A1 Journal article (refereed)
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks (2019)

Asghar, M. Z., Abbas, M., Zeeshan, K., Kotilainen, P., & Hämäläinen, T. (2019). Assessment of Deep Learning Methodology for Self-Organizing 5G Networks. Applied Sciences, 9(15), Article 2975. https://doi.org/10.3390/app9152975

JYU authors or editors

Publication details

All authors or editors: Asghar, Muhammad Zeeshan; Abbas, Mudassar; Zeeshan, Khaula; Kotilainen, Pyry; Hämäläinen, Timo

Journal or series: Applied Sciences

eISSN: 2076-3417

Publication year: 2019

Volume: 9

Issue number: 15

Article number: 2975

Publisher: MDPI AG

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.3390/app9152975

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65169

Abstract

In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of autoencoders (AE) for cell outage detection. First, we briefly introduce deep learning (DL) and also shed light on why it is a promising technique to make self organizing networks intelligent, cognitive, and intuitive so that they behave as fully self-configured, self-optimized, and self-healed cellular networks. The concept of SON is then explained with applications of intrusion detection and mobility load balancing. Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator. Finally, we provide a comparative analysis of the proposed framework with the existing frameworks.

Free keywords: deep learning; self-organizing networks; 5G; autoencoder; mobility load balancing; cell outage detection; intrusion detection

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Ministry reporting: Yes

VIRTA submission year: 2019

JUFO rating: 1