Objective
A revelation in today´s mobile is networks is SON (Self-Organizing technology) technology, which is seen as a playing pivotal role towards reducing the management costs of networks. In legacy networks, still many network elements and associated parameters are manually configured. The associated operations costs are significant. Specialized expertise must be maintained to tune these network parameters, and the existing manual process is time-consuming and potentially error-prone. In addition, this manual tuning process inherently results in comparatively long delays in updating values in response to the often rapidly changing network topologies and operating conditions, resulting in sub-optimal network performance. SON is capable of collecting information from the network, so as to perform self-configuration, self-optimization, self-healing and etc, so as to reduce the operation cost through less human involvement, and to optimize the service quality through robust and prompt network optimization.
In this proposal, we aim to drive further cost savings in the way networks are managed today by amplifying further the coverage zone of SON within the network. We believe that key technologies such as network sharing and Coordinated Multipoint (CoMP) can benefit from SON technology solutions. We will consider a complex context-aware heterogeneous network that is slowly becoming a 5G reality, and investigate the notion of SON CoMP and SO network sharing, as key technologies to reduce cost and energy per bit in legacy and future emerging mobile technologies.
Fields of science
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksmobile network5G
- natural sciencesmathematicspure mathematicstopology
- natural sciencesmathematicsapplied mathematicsgame theory
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technology
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Coordinator
3750 102 Agueda
Portugal
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Participants (3)
265 04 Rio Patras
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SE10AA London
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2057 Nicosia
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.