no code implementations • 24 May 2024 • Kaidi Wang, Zhiguo Ding, Daniel K. C. So, Zhi Ding
To further improve learning performance by increasing device participation under the maximum time consumption constraint, we formulate an energy consumption minimization problem by including resource allocation and sub-channel assignment.
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.