Proactive Edge Caching in Vehicular Networks: An Online Bandit Learning Approach

Proactive Edge Caching in Vehicular Networks: An Online Bandit Learning Approach

Proactively caching content at the network edge is particularly effective in high-mobility vehicular networks, where intermittent connection is the major challenge for seamless content transmission. The objective of this paper is to achieve proactive caching in vehicular networks by mobility predict...

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Bibliographic Details
Main Authors: Qiao Wang, David Grace
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Proactive edge caching
reinforcement learning
multi-armed bandit
mobility prediction
vehicular networks
uncertainty analysis
Online Access:https://ieeexplore.ieee.org/document/9987495/

Internet

https://ieeexplore.ieee.org/document/9987495/

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