Joint Optimization Communication and Computing Resource for LEO Satellites with Edge Computing

Low earth orbit (LEO) satellites with wide coverage can carry mobile edge computing (MEC) servers with computing power to form the LEO satellite edge computing system, providing computing services for ground users that cannot access the core network. This paper studies the joint optimization problem of communication and computing resource in the LEO satellite edge computing system to minimize the utility function value of the system. Due to the fact that, general optimization tools cannot effectively solve this problem, this paper proposes a deep learning-based bandwidth allocation algorithm. The bandwidth allocation schemes are generated through multiple parallel deep neural networks (DNNs). The utility function values of the system are calculated according to the derived optimal CPU cycle frequency and optimal user transmission power. The bandwidth allocation scheme corresponding to the optimal system utility function value is stored in the memory to further train and improve all DNNs. The simulation results show that the proposed algorithm can achieve good convergence effect and the algorithm proposed in this paper outperforms the other four comparison algorithms with low average time cost.