14 papers
Generative Communications: Overview, Technologies, and Trends
Wenjun Zhang, Zhiyong Chen, Tong Wu +4
The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication par…
JSCGC: Joint Source-Channel-Generation Coding for Wireless Generative Communications
Tong Wu, Zhiyong Chen, Guo Lu +4
Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distor…
Joint Source-Channel-Generation Coding: From Distortion-oriented Reconstruction to Semantic-consistent Generation
Tong Wu, Zhiyong Chen, Guo Lu +4
Conventional communication systems, including both separation-based coding and AI-driven joint source-channel coding (JSCC), are largely guided by Shannon's rate-distortion theory.…
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 (…
Two Birds with One Stone: Multi-Task Semantic Communications Systems over Relay Channel
Yujie Cao, Tong Wu, Zhiyong Chen +3
In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstru…
MambaJSCC: Adaptive Deep Joint Source-Channel Coding with Generalized State Space Model
Tong Wu, Zhiyong Chen, Meixia Tao +4
Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architec…
DM-MIMO: Diffusion Models for Robust Semantic Communications over MIMO Channels
Yiheng Duan, Tong Wu, Zhiyong Chen +1
This paper investigates robust semantic communications over multiple-input multiple-output (MIMO) fading channels. Current semantic communications over MIMO channels mainly focus o…
Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels
Tong Wu, Zhiyong Chen, Meixia Tao +2
Degraded broadcast channels (DBC) are a typical multiuser communication scenario, Semantic communications over DBC still lack in-depth research. In this paper, we design a semantic…
MambaJSCC: Deep Joint Source-Channel Coding with Visual State Space Model
Tong Wu, Zhiyong Chen, Meixia Tao +3
Lightweight and efficient deep joint source-channel coding (JSCC) is a key technology for semantic communications. In this paper, we design a novel JSCC scheme named MambaJSCC, whi…
CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications
Tong Wu, Zhiyong Chen, Dazhi He +4
Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for e…