Author
Narmanlıoğlu, Ö., Zeydan, E., Kandemir, Melih, Kranda, T.
Publication Date
2017-01-01
Publication Place
-
IEEE
Subject
Bayesian neural networks, Long term evolution, Self-organizing networks, Short-term prediction
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2377-8652
Record ID
1e9e5a32-d4ae-42e7-86ba-55e808e62681
Library Location
Computer Science
Date
2017-01-01
Sample Text
Internet-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.
DOI
10.1109/NOF.2017.8251223
Cilt
2018-January
