most citedNetLLM: Adapting Large Language Models for Networking

161 citations · 162 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NI2026

Morphe: High-Fidelity Generative Video Streaming with Vision Foundation Model

Tianyi Gong, Zijian Cao, Zixing Zhang +4

Video streaming is a fundamental Internet service, while the quality still cannot be guaranteed especially in poor network conditions such as bandwidth-constrained and remote areas…

cs.IT2024

Generative Semantic Communication: Architectures, Technologies, and Applications

Jinke Ren, Yaping Sun, Hongyang Du +7

This paper delves into the applications of generative artificial intelligence (GAI) in semantic communication (SemCom) and presents a thorough study. Three popular SemCom systems e…

cs.IT2024

Receiver-Centric Generative Semantic Communications

Xunze Liu, Yifei Sun, Zhaorui Wang +4

This paper investigates semantic communications between a transmitter and a receiver, where original data, such as videos of interest to the receiver, is stored at the transmitter.…

cs.SD2024★ 1 cited

Generative Semantic Communication for Text-to-Speech Synthesis

Jiahao Zheng, Jinke Ren, Peng Xu +5

Semantic communication is a promising technology to improve communication efficiency by transmitting only the semantic information of the source data. However, traditional semantic…

cs.NI2024★ 161 cited

NetLLM: Adapting Large Language Models for Networking

Duo Wu, Xianda Wang, Yaqi Qiao +4

Many networking tasks now employ deep learning (DL) to solve complex prediction and optimization problems. However, current design philosophy of DL-based algorithms entails intensi…