A Mean Field Game-Based Distributed Edge Caching in Fog Radio Access Networks

• Published in: IEEE transactions on communications Vol. 68; no. 3; pp. 1567 - 1580
• Main Authors: Jiang, Yanxiang, Hu, Yabai, Bennis, Mehdi, Zheng, Fu-Chun, You, Xiaohu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Caching; Computer simulation; Differential games; distributed edge caching; Fog radio access networks; fractional Knapsack problem; Fronthaul traffic load; Game theory; Greedy algorithms; Iterative algorithms; Iterative methods; Knapsack problem; mean field game; Optimization; request service delay; Traffic delay
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2019.2961081

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the optimization problem as a stochastic differential game (SDG) which captures the dynamics of F-AP states. To address both the intractability problem of the SDG and the caching capacity constraint, we propose to solve the optimization problem in a distributive manner. Firstly, a mean field game (MFG) is converted from the original SDG by exploiting the ultra-dense property of F-RANs, and the states of all F-APs are characterized by a mean field distribution. Then, an iterative algorithm is developed that enables each F-AP to obtain the mean field equilibrium and caching control without extra information exchange with other F-APs. Secondly, a fractional knapsack problem is formulated based on the mean field equilibrium, and a greedy algorithm is developed that enables each F-AP to obtain the final caching policy subject to the caching capacity constraint. Simulation results show that the proposed scheme outperforms the baselines.