Computation for Reinforcement Learning at the Mobile Edge Network

Zixu Zhao

doi:10.1051/shsconf/202214403016

All issues

Volume 144 (2022)

SHS Web Conf., 144 (2022) 03016

References

Open Access

Issue		SHS Web Conf. Volume 144, 2022 2022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)


Article Number		03016
Number of page(s)		5
Section		Application of Artificial Intelligence Technology and Machine Learning Algorithms
DOI		https://doi.org/10.1051/shsconf/202214403016
Published online		26 August 2022

Bi, S., Ho, C., & Zhang, R.. Wireless powered communication: opportunities and challenges. Communications Magazine, IEEE, 53(4) (2015) pp. 117-125. [CrossRef] [Google Scholar]
Chiang, M., & Tao, Z.. Fog and iot: an overview of research opportunities. IEEE Internet of Things Journal, 3(6) (2017) pp. 854-864. [Google Scholar]
Mao, Y., Zhang, J., & Letaief, K. B.. Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J. Sel. Areas Commun., 34(12) (2016) pp.3590-3605 [CrossRef] [Google Scholar]
You, C., Huang, K., Chae, H., & Kim, B. H.. “Energy-effificient resource allocation for mobileedge computation offloading, ” IEEE Trans. Wireless Commun., 16(3) (2017) pp. 1397–1411. [CrossRef] [Google Scholar]
Chen, X., Jiao, L., Li, W., & Fu, X.. Efficient multiuser computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking, 24(5) (2016) pp. 2795-2808. [CrossRef] [Google Scholar]
Wang, F., Xu, J., Wang, X., & Cui, S.. “Joint offloading and computing optimization in wireless powered mobile-edge computing systems, ” IEEE Trans. Wireless 17(3) (2018) pp. 1784–1797. [CrossRef] [Google Scholar]
S. Bi, Y.J.A. Zhang, “Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading, ” IEEE Trans. Wireless Commun., 17(6), 2018, pp.4177–4190. [CrossRef] [Google Scholar]
Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief, “A survey on mobile edge computing: The communication perspective, ” IEEE Commun. Surveys Tuts., 19(4) (2017) pp.2322–2358. [CrossRef] [Google Scholar]
T. X. Tran and D. Pompili, “Joint Task Offloading and Resource Allocation for Multi-Server MobileEdge Computing Networks, ” IEEE Transactions on Vehicular Technology, 68(1) (2019) pp. 856-868. [CrossRef] [Google Scholar]
Guo, S., Xiao, B., Yang, Y., & Yang, Y. Energyefficient dynamic offloading and resource scheduling in mobile cloud computing. IEEE INFOCOM 2016 IEEE Conference on Computer Communications. IEEE. pp. 1–9. 2016. [Google Scholar]
Dinh, T. Q., Tang, J., La, Q. D., & Quek, T.. Offloading in mobile edge computing: task allocation and computational frequency scaling. IEEE Transactions on Communications, 65(8) (2017) pp. 3571-3584. [Google Scholar]
Volodymyr, M., Koray, K., David, S., Rusu, A. A., Joel, V., & Bellemare, M. G., et al. Human-level control through deep reinforcement learning. Nature, 518(7540), (2015). p. 529. [NASA ADS] [CrossRef] [Google Scholar]
Dulac-Arnold G, Evans R, Hasselt H V, et al. Deep Reinforcement Learning in Large Discrete Action Spaces. Computer Science, 2015. [Google Scholar]
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning, ” Nature, 521(7553) (2015) p. 436 [CrossRef] [Google Scholar]
H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, N.D. Sidiropoulos, “Learning to optimize: Training deep neural networks for wireless resource management, ” in Proc. IEEE SPAWC, Jul. 2017, pp. 1–6. [Google Scholar]
Sun, Haoran, Chen, Xiangyi, & Shi, Qingjiang, et al. Learning to optimize: training deep neural networks for interference management. IEEE Transactions on Signal Processing: A publication of the IEEE Signal Processing Society, 66(20), (2018) pp. 5438-5453. [Google Scholar]
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, et al, “Continuous control with deep reinforcement learning, ” in Proc. ICLR, 2016. https://doi.org/10.48550/arXiv.1509.02971 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] Bi, S., Ho, C., & Zhang, R.. Wireless powered communication: opportunities and challenges. Communications Magazine, IEEE, 53(4) (2015) pp. 117-125. [CrossRef] [Google Scholar]

[2] Chiang, M., & Tao, Z.. Fog and iot: an overview of research opportunities. IEEE Internet of Things Journal, 3(6) (2017) pp. 854-864. [Google Scholar]

[3] Mao, Y., Zhang, J., & Letaief, K. B.. Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J. Sel. Areas Commun., 34(12) (2016) pp.3590-3605 [CrossRef] [Google Scholar]

[4] You, C., Huang, K., Chae, H., & Kim, B. H.. “Energy-effificient resource allocation for mobileedge computation offloading, ” IEEE Trans. Wireless Commun., 16(3) (2017) pp. 1397–1411. [CrossRef] [Google Scholar]

[5] Chen, X., Jiao, L., Li, W., & Fu, X.. Efficient multiuser computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking, 24(5) (2016) pp. 2795-2808. [CrossRef] [Google Scholar]

[6] Wang, F., Xu, J., Wang, X., & Cui, S.. “Joint offloading and computing optimization in wireless powered mobile-edge computing systems, ” IEEE Trans. Wireless 17(3) (2018) pp. 1784–1797. [CrossRef] [Google Scholar]

[7] S. Bi, Y.J.A. Zhang, “Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading, ” IEEE Trans. Wireless Commun., 17(6), 2018, pp.4177–4190. [CrossRef] [Google Scholar]

[8] Y. Mao, C. You, J. Zhang, K. Huang, K. B. Letaief, “A survey on mobile edge computing: The communication perspective, ” IEEE Commun. Surveys Tuts., 19(4) (2017) pp.2322–2358. [CrossRef] [Google Scholar]

[9] T. X. Tran and D. Pompili, “Joint Task Offloading and Resource Allocation for Multi-Server MobileEdge Computing Networks, ” IEEE Transactions on Vehicular Technology, 68(1) (2019) pp. 856-868. [CrossRef] [Google Scholar]

[10] Guo, S., Xiao, B., Yang, Y., & Yang, Y. Energyefficient dynamic offloading and resource scheduling in mobile cloud computing. IEEE INFOCOM 2016 IEEE Conference on Computer Communications. IEEE. pp. 1–9. 2016. [Google Scholar]

[11] Dinh, T. Q., Tang, J., La, Q. D., & Quek, T.. Offloading in mobile edge computing: task allocation and computational frequency scaling. IEEE Transactions on Communications, 65(8) (2017) pp. 3571-3584. [Google Scholar]

[12] Volodymyr, M., Koray, K., David, S., Rusu, A. A., Joel, V., & Bellemare, M. G., et al. Human-level control through deep reinforcement learning. Nature, 518(7540), (2015). p. 529. [NASA ADS] [CrossRef] [Google Scholar]

[13] Dulac-Arnold G, Evans R, Hasselt H V, et al. Deep Reinforcement Learning in Large Discrete Action Spaces. Computer Science, 2015. [Google Scholar]

[14] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning, ” Nature, 521(7553) (2015) p. 436 [CrossRef] [Google Scholar]

[15] H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, N.D. Sidiropoulos, “Learning to optimize: Training deep neural networks for wireless resource management, ” in Proc. IEEE SPAWC, Jul. 2017, pp. 1–6. [Google Scholar]

[16] Sun, Haoran, Chen, Xiangyi, & Shi, Qingjiang, et al. Learning to optimize: training deep neural networks for interference management. IEEE Transactions on Signal Processing: A publication of the IEEE Signal Processing Society, 66(20), (2018) pp. 5438-5453. [Google Scholar]

[17] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, et al, “Continuous control with deep reinforcement learning, ” in Proc. ICLR, 2016. https://doi.org/10.48550/arXiv.1509.02971 [Google Scholar]