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Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Feb 15, 2021
Cong T. Nguyen, Nguyen Van Huynh, Nam H. Chu, Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham, Dusit Niyato, Eryk Dutkiewicz, Won-Joo Hwang


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Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation

Feb 05, 2021
Chunmei Xu, Shengheng Liu, Zhaohui Yang, Yongming Huang, Dusit Niyato, Kai-Kit Wong


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Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning

Dec 23, 2020
Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, Massimo Tornatore, Stefano Secci

* This paper appears in the Proceedings of IEEE Global Communications Conference (GLOBECOM) 2020. A full version appears in IEEE Transactions on Wireless Communications. arXiv:2004.12539 

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A Comprehensive Survey of 6G Wireless Communications

Dec 21, 2020
Yang Zhao, Wenchao Zhai, Jun Zhao, Tinghao Zhang, Sumei Sun, Dusit Niyato, Kwok-Yan Lam


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Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

Dec 08, 2020
Yi Liu, Ruihui Zhao, Jiawen Kang, Abdulsalam Yassine, Dusit Niyato, Jialiang Peng


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Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks

Aug 29, 2020
Shimin Gong, Jiaye Lin, Jinbei Zhang, Dusit Niyato, Dong In Kim, Mohsen Guizani

* submitted to IEEE Communications Magazine 

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Toward Smart Security Enhancement of Federated Learning Networks

Aug 19, 2020
Junjie Tan, Ying-Chang Liang, Nguyen Cong Luong, Dusit Niyato


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Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach

Jul 07, 2020
Kevin Shen Hoong Ong, Dusit Niyato, Chau Yuen

* 6 pages, 5 figures, accepted in IEEE WF-IoT 2020 

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Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning

Jul 07, 2020
Kevin Shen Hoong Ong, Yang Zhang, Dusit Niyato

* 5 pages, 2 figures, accepted in IEEE VTC-Fall 2019 

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Federated Learning for 6G Communications: Challenges, Methods, and Future Directions

Jun 04, 2020
Yi Liu, Xingliang Yuan, Zehui Xiong, Jiawen Kang, Xiaofei Wang, Dusit Niyato


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Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning

May 31, 2020
Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Cyril Leung, Chunyan Miao, Qiang Yang


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Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications

May 25, 2020
Jiaye Lin, Yuze Zou, Xiaoru Dong, Shimin Gong, Dinh Thai Hoang, Dusit Niyato


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A Secure Federated Learning Framework for 5G Networks

May 12, 2020
Yi Liu, Jialiang Peng, Jiawen Kang, Abdullah M. Iliyasu, Dusit Niyato, Ahmed A. Abd El-Latif

* This paper was accepted by IEEE Wireless Communications Magazine 

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Demand-Side Scheduling Based on Deep Actor-Critic Learning for Smart Grids

May 05, 2020
Joash Lee, Wenbo Wang, Dusit Niyato


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Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach

May 01, 2020
Nguyen Quang Hieu, Tran The Anh, Nguyen Cong Luong, Dusit Niyato, Dong In Kim, Erik Elmroth


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Local Differential Privacy based Federated Learning for Internet of Things

Apr 19, 2020
Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, Kwok Yan Lam


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Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game

Apr 04, 2020
Wenbo Wang, Amir Leshem, Dusit Niyato, Zhu Han

* 30 pages, 10 figures, submitted to IEEE TWC 

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Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach

Mar 19, 2020
Yi Liu, James J. Q. Yu, Jiawen Kang, Dusit Niyato, Shuyu Zhang

* This paper is in the second round of under review of the IEEE Internet of Things Journal 

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A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network

Mar 16, 2020
Rong Xia, Yong Xiao, Yingyu Li, Marwan Krunz, Dusit Niyato

* 6 pages, 8 figures. Accepted at IEEE International Conference on Communications (ICC), Dublin, Ireland, June 2020 

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Optimal Pricing of Internet of Things: A Machine Learning Approach

Feb 14, 2020
Mohammad Abu Alsheikh, Dinh Thai Hoang, Dusit Niyato, Derek Leong, Ping Wang, Zhu Han

* IEEE Journal on Selected Areas in Communications, 2020 dIEEE Journal on Selected Areas in Communications IEEE Journal on Selected Areas in Communications 
* 17 pages 

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Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

Jan 27, 2020
Inaam Ilahi, Muhammad Usama, Junaid Qadir, Muhammad Umar Janjua, Ala Al-Fuqaha, Dinh Thai Hoang, Dusit Niyato


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Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach

Oct 21, 2019
Huy T. Nguyen, Nguyen Cong Luong, Jun Zhao, Chau Yuen, Dusit Niyato


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Re-route Package Pickup and Delivery Planning with Random Demands

Jul 24, 2019
Suttinee Sawadsitang, Dusit Niyato, Kongrath Suankaewmanee, Puay Siew Tan

* 2019 IEEE 90th Vehicular Technology Conference: VTC2019-Fall 
* 6 pages, 4 figures, 2 tables 

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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Jul 19, 2019
Yiwen Han, Xiaofei Wang, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen

* This paper has been submitted to IEEE Communications Surveys and Tutorials for possible publication 

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Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System

Jun 26, 2019
Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato


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Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

May 16, 2019
Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, Dong In Kim

* submitted to the conference for potential publication 

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Distributed Learning for Channel Allocation Over a Shared Spectrum

Feb 19, 2019
S. M. Zafaruddin, Ilai Bistritz, Amir Leshem, Dusit Niyato


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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Oct 18, 2018
Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim

* 37 pages, 13 figures, 6 tables, 174 reference papers 

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