A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Asghar, Muhammad
Abbas, Mudassar
Zeeshan, Khaula
Kotilainen, Pyry
Hämäläinen, Timo

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Asghar, Muhammad Zeeshan; Abbas, Mudassar; Zeeshan, Khaula; Kotilainen, Pyry; Hämäläinen, Timo

Lehti tai sarja: Applied Sciences

eISSN: 2076-3417

Julkaisuvuosi: 2019

Volyymi: 9

Lehden numero: 15

Artikkelinumero: 2975

Kustantaja: MDPI AG

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Kokonaan avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/65169

Tiivistelmä

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.

Vapaat asiasanat: 5G-tekniikka

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

OKM-raportointi: Kyllä

Raportointivuosi: 2019

JUFO-taso: 1