LLM-Empowered Agentic AI for QoE-Aware Network Slicing Management in Industrial IoT
Xudong Wang, Lei Feng, Ruichen Zhang, Fanqin Zhou, Hongyang Du, Wenjing Li, Dusit Niyato, Abbas Jamalipour, Ping Zhang

TL;DR
This paper proposes an LLM-empowered agentic AI framework for QoE-aware network slicing in IIoT, combining reasoning, planning, and adaptation to improve network performance under dynamic conditions.
Contribution
It introduces an integrated LLM-based agentic AI approach with RAG, DRL, and memory mechanisms for dynamic network slicing management in IIoT environments.
Findings
Achieves up to 19% improvement in slice availability ratio.
Significantly outperforms baseline methods in balancing latency, reliability, and cost.
Demonstrates effective continual learning and semantic intent inference.
Abstract
The Industrial Internet of Things (IIoT) requires networks that deliver ultra-low latency, high reliability, and cost efficiency, which traditional optimization methods and deep reinforcement learning (DRL)-based approaches struggle to provide under dynamic and heterogeneous workloads. To address this gap, large language model (LLM)-empowered agentic AI has emerged as a promising paradigm, integrating reasoning, planning, and adaptation to enable QoE-aware network management. In this paper, we explore the integration of agentic AI into QoE-aware network slicing for IIoT. We first review the network slicing management architecture, QoE metrics for IIoT applications, and the challenges of dynamically managing heterogeneous network slices, while highlighting the motivations and advantages of adopting agentic AI. We then present the workflow of agentic AI-based slicing management,…
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Taxonomy
TopicsSoftware-Defined Networks and 5G · Software System Performance and Reliability · IoT and Edge/Fog Computing
