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.

中文翻译：

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雾无线接入网络中基于平均场博弈的分布式边缘缓存

在本文中，研究了雾无线电接入网络（F-RAN）中的边缘缓存优化问题。考虑到时变用户请求和雾接入点（F-AP）的超密集部署，我们提出了一种分布式边缘缓存方案，以共同最小化请求服务延迟和前传流量负载。考虑到 F-AP 之间的交互关系，我们将优化问题建模为随机微分博弈 (SDG)，它捕捉 F-AP 状态的动态。为了同时解决 SDG 的棘手问题和缓存容量约束，我们建议以分布式方式解决优化问题。首先，利用F-RANs的超密集特性从原始SDG转换平均场博弈（MFG），所有F-APs的状态都以平均场分布为特征。然后，开发了一种迭代算法，使每个 F-AP 能够在不与其他 F-AP 交换额外信息的情况下获得平均场平衡和缓存控制。其次，基于平均场均衡制定了分数背包问题，并开发了贪心算法，使每个F-AP能够在缓存容量约束下获得最终的缓存策略。仿真结果表明，所提出的方案优于基线。并开发了贪心算法，使每个F-AP能够在缓存容量约束下获得最终的缓存策略。仿真结果表明，所提出的方案优于基线。并开发了贪心算法，使每个F-AP能够在缓存容量约束下获得最终的缓存策略。仿真结果表明，所提出的方案优于基线。