Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/5150
Title: Online Learning Models for Content Popularity Prediction in Wireless Edge Caching
Authors: Bhatia, Vimal
Keywords: Computer circuits;Learning systems;Caching performance;Content popularities;Edge caching;Follow the leaders;Follow the regularized leaders;Online learning;Poisson point process;Prediction problem;E-learning
Issue Date: 2019
Publisher: IEEE Computer Society
Citation: Garg, N., Sellathurai, M., Bettagere, B., Bhatia, V., & Ratnarajah, T. (2019). Online learning models for content popularity prediction in wireless edge caching. Paper presented at the Conference Record - Asilomar Conference on Signals, Systems and Computers, , 2019-November 337-341. doi:10.1109/IEEECONF44664.2019.9048682
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. © 2019 IEEE.
URI: https://doi.org/10.1109/IEEECONF44664.2019.9048682
https://dspace.iiti.ac.in/handle/123456789/5150
ISBN: 9781728143002
ISSN: 1058-6393
Type of Material: Conference Paper
Appears in Collections:Department of Electrical Engineering

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