Real-Time Data Transmission Optimization on 5G Remote-Controlled Units Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Real-Time Data Transmission Optimization on 5G Remote-Controlled Units Using Deep Reinforcement Learning

Published:2023 Issue: Volume: Page:281-295
ISSN:0302-9743
Container-title:Architecture of Computing Systems
language:
Short-container-title:

Author:

Smirnov Nikita^ORCID,Tomforde Sven^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-42785-5_19

Reference20 articles.

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2. Cui, L., Su, D., Yang, S., Wang, Z., Ming, Z.: TCLiVi: transmission control in live video streaming based on deep reinforcement learning. IEEE Trans. Multimedia 23, 651–663 (2021). https://doi.org/10.1109/TMM.2020.2985631

3. Dao, N.N., Tran, A.T., Tu, N.H., Thanh, T.T., Bao, V.N.Q., Cho, S.: A contemporary survey on live video streaming from a computation-driven perspective. ACM Comput. Surv. 54(10), 1–38 (2022). https://doi.org/10.1145/3519552

4. Feng, Y., Liu, S., Zhu, Y.: Real-time spatio-temporal lidar point cloud compression (2020)

5. Huang, S., Dossa, R.F.J., Raffin, A., Kanervisto, A., Wang, W.: The 37 implementation details of proximal policy optimization (2022). https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/. Accessed 08 Aug 2023

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