Adaptive Delivery in Caching Networks

The problem of content delivery in caching networks is investigated for the scenarios where multiple users request identical files. An adaptive method is proposed for the delivery of redundant demands in caching networks. Based on the redundancy pattern in the current demand vector, the proposed method decides between the transmission of uncoded messages or the coded messages of Maddah-Ali and Niesen for delivery. Moreover, a lower bound on the delivery rate of redundant requests is derived. The performance of the adaptive method is investigated through numerical examples and Monte Carlo simulations. It is shown that the adaptive method considerably reduces the performance gap to the lower bound for the specific ranges of network parameters.

[1]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[2]  Urs Niesen,et al.  Fundamental limits of caching , 2012, 2013 IEEE International Symposium on Information Theory.

[3]  Suhas N. Diggavi,et al.  Hierarchical coded caching , 2014, 2014 IEEE International Symposium on Information Theory.

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

[5]  Urs Niesen,et al.  Coded Caching With Nonuniform Demands , 2017, IEEE Transactions on Information Theory.

[6]  Christian Igel,et al.  An Introduction to Restricted Boltzmann Machines , 2012, CIARP.

[7]  Urs Niesen,et al.  Online Coded Caching , 2013, IEEE/ACM Transactions on Networking.

[8]  Urs Niesen,et al.  Decentralized coded caching attains order-optimal memory-rate tradeoff , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[9]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[10]  Suhas N. Diggavi,et al.  Content caching and delivery over heterogeneous wireless networks , 2014, 2015 IEEE Conference on Computer Communications (INFOCOM).

[11]  Li Fan,et al.  Web caching and Zipf-like distributions: evidence and implications , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[12]  Xinbing Wang,et al.  Coded caching under arbitrary popularity distributions , 2015, 2015 Information Theory and Applications Workshop (ITA).

[13]  Suhas N. Diggavi,et al.  Effect of number of users in multi-level coded caching , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[14]  Jaime Llorca,et al.  On the average performance of caching and coded multicasting with random demands , 2014, 2014 11th International Symposium on Wireless Communications Systems (ISWCS).

[15]  Mehdi Bennis,et al.  Living on the edge: The role of proactive caching in 5G wireless networks , 2014, IEEE Communications Magazine.