无线HetNets的性能分析:流量、能源和保密考虑

Georgios Smpokos
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摘要

到目前为止,大多数通信网络的特点是“单片”操作方法。网络元素的配置和运行无需任何重新配置很长一段时间。软件化,即专用元件被更通用的设备所取代,最近已经挑战了这种现有的方法。通过软件对基础设施进行虚拟化可以为最终用户和运营商提供显著的好处,支持更灵活的服务部署,提供实时监控和操作更改。在这篇许可论文中,我们考虑可以用于虚拟网络的技术。本文研究了一种新的接入网分配技术和流量优化过程。蜂窝网络技术(即UMTS、LTE、LTE- a)将与非蜂窝小基站共存,并将流量从蜂窝网络转移到主要在3GPP Wi-Fi (IEEE 802.11标准)中运行的非蜂窝网络。这是一种用于室内无线接入实现的场景,其中卸载机制可以改善运营商提供的QoS,并减少由接入前传处理的流量。对一种新型优化算法的分析展示了一种用于接入核心互联的整体解决方案,其中LWA (LTE-WiFi聚合)为最终用户提供了更好的性能。分析了如何影响核心网数据中心(冷却系统)的功耗。通过使用数据中心的数据应用机器学习技术,我们能够根据大气天气条件预测电力消耗并分析其准确性。
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Performance Analysis in Wireless HetNets: Traffic, Energy, and Secrecy Considerations
To this day, most of the communication networks are characterized by a “monolithic” operating approach. Network elements are configured and operate without any reconfiguration for long time periods. Softwarization, whereby dedicated elements are being replaced by more general-purpose devices, has been lately challenging this existing approach. Virtualizing the infrastructure through the softwarization can provide significant benefits to end users and operators, supporting more flexible service deployment, providing real time monitoring and operational changes. In this licentiate thesis, we consider techniques that can be used towards virtual networking. In Paper I we study a novel allocation technique and traffic optimization process for the access network. Cellular network technologies (i.e. UMTS, LTE, LTE-A) will coexist with non-cellular small cells and offload traffic from cellular to non-cellular networks mainly operating in 3GPP Wi-Fi (IEEE 802.11 standards). This is a scenario for indoor wireless access implementations where offloading mechanisms can improve the QoS offered by the operators, and reduce the traffic handled by the access fronthaul. The analysis of a novel optimization algorithm exhibited a holistic solution for access-core interworking where LWA (LTE-WiFi Aggregation) offers improved performance for the end users. analysed how can affect the power consumption of core network data centers (cooling systems). By applying machine learning techniques using data from a data center, we were able to forecast the power consumption based on to atmospheric weather conditions and analyse its accuracy.
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