5 papers · 1 filter
Fed-PELAD: Communication-Efficient Federated Learning for Massive MIMO CSI Feedback with Personalized Encoders and a LoRA-Adapted Shared Decoder
Yixiang Zhou, Tong Wu, Meixia Tao +1
This paper addresses the critical challenges of communication overhead, data heterogeneity, and privacy in deep learning for channel state information (CSI) feedback in massive MIM…
Text-Guided Diffusion Model-based Generative Communication for Wireless Image Transmission
Shengkang Chen, Tong Wu, Zhiyong Chen +3
Reliable image transmission over wireless channels is particularly challenging at extremely low transmission rates, where conventional compression and channel coding schemes fail t…
Mixture of Semantics Transmission for Generative AI-Enabled Semantic Communication Systems
Junjie Ni, Tong Wu, Zhiyong Chen +3
In this paper, we propose a mixture of semantics (MoS) transmission strategy for wireless semantic communication systems based on generative artificial intelligence (AI). At the tr…
ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications
Tong Wu, Zhiyong Chen, Dazhi He +5
Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals m…
Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning
Zhongwei Wang, Tong Wu, Zhiyong Chen +3
Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (…