Optimizing Power Allocation in HAPs Assisted LEO Satellite Communications

Abstract

The next generation of communication devices will require robust connectivity for millions of ground devices such as sensors or mobile devices in remote or disaster-stricken areas to be connected to the network. Non-terrestrial network (NTN) nodes can play a vital role in fulfilling these requirements. Specifically, low-earth orbiting (LEO) satellites have emerged as an efficient and cost-effective technique to connect devices over long distances through space. However, due to their low power and environmental limitations, LEO satellites may require assistance from aerial devices such as high-altitude platforms (HAPs) or unmanned aerial vehicles to forward their data to the ground devices. Moreover, the limited power available at the NTNs makes it crucial to utilize available resources efficiently. In this paper, we present a model in which a LEO satellite communicates with multiple ground devices with the help of HAPs that relay LEO data to the ground devices. We formulate the problem of optimizing power allocation at the LEO satellite and all the HAPs to maximize the sum-rate of the system. To take advantage of the benefits of free-space optical (FSO) communication in satellites, we consider the LEO transmitting data to the HAPs on FSO links, which are then broadcast to the connected ground devices on radio frequency channels. We transform the complex non-convex problem into a convex form and compute the Karush-Kuhn-Tucker (KKT) conditions-based solution of the problem for power allocation at the LEO satellite and HAPs. Then, to reduce computation time, we propose a soft actor-critic (SAC) reinforcement learning (RL) framework that provides the solution in significantly less time while delivering comparable performance to the KKT scheme. Our simulation results demonstrate that the proposed solutions provide excellent performance and are scalable to any number of HAPs and ground devices in the system.