“AI-Based Beam Management for FR3 FDD MIMO via Online Channel Synthesis” has been accepted to IEEE JSAC.
PRINCIPAL INVESTIGATOR
Prof. Hyung-Joo Moon
Wireless AI Systems Lab (WAISL) · DGIST EECS
I lead the Wireless AI Systems Lab at DGIST. I received my PhD from Yonsei University in 2026 under the supervision of Prof. Chan-Byoung Chae. My research combines wireless communications, machine learning, and prototype validation, with a focus on AI-native wireless systems, latent communications for edge intelligence, and wireless digital twins.
WAISL · DGIST
Wireless AI Systems Lab
We study learning-based wireless systems, from radio and network control to learned signaling for edge intelligence.
Our four research areas connect communication theory, deep learning, RF environment modeling, and system validation.
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
Selected publications
AI-Based Beam Management for FR3 FDD MIMO via Online Channel Synthesis
IEEE J. Sel. Areas Commun., vol. 44, pp. 3444-3458, Jan. 2026.
MAP-X: Massive Field Data Processing for Real-Time Wide-Area Mapping Using High-Altitude Platforms with MIMO
IEEE Trans. Wireless Commun., vol. 25, pp. 4358-4373, Jan. 2026.