5G Research

The research in the project cover the following five areas:

1. Key technologies for 5G in sparsely populated areas
This activity focuses on the development of key technologies for 5G in sparsely populated areas, network intelligence for 5G systems and solutions for container orchestration and profiling for multi-access edge computing environments.

Improved network intelligence is needed to handle large amounts of sensors that connect within geographically extensive areas. Multi-access edge computing offers that intelligence and computing capabilities are placed close to end users so that services in 5G networks can be made more responsive to user mobility no matter how they move across large areas.

Taken together, these selected development areas are very important building blocks for turning modern products and services into businesses and to serve people who are far from the large cities' well-developed infrastructures.

2. Multi-Access Edge Computing for enhanced 5G functionality in sparsely populated areas

The sub-activities deals with container orchestration and container profiling for multi-access custom computing where the server side of mobile services can be easily moved between different locations in the mobile network's property, depending on the mobility of the users.

This will be studied partly from a mobile service operator perspective, where resource management and distribution of containers takes place in a more or less dynamic way, and on the other hand from an end-user perspective, where different network configurations and architectures are compared against each other from capacity and performance points of view.

Innovative solutions for resource allocation and distribution of containers will be developed and tested. The sub-activity also studies various cloud-based solutions with Multi-Access Edge Computing. Various network configurations and their effects on delay and performance will be studied in experiments with local connection where possible cross-border services to verify compatibility between different hardware and systems are tested.

3. Innovative 5G applications

The activity is about developing and testing software that uses improved communication capabilities in distributed, data-intensive environments produced by sensors, the Internet of Things and cyber-physical systems.
The focus is on collecting, filtering, utilizing and disseminating relevant contextual and situational awareness data, which come from a variety of heterogeneous data sources. These gives rise to complexity, which needs to be addressed in future 5G environments.

Specifically, the activity is about developing 5G applications and a framework that will provide support to SMEs to develop innovative products and services for 5G. The software will also provide opportunities to create innovations that can be easily deployed in 5G-web.

The activities develop various methods, models and frameworks with software for:
1) data collection from several heterogeneous, data intensive devices, such as sensors, IoT and CPS, used in various situations
2) filtering and utilization of relevant data
3) dissemination of context-dependent and real-time critical data to units in the environment

4. 5G applications for cross-border transport

The sub-activities focuses on cross-border transport of time-critical cargo transported by (semi) autonomous vehicles. It requires real-time traffic information obtained through-traffic monitoring and traffic forecasts using 5G.

This includes collecting data and information from multiple data sources such as cars, trucks and other available sensors in the environment, e.g. road sensors, which will produce a rich and complex amount of data. It is necessary to provide situation-dependent and real-time data to the objects in the environment in order to provide the right service to the right vehicle or person at the right time.

5. Connected 5G services for healthcare

The sub-activities focuses on connected 5G applications for care where the applications will support personal, time-critical and situation-dependent healthcare in sparsely populated areas.

The work involves the collection, filtering and utilization of context-dependent data and information, which include user preferences. The sub-activities includes combining heterogeneous, data-intensive devices, such as IoT and CPS, and providing the right product and service, to the right person, and at the right time.