RAIK: Regional analysis with geodata and crowdsourcing to infer key performance indicators
RAIK: Regional analysis with geodata and crowdsourcing to infer key performance indicators
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DOI：
10.1109/wcnc.2018.8377405
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发表时间：
2018-04
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影响因子：
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通讯作者：
R. Enami;D. Rajan;J. Camp
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文献类型：
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作者：
R. Enami;D. Rajan;J. Camp
关键性能指标(KPI)是衡量蜂窝网络服务质量的重要指标。移动运营商和5G标准化做出了多项努力，以利用KPI将驾驶测试(MDT)降至最低，并通过用户反馈自组织网络以实现最佳性能。这种方法根据用户设备的正常使用来考虑其操作领域中的用户设备，并且绕过了传统上由运营商直接或通过第三方支付的许多成本(例如，人力、设备)。在本文中，我们构建了一个区域分析来推断关键绩效指标(RAIK)框架，以建立地理数据和用户数据之间的关系，使用众包测量。为此，我们使用神经网络和用户设备(UE)获得的众包数据来根据参考信号的接收功率(RSRP)和路径损耗估计来预测KPI。由于这些KPI是地形类型的函数，因此我们通过在三维地理地图上叠加性能层来提供两层覆盖图。因此，我们可以高效地使用众包数据(不过度扩展用户带宽和电池)，并在尚未或无法执行测量的区域推断KPI。例如，我们表明，在缺乏信号质量数据的地区，RAIK可以仅使用地理信息来预测KPI，其均方误差可以忽略不计，与最先进的解决方案相比，误差减少了七分之一。
Key Performance Indicators (KPIs) are important measures of the quality of service in cellular networks. There are multiple efforts by cellular carriers and 5G standardization to leverage the KPIs to minimize drive tests (MDT) and self-organize the network for optimal performance via user feedback. Such an approach accounts for user devices in the field of their operation according to their normal usage and circumvents a number of costs (e.g., manpower, equipment) traditionally covered by the carrier, either directly or through a third party. In this paper, we build a Regional Analysis to Infer KPIs (RAIK) framework to establish a relationship between geographical data and user data using crowdsourced measurements. To do so, we use a neural network and crowdsourced data obtained by user equipment (UE) to predict the KPIs in terms of the reference signal's received power (RSRP) and path loss estimation. Since these KPIs are a function of terrain type, we provide a two-layer coverage map by overlaying a performance layer on a 3-dimensional geographical map. As a result, we can efficiently use crowdsourced data (to not overextend user bandwidth and battery) and infer KPIs in areas where measurements have not or can not be performed. For example, we show that RAIK can use only geographical information to predict the KPIs in areas that lack signal quality data with a negligible mean squared error, a seven-fold reduction in error from state-of-the-art solutions.