Agentic AI for Intelligent 6G Network Management and Resource Optimization

Abstract: With regards to network intelligence, resource optimization, ultra low-latency, and autonomous service management, rapid development of sixth generation (6G) wireless communication technologies brings new challenges. The future 6G networks will be dynamic, heterogeneous, and geographically dispersed, being impossible to manage using conventional network management techniques. The concept of intelligent network orchestration can experience a revolution because of the advent of Agentic Artificial Intelligence (Agentic AI) that has such features as autonomous reasoning, planning, collaboration, and adaptation. In order to perform intelligent management and optimization of 6G wireless networks, an Agentic AI framework consisting of multiple interacting AI agents for handling fault recovery, orchestration, monitoring, forecasting, and dynamic resource allocation is proposed in the paper. The proposed solution constantly assesses network status and optimizes energy efficiency, spectrum efficiency, Quality of Experience (QoE), and Quality of Service (QoS). The simulation-based evaluation shows improved latency, throughput, network reliability, and resource efficiency as compared to traditional AI-based approaches. Scalable, robust, and self-optimizing 6G ecosystems are enabled by the proposed architecture for next generation autonomous wireless networks.

Keywords: Agentic AI, 6G Networks, Network Intelligence, Resource Optimization, Multi-Agent Systems, Autonomous Networking, QoS, QoE, Deep Reinforcement Learning, Edge Intelligence