Learning Distributed Coded Caching Strategy in a Cellular Network

Yash Doshi, B. N. Bharath, Navneet Garg, Vimal Bhatia, Tharmalingam Ratnarajah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The caching of popular contents in a cellular network is known to reduce the data load in the backhaul link, and have been an active area of research. This paper considers the problem of efficient distributed content coded caching in a small-cell Base Station (sBS) wireless network to improve the cache hit performance. The demands at each sBS across time and sBSs is assumed to be correlated, and is unknown. A new weighted (across time and sBS) caching strategy is proposed. A high probability lower bound on the cache hit is derived, which is obtained using the proposed strategy as a function of the cache hit of the optimal caching strategy. The bound is shown to depend on (i) the weighted average of cache hits, (ii) regret, and (iii) the discrepancy across time and sBSs (a measure of correlation of demands across time and sBSs). This provides the following insight on obtaining the caching strategy: (i) find a sequence of caching strategies by running regret minimization across time at each sBS, and (ii) maximize an estimate of the bound to obtain a set of weights. The insight is shown to result in an iterative distributed algorithm to obtain caching strategies at each sBS. The performance of the proposed caching strategy is shown to outperform Least Recently Frequently Used (LRFU) algorithm by a large margin.

Original languageEnglish
Title of host publication2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189642
DOIs
Publication statusPublished - 15 Jun 2021
Event93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
Duration: 25 Apr 202128 Apr 2021

Publication series

NameIEEE Vehicular Technology Conference
Volume2021-April
ISSN (Print)1550-2252

Conference

Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
CityVirtual, Online
Period25/04/2128/04/21

Keywords / Materials (for Non-textual outputs)

  • Caching
  • online learning
  • reinforcement learning

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