Integrating Convex Optimization and Deep Learning for Downlink Resource Allocation in LEO Satellites Networks

Abstract

This paper investigates the satellite communication network (SCN) to optimize the channel allocation of the fixed ground base station and the transmission power allocation of the Low Earth orbit (LEO) satellites jointly while considering the freshness of the information. We present a mathematical model for the problem and formulate it as a mixed-integer programming (MIP) problem, which is NP-hard. To tackle this challenge, we propose a two-step approach that decomposes the problem into a channel allocation problem and a power allocation problem. For the power allocation problem, we propose a convex optimization algorithm termed as Opt. For the channel allocation problem, we introduce two learning-based schemes, Ptr and DNN-Ptr. Combining these two steps together, we develop two novel algorithms, i.e., Opt-Ptr and Opt-DNN-Ptr. In particular, the Opt-Ptr algorithm devises a novel Pointer Network to obtain the channel allocation decision and then solves the remaining power allocation problem using convex optimization algorithms. To further improve the performance, the Opt-DNN-Ptr algorithm utilizes a DNN to predict a transmission power allocation, which is then combined with the channel allocation decision obtained from the pointer network to solve the remaining power allocation problem. The simulation results verify the superiority of the proposed algorithm.