11 citations · 19 across the 19 of their papers we have counts for
19 papers
SPrune: Spatially Structured Visual Token Pruning for Multimodal Large Language Models
Yuanyuan Jia, Shunpu Tang, Qianqian Yang
Visual token pruning reduces the inference overhead of multimodal large language models (MLLMs) by retaining only a subset of visual tokens. Existing methods usually select tokens…
When Eavesdroppers Reason: Agentic Eavesdropping Attacks on Semantic Communication
Shunpu Tang, Qianqian Yang, Zhiguo Shi +2
Semantic communication (SemCom) has emerged as a promising paradigm for next-generation networks. However, its typical end-to-end joint source--channel coding (JSCC) architecture a…
AgenticPrecoding: LLM-Empowered Multi-Agent System for Precoding Optimization
Zijiu Yang, Zixiang Zhang, Shunpu Tang +2
Precoding is a key technique for interference management and performance improvement in multi-antenna wireless systems. However, existing precoding methods are typically developed…
CoVSpec: Efficient Device-Edge Co-Inference for Vision-Language Models via Speculative Decoding
Yuanyuan Jia, Shunpu Tang, Qianqian Yang
Vision-language models (VLMs) have demonstrated strong capabilities in multimodal perception and reasoning. However, deploying large VLMs on mobile devices remains challenging due…
Evolving Token Communication with Parametric Memory Network
Weixuan Chen, Qianqian Yang
Token communication has emerged as a promising framework for efficient wireless transmission by representing source data as compact semantic tokens. However, transmitting full sema…
Privacy-Preserving Semantic Communication over Wiretap Channels with Learnable Differential Privacy
Weixuan Chen, Qianqian Yang, Shuo Shao +3
While semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information, it also raises critical privacy concerns. Many existing secure SemC…
Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy
Weixuan Chen, Shunpu Tang, Qianqian Yang +2
Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information. However, transmitting semantic-rich data over insecure channels introduce…
Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation
Zhaoyang Li, Qianqian Yang, Zehui Xiong +2
Accurate channel prediction is essential in massive multiple-input multiple-output (m-MIMO) systems to improve precoding effectiveness and reduce the overhead of channel state info…
Enabling Training-Free Semantic Communication Systems with Generative Diffusion Models
Shunpu Tang, Yuanyuan Jia, Qianqian Yang +3
Semantic communication (SemCom) has recently emerged as a promising paradigm for next-generation wireless systems. Empowered by advanced artificial intelligence (AI) technologies,…
Towards Secure Semantic Communications in the Presence of Intelligent Eavesdroppers
Shunpu Tang, Yuhao Chen, Qianqian Yang +3
Semantic communication has emerged as a promising paradigm for enhancing communication efficiency in sixth-generation (6G) networks. However, the broadcast nature of wireless chann…
Retrieval-augmented Generation for GenAI-enabled Semantic Communications
Shunpu Tang, Ruichen Zhang, Yuxuan Yan +4
Semantic communication (SemCom) is an emerging paradigm aiming at transmitting only task-relevant semantic information to the receiver, which can significantly improve communicatio…
Semantic Communication for Efficient Point Cloud Transmission
Shangzhuo Xie, Qianqian Yang, Yuyi Sun +3
As three-dimensional acquisition technologies like LiDAR cameras advance, the need for efficient transmission of 3D point clouds is becoming increasingly important. In this paper,…
Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling
Haowen Wan, Qianqian Yang, Jiancheng Tang +1
In this paper, we propose a semantic communication approach based on probabilistic graphical model (PGM). The proposed approach involves constructing a PGM from a training dataset,…
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
Yuxuan Yan, Qianqian Yang, Shunpu Tang +1
Despite their exceptional performance on various tasks after fine-tuning, pre-trained language models (PLMs) face significant challenges due to growing privacy concerns with data i…
Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers
Shunpu Tang, Chen Liu, Qianqian Yang +2
Semantic communication (SemCom) has emerged as a key technology for the forthcoming sixth-generation (6G) network, attributed to its enhanced communication efficiency and robustnes…
Minimizing End-to-End Latency for Joint Source-Channel Coding Systems
Kaiyi Chi, Qianqian Yang, Yuanchao Shu +2
While existing studies have highlighted the advantages of deep learning (DL)-based joint source-channel coding (JSCC) schemes in enhancing transmission efficiency, they often overl…
Deep Learning Enabled Semantic Communication Systems for Video Transmission
Zhenguo Zhang, Qianqian Yang, Shibo He +1
Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic com…
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +3
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to…
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +4
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applica…