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JOINT CONTENT POPULARITY PREDICTION AND CONTENT DELIVERY POLICY FOR CACHE-ENABLED D2D NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH

Abstract: 

Compared with traditional device-to-device (D2D) communication networks, the users in the cache-enabled D2D communication networks can easily obtain their requested contentsfromthenearbyusers,andreducethebackhaulcosts. In this paper, we investigate the caching strategy for the cacheenabled D2D communication networks, with the consideration of caching placement and caching delivery. The content popularity and user mobility are predicted by a machine learning approach of echo state networks (ESNs) in order to determine which content to cache and where to cache. Furthermore,adeepQ-learningnetwork(DQN)algorithmisproposed to optimize the content delivery problem, with taking the delays and energy consumption into consideration. Simulation results show that the content hit rate and the traffic offloadingcanberemarkablyimprovedwiththeproposedapproach, compared to the random caching strategy.

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Paper Details

Authors:
Jiaying Yin, Lixin Li, Yang Xu, Wei Liang, Huisheng Zhang, and Zhu Han
Submitted On:
27 March 2019 - 9:05am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Jiaying Yin
Paper Code:
1288
Document Year:
2018
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Document Files

GlobalSIP_YJY.pdf

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[1] Jiaying Yin, Lixin Li, Yang Xu, Wei Liang, Huisheng Zhang, and Zhu Han, "JOINT CONTENT POPULARITY PREDICTION AND CONTENT DELIVERY POLICY FOR CACHE-ENABLED D2D NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3715. Accessed: May. 26, 2019.
@article{3715-18,
url = {http://sigport.org/3715},
author = {Jiaying Yin; Lixin Li; Yang Xu; Wei Liang; Huisheng Zhang; and Zhu Han },
publisher = {IEEE SigPort},
title = {JOINT CONTENT POPULARITY PREDICTION AND CONTENT DELIVERY POLICY FOR CACHE-ENABLED D2D NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH},
year = {2018} }
TY - EJOUR
T1 - JOINT CONTENT POPULARITY PREDICTION AND CONTENT DELIVERY POLICY FOR CACHE-ENABLED D2D NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH
AU - Jiaying Yin; Lixin Li; Yang Xu; Wei Liang; Huisheng Zhang; and Zhu Han
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3715
ER -
Jiaying Yin, Lixin Li, Yang Xu, Wei Liang, Huisheng Zhang, and Zhu Han. (2018). JOINT CONTENT POPULARITY PREDICTION AND CONTENT DELIVERY POLICY FOR CACHE-ENABLED D2D NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH. IEEE SigPort. http://sigport.org/3715
Jiaying Yin, Lixin Li, Yang Xu, Wei Liang, Huisheng Zhang, and Zhu Han, 2018. JOINT CONTENT POPULARITY PREDICTION AND CONTENT DELIVERY POLICY FOR CACHE-ENABLED D2D NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH. Available at: http://sigport.org/3715.
Jiaying Yin, Lixin Li, Yang Xu, Wei Liang, Huisheng Zhang, and Zhu Han. (2018). "JOINT CONTENT POPULARITY PREDICTION AND CONTENT DELIVERY POLICY FOR CACHE-ENABLED D2D NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH." Web.
1. Jiaying Yin, Lixin Li, Yang Xu, Wei Liang, Huisheng Zhang, and Zhu Han. JOINT CONTENT POPULARITY PREDICTION AND CONTENT DELIVERY POLICY FOR CACHE-ENABLED D2D NETWORKS: A DEEP REINFORCEMENT LEARNING APPROACH [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3715