고급지능시스템 연구실

Pioneering the convergence of 6G Wireless Communications, Artificial Intelligence, and Robotics to build the intelligent infrastructure of the future. We design AI-native RAN intelligence that translates high-level human intent into real-time network actions. An Intent LMM interprets operator inputs and orchestrates control apps across the O-RAN RIC stack. Intent LMM: Translates natural language to structured network policies. Proactive Control: Resources allocated efficiently before demand peaks. GPU-Accelerated: High-reliability services via NVIDIA Sionna and Aerial frameworks. We study distributed learning for battery-limited wireless edge systems. Devices update lightweight models using local data and share privacy-preserving parameters with a central server. Split Computing: Joint optimization of Device and Edge computation capabilities. Channel-Aware: Adaptive task offloading based on real-time channel states. Privacy-First: Secure model updates without sharing raw local data. We implement a two-level intelligence loop for connected robotics. Robots execute fast on-device control for immediate safety, while an Edge LMM aggregates multi-robot telemetry for global planning. Two-Level Loop: Combines fast local control with intelligent global planning. Edge LMM: Resolves fleet-wide congestion and routing conflicts dynamically. Scalability: Efficient and reliable coordination for large-scale robot fleets.