Reinforcement Learning-Based Resource Allocation and Energy Efficiency Optimization for a Space-Air-Ground-Integrated Network

With the construction and development of the smart grid, the power business puts higher requirements on the communication capability of the network. In order to improve the energy efficiency of the space-air-ground-integrated power three-dimensional fusion communication network, we establish an optimization problem for joint air platform (AP) flight path selection, ground power facility (GPF) association, and power control. In solving the problem, we decompose the problem into two subproblems, one is the AP flight path selection subproblem and the other is the GPF association and power control subproblem. Firstly, based on the GPF distribution and throughput weights, we model the AP flight path selection subproblem as a Markov Decision Process (MDP) and propose a multi-agent iterative optimization algorithm based on the comprehensive judgment of GPF positions and workload. Secondly, we model the GPF association and power control subproblem as a multi-agent, time-varying K-armed bandit model and propose an algorithm based on multi-agent Temporal Difference (TD) learning. Then, by alternately iterating between the two subproblems, we propose a reinforcement learning (RL)-based joint optimization algorithm. Finally, the simulation results indicate that compared to the three baseline algorithms (random path, average transmit power, and random device association), the proposed algorithm improves an overall energy efficiency of the system of 16.23%, 86.29%, and 5.11% under various conditions (including different noise power levels, GPF bandwidth, and GPF quantities), respectively.