Edge Caching Replacement Optimization for D2D Wireless Networks via Weighted Distributed DQN

Ruibin Li, Yiwei Zhao, Chenyang Wang, Xiaofei Wang, Victor C.M. Leung, Xiuhua Li, Tarik Taleb

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)
54 Downloads (Pure)

Abstract

Duplicated download has been a big problem that affects the users' quality of service/experience (QoS/QoE) of current mobile networks. Edge caching and Device-to-Device communication are two promising technologies to release the pressure of repeated traffic downloading from the cloud. There are many researches about the edge caching policy. However, these researches have some limitations in the real scenarios. Traditional methods are lacking the self-adaptive ability in the dynamic environment and privacy issues will occur in centralized learning methods. In this paper, based on the virtue of Deep Q-Network (DQN), we propose a weighted distributed DQN model (WDDQN) to solve the cache replacement problem. Our model enables collaboratively to learn a shared predictive model. Trace-driven simulation results show that our proposed model outperforms some classical and state-of-the-art schemes.

Original languageEnglish
Title of host publication2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728131061
DOIs
Publication statusPublished - May 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Wireless Communications and Networking Conference - Seoul, Korea, Republic of
Duration: 25 May 202028 May 2020

Publication series

NameIEEE Wireless Communications and Networking Conference
ISSN (Print)1525-3511
ISSN (Electronic)1558-2612

Conference

ConferenceIEEE Wireless Communications and Networking Conference
Abbreviated titleWCNC
CountryKorea, Republic of
CitySeoul
Period25/05/202028/05/2020

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