Building an Intelligent System of Insight and Action for 5G Network Management


5G will realise a true Internet of Things, a network capable of supporting potentially trillions of wireless connected devices and with overall bandwidth one thousand times higher that today’s wireless networks. Current 4G technology is approaching the limits of what is possible with this generation of radio technology and to address this, one of the key requirements of 5G will be to create a network that is highly optimised to make maximum use of available radio spectrum and bandwidth for QoS, and because of the network size and number of devices connected, it will be necessary for it to largely manage itself and deal with organisation, configuration, security, and optimization issues.

Virtualisation will also play an important role as the network will need to provision itself dynamically to meet changing demands for resources and Network Function Virtualisation (NFV), the virtualising of network nodes functions and links, will be the key technology for this.We believe that Autonomic Network Management based on Machine Learning will be a key technology enabling an (almost) self administering and self managing network. Network software will be capable of forecasting resource demand requirements through usage prediction, recognizing error conditions, security conditions, outlier events such as fraud, and responding and taking corrective actions.

Energy efficiency will also be a key requirement with the possibility to reconfigure the NFV to for example avail of cheaper or greener energy when it is available and suitable. Again this is directly related to usage prediction both at a macro level, across an entire network, and at a micro level within specific cells.The Cognet project will focus on applying Machine Learning research to these domains to enable the level of Network Management technology required to fulfil the 5G vision.

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Aim and Impact

CogNet developed solutions to provide a highly automated and more intelligent level of network monitoring and management, improve operational and energy efficiencies, quality of experience for the end user and facilitate the requirements of 5G.

  • Will help maintain Europe’s lead in Network Management.
  • Reduced cost and improve quality of services in future network
  • Optimized networks, better security and improved resource utilisation
  • Increase sales, new market opportunities and more jobs

The goal of the CogNet project was to make a major contribution towards autonomic management of telecoms network infrastructure through using the available network data and applying Machine Learning algorithms to yield insights, recognise events and conditions and respond correctly to them. The project goal was to develop solutions that would provide a higher and more intelligent level of automated monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of 5G.

The project conducted and exploited leading research in the areas of data gathering, machine learning, data analytics and autonomic network management. The ultimate objective was to enable the larger and more dynamic network topologies necessary in 5G, improve the end-user QoS, and to lower capital and operational costs through improved efficiencies and the use of node, link and function virtualisation.


Building an Intelligent System of Insights and Action for 5G Network Management