Graph-based Cooperative Caching in Fog-RAN
Graph-based Cooperative Caching in Fog-RAN
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DOI：
10.1109/iccnc.2018.8390300
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Xiaoting Cui;Yanxiang Jiang;Xuan Chen;F. Zheng;X. You
中科院分区：
文献类型：
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作者：
Xiaoting Cui;Yanxiang Jiang;Xuan Chen;F. Zheng;X. You
本文研究了雾无线接入网络（F-RAN）中的协作缓存问题。为了最大化增量卸载流量，我们考虑了协作缓存和本地内容流行度，制定了集群优化问题，这属于组合编程的范围。然后，我们提出一种有效的基于图的方法来解决这个具有挑战性的问题。首先，构建节点图，其顶点集代表所考虑的雾接入点（F-AP），其边集反映F-AP之间的潜在合作。然后，通过利用节点图每个顶点的邻接表，我们建议通过间接搜索最大完整子图来获得完整子图，以降低搜索复杂度。此外，通过使用如此获得的完整子图，构建加权图。通过将带权图的顶点的权重设置为其对应的完整子图的增量分流流量，可以将原始的聚类优化问题转化为等效的0-1整数规划问题。通过我们提出的贪心算法解决上述优化问题，可以很容易地获得加权图的顶点集的最大权重独立子集，其相当于目标簇集。与具有指数复杂性的蛮力方法相比，我们提出的基于图的方法具有明显较低的复杂性。仿真结果表明，使用我们提出的方法在卸载增益方面取得了显着的改进。
In this paper, the cooperative caching problem in fog radio access networks (F-RAN) is investigated. To maximize the incremental offloaded traffic, we formulate the clustering optimization problem with the consideration of cooperative caching and local content popularity, which falls into the scope of combinatorial programming. We then propose an effective graph-based approach to solve this challenging problem. Firstly, a node graph is constructed with its vertex set representing the considered fog access points (F-APs) and its edge set reflecting the potential cooperations among the F-APs. Then, by exploiting the adjacency table of each vertex of the node graph, we propose to get the complete subgraphs through indirect searching for the maximal complete subgraphs for the sake of a reduced searching complexity. Furthermore, by using the complete subgraphs so obtained, a weighted graph is constructed. By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs, the original clustering optimization problem can be transformed into an equivalent 0–1 integer programming problem. The max-weight independent subset of the vertex set of the weighted graph, which is equivalent to the objective cluster sets, can then be readily obtained by solving the above optimization problem through the greedy algorithm that we propose. Our proposed graph-based approach has an apparently low complexity in comparison with the brute force approach which has an exponential complexity. Simulation results show the remarkable improvements in terms of offloading gain by using our proposed approach.