Adaptive Power Allocation and User Scheduling for LEO Satellites using Channel Predictions

TL;DR

This paper introduces APASS, an adaptive scheme for power allocation and user scheduling in LEO satellite systems that uses channel predictions to enhance fairness and performance despite channel variability.

Contribution

It presents a novel channel model and formulates a non-convex optimization problem solved by APASS, which dynamically allocates power and schedules users based on predicted channel gains.

Findings

APASS improves minimum user rate by nearly 3 times over equal power allocation.

APASS maintains high fairness with Jain's index above 0.99.

Performance remains strong even with significant prediction errors.

Abstract

Low earth orbit (LEO) satellites are a key technology to enable connectivity for rural and remote users. Communication satellites in LEO can provide coverage to much larger areas than terrestrial or aerial systems, while offering improved data rates when compared with geostationary systems. However, a major challenge with LEO satellite communications is the high mobility of the satellite, which results in a rapidly changing communication channel. Due to this, it is challenging to fairly allocate communication resources to multiple users in the system. This work proposes an Adaptive Power Allocation and Scheduling Scheme (APASS) to ensure user fairness in the downlink of a LEO satellite system serving mobile ground users. First, a novel channel and transmission model is introduced to capture the variability in channel statistics due to the satellite's trajectory. Then, a non-convex optimization problem is formulated to maximize the minimum rate across all ground users over a fixed set of time slots. To solve this problem, the proposed APASS dynamically allocates power and schedules transmissions based on predicted future channel gains. Numerical results show that APASS achieves strong performance even with substantial prediction errors, faring close to an upper bound that assumes perfect future channel knowledge. Furthermore, it improves the minimum user rate by a factor of 2.98 compared to equal-power allocation and maintains user fairness with a Jain's fairness index of well above 0.99.