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- Load forecasting in WIFI access points over the LTE networkPublication . Marques, Hugo; Torres, Pedro; Marques, Paulo; Dionísio, Rogério Pais; Rodriguez, JonathanThe concept of smart cities grew with the need to rethink the use of urban spaces based on the constant technological advances and respecting sustainability. Today the urbanism and the methodologies to think about the city are changing, as citizens want more access to digital information on almost everything. Therefore, cities need to be planned and equipped with infrastructures that enable connectivity between the citizens’ devices and the digital information. This challenge raises technological problems, such as traffic management, in an attempt to guarantee fair network access to all users. Solutions based on wireless resource management and self-organizing networks are key when design the connectivity for these smart cities. This paper presents a study on forecasting the daily load of Wi-Fi city hotspots, taking also in consideration the weather conditions. This is particularly interesting to predict the network load and resource requirements needed to ensure proper quality of service is provided to the hotspot users. The study was performed in a Wi-Fi hotspot located in the city of Castelo Branco, Portugal. The results show the ARIMA model is capable of identifying and forecasting seasonality events for one week in advance including its capability to correlate the number of hotspot users with weather conditions.
- 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.