CSON - Cognitive Self-organizing Networks (CSON)
Main funder
Funder's project number: 1916/31/2017
Funds granted by main funder (€)
- 391 000,00
Funding program
Project timetable
Project start date: 01/07/2017
Project end date: 30/06/2019
Summary
The dramatic increase of the mobile data traffic is expected in future. One of the reason for the mobile traffic growth is the use of new types of services, application and devices. The mobile traffic demand per user is expected to grow from 0.5 GB to 15 GB per month . The ever increasing capacity demands are setting rigid requirements on capacity improvements and efficient network management in mobile networks. One of the solutions is to deploy base stations on large scale leading to network densification, however, this in return increase the operational costs for the operator. In addition, the traffic per user is increasing quickly while the revenue per user is more or less static, leading to the scissor effect because the traffic is increasing but the revenue is probably based on the flat rate pricing. Therefore, there is a clear need for introducing automation and self-organization into the cellular networks. There are two clear benefits of introducing automation in operator’s planning, deployment and operational tasks. First benefit is that it would reduce capital expenditures by reducing human involvement in the repetitive operational tasks. Secondly, it would improve the coverage, capacity and quality of service by better utilization of human and network resources. In this way the human role is shifted to higher level, defining policies and monitoring the SON functions execution. The SON functionalities can be divided into three, namely self-configuration, self-optimization and self-healing that can be integrated to the operator’s network management. The self-configuration aims to automate the initial steps of the network element installation and autonomous integration to operation administration and maintenance (OAM). The self-optimization aims at continuous monitoring, detecting the under-performing cell and adjusting network parameters thus ensuring faster response to the dynamically changing network conditions. The self-healing aims at automatic fault management to reduce the number of outages and duration of outages.
This project CSON refers to the comprehensive and innovative self-organizing network management software solutions for the future wireless-networks. CSON addresses the challenges of network operations that arise due to the complexity involved in the operational processes that will be difficult to handle for manual network management, and develop knowledge mining assisted self-optimization and self-healing solutions that can reduce the operational expenses at one hand and improves the network coverage, capacity and service quality.
Specifically, the following challenges will be addressed: 1) Modeling degradations and network outages in the cellular network 2) Investigating the sleeping cells in the network and cognitive recovery of outages 3) Designing self-healing and self-optimizing algorithms using machine learning and advanced knowledge mining techniques. 4) Enriching SON with big data that is harnessed in cellular network 5) Designing proactive and context-aware SON solutions to meet the zero latency requirements of 5G networks
This project CSON refers to the comprehensive and innovative self-organizing network management software solutions for the future wireless-networks. CSON addresses the challenges of network operations that arise due to the complexity involved in the operational processes that will be difficult to handle for manual network management, and develop knowledge mining assisted self-optimization and self-healing solutions that can reduce the operational expenses at one hand and improves the network coverage, capacity and service quality.
Specifically, the following challenges will be addressed: 1) Modeling degradations and network outages in the cellular network 2) Investigating the sleeping cells in the network and cognitive recovery of outages 3) Designing self-healing and self-optimizing algorithms using machine learning and advanced knowledge mining techniques. 4) Enriching SON with big data that is harnessed in cellular network 5) Designing proactive and context-aware SON solutions to meet the zero latency requirements of 5G networks
Principal Investigator
Primary responsible unit
Related publications and other outputs
- Artificial Intelligence Enabled Self-healing for Mobile Network Automation (2021) Asghar, Muhammad Zeeshan; et al.; A4; 978-1-6654-2390-8
- Location-Awareness for Failure Management in Cellular Networks : An Integrated Approach (2021) Fortes, Sergio; et al.; A1; OA
- Dual Connectivity in Non-Stand Alone Deployment mode of 5G in Manhattan Environment (2020) Sheik, Muhammad Usman; et al.; A4; OA; 978-1-7281-6289-8
- Assessment of Deep Learning Methodology for Self-Organizing 5G Networks (2019) Asghar, Muhammad Zeeshan; et al.; A1; OA
