节能5G RAN的DRX数据驱动选择

Diarmuid Corcoran, Loghman Andimeh, Andreas Ermedahl, P. Kreuger, Christian Schulte
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引用次数: 9

摘要

连接移动设备的数量正在迅速增长,预计到2022年将超过100亿。他们的总能源消耗引起了社会的重大关注。目前的3gpp(第三代合作伙伴计划)LTE/LTE- advanced标准采用了一种称为不连续接收(DRX)的节能技术。预计5G将使用该方案的演进版本。一般来说,每个设备的DRX参数的单一选择是不平凡的。本文介绍了如何通过基于每台设备的流量概况选择DRX来提高移动设备的能源效率。我们的特殊方法使用两阶段数据驱动策略,该策略根据智能快速能量模型调整DRX参数的选择。第一阶段涉及针对特定流量组合的可行DRX组合的离线选择。第二阶段涉及从这个可行清单中在线选择DRX。该方法试图保证延迟不低于选定的阈值。另外,可以用延长设备的电池寿命来换取增加的延迟。我们建立了该系统的实验室原型,以验证该技术在实际LTE系统上的工作和扩展。我们还设计了一个基于实际用户数据轨迹的复杂流量生成器。通过对LTE网络记录数据的详尽离线仿真,对该方法进行了验证。我们的方法显示出显著的设备节能效果,这将为数十亿台设备的绿色节能网络做出真正的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data driven selection of DRX for energy efficient 5G RAN
The number of connected mobile devices is increasing rapidly with more than 10 billion expected by 2022. Their total aggregate energy consumption poses a significant concern to society. The current 3gpp (3rd Generation Partnership Project) LTE/LTE-Advanced standard incorporates an energy saving technique called discontinuous reception (DRX). It is expected that 5G will use an evolved variant of this scheme. In general, the single selection of DRX parameters per device is non trivial. This paper describes how to improve energy efficiency of mobile devices by selecting DRX based on the traffic profile per device. Our particular approach uses a two phase data-driven strategy which tunes the selection of DRX parameters based on a smart fast energy model. The first phase involves the off-line selection of viable DRX combinations for a particular traffic mix. The second phase involves an on-line selection of DRX from this viable list. The method attempts to guarantee that latency is not worse than a chosen threshold. Alternatively, longer battery life for a device can be traded against increased latency. We built a lab prototype of the system to verify that the technique works and scales on a real LTE system. We also designed a sophisticated traffic generator based on actual user data traces. Complementary method verification has been made by exhaustive off-line simulations on recorded LTE network data. Our approach shows significant device energy savings, which has the aggregated potential over billions of devices to make a real contribution to green, energy efficient networks.
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