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Ying He
Researcher at Carleton University
Publications - 46
Citations - 4330
Ying He is an academic researcher from Carleton University. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 20, co-authored 35 publications receiving 3431 citations. Previous affiliations of Ying He include Dalian University of Technology & Shenzhen University.
Papers
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Journal ArticleDOI
Efficient Resource Allocation for Multi-Beam Satellite-Terrestrial Vehicular Networks: A Multi-Agent Actor-Critic Method With Attention Mechanism
TL;DR: A multi-agent actor-critic method with attention mechanism is proposed to allocate resources for vehicles with strict delay requirements and minimum bandwidth consumption, where all the agents can well cooperative to achieve efficient resource allocation on-demand for the vehicles under strictly limited bandwidth resources.
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Meta-Hierarchical Reinforcement Learning (MHRL)-Based Dynamic Resource Allocation for Dynamic Vehicular Networks
TL;DR: This paper model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs), and it combines hierarchical reinforcement learning with meta-learning, which makes the proposed framework quickly adapt to a new environment by only fine-tuning the top-level master network.
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Bift: A Blockchain-Based Federated Learning System for Connected and Autonomous Vehicles
TL;DR: Bift enables distributed CAVs to train ML models locally using their own driving data and then to upload the local models to get a better global model and provides a consensus algorithm named Proof of Federated Learning to resist possible adversaries.
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An Efficient Ciphertext-Policy Attribute-Based Encryption Scheme Supporting Collaborative Decryption with Blockchain
TL;DR: Based on the linear secret sharing scheme (LSSS), an efficient scheme is proposed to realize a collaborative decryption function and a multiauthorization model is created based on the Bohen–Lynn–Shacham technology in order to solve the key-management issue.
Proceedings ArticleDOI
Video Rate Adaptation and Traffic Engineering in Mobile Edge Computing and Caching-Enabled Wireless Networks
TL;DR: This paper jointly considers SDMNs, in- network caching, and MEC to enhance the video service in next generation mobile networks and utilizes dual-decomposition method to decouple those three sets of variables.