Project

AI-based wireless access network design for future (B)5G networks under energy- and EMF-constraints

Duration
01 October 2020 → Ongoing
Funding
Research Foundation - Flanders (FWO)
Promotor
Research disciplines
  • Engineering and technology
    • Wireless communications
Keywords
Network planning energy-aware EMF-aware 5G Beyond-5G machine learning renewable energy sources UABS UAV
 
Project description

In the last decades, not only an increase in mobile devices has been noticed, but these devices are also becoming very powerful, allowing more demanding services such as data streaming or video calling. In the future, wireless access networks need to expand to cope with these extra demands to keep the user satisfied. However, network planning is currently already a very complex problem due to the many targets such as cost, performance, etc. To meet the requirements of the next-generation networks, network should be taken to the next level. Future network design should be EMF- (Electromagnetic Field) and energy-aware, but also have a high degree of flexibility. 5G for example will use a variety of technologies in a single network such as beam forming, mmWave frequencies and Massive MIMO (Multiple Input Multiple Output) requiring advanced machine learning techniques to deal with this network complexity and ensure the energy- and EMF-awareness. For energy-awareness, one should also look at using renewable energy sources to feed the wireless access network instead of using fossil fuels like today. Finally, to allow a high degree of network flexibility, one should use mobile base stations. This can be done by UAV (Unmanned Aerial Vehicle) aided networks. In this type of networks, mobile stations are mounted on UAVs or drones to provide additional performance on a more local scale. Each of the above requirements will be addressed in this research proposal.