no code implementations • 5 Mar 2024 • Yushen Lin, Kaidi Wang, Zhiguo Ding
This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-orthogonal multiple access (NOMA) under non-independent and identically distributed (non-IID) datasets, where multiple devices participate in the aggregation with time limitations and a finite number of sub-channels.
no code implementations • 4 Jan 2024 • Chunjiang Liu, Yikun Han, Haiyun Xu, Shihan Yang, Kaidi Wang, Yongye Su
This study presents a novel approach that synergizes community detection algorithms with various Graph Neural Network (GNN) models to bolster link prediction in scientific literature networks.
no code implementations • 14 Sep 2022 • Kaidi Wang, Yi Ma, Mahdi Boloursaz Mashhadi, Chuan Heng Foh, Rahim Tafazolli, Zhi Ding
In this paper, federated learning (FL) over wireless networks is investigated.
no code implementations • Physical Communication 2022 • Yunus Dursun, Kaidi Wang, Zhiguo Ding
Non-orthogonal multiple access (NOMA), as a well-qualified candidate for sixth-generation (6G) mobile networks, has been attracting remarkable research interests due to high spectral efficiency and massive connectivity.
no code implementations • 2 Dec 2020 • Kaidi Wang, Wenwen Zhang
The technology of Shared Automated Vehicles (SAVs) has advanced significantly in recent years.
Computers and Society J.4; J.6
no code implementations • 14 Sep 2020 • Fang Fang, Kaidi Wang, Zhiguo Ding, Victor C. M. Leung
In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions.