Deep RAN：一个可扩展的数据驱动平台，用于使用递归卷积神经网络检测活细胞网络中的异常
Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network
摘要：
在本文中，我们提出了一种新的算法来检测关键参数指标（KPI）的活细胞网络的基础上的组合的递归神经网络（RNN），卷积神经网络（CNN），作为递归卷积神经网络（R-CNN）的异常。CNN对空间相关性和信息进行建模，而RNN对时间相关性和信息进行建模。本文研究的蜂窝网络包括2G、3G、4G和4.5G技术，KPI包括小区的语音和数据流量。数据和语音流量对于无线网络的所有者来说非常重要，因为它直接关系到用户体验的收入和服务质量。这些流量变化的发生有几个原因：由于特殊事件，使相邻站点处于空中或关闭状态，或者通过将流量转移到其他技术，例如将流量从3G转移到4G，用户行为发生变化。传统上，为了保持网络的稳定，需要在每个时间间隔内逐层观察流量，以检测KPI的重大变化，在大规模的电信网中，这将是过于耗时和异常检测的准确性低。然而，所提出的算法能够以时间有效和准确的方式检测网络的每个元素的异常KPI。它观察流量层的趋势，并将其分为8个流量类别：正常，突然增加，逐渐增加，突然减少，逐渐减少，快速站点，新站点和停机站点。此分类任务使供应商和运营商能够检测其实时网络中的异常情况，以保持KPI处于正常趋势。该算法是训练和测试的真实数据集上的细胞网络超过25000个细胞。
英文摘要：
In this paper, we propose a novel algorithm to detect anomaly in terms of Key Parameter Indicators (KPI)s over live cellular networks based on the combination of Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), as Recurrent Convolutional Neural Networks (R-CNN). CNN models the spatial correlations and information, whereas, RNN models the temporal correlations and information. In this paper, the studied cellular network consists of 2G, 3G, 4G, and 4.5G technologies, and the KPIs include Voice and data traffic of the cells. The data and voice traffic are extremely important for the owner of wireless networks because it is directly related to the revenue and quality of service that users experience. These traffic changes happen due to a couple of reasons: the subscriber behavior changes due to special events, making neighbor sites on-air or down, or by shifting the traffic to the other technologies, e.g. shifting the traffic from 3G to 4G. Traditionally, in order to keep the network stable, the traffic should be observed layer by layer during each interval to detect major changes in KPIs, in large scale telecommunication networks it will be too time-consuming with the low accuracy of anomaly detection. However, the proposed algorithm is capable of detecting the abnormal KPIs for each element of the network in a time-efficient and accurate manner. It observes the traffic layer trends and classifies them into 8 traffic categories: Normal, Suddenly Increasing, Gradually Increasing, Suddenly Decreasing, Gradually Decreasing, Faulty Site, New Site, and Down Site. This classification task enables the vendors and operators to detect anomalies in their live networks in order to keep the KPIs in a normal trend. The algorithm is trained and tested on the real dataset over a cellular network with more than 25000 cells.
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。
