Distributionally Robust Planning for Data Delivery in Distributed Satellite Cluster Network

Abstract

The emerging distributed satellite cluster network (DSCN) holds great promise in various practical fields, including earth observation, disaster rescue, and tracking of forest fires. In the DSCN environment, it is essential to achieve the best data delivery performance by coordinating multi-dimensional heterogeneous and dynamic resources. However, in real-world applications, the distribution of long-term data arrival is not often fully known. Motivated by this fact, we propose a distributionally robust two-stage stochastic optimization framework with considering the dynamic network resources and the partially known distribution information of long-term data arrival. Aiming at maximizing the total network reward, we formulate a two-stage stochastic flow optimization problem based on the extended time expanded graph. Then, we introduce an ambiguity set for the uncertain distribution of the long-term random data arrival inspired by the idea from the distributionally robust optimization. On the basis of the proposed ambiguity set, we further propose a data arrival distribution robust two-stage recourse (DADR-TR) algorithm by converting the original stochastic optimization problem into a deterministic cone optimization problem, which is computationally tractable. The extensive simulations have been conducted to evaluate the impact of various network parameters on the algorithm performance and further validate that the proposed DADR-TR algorithm can achieve high data delivery performance without full distribution information of the long-term data arrival.