146 citations · 641 across the 84 of their papers we have counts for
13 papers · 1 filter
In-Context Source and Channel Coding
Ziqiong Wang, Tianqi Ren, Rongpeng Li +2
Separate Source-Channel Coding (SSCC) remains attractive for text transmission due to its modularity and compatibility with mature entropy coders and powerful channel codes. Howeve…
Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Services
Renxuan Tan, Rongpeng Li, Xiaoxue Yu +3
Federated learning (FL) in multi-service provider (SP) ecosystems is fundamentally hampered by non-cooperative dynamics, where privacy constraints and competing interests preclude…
AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air
Shiyi Yang, Xiaoxue Yu, Rongpeng Li +3
Operating Large Language Models (LLMs) on edge devices is increasingly challenged by limited communication bandwidth and strained computational and memory costs. Thus, cloud-assist…
Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning
Chongyang Tan, Ruoqi Wen, Rongpeng Li +3
Federated Learning (FL) enables distributed model training across edge devices in a privacy-friendly manner. However, its efficiency heavily depends on effective device selection a…
MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification
Yuxuan Chen, Rongpeng Li, Zhifeng Zhao +1
We present MERLOT, a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification. By applying model distillat…
Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach
Yuxuan Chen, Rongpeng Li, Xiaoxue Yu +2
Optimizing the deployment of large language models (LLMs) in edge computing environments is critical for enhancing privacy and computational efficiency. Toward efficient wireless L…
NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services
Yuxuan Chen, Rongpeng Li, Zhifeng Zhao +4
Large language models (LLMs) have triggered tremendous success to empower our daily life by generative information. The personalization of LLMs could further contribute to their ap…
AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching
Jianhang Zhu, Rongpeng Li, Guoru Ding +4
Along with the fast development of network technology and the rapid growth of network equipment, the data throughput is sharply increasing. To handle the problem of backhaul bottle…
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning
Xing Xu, Rongpeng Li, Zhifeng Zhao +1
The paper considers independent reinforcement learning (IRL) for multi-agent decision-making process in the paradigm of federated learning (FL). We show that FL can clearly improve…
Reinforcement Learning-powered Semantic Communication via Semantic Similarity
Kun Lu, Rongpeng Li, Xianfu Chen +2
We introduce a new semantic communication mechanism - SemanticRL, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Unlik…
Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty
Rongpeng Li
Online reinforcement learning (RL) has been widely applied in information processing scenarios, which usually exhibit much uncertainty due to the intrinsic randomness of channels a…
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication
Xing Xu, Rongpeng Li, Zhifeng Zhao +1
The paper considers independent reinforcement learning (IRL) for multi-agent collaborative decision-making in the paradigm of federated learning (FL). However, FL generates excessi…
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing
Chen Qi, Yuxiu Hua, Rongpeng Li +2
Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep -learning, DQL) is…