15 citations · 22 across the 9 of their papers we have counts for
5 papers · 1 filter
FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
Sijing Xie, Dingzhu Wen, Changsheng You +3
Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further…
Collaborative Edge AI Inference over Cloud-RAN
Pengfei Zhang, Dingzhu Wen, Guangxu Zhu +3
In this paper, a cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture rea…
Artificial Intelligence Enabled NOMA Towards Next Generation Multiple Access
Xiaoxia Xu, Yuanwei Liu, Xidong Mu +3
This article focuses on the application of artificial intelligence (AI) in non-orthogonal multiple-access (NOMA), which aims to achieve automated, adaptive, and high-efficiency mul…
Cluster-Free NOMA Communications Towards Next Generation Multiple Access
Xiaoxia Xu, Yuanwei Liu, Xidong Mu +2
A generalized downlink multi-antenna non-orthogonal multiple access (NOMA) transmission framework is proposed with the novel concept of cluster-free successive interference cancell…
Embedding LTE-U within Wi-Fi Bands for Spectrum Efficiency Improvement
Qimei Chen, Guanding Yu, Hesham M. Elmaghraby +2
Driven by growing spectrum shortage, Long-term Evolution in unlicensed spectrum (LTE-U) has recently been proposed as a new paradigm to deliver better performance and experience fo…