蜂窝网络中基站关系与业务关系网络与应用  

Base station and service social network and its application in cellular networks

作　　者：张佳鑫[1] 张兴[1] 李永竞 王硕[1] 杨居沃 梅承力[2] 王文博[1] 

机构地区：[1]北京邮电大学泛网无线通信教育部重点实验室,北京100876 [2]中国电信股份有限公司技术创新中心,北京100032

出　　处：《中国科学：信息科学》2017年第5期648-663,共16页Scientia Sinica(Informationis)

基　　金：国家自然科学基金(批准号:61372114;61631005);北京市科技新星计划(批准号:Z151100000315077);国家重点基础研究发展计划(批准号:2012CB316005)资助项目

摘　　要：随着无线网络的迅猛发展,4G网络逐渐推广普及,5G网络也预计将于2020年商用,各种各样的新型业务形态逐渐渗透到移动终端,使得下一代通信业务类型将更加多样化,数据的规模、维度、类型也日益复杂.通过认知蜂窝网络业务规律,优化对网络基站部署和资源调度策略,以满足用户多类型业务需求,成为当前的研究热点.本文通过对大规模实测蜂窝网络数据的研究分析,创新性地引入社会网络分析方法,提出了蜂窝网络业务关系网络和基站关系网络模型,并基于基站关系网络和重要基站挖掘,实现了典型无线网络场景识别、基站流量预测和缓存策略优化等应用.本研究对于利用蜂窝无线网络大数据,实现业务流量监控与拥塞预防、网络架构设计以及资源调度优化提供了一种可能的研究方案和途径.With the rapid development of wireless networks, the fourth-generation （4G） communication network is becoming increasingly popular and the fifth-generation （5G） network is expected to be commercialized by 2020. Many types of new services are being gradually applied mobile terminals with increasingly complex data in terms of diversity, scale, and dimension. It has become a popular research topic to optimize the base station deployment as well as resource scheduling strategy to meet various service requirements based on the cognizing of cellular network service pattern. We introduce a social network analysis method and propose a novel method to build service social network and base station social network （BSSN） for typical wireless network based on a study of wireless big data in practical networks. Additionally, traffic fluctuations can be predicted based on selected several typical very important base stations （VIBS）, and caching strategies for cellular network can be optimized based on BSSN. This work provides insights and possible solutions for traffic monitoring, congestion prevention, network deployment and optimization, as well as resource scheduling optimization based on the characteristics of big data in cellular network.

关 键 词：蜂窝网络 用户行为 社会网络分析方法 基站关系网络 无线网络大数据 

分 类 号：TN929.5[电子电信—通信与信息系统]