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
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
- CSON - Kognitiivisia itseorganisoituvia verkonhallintaratkaisuja
- Asghar, Muhammad
- TEKES
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2019
JUFO-taso: 1