Abstract
The increased level of heterogeneity among the network components of current mobile Radio Access Networks (RANs), has meant that conventional network troubleshooting techniques are no longer sustainable; impacting negatively on customer experience and Quality of Service (QoS). Intelligent Fault Management (IFM) is a promising approach that utilizes the power of data mining in addressing emerging and future challenges in RAN network troubleshooting and performance management. This paper provides a comprehensive integrated RAN modelling framework in order to reflect protocol, topology, service inventory, and correlations between Alarms in a typical Mobile Radio Access Network (RAN). It proposes and presents results from a RAN model leading towards an effective Network Management System (NMS) datasets that could be analysed to discover alarm rules, associations and core drivers using different data mining techniques, graph theory measures and data benchmarking methodologies.
Original language | English |
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Title of host publication | Proceedings of 2016 Conference of Basic Sciences and Engineering Studies, SGCAC 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 43-49 |
Number of pages | 7 |
ISBN (Electronic) | 9781509018123 |
DOIs | |
Publication status | Published - 25 Apr 2016 |
Event | Conference of Basic Sciences and Engineering Studies, SGCAC 2016 - Khartoum, Sudan Duration: 20 Feb 2016 → 23 Feb 2016 |
Conference
Conference | Conference of Basic Sciences and Engineering Studies, SGCAC 2016 |
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Country/Territory | Sudan |
City | Khartoum |
Period | 20/02/16 → 23/02/16 |
Keywords
- Data Mining
- Intelligent Fault Management
- Network Graph and RAN Model
- Radio Access Network (RAN)