Deep Q-Learning-Based Content Caching With Update Strategy for Fog Radio Access Networks

Deep Q-Learning-Based Content Caching With Update Strategy for Fog Radio Access Networks

In order to improve the edge caching efficiency of the fog radio access network (F-RAN), this paper put forward a distributed deep Q-learning-based content caching scheme based on user preference prediction and content popularity prediction. Given that the constraint that the storage capacity of eac...

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Bibliographic Details
Main Authors: Fan Jiang, Zeng Yuan, Changyin Sun, Junxuan Wang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Deep Q-learning-based content caching
caching hit rate
DQN
user preference
content update
Online Access:https://ieeexplore.ieee.org/document/8758962/

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