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

2026-04-17

AI Summary

This work addresses the challenge of ensuring multi-user fairness in low Earth orbit (LEO) satellite communications, where rapid channel variations induced by high-speed mobility hinder reliable performance. To tackle this issue, the authors propose an Adaptive Power Allocation and Scheduling Scheme (APASS). APASS introduces a novel model that captures the statistical dynamics of channel variations driven by satellite trajectories and uniquely integrates channel gain prediction with non-convex optimization. By jointly optimizing power allocation and user scheduling in the downlink to maximize the worst-case user rate, the scheme achieves robust performance even under significant prediction errors. Experimental results demonstrate that APASS attains near-optimal performance, improving the minimum user rate by a factor of 2.98 compared to equal power allocation and achieving a Jain’s fairness index exceeding 0.99.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Learning to Search
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements

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.

Problem

• LEO satellites
• channel variability
• user fairness
• resource allocation
• high mobility

Innovation

• LEO satellite communications
• adaptive resource allocation
• channel prediction
• user fairness
• non-convex optimization