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

TL;DR

This paper introduces a mean-field game theoretic approach to edge caching in ultra-dense networks, enabling scalable, distributed caching that adapts to user demand and interference, significantly reducing costs and redundancy.

Contribution

It develops a novel distributed caching algorithm based on mean-field game theory for ultra-dense networks, addressing complexity and demand dynamics.

Findings

Reduces long-term average network cost by 24%

Decreases redundant cached data by 42%

Maintains robustness with imperfect popularity information

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.