Online Content Popularity Prediction and Learning in Wireless Edge Caching

Navneet Garg, Mathini Sellathurai, Vimal Bhatia, B. N. Bharath, Tharmalingam Ratnarajah

Research output: Contribution to journalArticle

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

Caching popular contents in advance is an important technique to achieve low latency and reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process, optimal content placement caching probabilities are obtained to maximize the average success probability (ASP) for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. In this paper, we first propose two online prediction (OP) methods for forecasting CP viz., popularity prediction model (PPM) and Grassmannian prediction model (GPM), where the unconstrained coefficients for linear prediction are obtained by solving constrained non-negative least squares. To reduce the higher computational complexity per online round, two online learning (OL) approaches viz., weighted-follow-the-leader and weighted-follow-the-regularized-leader are proposed, inspired by the OP models. In OP, ASP difference (i.e, the gap between the ASP achieved by prediction and that by known content popularity) is bounded, while in OL, sub-linear MSE regret and linear ASP regret bounds are obtained. With MovieLens dataset, simulations verify that OP methods are better for MSE and ASP difference minimization, while the OL approaches perform well for the minimization of the MSE and ASP regrets.
Original languageEnglish
Pages (from-to)1087-1100
Number of pages14
JournalIEEE Transactions on Communications
Volume68
Issue number2
Early online date26 Nov 2019
DOIs
Publication statusPublished - Feb 2020

Keywords

  • Linear prediction
  • Poisson point process (PPP)
  • caching
  • online learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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