软件定义超蜂窝网络中的通信与计算协同优化

Synergetic communication-and-computation optimization in software-defined hyper-cellular networks

近年提出的云无线接入网,通过集中化改善了小区为中心无线接入网在成本和灵活性方面的瓶颈.但是,云无线接入网需要通过前传网大范围汇聚带宽极高的前传采样,造成了过高的建设与运营成本.本文针对云无线接入网过度依赖前传通信资源的问题,依据控制与数据分离的思想设计了一种软件定义超蜂窝网络新型架构,并提出了在该架构下通过通信与计算协同的资源部署大幅降低前传汇聚带宽的具体方案.首先基于排队论给出了虚拟基站池中计算资源统计复用增益与池规模的定量关系,并依据统计复用增益边际效应迅速递减的性质得到了部署中等规模基站池更经济的指导方针.然后基于图聚类框架针对基带处理功能分割部署问题给出了一种遗传算法,该算法给出的分割方案可以根据设计偏好在前传带宽成本与计算代价间进行灵活折衷.

Recently,the cloud radio access network（C-RAN） architecture has been proposed to enhance the cost effectiveness and flexibility of traditional cell-centric radio access networks. However,the massive fronthaul bandwidth required to centralize baseband computations in C-RAN results in extremely high costs. This paper summarizes our previous efforts toward solving this problem. We proposed the software-defined hyper-cellular network（SDHCN） based on the control/data separation principle. Under the proposed SDHCN framework,we studied two mechanisms that can greatly reduce fronthaul costs through the joint deployment of communicational and computational resources. First,we quantitatively characterized the relationship between the size of virtual base station（VBS） pools and the gains from computational statistical multiplexing by using queueing theory. We then showed that the marginal gain diminishes quickly with a growing pool size. Therefore,it is most economical to deploy mid-sized VBS pools. Finally,we proposed a genetic algorithm for baseband function splitting within a graph-clustering framework. This algorithm provides splitting schemes that can flexibly achieve different tradeoffs between fronthaul and computational costs based on different design preferences.