超高密度HetNets满足大数据需求：绿色框架、技术和方法
Ultra-Dense HetNets Meet Big Data: Green Frameworks, Techniques, and Approaches
摘要：
超密集异构网络（Ud-HetNets）已经被提出来提高下一代无线网络的网络容量。然而，与5G愿景相反，网络的超密集部署将显着增加能耗，从而降低网络能源效率，这是传统的最坏情况网络设计理念。当Ud-HetNets遇到大数据时，由于传统的反应式请求-发送服务模式，这个问题变得特别严重。鉴于此，本文首先开发了一个基于大数据感知的人工智能的Ud-HetNets节能运营框架。基于该框架，我们确定了四个有前途的技术，即大数据分析，自适应基站操作，主动缓存，和干扰感知资源分配，以减少能源成本在大规模和小规模。我们进一步开发了一个负载感知的随机优化方法，以显示我们提出的框架和技术在节能方面的潜力。简而言之，我们致力于构建具有学习和推理能力的大数据绿色Ud-HetNet，将控制的灵活性从最坏情况提高到自适应设计，并将服务方式从被动模式转变为主动模式。
英文摘要：
Ultra-dense heterogeneous networks (Ud-HetNets) have been put forward to improve the network capacity for next-generation wireless networks. However, counter to the 5G vision, ultra-dense deployment of networks would significantly increase energy consumption and thus decrease network energy efficiency suffering from the conventional worst-case network design philosophy. This problem becomes particularly severe when Ud-HetNets meet big data because of the traditional reactive request-transmit service mode. In view of these, this article first develops a big-data-aware artificial intelligent based framework for energy-efficient operations of Ud-HetNets. Based on the framework, we then identify four promising techniques, namely big data analysis, adaptive base station operation, proactive caching, and interference-aware resource allocation, to reduce energy cost on both large and small scales. We further develop a load-aware stochastic optimization approach to show the potential of our proposed framework and techniques in energy conservation. In a nutshell, we devote to constructing green Ud-HetNets of big data with the abilities of learning and inferring by improving the flexibility of control from worst-case to adaptive design and shifting the manner of services from reactive to proactive modes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
