Search Results for author: Li-Chun Wang

Found 6 papers, 1 papers with code

Long-Lasting UAV-aided RIS Communications based on SWIPT

1 code implementation IEEE Wireless Communications and Networking Conference (WCNC) 2022 Haoran Peng, Li-Chun Wang, Geoffrey Ye Li, Ang-Hsun Tsai

Reconfigurable intelligent surface (RIS) is a promising technology for energy efficient wireless communications and has drawn significant attention recently.

Feature Fusion Use Unsupervised Prior Knowledge to Let Small Object Represent

no code implementations17 Dec 2019 Tian Liu, Li-Chun Wang, Shaofan Wang

Fusing low level and high level features is a widely used strategy to provide details that might be missing during convolution and pooling.

Communications and Networking Technologies for Intelligent Drone Cruisers

no code implementations25 Sep 2019 Li-Chun Wang, Chuan-Chi Lai, Hong-Han Shuai, Hsin-Piao Lin, Chi-Yu Li, Teng-Hu Cheng, Chiun-Hsun Chen

Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd.

A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network

no code implementations25 Sep 2019 Haoran Peng, Chao Chen, Chuan-Chi Lai, Li-Chun Wang, Zhu Han

In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories.

Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach

no code implementations10 Dec 2018 Tran The Anh, Nguyen Cong Luong, Dusit Niyato, Dong In Kim, Li-Chun Wang

In this letter, we propose to adopt a deep- Q learning algorithm that allows the server to learn and find optimal decisions without any a priori knowledge of network dynamics.

Networking and Internet Architecture

Reinforcement Learning-based Energy Trading for Microgrids

no code implementations19 Jan 2018 Liang Xiao, Xingyu Xiao, Canhuang Dai, Mugen Pengy, Li-Chun Wang, H. Vincent Poor

The Nash quilibrium (NE) of the game is provided, revealing the conditions under which the local energy generation satisfies the energy demand of the MG and providing the performance bound of the energy trading scheme.

Systems and Control

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