|Start:||24 June 2019|
|End:||31 January 2020|
|Department:||Array and Multi-Sensor Processing (A&MSP)|
|Code:||ARTES FPE 1A.104|
The activity will identify multiple use cases in the domain of satellite communications that could be addressed by using ML/AI techniques. Such use cases could either be existing problems or new concepts that could enable new capabilities for satellite communication systems. The use case could address any phase in the lifecycle of satellite communication systems.
As a minimum, some use cases shall be investigated that address the reduction of interference, the optimisation of spectrum usage and radio resources in scenarios where satellite systems interfere with each other, or in which satellite systems interfere with terrestrial systems. Other use cases shall could be of interest is optimisation of network management and operations for large complex satellite constellations.
The activity shall evaluate a number use cases and ML/AI techniques, including supervised and unsupervised learning, deep and continuous learning and constrained based learning. The activity shall actively seek the feedback from industry on the practical applicability of identified techniques. Based on justified trade-off criteria and the consultations with industry, a subset of the identified use cases shall be simulated or emulated to make a convincing demonstration of the added value of such ML/AI techniques. The activity shall identify additional activities which shall be initiated.
The activity will generate the following outcomes: