6G Networks and AI-Orchestrated Resource Allocation

Danang Danang (Universitas Sains dan Teknologi Komputer)
Tameem Raif (University of Technology Bahrain)
Zubair Hadi Faisal (University of Technology Bahrain)
Munir Fadlan Karim (University of Technology Bahrain)

Proceeding of The International Conference on Electrical Engineering and Informatics
Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and

As 6G networks promise unprecedented speeds and ultra-low latency, AI-based resource allocation plays a crucial role in optimizing network performance. This study explores AI-driven techniques for spectrum management, energy efficiency, and real-time data processing. By leveraging machine learning and deep learning models, AI enhances network adaptability, reduces congestion, and improves overall efficiency. The proposed approaches enable intelligent decision-making, dynamic resource allocation, and predictive analytics to meet the growing demands of future wireless communication. The findings highlight the potential of AI in revolutionizing 6G networks, ensuring seamless connectivity, and maximizing network capacity while minimizing power consumption. These advancements contribute to the development of more sustainable and intelligent telecommunication infrastructures.