This work considers cache placement for ultra-dense fog radio access networks (F-RANs). In an F-RAN, the fog access points (F-APs) form overlapping clusters based on their geographical locations. A cluster of F-APs then acts as a distributed cache to cooperatively serve user requests. The fronthaul traffic minimization problem is formulated in information-theoretic terms. For the $k$ -association networks, cache placement can be optimized by concatenating an MDS code with a repetition code. By repeating the same packet in some F-APs, multicasting over the fronthaul link can be done in cache placement, which saves energy and bandwidth. Such an idea is applied to both uncoded and coded caching schemes based on hypergraph coloring. Their associated optimization problems are shown to be NP-complete. For the uncoded case, a suboptimal algorithm is proposed, which carefully repeats the subfiles. For the coded case, a heuristic algorithm that minimizes the field size requirement of the MDS repetition scheme is proposed. Simulation results demonstrate the outstanding performance of the proposed uncoded and coded caching schemes during both the cache placement phase and content delivery phase in reducing fronthaul traffic load and energy consumption.

中文翻译：

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超密集 Fog-RAN 的协同缓存：信息优化和超图着色

这项工作考虑了超密雾无线电接入网络 (F-RAN) 的缓存放置。在 F-RAN 中，雾接入点 (F-AP) 根据其地理位置形成重叠集群。然后，一组 F-AP 充当分布式缓存，以协同服务用户请求。前传流量最小化问题是用信息论术语表述的。为了 $千$ - 关联网络，可以通过将 MDS 代码与重复代码连接来优化缓存放置。通过在某些 F-AP 中重复相同的数据包，可以在缓存放置中完成前传链路上的多播，从而节省能源和带宽。这种想法适用于基于超图着色的未编码和编码缓存方案。他们的相关优化问题被证明是 NP 完全的。对于未编码的情况，提出了一种次优算法，它仔细地重复子文件。对于编码情况，提出了最小化MDS重复方案的字段大小要求的启发式算法。