Online Learning Models for Content Popularity Prediction in Wireless Edge Caching

被引：0

作者：

Garg, Navneet ^{[1
,2
]}

Sellathurai, Mathini ^{[2
]}

Bettagere, Bharath ^{[3
]}

Bhatia, Vimal ^{[4
]}

Ratnarajah, Tharmalingam ^{[1
]}

机构：

[1] Univ Edinburgh, Edinburgh, Midlothian, Scotland

[2] Heriot Watt Univ, Edinburgh, Midlothian, Scotland

[3] Tlndian Inst Technol Dharwad, Dharwad, Karnataka, India

[4] Indian Inst Technol Indore, Indore, India

来源：

CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS | 2019年

基金：

英国工程与自然科学研究理事会;

关键词：

DELIVERY; POLICY;

D O I：

10.1109/ieeeconf44664.2019.9048682

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：337 / 341

页数：5

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