“AI-Based Beam Management for FR3 FDD MIMO via Online Channel Synthesis” has been accepted to IEEE JSAC.
WAISL · DGIST EECS
Wireless AI Systems Lab
The Wireless AI Systems Lab (WAISL) at DGIST EECS is led by Prof. Hyung-Joo Moon.
We study learning-based wireless systems, from radio and network control to learned signaling for edge intelligence, and validate our ideas through real-world prototyping.
Research overviewCHANNEL2WORLDRF → ENVIRONMENT
DIRECTREFLECTED
RESEARCH
Research areas
01
AI-native wireless
Site-specific AI for channel estimation, beam management, and cross-layer control in wireless networks.
02
Latent communications for edge intelligence
Deep-learning-driven signaling for time-critical communication involving mobile or resource-constrained agents.
03
Wireless digital twins
RF environment representations and calibrated digital models for learning and evaluating wireless systems.
04
Non-terrestrial networks
Resource allocation, wide-area sensing, and optical wireless links across ground, air, and space.
Hyung-Joo Moon won first place in the IEEE ComSoc Four-Minute Competition (1st out of 55).
“MAP-X: Massive Field Data Processing for Real-Time Wide-Area Mapping Using High-Altitude Platforms with MIMO” has been accepted to IEEE TWC.
PUBLICATIONS