Search Results for author: Tom H. Luan

Found 8 papers, 2 papers with code

Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning

no code implementations7 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.

Graph Learning Multi-agent Reinforcement Learning +1

Label-free Deep Learning Driven Secure Access Selection in Space-Air-Ground Integrated Networks

no code implementations28 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.

A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions

no code implementations25 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.

An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

1 code implementation15 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.

Federated Learning

Collaborative Driving: Learning- Aided Joint Topology Formulation and Beamforming

no code implementations18 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.

Autonomous Driving

Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles

no code implementations28 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.

Autonomous Vehicles Scheduling

Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform

2 code implementations20 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

See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS

no code implementations8 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.

Scheduling Time Series +1

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