The surging data traffic and dynamic user mobility in 5G networks have posed significant demands for mobile operators to increase data processing capacity and ensure user handover quality. Specifically, a cost-effective and quality-aware radio access network (RAN) is in great necessity. With the emergence of fog-computing-based RAN architecture (Fog-RAN), the data processing units (BBUs) can be separated from base stations (RRHs) and hosted in distributed fog servers, where each server accommodates a community of RRHs to handle data traffic and user handover. The key problem in Fog-RAN optimization is how to cluster complementary RRHs into communities and allocate adequate numbers of BBUs for the fog servers, since real-world traffic and mobility patterns are highly dynamic to model, and it is not trivial to find an optimal RRH clustering and BBU allocation scheme from potentially enormous numbers of candidates. In this work, we propose a data-driven framework for cost-effective and quality-aware Fog-RAN optimization. In the RRH clustering phase, we build a weighted graph model to characterize user mobility patterns across RRHs, and propose a size-constrained community detection (SCUD) algorithm to cluster RRHs into communities with frequent internal handover events. In the BBU allocation phase, we formulate BBU allocation in each community fog server as a set partitioning problem, and propose a column-reduced integer programming (CLIP) algorithm to find optimal BBU allocation schemes that maximize BBU utilization rate. Evaluations using two large-scale real-world datasets collected from Ivory Coast and Senegal show that compared to the traditional RAN architecture, our framework effectively reduces the average handover overhead to 12.8% and 27.3%, and increases the average BBU utilization rate to 49.7% and 52.3% in both cities, respectively, which consistently outperforms the state-of-the-art baseline methods.

5G网络中激增的数据流量和动态的用户移动性对移动运营商提高数据处理能力和保证用户切换质量提出了巨大的需求。具体而言，非常需要具有成本效益和质量意识的无线电接入网络 (RAN)。随着基于雾计算的 RAN 架构 (Fog-RAN) 的出现，数据处理单元 (BBU) 可以与基站 (RRH) 分离并托管在分布式雾服务器中，其中每个服务器容纳一个 RRH 社区来处理数据流量和用户切换。Fog-RAN 优化的关键问题是如何将互补的 RRH 集群到社区中，并为雾服务器分配足够数量的 BBU，因为现实世界的流量和移动模式是高度动态的建模，从潜在的大量候选者中找到最佳的 RRH 聚类和 BBU 分配方案并非易事。在这项工作中，我们提出了一个数据驱动的框架，用于具有成本效益和质量意识的 Fog-RAN 优化。在 RRH 聚类阶段，我们构建了一个加权图模型来表征跨 RRH 的用户移动模式，并提出了一种大小受限的社区检测 (SCUD) 算法将 RRH 聚类到具有频繁内部切换事件的社区中。在 BBU 分配阶段，我们将每个社区雾服务器中的 BBU 分配制定为一个集合分区问题，并提出了列约简整数规划（CLIP）算法来寻找最大化 BBU 利用率的最优 BBU 分配方案。