Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots

2018 52nd Asilomar Conference on Signals, Systems, and Computers ◽

10.1109/acssc.2018.8645103 ◽

2018 ◽

Author(s):

Harald Bayerlein ◽

Rajeev Gangula ◽

David Gesbert

Keyword(s):

Base Station ◽

Learning Approach ◽

Trajectory Design ◽

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Optimized Trajectory Design in UAV Based Cellular Networks: A Double Q-Learning Approach

2018 IEEE International Conference on Communication Systems (ICCS) ◽

10.1109/iccs.2018.8689249 ◽

2018 ◽

Author(s):

Xuanlin Liu ◽

Mingzhe Chen ◽

Changchuan Yin

Keyword(s):

Cellular Networks ◽

Learning Approach ◽

Trajectory Design ◽

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Energy-efficient UAV trajectory design for backscatter communication: A deep reinforcement learning approach

China Communications ◽

10.23919/jcc.2020.10.009 ◽

2020 ◽

Vol 17 (10) ◽

pp. 129-141

Author(s):

Yiwen Nie ◽

Junhui Zhao ◽

Jun Liu ◽

Jing Jiang ◽

Ruijin Ding

Keyword(s):

Reinforcement Learning ◽

Energy Efficient ◽

Learning Approach ◽

Trajectory Design ◽

Backscatter Communication

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Trajectory Design for UAV-Assisted Emergency Communications: A Transfer Learning Approach

GLOBECOM 2020 - 2020 IEEE Global Communications Conference ◽

10.1109/globecom42002.2020.9348212 ◽

2020 ◽

Author(s):

Xinruo Zhang ◽

Gan Zheng ◽

Sangarapillai Lambotharan

Keyword(s):

Transfer Learning ◽

Learning Approach ◽

Trajectory Design ◽

Emergency Communications

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Energy and Service-Priority aware Trajectory Design for UAV-BSs using Double Q-Learning

2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) ◽

10.1109/ccnc49032.2021.9369472 ◽

2021 ◽

Author(s):

Sayed Amir Hoseini ◽

Ayub Bokani ◽

Jahan Hassan ◽

Shavbo Salehi ◽

Salil S. Kanhere

Keyword(s):

Trajectory Design ◽

Service Priority

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Mobility-Aware Trajectory Design for Aerial Base Station Using Deep Reinforcement Learning

2020 International Conference on Wireless Communications and Signal Processing (WCSP) ◽

10.1109/wcsp49889.2020.9299676 ◽

2020 ◽

Author(s):

Guoliang Hao ◽

Wanli Ni ◽

Hui Tian ◽

Leilei Cao

Keyword(s):

Reinforcement Learning ◽

Base Station ◽

Trajectory Design

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DDQP: A Double Deep Q-Learning Approach to Online Fault-tolerant SFC Placement

IEEE Transactions on Network and Service Management ◽

10.1109/tnsm.2021.3049298 ◽

2021 ◽

pp. 1-1

Author(s):

Lei Wang ◽

Weixi Mao ◽

Jin Zhao ◽

Yuedong Xu

Keyword(s):

Fault Tolerant ◽

Learning Approach ◽

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Data-driven tracking control approach for linear systems by on-policy Q-learning approach

2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV) ◽

10.1109/icarcv50220.2020.9305479 ◽

2020 ◽

Author(s):

Zhang Yihan ◽

Xiao Zhenfei ◽

Li Jinna

Keyword(s):

Linear Systems ◽

Tracking Control ◽

Data Driven ◽

Learning Approach ◽

Control Approach ◽

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Dynamic traffic steering based on fuzzy Q-Learning approach in a multi-RAT multi-layer wireless network

Computer Networks ◽

10.1016/j.comnet.2014.06.003 ◽

2014 ◽

Vol 71 ◽

pp. 100-116 ◽

Author(s):

P. Muñoz ◽

D. Laselva ◽

R. Barco ◽

P. Mogensen

Keyword(s):

Wireless Network ◽

Learning Approach ◽

Dynamic Traffic ◽

Traffic Steering

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Dynamic Pricing Decision for Perishable Goods: A Q-Learning Approach

2008 4th International Conference on Wireless Communications, Networking and Mobile Computing ◽

10.1109/wicom.2008.2786 ◽

2008 ◽

Author(s):

Yan Cheng

Keyword(s):

Dynamic Pricing ◽

Learning Approach ◽

Perishable Goods ◽

Pricing Decision

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A Q-learning approach to automated unmanned air vehicle demining

The Journal of Defense Modeling and Simulation Applications Methodology Technology ◽

10.1177/1548512911414599 ◽

2011 ◽

Vol 9 (1) ◽

pp. 83-92 ◽

Author(s):

Silvia Ferrari ◽

Greyson Daugherty

Keyword(s):

Learning Approach ◽

Unmanned Air Vehicle ◽

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