collaborators

9 papers

eess.SP2026

Wireless Intelligence Needs a Cerebellum: Score-Based Foundation Models Toward Real-Time Physical-Layer Inference

Chang Cai, Boyu Teng, Xiaojun Yuan +1

Wireless intelligence requires not only large foundation models for network-wide planning and decision-making, but also a compact "cerebellum" for fast and precise physical-layer i…

eess.SP2026

Near-Field Position and Orientation Tracking With Hybrid ELAA Architecture

Lin Chen, Xiaojun Yuan, Ying-Jun Angela Zhang

This paper investigates near-field (NF) position and orientation tracking of a multi-antenna mobile station (MS) using an extremely large antenna array (ELAA)-equipped base station…

cs.LG2026

FedCova: Robust Federated Covariance Learning Against Noisy Labels

Xiangyu Zhong, Xiaojun Yuan, Ying-Jun Angela Zhang

Noisy labels in distributed datasets induce severe local overfitting and consequently compromise the global model in federated learning (FL). Most existing solutions rely on select…

eess.SP2026

Joint Activity Detection and Channel Estimation for Massive Connectivity: Where Message Passing Meets Score-Based Generative Priors

Chang Cai, Wenjun Jiang, Xiaojun Yuan +1

Massive connectivity supports the sporadic access of a vast number of devices without requiring prior permission from the base station (BS). In such scenarios, the BS must perform…

cs.CV2025

Score-Based Turbo Message Passing for Plug-and-Play Compressive Imaging

Chang Cai, Hao Jiang, Xiaojun Yuan +1

Message-passing algorithms have been adapted for compressive imaging by incorporating various off-the-shelf image denoisers. However, these denoisers rely largely on generic or han…

eess.IV2025

Score-Based Turbo Message Passing for Plug-and-Play Compressive Image Recovery

Chang Cai, Xiaojun Yuan, Ying-Jun Angela Zhang

Message passing algorithms have been tailored for compressive imaging applications by plugging in different types of off-the-shelf image denoisers. These off-the-shelf denoisers mo…