Accessibility statement

Resource and Topology Management for 5G Systems

This project will explore how resource and topology management should be controlled in future 5G networks. Such networks will need to be highly adaptive to deal with hot spots of ultra-high capacity density, along with a need to be energy efficient. The purpose of the project will be to show how resource and topology management can be better controlled by applying learning strategies, such as machine learning, both on a local and system wide basis. The project will establish where the learning/reasoning should best reside (nodes and/or network), and also the degree of control information exchange required between nodes. A mixture of simulation and analysis will be used to assess performance. Game theory and Markov analysis are likely to be important analytical tools.

Key objectives

  • To investigate how cognitive radio techniques can be applied to Software Defined   Network (SDN) in 5G networks.
  • To explore how backhaul diversity can be achieved in the network.

Outputs

  • Adaptive and energy efficient topology management and resource allocation schemes.
  • Simulation and analysis showing the benefits/drawbacks of SDN based 5G networks.
  • Contributions to conference and journal papers.

Members

  • Jialu Lun
  • David Grace

Dates

  • Start: October 2013

Research