SNCF and Nokia to develop a 5G Lab to prepare for transition from GSM-R to FRMCS

PARIS: FRENCH National Railways (SNCF) has signed an agreement with Nokia to develop a 5G laboratory for rail and non-rail uses and to prepare for the switch from GSM-R to the Future Railway Mobile Communication System (FRMCS) in the mid-2020s.

FRMCS, which is being developed by a team led by the International Union of Railways (UIC), will be designed for 5G, which is starting to roll-out. 5G offers reliable, high-speed, low-latency performance and much greater capacity than GSM-R to improve existing telecommunications services and allow the development of new ones for railways.

SNCF and Nokia will evaluate FRMCS applications in the laboratory and out in the field. Infrastructure manager SNCF Network and SNCF’s innovation and research department will be closely involved in the project.

“SNCF’s ambition is to create a universal telecommunications system capable of meeting the needs of the rail system in the future,” says Ms Carole Desnost, director SNCF innovation and research.

“This collaboration highlights the full potential of 5G to address industrial use cases where current technologies are reaching their limits,” says Mr Matthieu Bourguignon, vice-president, Europe, with Nokia Enterprise.

It may not be out of place to mention here that to rollout 5G, telcos need to have accurate, detailed and up-to-date mapping of the markets they serve.

By using aerial imagery, LiDAR, and object-based image analysis technology, producing both 3D building and tree vectors and 1-meter resolution land-cover classifications of metropolitan areas is quite possible. With this telecom-tailored information, companies can accurately analyze urban & residential areas to determine the optimal locations for 5G sensors so their signals reach the highest number of users, helping them remain strong contenders in the race for a future that is predicted to be lightning fast.

Critical infra needed to enable the next wave of tech advancements in IoT, Smart Cities, VR & autonomous vehicles comes with the combination of 5G supported by LiDAR datasets, customized mapping of select Areas of Interest, 3D models of Areas of Interest with layered views of 3D building footprints, classified land cover/tree contours and their heights – all become part of critical information to analyze Line of Sight potential for 5G sensors to determine best strategy for the Telecos.

5G is the culmination of the process that will take us on a new trajectory of technological innovation, with the help of new framework that merges Cloud, Big Data, IoT, automation and AI to gain insights generated from billions of connected devices. Due to the extensive technological sophistication of 5G, selecting a site for 5G cells requires certain is more complex than selecting a site for 3G or 4G.

5G need minimum 50 times more antenna locations than 3G or 4G. Also, 5G signals are more powerful but they are unable to travel far and wide like its previous incarnations.

The main advantage of 5G is that of one cell site is not working; the other site would cater to customer demands. Instead of concentrating a large number of cells at a single location, 5G cells are spread at different locations so that in case of a network breakdown issues, users can continue to get efficient services. 5G’s higher frequencies which is needed to carry huge amounts of data have a very short range which can be impacted by smallest of the obstructions. The signal is so sensitive that it can be blocked by the palm of your hand, or even a raindrop.

5G will also require denser telecom network more towers placed selectively and strategically. Therefore, accurate, authoritative geospatial data is fundamental here to plan network towers.

Since the 5G network cells would be evenly distributed in a large area, it is important to select a location. As such, 5G and geospatial will together power future cities.

The solution includes machine learning and a service delivery framework, expertise in RF and C-RAN design from Shields and large, precise, 3D datasets derived from terrestrial/aerial LiDAR and other remote sensed content.

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