<p>This paper considers resource allocation for a high-throughput satellite communications system operating in geostationary earth orbit. The main contributions of this paper include the design, development, and testing of three beamforming algorithms, each using a different paradigm for candidate beam collection and evaluation, but the same integer programming model for optimizing the selection of the beams to be used from a set of candidate beams.</p>

SummaryModern satellite communication systems are required to serve heterogeneous and geographically dispersed user demands with limited resources. In this paper, we investigate methodologies for dynamic resource allocation in Geosynchronous Earth Orbit (GEO) High‐throughput Satellite (HTS) systems. We designed three solution approaches FlexBeamOpt v1, FlexBeamOpt v2, and FlexBeamOpt v3, each as a hybridization of custom heuristics, integer linear programming, and/or constraint programming. We test the performance of the three approaches on 12 test instances that vary in user distribution (realistic, random, and clustered), user numbers (500 vs. 5000 users), and demand distribution (uniform vs. random). We observed that FlexBeamOpt v1  consistently outperformed FlexBeamOpt v2  and FlexBeamOpt v3  in terms of demand coverage and number of users covered for realistic and random user distribution test instances but at the cost of computation time. FlexBeamOpt v3  is the fastest in these instances. For clustered user distribution instances, FlexBeamOpt v3  performed better in terms of demand coverage and number of users covered, at the cost of using more beams. For these test instances, FlexBeamOpt v2  is the fastest in terms of computation time while providing a comparable solution quality.

<p>This paper considers resource allocation for a high-throughput satellite communications system operating in geostationary earth orbit. The main contributions of this paper include the design, development, and testing of three beamforming algorithms, each using a different paradigm for candidate beam collection and evaluation, but the same integer programming model for optimizing the selection of the beams to be used from a set of candidate beams.</p>