges remain involving explainable AI, real-time edge processing constraints, data availability, and integration with existing infrastructure. The article proposes a research agenda focused on developing standardized frameworks, enabling cross-layer integration, and hybridizing AI with classical methods. By examining both current achievements and future directions, this work illuminates AI’s critical role in making wireless networks more autonomous, efficient, and user-centric.

  Keywords

Artificial Intelligence (AI), Wireless Networks, Mobile Networks, Machine Learning, Deep Learning, 5G, 6G, Resource Allocation, Edge Computing, Network Optimization, Reinforcement Learning, QoS, QoE, Federated Learning, Self-Organizing Networks