Details of Research Outputs

TitleDeep Reinforcement Learning Based Mobility Load Balancing under Multiple Behavior Policies
Author (Name in English or Pinyin)
Yue Xu1,2; Wenjun Xu1; Zhi Wang3; Jiaru Lin1; Shuguang Cui3,4
Date Issued2019-05-20
Conference Name2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
Source PublicationICC 2019 - 2019 IEEE International Conference on Communications (ICC)
Conference PlaceShanghai, China
DOI10.1109/ICC.2019.8761343
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
Published range国外学术期刊
Volume Issue Pagesv 2019-May,
References
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[2] J. Park, Y. Kim, and J. Lee, "Mobility load balancing method for selforganizing wireless networks inspired by synchronization and matching with preferences, " IEEE Trans. Veh. Techno/., vol. 67, no. 3, pp. 25942606, March 2018.
[3] S. S. Mwanje, L. C. Schmelz, and A. Mitschele-Thiel, "Cognitive cellular networks: A Q-Iearning framework for self-organizing networks, " IEEE Transactions on Network and Service Management, vol. 13, no. I, pp. 85-98, March 2016.
[4] R. Kwan, R. Amott, R. Paterson, and R. Trivisonno, "On mobility load balancing for LTE systems, " in IEEE Vehicular Technology Conference (VTC Fall), Taipei, Taiwan, September 2010, pp. 1-5.
[5] Y. Yang, P. Li, X. Chen, and W. Wang, "A high-efficient algorithm of mobile load balancing in LTE system, " in IEEE Vehicular Technology Conference (VTC Fall), Yokohama, Japan, September 2012, pp. 1-5.
[6] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, and G. Ostrovski, "Human-level control through deep reinforcement learning, " Nature, vol. 518, no. 7540, pp. 529-533, February 2015.
[7] T. P. Lillic.rap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, "Continuous control with deep reinforcement learning, " in International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, May 2016, pp. 1-14.
[8] Z. Wang, L. Li, Y. Xu, H. Tian, and S. Cui, "Handover control in wireless systems via asynchronous multi-user deep reinforcement learning, " IEEE Internet of Things Journal, June 2018, to appear.
[9] 3GPP, "TS 36.331 Evolved universal terrestrial.radio access (E-UTRAN); Radio resource control (RRC); Protocol specification, " Tech. Rep. Release 8, July 2009.
[10] R. S. Sutton, A. G. Barto, and F. Bach, Reinforcement Learning: An introduction. MIT Press, 1998.
[11] [II] M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and C. S. Hong, "Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience, " IEEE J. Se/. Areas Commun., vol. 35, no. 5, pp. 1046-1061, May 2017.
[12] D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller, "Deterministic policy gradient algorithms, " in International Conference on Machine Learning (ICML), Beijing, China, June 2014, pp. 387-395.
[13] T. Degris, M. White, and R. S. Sutton, "Off-policy actor-critic, " in International Conference on Machine Learning (ICML), Edinburgh, Scotland, June 2012, pp. 179-186.
[14] H. R. Maei, C. Szepesvri, S. Bhatnagar, and R. S. Sutton, "Toward offpolicy learning control with function approximation, " in International Conference on Machine Learning (ICML) , Haifa, Israel, June 2010, pp. 719-726.
[15] R. S. Sutton, H. R. Maei, D. Precup, S. Bhatnagar, D. Silver, C. Szepesvari, and E. Wiewiora, "Fast gradient-descent methods for temporal-difference learning with linear function approximation, " in International Conference on Machine Learning (ICML), Montreal, Canada, June 2009, pp. 993-1000.
[16] V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, "Asynchronous methods for deep reinforcement learning, " in International Conference on Machine Learning (ICML) , New York, USA, June 2016, pp. 1928-1937.
[17] A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. D. Maria, V. Panneershelvam, M. Suleyman, C. Beattie, and S. Petersen, "Massively parallel methods for deep reinforcement learning, " arXiv preprint:J507.04296, July 2015. [Online]. Available:
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/984
CollectionSchool of Science and Engineering
Affiliation
1.Beijing University of Posts and Telecommunications
2.深圳市大数据研究院
3.Department ofElectrical and Computer Engineering University of California, Davis
4.理工学院
First Author AffilicationShenzhen Research Institute of Big Data
Recommended Citation
GB/T 7714
Yue Xu,Wenjun Xu,Zhi Wanget al. Deep Reinforcement Learning Based Mobility Load Balancing under Multiple Behavior Policies[C],2019.
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