In next generation cellular networks (5G) the access points (APs) are anticipated to be equipped with storage devices to serve locally requests for reusable popular contents by caching them at the edge of the network. The ultimate goal is to shift part of the load on the back-haul links from on-peak to off-peak periods, contributing to a better overall network performance and service experience. In order to enable the APs with efficient (optimal) fetch-cache decision making schemes able to work in dynamic settings, we introduce simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions in every time slot influence the content availability in future instants, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming, for which efficient reinforcement-learning-based solvers are proposed. The performance of our algorithms is assessed via numerical tests, and discussions on the inherent fetching-versus-caching trade-off are provided.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Sep 10 2018|
|Event||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada|
Duration: Apr 15 2018 → Apr 20 2018
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018|
|Period||4/15/18 → 4/20/18|
Bibliographical noteFunding Information:
The work in this paper has been supported by USA NSF grants 1423316, 1508993, 1514056, 1711471, and by the Spanish MINECO grant OMI-CROM (TEC2013-41604-R).
© 2018 IEEE.
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