Hang Li
Texas A&M University
61 Papers
37 Citations
Hang Li is an academic researcher from Texas A&M University. The author has contributed to research in topics: Computer science & Visible light communication. The author has an hindex of 13, co-authored 43 publications. Previous affiliations of Hang Li include The Chinese University of Hong Kong & University of California, Davis.
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Papers
Reinforcement Learning-Based Multiaccess Control and Battery Prediction With Energy Harvesting in IoT Systems
TL;DR: In this article, the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels were investigated.
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A general utility optimization framework for energy-harvesting-based wireless communications
TL;DR: A general optimization framework to maximize the utility of EH wireless communication systems is developed that encapsulates a variety of design problems, such as throughput maximization and outage probability minimization in single-user and multiuser setups, and provides useful guidelines to the practical design of general EH-based communication systems.
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Simultaneous Lightwave Information and Power Transfer in Visible Light Communication Systems
TL;DR: This paper systematically analyzing both the information receiver and the energy harvester, and obtaining the explicit expressions to characterize the illumination-rate-energy region, investigates the downlink unicast transmission of multi-LED multi-user SLIPT VLC networks, and studies the total transmit power minimization problem.
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Optimal and Robust Secure Beamformer for Indoor MISO Visible Light Communication
TL;DR: This paper investigates the physical-layer secrecy problem for the indoor multiple-input single-output (MISO) visible light communication systems and proposes two kinds of optimal secure beamformers, designed to minimize the transmit power and to maximize the secrecy rate.
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Power Control in Energy Harvesting Multiple Access System With Reinforcement Learning
TL;DR: This paper proposes an actor–critic deep $Q$ -network (DQN) RL algorithm to simultaneously deal with the access and continuous power control problem, by considering both the sum rate and prediction loss.
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