Digital Communications Lab

AI-native design principles for next-generation radio access networks integrating machine learning at every layer. Extreme massive MIMO systems enhanced by AI-driven beamforming and integrated sensing capabilities for 6G. Digital twin framework for AI-native RAN simulation, optimization, and real-time network management. Our approach combines reinforcement learning-based resource management with transformer-based channel estimation. This hybrid methodology enables dynamic spectrum allocation, predictive beamforming, and adaptive modulation schemes that respond in real-time to changing channel conditions and user demands. Building tomorrow's wireless networks — from tactile internet and beyond-5G systems to V2X and intelligent spectrum sensing. We are dedicated to advancing wireless communication technologies from theoretical foundations to practical implementations. Our research spans 6G networks, AI-integrated massive MIMO systems, Hydra-RAN architectures, and ultra-reliable low-latency communications. Building on decades of expertise in signal detection and coding theory, we bridge the gap between Shannon's information theory and next-generation network solutions.