Traffic management for mobile networks with harvested ambient energy

Contact names: Dr. Paolo Dini and Dr. Jose Nuñez

Institution: CTTC

Application deadline:

Description: This project deals with the optimal energy allocation at the BSs with energy harvesting capabilities, while sustaining the traffic demand. Methods to balance the traffic load among the multiple tiers of the 5G network (i.e., macro and small cells) and also to dynamically reconfigure the different BSs will be devised. Software Defined Networking and Network Function Virtualization will be considered for a better sharing of the computational load and consequently the energy cost among the network elements (mainly in the radio access and backhaul). Self-organising and self-optimising methods will be targeted, designing traffic management algorithms able to adapt to the spatio-temporal dynamics of energy arrivals and traffic demands.

Expected Results: Understand the impact of the energy harvesters and storage systems in traffic management methods. Define self-organising and implementable load balancing methods.

Mobility Schedule: The candidate will be mainly hosted at CTTC.
After 24 months, the candidate will join Huawei Finland under the supervision of Dr. Kari Heiska to validate traffic management algorithms in the defined network architecture.

Requirements of the candidate:

  • At the time of recruitment, the applicant must not have lived in Spain for more than 12 months in the previous 36 months

  • A Master degree in Telecommunications, Computer Science or equivalent

  • Once selected by the SCAVENGE consortium for the ESR positions, the applicants must apply for PhD studentship at Universidad Politecnica de Catalunya (UPC).

  • Knowledge on mobile networks, sensor networks, big data

  • Knowledge on energy systems and management

  • Ability and motivation to conduct high-quality research, including publishing the results in relevant venues

  • Strong programming skills (e.g. C/C++, Java, Python).

  • Working experience in the areas of Mobile Networks, Sensor Networks, Smart Grid, Energy Harvesting, Big Data management, Cloud computing is an advantage.

  • Knowledge on Optimization Theory and Machine Learning is an advantage.

  • Knowledge on SDN/NFV is an advantage.

  • Very good communication skills in oral and written English.

  • Open-mindedness, strong integration skills and team spirit.