Bayesian and neural network schemes for call admission control in LTE systems

Cognitive networking paradigms may help meet the challenges of operating complex wireless communications networks. In this paper, we contrast the neural network (NN) and the Bayesian network (BN) models to extract information from real-time observations and optimize network performance. In particular, we apply these two models to the problem of call admission control (CAC) for a long term evolution (LTE) system. We simulate a realistic LTE scenario with mobility in ns-3 and we select the most relevant features that can be observed by the base station. Then, we design two new CAC schemes that autonomously learn the network behavior from the observation of the selected features. Furthermore, we propose a performance comparison among these two schemes and a state-of-the-art CAC scheme, showing that the NN and the BN schemes are very promising solutions for CAC in LTE systems.