Terrestrial Networks

For future commercial broad-band data satellite networks, satellite network topology, link capacity, and routing have major impacts on the cost of the network and the amount of revenue the network can generate. In this thesis, we establish a mathematical framework, using two-stage stochastic programming, to assist network designers in selecting the most cost-effective network topology for data applications. The solution to the stochastic programming formulation gives optimal link capacities and an optimal routing strategy for different network topologies, taking into account uncertainties in long-term aggregate traffic statistic estimation. For several classes of satellite topologies of interest, analytical solutions have been derived. In particular, we give the optimal topology selection criteria for a general GEO satellite network and identify regions where a hybrid satellite-terrestrial topology is the most cost effective. For LEO and MEO satellite networks, comparisons between different routing strategies for polar constellations with and without seam are presented. The analytical solutions we have derived capture the salient networ