Online Learning Models for Content Popularity Prediction in Wireless Edge Caching

Navneet Garg, Mathini Sellathurai, Bharath Bettagere, Vimal Bhatia, Tharmalingam Ratnarajah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the geographical edge caching, where base stations (BSs) and users are distributed as Poisson point process (PPP) and the caching performance is measured using average success probability (ASP), we consider the content popularity (CP) prediction problem to maximize the ASP. Two online learning (OL) models are proposed based on weighted-follow-the-leader (FTL) and weighted-follow-the-regularized-leader (FoReL). Regret analysis concludes that OL methods results in sub-linear MSE regret and linear ASP regret. With MovieLens dataset, simulations verify that the FTL yields better MSE regret while FoReL has lower ASP regret.

Original languageEnglish
Title of host publication53rd Asilomar Conference on Signals, Systems, and Computers 2019
PublisherIEEE
Pages337-341
Number of pages5
ISBN (Electronic)9781728143002
DOIs
Publication statusPublished - 30 Mar 2020
Event53rd Asilomar Conference on Circuits, Systems and Computers 2019 - Pacific Grove, United States
Duration: 3 Nov 20196 Nov 2019

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers 2019
Abbreviated titleACSSC 2019
Country/TerritoryUnited States
CityPacific Grove
Period3/11/196/11/19

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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