15 citations · 17 across the 10 of their papers we have counts for
12 papers
Spatial Token Interleaving and Two-Pass BP Decoding for Semantic Image Transmission
Yifan Feng, Chentao Yue, Haiyao Yu +5
Semantic error control coding (SECC) can correct the residual errors of a standard channel code with a foundation model that supplies a prior on the source content, but existing SE…
Cross-View Vision-Aided Proactive BS Selection and Beam Prediction for mmWave V2I Communications
Zijiao Hu, Haiyao Yu, Gaoyang Pang +6
This paper investigates environmental-sensing-aided proactive base station (BS) selection and beam prediction for millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) wireless…
Semantic Error Control Coding with Foundation Models for Future Communications
Chentao Yue, Gaoyang Pang, Branka Vucetic +1
Classical channel decoding typically treats all information sequences as equally likely and relies primarily on the channel observations and code structure, without exploiting stat…
Toward Wireless Human-Machine Collaboration in the 6G Era
Gaoyang Pang, Wanchun Liu, Chentao Yue +4
The next industrial revolution, Industry 5.0, will be driven by advanced technologies that foster human-machine collaboration (HMC). It will leverage human creativity, judgment, an…
Policy-Guided MCTS for near Maximum-Likelihood Decoding of Short Codes
Y. Tian, C. Yue, P. Cheng +3
In this paper, we propose a policy-guided Monte Carlo Tree Search (MCTS) decoder that achieves near maximum-likelihood decoding (MLD) performance for short block codes. The MCTS de…
Medical Referring Image Segmentation via Next-Token Mask Prediction
Xinyu Chen, Yiran Wang, Gaoyang Pang +4
Medical Referring Image Segmentation (MRIS) involves segmenting target regions in medical images based on natural language descriptions. While achieving promising results, recent a…
Wireless Human-Machine Collaboration in Industry 5.0
Gaoyang Pang, Wanchun Liu, Dusit Niyato +3
Wireless Human-Machine Collaboration (WHMC) represents a critical advancement for Industry 5.0, enabling seamless interaction between humans and machines across geographically dist…
Communication-Control Codesign for Large-Scale Wireless Networked Control Systems
Gaoyang Pang, Wanchun Liu, Dusit Niyato +2
Wireless Networked Control Systems (WNCSs) are essential to Industry 4.0, enabling flexible control in applications, such as drone swarms and autonomous robots. The interdependence…
BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination
Xin Hao, Bahareh Nakisa, Mohmmad Naim Rastgoo +1
Deep reinforcement Learning (DRL) offers a powerful framework for training AI agents to coordinate with human partners. However, DRL faces two critical challenges in human-AI coord…
DRL-based Resource Allocation in Remote State Estimation
Gaoyang Pang, Wanchun Liu, Yonghui Li +1
Remote state estimation, where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-critical…
Deep Reinforcement Learning for Radio Resource Allocation in NOMA-based Remote State Estimation
Gaoyang Pang, Wanchun Liu, Yonghui Li +1
Remote state estimation, where many sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-cri…
Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control
Gaoyang Pang, Kang Huang, Daniel E. Quevedo +3
We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels. Using elemen…