An analytical framework for remote sensing satellite networks based on the model predictive control with convex optimization. (15th November 2017) - Zheng, Yongxing; Zhao, Shanghong; Tan, Qinggui; Li, YongJun; Jiang, Yong; Xin, Ning
Summary: In this paper, we consider the problem of maximizing the throughput of remote sensing satellite networks. In such networks, the link capacities and routing matrices are varying over time. We propose a convex optimization‐based analytical framework for the problem. To maximize the network throughput under the premise of satisfying the delay constraint, we formulate the data transmission schedule into an optimization problem aiming at maximizing the delay‐constrained throughput. Considering the fact that the future link capacities cannot be accurately known in the actual situation, we propose a heuristic and distributed framework on the basis of model predictive control for approximately solving the problem. This framework can be used to design remote sensing data transmission schedules under various scenarios. We adopt a generic example to simulate and analyze the framework. The simulation results show that the proposed analytic framework can obtain the approximate solution that is very close to the optimal solution by solving the convex optimization problem step‐by‐step. The heuristic algorithm based on model predictive control can obtain the approximate solution, which is very close to the optimal solution in distributed scenario. Abstract : We propose an effective method to solve the throughput maximization problem in the remote sensing satellite networks based on the MPC theory and convex optimization framework. We decouple the original global optimization problem to the distributed subproblems and adopt a heuristic scheme to solve them. Our method can obtain the suboptimal MPC solution, which is very close to the exact solution, and greatly reduces the computational complexity.