AI-DRIVEN MULTI-AGENT SYSTEM FOR QOS OPTIMIZATION IN 6G INDUSTRIAL NETWORKS

The emergence of sixth-generation (6G) wireless technology will unlock unprecedented capabilities for Industrial Internet of Things (IIoT) networks by enabling terabit-per-second data rates, sub-millisecond latency, and extreme reliability. These advances will support mission-critical applications such as realtime robotics, autonomous manufacturing, and immersive automation. This paper presents an AI-driven Multi-Agent System (MAS) for real-time Quality of Service (QoS) anomaly detection and adaptive network optimization in 6G industrial environments. The MAS integrates three cooperating agents: a Monitoring Agent for telemetry collection, an AI-based Anomaly Detection Agent using Isolation Forest and deep Autoencoders, and a Reinforcement Learning Optimization Agent employing Proximal Policy Optimization (PPO) to self-tune network parameters. Experiments conducted on a Firecell 5G Standalone testbed emulating 6G conditions demonstrate the system’s effectiveness. The MAS reduced average latency by ≈40%, increased throughput by 15–20%, and lowered packet loss by up to 70% compared to static management baselines. These results validate the MAS’s ability to maintain consistent QoS under dynamic industrial workloads. Key contributions include: (1) a unified MAS architecture for closed-loop QoS control, (2) integration of hybrid AI models for anomaly detection and adaptive optimization, and (3) real-world testbed validation bridging 5G SA and 6G-IIoT research. For access to the code, data, and experimental results, visit our GitHub repository (Didilish/AI_Driven_MAS_For_Anomaly-Detection-QoS-Optimization-6G-IIOT).