2 citations · 3 across the 7 of their papers we have counts for
7 papers
RNet: 2D Deep Residual Learning with Height Embedding for 3D Radio Map Estimation
Huiting Rao, Junyuan Wang, Huiling Zhu +1
Acquiring channel knowledge is required by many applications. For instance, handover in cellular networks is mainly decided based on the knowledge of pathloss. In contrast to tradi…
Data Heterogeneity-Aware Client Selection for Federated Learning in Wireless Networks
Yanbing Yang, Huiling Zhu, Wenchi Cheng +3
Federated Learning (FL) enables mobile edge devices, functioning as clients, to collaboratively train a decentralized model while ensuring local data privacy. However, the efficien…
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing
Cui Zhang, Xiao Xu, Qiong Wu +4
In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global…
Spectral Efficiency Maximization for Active RIS-aided Cell-Free Massive MIMO Systems with Imperfect CSI
Mahdi Eskandari, Huiling Zhu, Jiangzhou Wang
A cell-free network merged with active reconfigurable reflecting surfaces (RIS) is investigated in this paper. Based on the imperfect channel state information (CSI), the aggregate…
Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systems
Qiong Wu, Xiaobo Wang, Pingyi Fan +3
Federated learning (FL) is a promising technology for vehicular networks to protect vehicles' privacy in Internet of Vehicles (IoV). Vehicles with limited computation capacity may…
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
Qiong Wu, Wenhua Wang, Pingyi Fan +3
Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users'…
On Consideration of Content Preference and Sharing Willingness in D2D Assisted Offloading
Yijin Pan, Cunhua Pan, Huiling Zhu +3
Device-to-device (D2D) assisted offloading heavily depends on the participation of human users. The content preference and sharing willingness of human users are two crucial factor…