SIRADEL is an expert in simulating the physical layer of wireless communication networks, in 3D modelling of dynamic scenarios, in evaluating radio link performance, and in optimising the deployment and configuration of access points. In other words, SIRADEL contributes to implementing the RAN digital twin (DT) for radio link emulation, system performance prediction, and radio network planning.
As provider of software and data solutions, we have strong expertise in the production of mapping datasets, ray tracing based channel simulation, the development and integration of simulation modules, and the measurement of field performance.
Our simulation capabilities apply to a wide variety of network types, including access, backhaul and sensing networks; private networks; smart factory scenarios; rail environments; regional or national networks; drones; and NTN systems. These use cases share a common foundation: the exploitation of accurate data for modelling the environment, antennas, objects, and more.
We are interested in collaborations on the following topics:
– Digital twin technologies to generate synthetic data for AI algorithms; or applied to spectrum management;
– Simulation and optimisation of JCAS scenarios, e.g. for drone detection or robot positioning;
– Coexistence between NTN and terrestrial networks.
SIRADEL is a company that has been delivering innovative digital solutions since 1994 in the fields of telecommunications and territorial management, thanks to its combined expertise in modelling, simulation, 3D mapping and spatial analysis.
Today, Siradel supports operators, equipment manufacturers and R&D teams in optimising the deployment of 5G/6G networks through software solutions and consulting studies. In addition, as a leader in geospatial digital services, Siradel helps companies and local authorities harness the potential of geographic data to make better decisions, take action and communicate more effectively.
SIRADEL has strong experience in collaborative projects at both national and European levels. We can contribute by developing the digital twin of the PHY-layer or RAN (at the scale of a private network, macro network or NTN), by generating deterministic datasets (e.g., for dynamic scenarios), or by optimising the deployment of radio infrastructure.