Mobile Traffic Prediction from Raw Data Using LSTM Networks

Predictive analysis on mobile network traffic is becoming of fundamental importance for the next generation cellular network. Proactively knowing the user demands, allows the system for an optimal resource allocation. In this paper, we study the mobile traffic of an LTE base station and we design a system for the traffic prediction using Recurrent Neural Networks. The mobile traffic information is gathered from the Physical Downlink Control CHannel (PDCCH) of the LTE using the passive tool presented in [1]. Using this tool we are able to collect all the control information at 1 ms resolution from the base station. This information comprises the resource blocks, the transport block size and the modulation scheme assigned to each user connected to the eNodeB. The design of the prediction system includes long short term memory units. With respect to a Multilayer Perceptron Network, or other artificial neurons structures, recurrent networks are advantageous for problems with sequential data (e.g. language modeling) [2]. In our case, we state the problem as a supervised multivariate prediction of the mobile traffic, where the objective is to minimize the prediction error given the information extracted from the PDCCH. We evaluate the one-step prediction and the long-term prediction errors of the proposed methodology, considering different numbers for the duration of the observed values, which determines the memory length of the LSTM network and how much information must be stored for a precise traffic prediction.