Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks

Name: Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks
Author: Narmanlıoğlu, Ö., Zeydan, E., Kandemir, Melih, Kranda, T.

İsim	Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks
Yazar	Narmanlıoğlu, Ö., Zeydan, E., Kandemir, Melih, Kranda, T.
Basım Tarihi:	2017-01-01
Basım Yeri	- IEEE
Konu	Bayesian neural networks, Long term evolution, Self-organizing networks, Short-term prediction
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2377-8652
Kayıt Numarası	1e9e5a32-d4ae-42e7-86ba-55e808e62681
Lokasyon	Computer Science
Tarih	2017-01-01
Örnek Metin	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

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Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks

Yazar Narmanlıoğlu, Ö., Zeydan, E., Kandemir, Melih, Kranda, T.

Basım Tarihi 2017-01-01

Basım Yeri - IEEE

Konu Bayesian neural networks, Long term evolution, Self-organizing networks, Short-term prediction

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2377-8652

Kayıt Numarası 1e9e5a32-d4ae-42e7-86ba-55e808e62681

Lokasyon Computer Science

Tarih 2017-01-01

Örnek Metin 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