Video delivery and caching over the millimeter-wave (mmWave) spectrum is a promising technology for high data rate and efficient frequency utilization in many applications, including distributed vehicular networks. However, due to the short handoff duration, calibrating both optimal power allocation of each base station toward its associated vehicles and cache allocation are challenging for their computational complexity. Heretofore, most video delivery applications were based on on-line or off-line algorithms, and they were limited to compute and optimize high dimensional objectives within low-delay in large scale vehicular networks. On the other hand, deep reinforcement learning is shown for learning such scale of a problem with an optimized policy learning phase. In this paper, we propose deep deterministic policy gradient-based power control of mmWave base station (mBS) and proactive cache allocation toward mBSs in distributed mmWave Internet-of-vehicle (IoV) networks. Simulation results validate the performance of the proposed caching scheme in terms of quality of the provisioned video and playback stall in various scales of IoV networks.
Bibliographical noteFunding Information:
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00170, Virtual Presence in Moving Objects through 5G) and also by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2017-0-01637) supervised by the IITP (Institute for Information & Communications Technology Promotion). J. Kim, A. Mo-haisen, and W. Lee are the corresponding authors of this paper.
Manuscript received Nov. 21, 2020; revised June 15, 2020; approved for publication by Tim O’Shea, Guest Editor, July 15, 2020. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2017-0-01637) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation); and also by MSIT, Korea, under ITRC support program (IITP-2018-0-01396) supervised by IITP. D. Kwon is with Hyundai-Autoever, Seoul, Korea, email: firstname.lastname@example.org. J. Kim is with the School of Electrical Engineering, Korea University, Seoul, Korea, e-mail: email@example.com. D. A. Mohaisen is with the Department of Computer Science, University of Central Florida, Orlando, FL, USA, e-mail: firstname.lastname@example.org. W. Lee is with the School of Cybersecurity, Korea University, Seoul, Korea, e-mail: email@example.com. J. Kim, D.A. Mohaisen, and W. Lee are corresponding authors. Digital Object Identifier: 10.1109/JCN.2020.000022 Fig. 1. Considered power-cache aware video caching scheme in distributed IoV networks.
© 2011 KICS.
- Deep reinforcement learning
- Internet-of-vehicle caching
- video caching
ASJC Scopus subject areas
- Information Systems
- Computer Networks and Communications