Doeon Kim

A CSI-native self-supervised framework that learns WiFi-CSI representations by predicting masked latent embeddings rather than reconstructing raw CSI, using CSI-aware tokenization and a ray-tracing-based synthetic-CSI pretraining pipeline. It improves 3D human-pose accuracy over supervised and prior CSI baselines, including in multi-person settings.

PRISM, a framework that exploits the spatial concentration of RF reflections to make mmWave human pose estimation schedulable on resource-limited edge hardware, where cameras are inadmissible and latency must be predictable. It combines physics-bounded integral processing (constant-time integral queries), physics-adaptive instance proposals that decompose multi-person scenes into bounded local subproblems, and deadline-aware operation profiles with offline-verified worst-case bounds. Across four public datasets, PRISM cuts 99th-percentile latency by 24-58% relative to deadline-missing baselines with a 0.0% miss rate, and attains the highest pose accuracy among deadline-feasible configurations.

A multi-conditioned dense-prediction framework that generates urban EMF maps from building layouts and antenna configurations, using a vision-transformer backbone with feature-modulation and cross-attention conditioning and transmitter-relative spatial channels. It improves EMF-map accuracy over convolutional and unconditioned baselines.

A multimodal Transformer that keeps image patches as a dense spatial stream and fuses tabular antenna metadata via late cross-attention, with masked-image and masked-tabular pretraining objectives. It achieves low error across radio-map prediction settings, outperforming convolutional and Transformer baselines.

A systematic study of Vision Transformer architectures for predicting 5G electromagnetic-field strength from physically-informed, ray-tracing-generated inputs (building maps, antenna radiation patterns, base-station parameters). Among the compared models an HRFormer-based predictor achieves the best masked MAE of 5.45%, about a 31% error reduction over a U-Net baseline.