Online Tensor Completion Based Prediction For Wireless Edge Caching

Wireless edge caching is a popular strategy to avoid backhaul congestion in the next generation networks, where the content is cached in advance at base stations to serve redundant requests during peak congestion periods. In the edge caching data, the missing observations are inevitable due to dynamic selective popularity. Among the completion methods, the tensorbased models have been shown to be the most advantageous for missing data imputation. Also, since the observations are correlated across time, files, and base stations, in this paper, we formulate the cooperative caching with recommendations as a fourth-order tensor completion and prediction problem. Since the content library can be large leading to a large dimension tensor, we modify the latent norm-based Frank-Wolfe (FW) algorithm with towards a much lower time complexity using multi-rank updates, rather than rank-1 updates in literature. This significantly lower time computational overhead leads in developing an online caching algorithm. With MovieLens dataset, simulations show lower reconstruction errors for the proposed algorithm as compared to that of the recent FW algorithm, albeit with lower computation overhead. It is also demonstrated that the completed tensor improves normalized cache hit rates for linear prediction schemes.

[1]  V. Aggarwal,et al.  Efficient Low Rank Tensor Ring Completion , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[2]  Hong Cheng,et al.  Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data , 2015, IEEE Transactions on Cybernetics.

[3]  Iordanis Koutsopoulos,et al.  Jointly Optimizing Content Caching and Recommendations in Small Cell Networks , 2019, IEEE Transactions on Mobile Computing.

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

[5]  R. Bro.  PARAFAC. Tutorial and applications , 1997 .

[6]  Tensor Completion based Prediction in Wireless Edge Caching , 2020, 2020 54th Asilomar Conference on Signals, Systems, and Computers.

[7]  L. Tucker,et al.  Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.

[8]  Masashi Sugiyama,et al.  Learning Efficient Tensor Representations with Ring-structured Networks , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Naoto Yokoya,et al.  Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Xiaojiang Du,et al.  Cooperative Content Caching for Mobile Edge Computing With Network Coding , 2019, IEEE Access.

[12]  Chao Li,et al.  Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion , 2018, AAAI.

[13]  Naoto Yokoya,et al.  Hyperspectral Image Compressive Sensing Reconstruction Using Subspace-Based Nonlocal Tensor Ring Decomposition , 2020, IEEE Transactions on Image Processing.

[14]  Liqing Zhang,et al.  Tensor Ring Decomposition , 2016, ArXiv.

[15]  Hong-Yuan Mark Liao,et al.  Simultaneous Tensor Decomposition and Completion Using Factor Priors , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Chenyang Yang,et al.  Temporal-Spatial Recommendation for Caching at Base Stations via Deep Reinforcement Learning , 2019, IEEE Access.

[17]  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.

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

[19]  Massimiliano Pontil,et al.  A New Convex Relaxation for Tensor Completion , 2013, NIPS.

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

[21]  Ling Luo,et al.  Caching Placement with Recommendation Systems for Cache-Enabled Mobile Social Networks , 2017, IEEE Communications Letters.

[22]  Tharmalingam Ratnarajah,et al.  Modeling and Performance of Uplink Cache-Enabled Massive MIMO Heterogeneous Networks , 2018, IEEE Transactions on Wireless Communications.

[23]  Tharmalingam Ratnarajah,et al.  Content Placement Learning for Success Probability Maximization in Wireless Edge Caching Networks , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[25]  Nikos D. Sidiropoulos,et al.  Coupled Graphs and Tensor Factorization for Recommender Systems and Community Detection , 2018, IEEE Transactions on Knowledge and Data Engineering.

[26]  Yuning Qiu,et al.  An Efficient Tensor Completion Method Via New Latent Nuclear Norm , 2019, IEEE Access.

[27]  Jinkuan Wang,et al.  Multidimensional data learning-based caching strategy in information-centric networks , 2017, 2017 IEEE International Conference on Communications (ICC).

[28]  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).

[29]  Qibin Zhao,et al.  Tensor-ring Nuclear Norm Minimization and Application for Visual : Data Completion , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[30]  Tamara G. Kolda,et al.  Scalable Tensor Factorizations for Incomplete Data , 2010, ArXiv.

[31]  Ivan V. Oseledets,et al.  Tensor methods and recommender systems , 2016, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

[32]  Jianting Cao,et al.  Higher-dimension Tensor Completion via Low-rank Tensor Ring Decomposition , 2018, 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

[33]  Tharmalingam Ratnarajah,et al.  Function Approximation Based Reinforcement Learning for Edge Caching in Massive MIMO Networks , 2021, IEEE Transactions on Communications.

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

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

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

[37]  Lifeng Wang,et al.  Performance Analysis and Optimization of Cache-Enabled Small Cell Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[38]  Qibin Zhao,et al.  An Effective Tensor Completion Method Based on Multi-linear Tensor Ring Decomposition , 2018, 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

[39]  Hidekata Hontani,et al.  Simultaneous Tensor Completion and Denoising by Noise Inequality Constrained Convex Optimization , 2018, IEEE Access.

[40]  Yipeng Liu,et al.  Low CP Rank and Tucker Rank Tensor Completion for Estimating Missing Components in Image Data , 2020, IEEE Transactions on Circuits and Systems for Video Technology.

[41]  Martin Jaggi,et al.  Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization , 2013, ICML.

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

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

[44]  Tharmalingam Ratnarajah,et al.  In-Network Caching for Hybrid Satellite-Terrestrial Networks Using Deep Reinforcement Learning , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[46]  Taiji Suzuki,et al.  Convex Tensor Decomposition via Structured Schatten Norm Regularization , 2013, NIPS.

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

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

[49]  Nicolas Le Roux,et al.  A latent factor model for highly multi-relational data , 2012, NIPS.

[50]  Tharmalingam Ratnarajah,et al.  Online Content Popularity Prediction and Learning in Wireless Edge Caching , 2020, IEEE Transactions on Communications.

[51]  Liqing Zhang,et al.  Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Thrasyvoulos Spyropoulos,et al.  Soft Cache Hits: Improving Performance Through Recommendation and Delivery of Related Content , 2018, IEEE Journal on Selected Areas in Communications.

[53]  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.

[54]  James T. Kwok,et al.  Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm , 2017, AAAI.

[55]  Andong Wang,et al.  Latent Schatten TT Norm for Tensor Completion , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[56]  Minh N. Do,et al.  Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train , 2016, IEEE Transactions on Image Processing.

[57]  Branka Vucetic,et al.  Localized Small Cell Caching: A Machine Learning Approach Based on Rating Data , 2019, IEEE Transactions on Communications.

[58]  Xiaobin Tan,et al.  Software-Defined Multimedia Streaming System Aided By Variable-Length Interval In-Network Caching , 2019, IEEE Transactions on Multimedia.

[59]  Symeon Chatzinotas,et al.  Trend-Aware Proactive Caching via Tensor Train Decomposition: A Bayesian Viewpoint , 2021, IEEE Open Journal of the Communications Society.