activity
20222025
most citedSemi-Federated Learning for Collaborative Intelligence in Massive IoT Networks

56 citations · 258 across the 11 of their papers we have counts for

collaborators

11 papers

cs.IT2025

Large Language Model Agents for Radio Map Generation and Wireless Network Planning

Hongye Quan, Wanli Ni, Tong Zhang +5

Using commercial software for radio map generation and wireless network planning often require complex manual operations, posing significant challenges in terms of scalability, ada…

eess.SP2024

Federated Contrastive Learning for Personalized Semantic Communication

Yining Wang, Wanli Ni, Wenqiang Yi +3

In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of lo…

eess.SP2024

OFDM-Based Digital Semantic Communication with Importance Awareness

Chuanhong Liu, Caili Guo, Yang Yang +2

Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance. However, existing works mai…

eess.SP202318 cited

Performance Analysis and Optimization of Reconfigurable Multi-Functional Surface Assisted Wireless Communications

Wen Wang, Wanli Ni, Hui Tian +1

Although reconfigurable intelligent surfaces (RISs) can improve the performance of wireless networks by smartly reconfiguring the radio environment, existing passive RISs face two…

eess.SP202342 cited

Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization

Wen Wang, Wanli Ni, Hui Tian +2

In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that onl…

cs.IT202325 cited

Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework

Jingheng Zheng, Wanli Ni, Hui Tian +3

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible…