219 citations · 546 across the 100 of their papers we have counts for
18 papers · 1 filter
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…
Adaptive Pruning for Large Language Models with Structural Importance Awareness
Haotian Zheng, Jinke Ren, Yushan Sun +6
The recent advancements in large language models (LLMs) have significantly improved language understanding and generation capabilities. However, it is difficult to deploy LLMs on r…
When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study
Yang Xiong, Ruichen Zhang, Yinqiu Liu +4
The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliab…
Hallucination-aware Optimization for Large Language Model-empowered Communications
Yinqiu Liu, Guangyuan Liu, Ruichen Zhang +5
Large Language Models (LLMs) have significantly advanced communications fields, such as Telecom Q\&A, mathematical modeling, and coding. However, LLMs encounter an inherent issue k…
STAR-RIS Enabled ISAC Systems: Joint Rate Splitting and Beamforming Optimization
Yuan Liu, Ruichen Zhang, Ruihong Jiang +5
This paper delves into an integrated sensing and communication (ISAC) system bolstered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)…
Toward Democratized Generative AI in Next-Generation Mobile Edge Networks
Ruichen Zhang, Jiayi He, Xiaofeng Luo +5
The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced…
Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO approach
Yangbo Guo, Jianhui Fan, Ruichen Zhang +4
This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting mu…
Generative Semantic Communication for Joint Image Transmission and Segmentation
Weiwen Yuan, Jinke Ren, Chongjie Wang +4
Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecti…
Optimizing Resource Allocation for Multi-modal Semantic Communication in Mobile AIGC Networks: A Diffusion-based Game Approach
Jian Liu, Ming Xiao, Jinbo Wen +6
Mobile Artificial Intelligence-Generated Content (AIGC) networks enable massive users to obtain customized content generation services. However, users still need to download a larg…
Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach
Jiakang Zheng, Jiayi Zhang, Hongyang Du +4
Cell-free (CF) massive multiple-input multipleoutput (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Ratesplitting (RS) e…
Maximum-Likelihood Estimation Based on Diffusion Model For Wireless Communications
Changyuan Zhao, Jiacheng Wang, Ruichen Zhang +3
Generative Artificial Intelligence (GenAI) models, with their powerful feature learning capabilities, have been applied in many fields. In mobile wireless communications, GenAI can…
Optimizing Generative AI Networking: A Dual Perspective with Multi-Agent Systems and Mixture of Experts
Ruichen Zhang, Hongyang Du, Dusit Niyato +4
In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixt…
Defining Problem from Solutions: Inverse Reinforcement Learning (IRL) and Its Applications for Next-Generation Networking
Yinqiu Liu, Ruichen Zhang, Hongyang Du +4
Performance optimization is a critical concern in networking, on which Deep Reinforcement Learning (DRL) has achieved great success. Nonetheless, DRL training relies on precisely d…
Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models
Jinbo Wen, Ruichen Zhang, Dusit Niyato +4
By integrating Artificial Intelligence (AI) with the Internet of Things (IoT), Artificial Intelligence of Things (AIoT) has revolutionized many fields. However, AIoT is facing the…
Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
Yang Lu, Yuhang Li, Ruichen Zhang +3
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are…
Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission
Ruichen Zhang, Hongyang Du, Yinqiu Liu +5
In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communicati…
Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions
Zhe Wang, Jiayi Zhang, Hongyang Du +4
Next-generation Multiple-Input Multiple-Output (MIMO) is expected to be intelligent and scalable. In this paper, we study Large Language Model (LLM)-enabled next-generation MIMO ne…
Interactive AI with Retrieval-Augmented Generation for Next Generation Networking
Ruichen Zhang, Hongyang Du, Yinqiu Liu +5
With the advance of artificial intelligence (AI), the emergence of Google Gemini and OpenAI Q* marks the direction towards artificial general intelligence (AGI). To implement AGI,…