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In this paper, we propose a novel algorithm to detect anomaly in terms of KeyParameter Indicators (KPI)s over live cellular networks based on thecombination of Recurrent Neural Networks (RNN), and Convolutional NeuralNetworks (CNN), as Recurrent Convolutional Neural Networks (R-CNN). CNN modelsthe spatial correlations and information, whereas, RNN models the temporalcorrelations and information. In this paper, the studied cellular networkconsists of 2G, 3G, 4G, and 4.5G technologies, and the KPIs include Voice anddata traffic of the cells. The data and voice traffic are extremely importantfor the owner of wireless networks because it is directly related to therevenue and quality of service that users experience. These traffic changeshappen due to a couple of reasons: the subscriber behavior changes due tospecial events, making neighbor sites on-air or down, or by shifting thetraffic to the other technologies, e.g. shifting the traffic from 3G to 4G.Traditionally, in order to keep the network stable, the traffic should beobserved layer by layer during each interval to detect major changes in KPIs,in large scale telecommunication networks it will be too time-consuming withthe low accuracy of anomaly detection. However, the proposed algorithm iscapable of detecting the abnormal KPIs for each element of the network in atime-efficient and accurate manner. It observes the traffic layer trends andclassifies 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 andoperators to detect anomalies in their live networks in order to keep the KPIsin a normal trend. The algorithm is trained and tested on the real dataset overa cellular network with more than 25000 cells.

Deep RAN: A Scalable Data-driven platform to Detect Anomalies in Live Cellular Network Using Recurrent Convolutional Neural Network