LSTM-Based Active User Number Estimation and Prediction for Cellular Systems

In current long term evolution (LTE) system, resource efficiency of the random access procedure is low due to the semi-static resource allocation strategy. With the massive access requirement in the future 5G network, this problem would become more and more serious. To effectively reduce the resource consumption while guaranteeing the access delay and reliability, intelligent access channel allocation must be applied, which requires the base station (BS) to accurately estimate the number of user equipments (UEs) that are performing random access. Motivated by this, we propose a novel LSTM-based machine learning approach for active UE number estimation in random access procedure. Meanwhile, a simple minimum distance method is devised as the baseline. Our proposed LSTM-based approach can also predict the UE number at the next random access procedure. Simulation results show that the proposed method achieves quite accurate estimation and prediction of UE number.