The development and management of telecommunications networks is becoming increasingly complex. To simplify them, new solutions are needed that guarantee users reliability and quality of experience, while ensuring that services remain cost-competitive for the companies and operators that provide them. The improved solutions in the architecture that TAPIR-CM will develop will influence network intelligence so that 5G and subsequent generations of mobile networks can achieve these objectives. TAPIR-CM builds upon the achievements of the recently completed TIGRE5-CM project, which offered an architecture based on Software-Defined Networks (SDN).
The Madrid-based IMDEA Networks Research Institute will coordinate the TAPIRCM project. IMDEA Networks is collaborating with the same partners it partnered with on TIGRE5: Carlos III University of Madrid and the University of Alcalá.
Domenico Giustiniano, Associate Research Professor at IMDEA Networks and principal investigator of the project, explains what the TAPIRCM researchers are trying to achieve: “With TAPIRCM, we are exploring new perspectives in research areas with which we are very familiar. Specifically, we aim to strengthen and reinforce two major research areas. The first is SDN combined with Network Function Virtualization (NFV) to achieve network improvement. This will bring flexibility and agility to the entire system lifecycle.”
The second area is the applicability of machine learning/artificial intelligence to networks. This promises to give operators the ability to accurately predict the behavior and characteristics of data traffic consumed by mobile users. With this information, operators will be able to improve the performance of their network functions, such as task scheduling, mobility management, orchestration, and resource allocation.
According to Domenico Giustiniano, the IMDEA Networks researchers will draw on specific areas of their expertise and their established experimentation-based approach: “Our team will focus on characterizing the mobile network access phase, which will help us obtain optimized solutions. We will also define how and where to collect the measurements needed to test machine learning algorithms for traffic classification and prediction. It is important that we do this with the same energetic experimental mindset that we apply to all our research activities.”.
According to Giustiniano, with the project's completion, the team hopes to showcase the fruits of their labor with a working prototype. "Our specific goal is to prototype the solutions we design in order to optimize them. Achieving our objectives will result in a set of solutions that dramatically improve the existing network architecture and pave the way beyond 5G networks."
TAPIR-CM is funded by the Department of Education and Research of the Autonomous Community of Madrid through the 2018 R&D technology program for research groups and co-financed by the European Social Fund (ESF) Operational Program and the European Regional Development Fund.
