most citedmmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud

6 citations · 6 across the 4 of their papers we have counts for

collaborators

9 papers

cs.IT2026

Flexible Intelligent Metasurface-Aided ISAC: User Fairness Optimization and Performance Evaluation

Hailun Huang, Yuwen Cao, Jiguang He +1

This paper investigates max-min user fairness optimization for flexible intelligent metasurface (FIM) and non-orthogonal multiple access (NOMA)-assisted integrated sensing and comm…

cs.CV2026

RePos: Relative-to-Absolute Pose Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation

Zhangcheng Hou, Tomoaki Ohtsuki

Device-free 3D human pose estimation from commodity WiFi Channel State Information (CSI) enables human sensing that preserves privacy and tolerates poor illumination, but its deplo…

cs.IT2026

Memristor-Based Meta-Learning for Fast mmWave Beam Prediction in Non-Stationary Environments

Yuwen Cao, Tomoaki Ohtsuki, Setareh Maghsudi +1

Traditional machine learning techniques have achieved great success in improving data-rate performance and reducing latency in millimeter wave (mmWave) communications. However, the…

cs.CV20266 cited

mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud

Abdullah Al Masud, Shi Xintong, Mondher Bouazizi +1

Pose estimation and human action recognition (HAR) are pivotal technologies spanning various domains. While the image-based pose estimation and HAR are widely admired for their sup…

cs.LG2025

Generative Model-Aided Continual Learning for CSI Feedback in FDD mMIMO-OFDM Systems

Guijun Liu, Yuwen Cao, Tomoaki Ohtsuki +2

Deep autoencoder (DAE) frameworks have demonstrated their effectiveness in reducing channel state information (CSI) feedback overhead in massive multiple-input multiple-output (mMI…

cs.IT2025

A Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications

Zhiqiang Xiao, Yuwen Cao, Mondher Bouazizi +2

Existing transfer learning-based beam prediction approaches primarily rely on simple fine-tuning. When there is a significant difference in data distribution between the target dom…