Multi-Satellite Resource Scheduling Based on Deep Neural Network

Abstract

Resource scheduling is one of the main problems for multi-satellite Tracking, Telemetry and Command (TT&C) networks. Traditional multi-resource joint scheduling algorithms are with long solution time, low efficiency, high computational cost, and simple description on the system. Deep Neural Network (DNN) provides a possible new way to solve those problems, but it is difficult to handle correlations among the input data. This motivates our work to solve the strong correlation problem based on the accumulated historical data, and thus enables DNN for TT&C resource scheduling. By discretizing the data, multiple constraints and related attributes are transformed into different flags, and some binary bits of the data are used to reflect the constraint relationship. Then, we can use DNN model and construct an intelligent TT&C resource scheduling system to handle multiple constraints and data attributes (such as priorities among tasks and others). This improves the efficiency of TT&C resources utilization and automation. Effectiveness of the proposed model is verified by simulations.