Multi-Armed Bandit Learning for Cache Content Placement in Vehicular Social Networks

Saeid Akhavan Bitaghsir, Aresh Dadlani, Muhammad Borhani, Ahmad Khonsari

Research output: Contribution to journalArticlepeer-review

Abstract

In this letter, the efficient dissemination of content in a socially-aware cache-enabled hybrid network using multi-armed bandit learning theory is analyzed. Specifically, an overlay cellular network over a vehicular social network is considered, where commuters request for multimedia content from either the stationary road-side units (RSUs), the base station, or the single mobile cache unit (MCU), if accessible. Firstly, we propose an algorithm to optimally distribute popular contents among the locally deployed RSU caches. To further maximize the cache hits experienced by vehicles, we then present an algorithm to find the best traversal path for the MCU based on commuters' social degree distribution. For performance evaluation, the asymptotic regret upper bounds of the two algorithms are also derived. Simulations reveal that the proposed algorithms outperform existing content placement methods in terms of overall network throughput.

Original languageEnglish
Article number8839820
Pages (from-to)2321-2324
Number of pages4
JournalIEEE Communications Letters
Volume23
Issue number12
DOIs
Publication statusPublished - Dec 2019

Keywords

  • cache content placement
  • cache hit rate
  • mobile cache unit
  • multi-armed bandit
  • Vehicular social networks

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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