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Mean-Field Game Theoretic Edge Caching in Ultra-Dense Networks

Abstract : This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventional caching algorithms are inapplicable as their complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand and inter-SBS interference. To overcome such difficulties, we propose a distributed caching algorithm under a stochastic geometric network model, as well as a spatio-temporal user demand model that characterizes the content popularity dynamics. By exploiting mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate that the proposed caching algorithm reduces not only the long run average cost of the network but also the redundant cached data respectively by 24% and 42%, compared to a baseline caching algorithm. The simulation results also show that the proposed caching algorithm is robust to imperfect popularity information, while ensuring a low computational complexity.
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Submitted on : Thursday, June 18, 2020 - 3:32:56 PM
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  • HAL Id : hal-02873810, version 1
  • ARXIV : 1801.07367


Hyesung Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim, Merouane Debbah. Mean-Field Game Theoretic Edge Caching in Ultra-Dense Networks. 2017 IEEE International Conference on Communications (ICC), May 2017, Paris, France. ⟨hal-02873810⟩



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