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Yuanwei Liu
Researcher at Queen Mary University of London
Publications - 477
Citations - 18977
Yuanwei Liu is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 53, co-authored 359 publications receiving 11049 citations. Previous affiliations of Yuanwei Liu include Xidian University & University of Houston.
Papers
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Performance Analysis of Intelligent Reflecting Surface Assisted NOMA Networks
Xinwei Yue,Yuanwei Liu +1 more
TL;DR: In this paper, the performance of an IRS-assisted NOMA network with imperfect successive interference cancellation (ipSIC) and perfect successive IC (pSIC), where a base station transmits superposed signals to multiple users by the virtue of an RIS, is investigated by invoking 1-bit coding scheme.
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Artificial Intelligence Aided Next-Generation Networks Relying on UAVs
TL;DR: In this article, an Artificial Intelligence (AI) enabled UAV aided wireless networks (UAWN) for overcoming the challenges imposed by the random fluctuation of wireless channels, blocking and user mobility effects is proposed.
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Enhancing the Fuel-Economy of V2I-Assisted Autonomous Driving: A Reinforcement Learning Approach
TL;DR: A novel framework for enhancing the driving safety and fuel economy of autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication networks is proposed and a deep reinforcement learning (DRL) approach is proposed for making collision-free decisions.
Posted Content
Application of Non-orthogonal Multiple Access in LTE and 5G Networks
TL;DR: In this article, the authors provide a systematic treatment of NOMA, from its combination with multiple-input multiple-output (MIMO) technologies, to cooperative and non-orthogonal multiple access (NOMA) and cognitive radio.
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Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks
TL;DR: A Q-learning based caching placement and resource allocation algorithm, where the UAV learns and selects action with soft ${\varepsilon }$-greedy strategy to search for the optimal match between actions and states, suitable for large-scale networks.