Mission Structure Learning-Based Resource Allocation in Space Information Networks

An efficient resource allocation algorithm plays a pivotal role in the performance improvement of space information networks (SIN). The dynamic of resources and mission requirements in the network has a great influence on resource allocation. Guiding rapid satellite resource allocation to adapt to dynamic changes of the network by discovering the similarity in the structure of changing missions is a key technology to improve SIN performance. In this paper, we firstly formulate satellite resource allocation as a problem aiming to maximize the total network benefits. Then, we propose a satellite resource allocation algorithm based on Hopfield to solve resource allocation problems in SIN. To accommodate the dynamics of satellite network missions and quickly solve resource allocation problems, we further propose a satellite resource allocation algorithm based on transfer learning to learn the node selection strategy in the Hopfield network. Specifically, we use a small number of additional training samples to better adapt to the changes in mission structure. Simulation results validate that the proposed method can achieve high performance while reducing computational complexity.