Narmanlıoğlu, Ö.Zeydan, E.Kandemir, MelihKranda, T.2024-03-262024-03-262017-01-012377-8652http://hdl.handle.net/10679/9321https://doi.org/10.1109/NOF.2017.8251223Internet-empowered electronic gadgets and content rich multimedia applications have expanded exponentially in recent years. As a consequence, heterogeneous network structures introduced with Long Term Evolution (LTE) Advanced have increasingly gaining momentum in order to handle with data explosion. On the other hand, the deployment of new network equipment is resulting in increasing both capital and operating expenditures. These deployments are done under the consideration of the busy hour periods which the network experiences the highest amount of traffic. However, these periods refer to only a couple of hours over a 24-hour period. In relation to this, accurate prediction of active user equipment (UE) number is significant for efficient network operations and results in decreasing energy consumption. In this paper, we investigate a Bayesian technique to design an optimal feed-forward neural network for shortterm predictor executed at the network management entity and providing proactivity to Energy Saving, a Self-Organizing Network function. We first demonstrate prediction results of active UE number collected from real LTE network. Then, we evaluate the prediction accuracy of the Bayesian neural network as comparing with low complex naive prediction method, Holt- Winter's exponential smoothing method, a deterministic feedforward neural network without Bayesian regularization term.engrestrictedAccessconferenceObject2018-January737800042714580001210.1109/NOF.2017.8251223Bayesian neural networksLong term evolutionSelf-organizing networksShort-term prediction2-s2.0-85049616181