Optimization of Communication Networks with Geodesic Dome Phased Array Antennas using Artificial Intelligence Techniques

The goal of this proposed effort is to develop a Mid-level Manager (MLM) that automatically handles all aspects associated with GDPAAs, including global optimized assignment of communication requests to the GDPAAs and dish antennas, intelligent smart beam assignment, multi level beam compatibility checking, and beam active area route planning. The ultimate system should outperform the existing software and human schedulers including better quality schedules; faster scheduling; handling larger, more complex sets of requests; and handling additional communication system capabilities, including multi-beam phased array antennas. The developed system should show superior performance on AFSCN/GDPAA scheduling/route planning problems, and demonstrate significant benefit for human future AFSCN schedulers. Each of the algorithms should be rigorously analyzed and have its most relevant properties proven, such as correctness, termination, and required conditions. Integration requirements with ESD will be investigated. The information content and timing between the MLM and the GDPAA managers will be determined. BENEFIT: The most direct target for the results of this effort is, of course, scheduling for the future AFSCN which will include GDPAAs and the other Space Communication Networks (DSN, GN, and SN). By both demonstrating significantly improved performance during Phase II and ensuring that the ultimate results can be integrated into the operational scheduling systems, the Air Force will have a strong incentive to operationalize the Phase II development of the proposed MLM. Beyond the Air Force and NASA there are several commercialization possibilities available in the government and private sector. Additionally, there are many similarities between communication scheduling and sensor scheduling. Thus the algorithms developed here could be marketed to our existing Air Force and Navy sensor customers interested in sensor scheduling and coordination. Finally, there are the commercial applications for the technology developed for the Scheduler. We are already marketing Aurora, our general intelligent planning and scheduling framework, and customizing it for a variety of domains. The additional capabilities developed for communication scheduling can be synergistically marketed to the same clients. We anticipate that this effort will result in additional scheduling algorithms that we will be able to incorporate into our existing scheduling products, thus increasing the benefits they provide and their value.