Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
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 se...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/9/15/2975 |
| _version_ | 1798046061524156416 |
|---|---|
| author | Muhammad Zeeshan Asghar Mudassar Abbas Khaula Zeeshan Pyry Kotilainen Timo Hämäläinen |
| author_facet | Muhammad Zeeshan Asghar Mudassar Abbas Khaula Zeeshan Pyry Kotilainen Timo Hämäläinen |
| author_sort | Muhammad Zeeshan Asghar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T23:32:37Z |
| format | Article |
| id | doaj.art-9b481d9db7924714a7168c7192d3f2ec |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-11T23:32:37Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9b481d9db7924714a7168c7192d3f2ec2022-12-22T03:57:06ZengMDPI AGApplied Sciences2076-34172019-07-01915297510.3390/app9152975app9152975Assessment of Deep Learning Methodology for Self-Organizing 5G NetworksMuhammad Zeeshan Asghar0Mudassar Abbas1Khaula Zeeshan2Pyry Kotilainen3Timo Hämäläinen4Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, FinlandFaculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, FinlandFaculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, FinlandFaculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, FinlandFaculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, FinlandIn 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.https://www.mdpi.com/2076-3417/9/15/2975deep learning (DL)self-organizing networks (SON)5Gautoencoder (AE)mobility load balancing (MLB)cell outage detectionintrusion detection |
| spellingShingle | Muhammad Zeeshan Asghar Mudassar Abbas Khaula Zeeshan Pyry Kotilainen Timo Hämäläinen Assessment of Deep Learning Methodology for Self-Organizing 5G Networks Applied Sciences deep learning (DL) self-organizing networks (SON) 5G autoencoder (AE) mobility load balancing (MLB) cell outage detection intrusion detection |
| title | Assessment of Deep Learning Methodology for Self-Organizing 5G Networks |
| title_full | Assessment of Deep Learning Methodology for Self-Organizing 5G Networks |
| title_fullStr | Assessment of Deep Learning Methodology for Self-Organizing 5G Networks |
| title_full_unstemmed | Assessment of Deep Learning Methodology for Self-Organizing 5G Networks |
| title_short | Assessment of Deep Learning Methodology for Self-Organizing 5G Networks |
| title_sort | assessment of deep learning methodology for self organizing 5g networks |
| topic | deep learning (DL) self-organizing networks (SON) 5G autoencoder (AE) mobility load balancing (MLB) cell outage detection intrusion detection |
| url | https://www.mdpi.com/2076-3417/9/15/2975 |
| work_keys_str_mv | AT muhammadzeeshanasghar assessmentofdeeplearningmethodologyforselforganizing5gnetworks AT mudassarabbas assessmentofdeeplearningmethodologyforselforganizing5gnetworks AT khaulazeeshan assessmentofdeeplearningmethodologyforselforganizing5gnetworks AT pyrykotilainen assessmentofdeeplearningmethodologyforselforganizing5gnetworks AT timohamalainen assessmentofdeeplearningmethodologyforselforganizing5gnetworks |