Online Learning Models for Content Popularity Prediction in Wireless Edge Caching

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.

[1]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[2]  Robert W. Heath,et al.  Grassmannian Differential Limited Feedback for Interference Alignment , 2011, IEEE Transactions on Signal Processing.

[3]  Jiqiang Wu,et al.  Modeling Dynamics of Online Video Popularity , 2016, IEEE Transactions on Multimedia.

[4]  Shai Shalev-Shwartz,et al.  Online Learning and Online Convex Optimization , 2012, Found. Trends Mach. Learn..

[5]  Alexandros G. Dimakis,et al.  FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers , 2013, IEEE Transactions on Information Theory.

[6]  Alireza Sadeghi,et al.  Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities , 2017, IEEE Journal of Selected Topics in Signal Processing.

[7]  Rick S. Blum,et al.  A Survey of Caching Techniques in Cellular Networks: Research Issues and Challenges in Content Placement and Delivery Strategies , 2018, IEEE Communications Surveys & Tutorials.

[8]  Dong Liu,et al.  Caching Policy Toward Maximal Success Probability and Area Spectral Efficiency of Cache-Enabled HetNets , 2016, IEEE Transactions on Communications.

[9]  Dong Liu,et al.  Caching at the wireless edge: design aspects, challenges, and future directions , 2016, IEEE Communications Magazine.

[10]  Bartlomiej Blaszczyszyn,et al.  Optimal geographic caching in cellular networks , 2014, 2015 IEEE International Conference on Communications (ICC).

[11]  Weiping Li,et al.  PPC: Popularity Prediction Caching in ICN , 2018, IEEE Communications Letters.

[12]  Konstantin Avrachenkov,et al.  Optimization of caching devices with geometric constraints , 2017, Perform. Evaluation.

[13]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[14]  Donald F. Towsley,et al.  The Role of Caching in Future Communication Systems and Networks , 2018, IEEE Journal on Selected Areas in Communications.

[15]  Urs Niesen,et al.  Fundamental Limits of Caching , 2014, IEEE Trans. Inf. Theory.

[16]  Omar Y. Al-Jarrah,et al.  Popularity-Based Video Caching Techniques for Cache-Enabled Networks: A Survey , 2019, IEEE Access.

[17]  Choong Seon Hong,et al.  Cache Aware User Association for Wireless Heterogeneous Networks , 2019, IEEE Access.

[18]  Chenyang Yang,et al.  Caching Policy for Cache-Enabled D2D Communications by Learning User Preference , 2017, IEEE Transactions on Communications.

[19]  Jasper Goseling,et al.  On Optimal Geographical Caching in Heterogeneous Cellular Networks , 2016, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[20]  Jasper Goseling,et al.  Optimal Geographical Caching in Heterogeneous Cellular Networks with Nonhomogeneous Helpers , 2017, ArXiv.

[21]  Wai-Xi Liu,et al.  Content Popularity Prediction and Caching for ICN: A Deep Learning Approach With SDN , 2018, IEEE Access.

[22]  R. Bro,et al.  A fast non‐negativity‐constrained least squares algorithm , 1997 .

[23]  Yu Zhang,et al.  Robust Grassmannian prediction for limited feedback multiuser MIMO systems , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[24]  Konstantinos Poularakis,et al.  On the Complexity of Optimal Content Placement in Hierarchical Caching Networks , 2016, IEEE Transactions on Communications.

[25]  Stavros Toumpis,et al.  Interference Functionals in Poisson Networks , 2016, IEEE Transactions on Information Theory.

[26]  Hiroki Nakayama,et al.  Caching algorithm for content-oriented networks using prediction of popularity of contents , 2015, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM).

[27]  Anja Klein,et al.  Context-Aware Proactive Content Caching With Service Differentiation in Wireless Networks , 2016, IEEE Transactions on Wireless Communications.

[28]  H. Vincent Poor,et al.  A Learning-Based Approach to Caching in Heterogenous Small Cell Networks , 2015, IEEE Transactions on Communications.

[29]  Michael L. Littman,et al.  Online Linear Regression and Its Application to Model-Based Reinforcement Learning , 2007, NIPS.

[30]  Zhu Han,et al.  A prediction-based coordination caching scheme for content centric networking , 2018, 2018 27th Wireless and Optical Communication Conference (WOCC).

[31]  Thomas J. Walsh,et al.  Knows what it knows: a framework for self-aware learning , 2008, ICML '08.

[32]  Mihaela van der Schaar,et al.  Trend-Aware Video Caching Through Online Learning , 2016, IEEE Transactions on Multimedia.

[33]  Konstantin Avrachenkov,et al.  A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints , 2017, Proc. ACM Meas. Anal. Comput. Syst..

[34]  Xiaohu You,et al.  User Preference Learning-Based Edge Caching for Fog Radio Access Network , 2018, IEEE Transactions on Communications.