Online Learning Models for Content Popularity Prediction in Wireless Edge Caching
Online Learning Models for Content Popularity Prediction in Wireless Edge Caching
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DOI：
10.1109/ieeeconf44664.2019.9048682
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
N. Garg;M. Sellathurai;Bharath Bettagere;V. Bhatia;T. Ratnarajah
中科院分区：
文献类型：
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作者：
N. Garg;M. Sellathurai;Bharath Bettagere;V. Bhatia;T. Ratnarajah
在地理边缘缓存，基站（BS）和用户分布的泊松点过程（PPP）和缓存性能的平均成功概率（ASP）的测量，我们考虑的内容流行度（CP）预测问题，以最大限度地提高ASP。提出了两种基于加权跟随领导（FTL）和加权跟随正则化领导（FoReL）的在线学习（OL）模型。遗憾分析表明，OL方法导致次线性MSE遗憾和线性ASP遗憾。利用MovieLens数据集，仿真验证了FTL产生更好的MSE遗憾，而FoReL具有更低的ASP遗憾。
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.