8 papers
Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning
Liwei Wang, Wen Chen, Jun Li +4
Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked…
Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization
Yujie Huang, Haibin Wan, Xiangcheng Li +4
With the rapid advances in programmable materials, reconfigurable intelligent surfaces (RIS) have become a pivotal technology for future wireless communications. The simultaneous t…
Fair Rate Maximization for Multi-User Multi-Cell MISO Communication Systems via Novel Transmissive RIS Transceiver
Yuan Guo, Wen Chen, Qingqing Wu +4
This paper explores a multi-cell multiple-input single-output (MISO) downlink communication system enabled by a unique transmissive reconfigurable intelligent surface (TRIS) transc…
Movable Antenna Enhanced Networked Integrated Sensing and Communication System
Yuan Guo, Wen Chen, Qingqing Wu +5
Integrated sensing and communication (ISAC) is a key technology for future 6G networks. Most existing studies focus on monostatic and/or bistatic setups with limited coverage and c…
Hierarchical Federated Learning for Social Network with Mobility
Zeyu Chen, Wen Chen, Jun Li +5
Federated Learning (FL) offers a decentralized solution that allows collaborative local model training and global aggregation, thereby protecting data privacy. In conventional FL f…
Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
Xuefeng Han, Wen Chen, Jun Li +6
Multimodal federated learning (MFL) is a distributed framework for training multimodal models without uploading local multimodal data of clients, thereby effectively protecting cli…