Brain Storm Optimization-Based Edge Caching in Fog Radio Access Networks

摘要

In this paper, the hierarchical cooperative caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content request delay, we formulate the hierarchical cooperative caching optimization problem based on local content popularity to find the optimal caching policy, where both horizontal cooperation among fog access points (F-APs) and vertical cooperation between the cloud server and F-APs are jointly considered. Considering the non-deterministic polynomial hard (NP-hard) property of this problem, we propose a brain storm optimization (BSO) approach which utilizes the penalty-based fitness function in individuals evaluation to meet the storage capacity constraint and the genetic algorithm (GA) in new individuals generation to meet the integer constraint, respectively. Moreover, to improve the initialization performance of the BSO approach, we propose to utilize Opposition-based Learning(OBL) to improve the initial solution space. To further reduce the computational complexity, we propose to implement the convergent operation in the objective space via individuals classification. We then analyze the global convergence and computational complexity of the proposed policy theoretically. Simulation results show that our proposed BSO-based hierarchical cooperative caching policy achieves remarkable performance in minimizing the content request delay.