Anticipatory Networking Techniques in 5G and Beyond
Welcome to ACT5G

The rapid growth in mobile broadband network data lead to the unprecedented speed of 4G LTE deployment. The evolution calls for sustainable capacity growth and performance improvement in the next generation of wireless systems, namely 5G. The success of 5G heavily relies on delivering satisfactory user experience at a low operational cost. In this context, research and development (R&D) is required not only for technological innovation at the device level, but also management system design for the flexibility necessary to predict and adapt to network usage. To this end, anticipation is a promising and new approach. By predicting and adapting to upcoming events at various time scales, an anticipatory-enabled 5G network dramatically improves the operation quality and efficiency in comparison to the existing systems. Deploying anticipatory networking with effective prediction of network behaviour offers a twin advantage: First, resources ranging from spectrum and power to network interfaces buffers can be managed optimally. Second, network operators can jointly plan how to share infrastructure and resources to provide service with substantially reduced expenditure.

The ACT5G project takes an inter-sector approach for anticipatory networking for 5G. The project fuses and integrates the scientific expertise of the participating academic sites with the industrial know-how of the consortium. The R&D core of ACT5G consists of models, methods, concepts, and algorithms for network anticipation and network reaction. The R&D tasks are carried out along with researcher training.

The PhD training programme of ACT5G is designed the maximize the partners' synergy, to promote career opportunities of the ACT5G Early-Stage Researchers in the European ICT arena, and thus generate new expertise and human capital for the European Research Area.

Project Partners
Link&oumlping University
Linköping University (LiU) [coordinator], Sweden




Follow ACT5G activities on Linkedin