Integrating Phased Array Path Planning with Intelligent Satellite Scheduling

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As the demand for satellite-driven communication increases in both the commercial and military sectors, so do the numbers of active satellite constellations and the parallel requirements for ground-based support capacity. Phased array antennae have been identified as a cost-effective hardware solution for increasing communications capacity at ground stations, due to their ability to support multiple contacts simultaneously and their design compatibility with cost-effective commercial components. However, with increased communications capacity comes added complexity for the task of scheduling satellite supports in a network of satellites and ground stations with multi-beam phased array antennae. This task breaks down into two inter-related goals. First, the network-level challenge remains to allocate contacts to specific local sites. This is already complex in the case where ground stations exclusively use traditional mechanically steered reflector antennae, as schedulers seek to optimize resource usage within the confines of satellite visibilities and equipment availability at different sites. Second, with the introduction of phased array antennae, there is an additional local-level challenge to calculate active areas and paths on the surface of the phased array, to determine whether a candidate allocation with multiple contacts can actually be supported. These are inter-related because the local path planning analysis is predicated on an allocation developed at the network-level, whereas the network-level reasoning is most effective if it can be informed by knowledge of incompatibilities manifested at the local level. This paper describes an Artificial Intelligence based approach for handling these mutual dependencies efficiently while generating nearly optimal solutions.