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- Using deep neural networks for forecasting cell congestion on LTE networks: a simple approachPublication . Torres, Pedro; Marques, Hugo; Marques, Paulo; Rodriguez, JonathanPredicting short-term cellular load in LTE networks is of great importance for mobile operators as it assists in the efficient managing of network resources. Based on predicted behaviours, the network can be intended as a proactive system that enables reconfiguration when needed. Basically, it is the concept of self-organizing networks that ensures the requirements and the quality of service. This paper uses a dataset, provided by a mobile network operator, of collected downlink throughput samples from one cell in an area where cell congestion usually occurs and a Deep Neural Network (DNN) approach to perform short-term cell load forecasting. The results obtained indicate that DNN performs better results when compared to traditional approaches.
- Data analytics for forecasting cell congestion on LTE networksPublication . Torres, Pedro; Marques, Paulo; Marques, Hugo; Dionísio, Rogério Pais; Alves, Tiago Ferreira; Pereira, Luis Miguel Cardoso; Ribeiro, Jorge Miguel AfonsoThis paper presents a methodology for forecasting the average downlink throughput for an LTE cell by using real measurement data collected by multiple LTE probes. The approach uses data analytics techniques, namely forecasting algorithms to anticipate cell congestion events which can then be used by Self-Organizing Network (SON) strategies for triggering network re-configurations, such as shifting coverage and capacity to areas where they are most needed, before subscribers have been impacted by dropped calls or reduced data speeds. The presented implementation results show the prediction of network behaviour is possible with a high level of accuracy, effectively allowing SON strategies to be enforced in time.