Learning-based optimization of cache content in a small cell base station

By Pol Blasco, Deniz Gündüz. 2014 IEEE International Conference on Communications (ICC), 2014

299 citations recorded by the source corpus.

Optimal cache content placement in a wireless small cell base station (sBS) with limited backhaul capacity is studied. The sBS has a large cache memory and provides content-level selective offloading by delivering high data rate contents to users in its coverage area. The goal of the sBS content controller (CC) is to store the most popular contents in the sBS cache memory such that the maximum amount of data can be fetched directly form the sBS, not relying on the limited backhaul resources during peak traffic periods. If the popularity profile is known in advance, the problem reduces to a knapsack problem. However, it is assumed in this work that, the popularity profile of the files is not known by the CC, and it can only observe the instantaneous demand for the cached content. Hence, the cache content placement is optimised based on the demand history. By refreshing the cache content at regular time intervals, the CC tries to learn the popularity profile, while exploiting the limited cache capacity in the best way possible. Three algorithms are studied for this cache content placement problem, leading to different exploitation-exploration trade-offs. We provide extensive numerical simulations in order to study the time-evolution of these algorithms, and the impact of the system parameters, such as the number of files, the number of users, the cache size, and the skewness of the popularity profile, on the performance. It is shown that the proposed algorithms quickly learn the popularity profile for a wide range of system parameters.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/433c5b1bcf0e06fa26e53c5aa6c517865fb5d2fd

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, background): “Many literatures have studied the performance of caching at SBSs [84], [85], [86], [87], [88].”
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, methodology): “Other solutions for caching optimization include the work in [85] and [86], where the caching problem is modeled as a game theory problem.”
Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching (2020, unknown): “placement [39] by using a multiarmed bandit (MAB) algorithm.”
Cache-enabled small cell networks: modeling and tradeoffs (2014, background): “Due to its non-tractable nature, the vast majority of these recent attempts focus on approximate approaches, or heuristics [15][16][17].”