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

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 computational complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand. 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 leverage mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate this consistent complexity of the proposed algorithm with respect to the number of SBSs. Also, it shows 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. Additionally, the simulation results show that the proposed caching algorithm is robust to imperfect popularity information, while ensuring a low computational complexity.

Kim Hyesung, Park Jihong, Bennis Mehdi, Kim Seong-Lyun, Debbah Mérouane

Publication type:
A1 Journal article – refereed

Place of publication:

Edge caching, mean-field game theory, spatio-temporal dynamics, stochastic geometry, ultra-dense networks


Full citation:
H. Kim, J. Park, M. Bennis, S. Kim and M. Debbah, “Mean-Field Game Theoretic Edge Caching in Ultra-Dense Networks,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 935-947, Jan. 2020.


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