Information System Laboratory
Embedded Files
#Wireless Communications #Signal Processing #Machine Learning #Artificial Intelligence
#Wireless Communications #Signal Processing #Machine Learning #Artificial Intelligence
ISLAB is focusing on machine learning, signal processing, and next-generation mobile communication systems. The research related to machine learning and signal processing includes compressive sensing, sparse signal recovery, matrix completion, financial big data analysis, and decision-making using supervised/unsupervised learning, radar/sonar/image signal analysis using reinforcement learning and meta-learning, etc. In relation to mobile communication systems, ISLab is conducting research on massive MIMO transmission techniques for 6G mobile communications, ultra-reliable and low-latency communication (URLLC), and the design of future wireless networks based on artificial intelligence.
Research Results
Research Results
Task-oriented communication using LMM
Task-oriented communication using LMM
Task-oriented communications for autonomous agent using LMM
Task-oriented communications for autonomous agent using LMM
Real-time 3D object detection using LiDAR data
Real-time 3D object detection using LiDAR data
Current Research Topics
Current Research Topics
Computer Vision-aided Communications
Computer Vision-aided Communications
Y. Ahn, J. Kim, S. Kim, K. Shim, J. Kim , S. Kim and B. Shim, "Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming," IEEE Wireless Communications
Visual Language Models
Visual Language Models
D. Kim, M. Bae, K. Shim, and B. Shim, "Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models," International Conference on Learning Representation, 2025.
Terahertz Beam Management
Terahertz Beam Management
S. Kim, J. Park, J. Moon and B. Shim, "Fast and Accurate Terahertz Beam Management via Frequency-dependent Beamforming", IEEE Transactions on Wireless Communications
Semantic Segmentation
Semantic Segmentation
S. Kim, D. Park and B. Shim, "Semantic-aware superpixel for weakly supervised semantic segmentation" In Proceedings of Conference on Artificial Intelligence (AAAI), Vol. 37, No. 1, pp. 1142-1150, 2023.
Page updated
Google Sites
Report abuse