SciRepID - 6G Networks and AI-Orchestrated Resource Allocation

📅 30 January 2025
DOI: 10.62951/iceei.v1i2.42

6G Networks and AI-Orchestrated Resource Allocation

Proceeding of the International Conference on Electrical Engineering and Informatics
Asosiasi Riset Teknik Elektro dan Informatika Indonesia (ARTEII)

📄 Abstract

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.
 

🔖 Keywords

#AI in Telecommunications; Future Wireless Networks; Low Latency; Spectrum Management; 6G Networks

ℹ️ Informasi Publikasi

Tanggal Publikasi
30 January 2025
Volume / Nomor / Tahun
Volume 1, Nomor 2, Tahun 2025

📝 HOW TO CITE

Danang Danang; Tameem Raif; Zubair Hadi Faisal; Munir Fadlan Karim, "6G Networks and AI-Orchestrated Resource Allocation," Proceeding of the International Conference on Electrical Engineering and Informatics, vol. 1, no. 2, Jan. 2025.

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