Jihoon Bang

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About

Jihoon Bang is a rising researcher at the forefront of next-generation wireless communications, specializing in the integration of artificial intelligence and digital twin technologies with radio-frequency (RF) channel sounding. His work addresses a critical bottleneck in modern channel modeling: the inability to accurately associate multipath components (MPCs) with physical scatterers in complex environments. Bang’s major contributions include developing a context-aware channel sounder that fuses camera and LiDAR data with RF measurements to reconstruct and semantically segment 3D environments, enabling AI-based classification of discrete objects. This approach, detailed in his 2024 paper (3 citations), bridges the gap between physical geometry and RF propagation. In his 2025 work (1 citation), he pioneers a digital-twin-assisted clustering method that overcomes the limitations of blind RF-only clustering, allowing MPCs to be linked directly to individual scatterers for more accurate and physically meaningful channel models. Though early in his career, Bang’s innovative fusion of sensing, AI, and digital twins positions him as a key contributor to the development of intelligent, context-aware wireless systems for 6G and beyond.