A Q-learning based proactive caching strategy for non-safety related services in vehicular networks

Hou, Lu, Lei, Lei, Zheng, Kan, and Wang, Xianbin (2019) A Q-learning based proactive caching strategy for non-safety related services in vehicular networks. IEEE Internet of Things Journal, 6 (3). pp. 4512-4520.

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Content caching has brought huge potential for the provisioning of non-safety related infotainment services in future vehicular networks. Assisted by multi-access edge computing, roadside units (RSUs) could become cache-capable and offer fast caching services to moving vehicles for content providers. On the other hand, deep learning makes it possible to accurately estimate the behavior of vehicles, which enables effective proactive caching strategies. However, caching services considering both the mobility of vehicles and storage could incur increased latency and considerable cost due to the cache size needed in RSUs. In this paper, we model such a problem using Markov decision processes, and propose a heuristic Q-learning solution together with vehicle movement predictions based on a long short-term memory network. The optimal caching strategy which minimizes the latency of caching services can be derived by our heuristic εn-greedy training processes. Numerical results demonstrate that our proposed strategy can achieve better performance compared with several baselines under different prediction accuracies.

Item ID: 57449
Item Type: Article (Research - C1)
ISSN: 2327-4662
Copyright Information: © 2018 IEEE.
Date Deposited: 14 Mar 2019 00:40
FoR Codes: 40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8901 Communication Networks and Services > 890103 Mobile Data Networks and Services @ 100%
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