no code implementations • 7 Nov 2023 • Yao Zhang, Zhiwen Yu, Jun Zhang, Liang Wang, Tom H. Luan, Bin Guo, Chau Yuen
Nevertheless, existing MARL algorithms ignore effective information aggregation which is fundamental for improving the learning capacity of decentralized agents.
no code implementations • 28 Aug 2023 • Zhaowei Wang, Zhisheng Yin, Xiucheng Wang, Nan Cheng, Yuan Zhang, Tom H. Luan
Considering the inherent co-channel interference due to spectrum sharing among multi-tier access networks in SAGIN, it can be leveraged to assist the physical layer security among heterogeneous transmissions.
no code implementations • 25 May 2023 • Yuntao Wang, Yanghe Pan, Miao Yan, Zhou Su, Tom H. Luan
Then, we investigate the taxonomy of security and privacy threats to AIGC and highlight the ethical and societal implications of GPT and AIGC technologies.
1 code implementation • 15 Aug 2022 • Chenhao Xu, Youyang Qu, Tom H. Luan, Peter W. Eklund, Yong Xiang, Longxiang Gao
Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models.
no code implementations • 18 Mar 2022 • Yao Zhang, Changle Li, Tom H. Luan, Chau Yuen Yuchuan Fu
Currently, autonomous vehicles are able to drive more naturally based on the driving policies learned from millions of driving miles in real environments.
no code implementations • 28 Dec 2021 • Jinkai Zheng, Tom H. Luan, Longxiang Gao, Yao Zhang, Yuan Wu
In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT.
2 code implementations • 20 Oct 2020 • Zhiping Jiang, Tom H. Luan, Han Hao, Jing Wang, Xincheng Ren, Kun Zhao, Wei Xi, Yueshen Xu, Rui Li
Three barriers always hamper the research: unknown baseband design and its influence, inadequate hardware, and the lack of versatile and flexible measurement software.
Hardware Architecture
no code implementations • 8 Jan 2017 • Xun Zhou, Changle Li, Zhe Liu, Tom H. Luan, Zhifang Miao, Lina Zhu, Lei Xiong
Based on the Gaussian distribution of traffic flow, a hybrid model with a Bayesian learning algorithm is developed which can effectively expand the application scenarios of SARIMA.