Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks

Navneet Garg; Mathini Sellathurai; Tharmalingam Ratnarajah

ICASSP 2019accepted0 citations

Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks

Navneet Garg, Mathini Sellathurai, Tharmalingam Ratnarajah

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.

BibTeX

@inproceedings{icassp2019_contentplacement,
  title = {Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks},
  author = {Navneet Garg and Mathini Sellathurai and Tharmalingam Ratnarajah},
  booktitle = {ICASSP 2019},
  year = {2019}
}