The paper discusses managing slice resources for 6G networks based on an open radio access network (ORAN) architecture. It introduces AI at the edge to optimize resource utilization, focusing on intra-slice and inter-slice intelligent agents working at different time scales. The study emphasizes the importance of AI and ML models for efficient resource management in future wireless networks.
The research addresses challenges in radio resource management (RRM) for RAN-edge domains, aiming to meet key performance indicators (KPIs) for various applications. It highlights the significance of dynamic and optimal resource management using machine learning and AI approaches. The proposed algorithms leverage deep reinforcement learning techniques to enhance mobile broadband, ultra-reliable low latency, and massive machine type communication slice categories.
By utilizing intelligent agents with DRL algorithms, the study aims to minimize maximum resource utilization within each slice while maintaining user performance. It discusses how AI can be integrated into network architectures to support multi-time scale management using intelligent agents. The paper also explores the implications of slicing technology in meeting diverse requirements across different use cases.
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by Suvidha Mhat... klo arxiv.org 03-08-2024
https://arxiv.org/pdf/2311.11668.pdfSyvällisempiä Kysymyksiä