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Content Placement Learning For Success Probability Maximization In Wireless Edge Caching Networks

Citation Author(s):
Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah
Submitted by:
Navneet Garg
Last updated:
10 May 2019 - 7:46am
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Navneet Garg
Paper Code:
MLSP-P2.8

Abstract 

Abstract: 

To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network. Given a set of available placement strategies,simulations show that the presented framework successfully learns and provides the best content placement to maximize the average success probability.

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