Energy Efficient Traffic Scheduling For Optical LEO Satellite Downlinks

TL;DR

This paper proposes energy-efficient traffic scheduling schemes for optical LEO satellite downlinks, balancing delivery ratio and energy use, with adaptive methods outperforming static ones under dynamic weather conditions.

Contribution

It introduces novel static and adaptive scheduling schemes, including reinforcement learning approaches, tailored for energy efficiency and delivery reliability in satellite optical communications.

Findings

Adaptive schemes outperform static ones in dynamic weather conditions.

Static schemes are simpler but less reliable under changing environments.

Reinforcement learning-based schemes improve energy efficiency and delivery ratio.

Abstract

In recent years, the number of satellites in orbit has increased rapidly, with megaconstellations like Starlink providing near-global, delay-sensitive communication services. However, not all satellite communication use cases have stringent delay requirements; services such as Earth observation (EO) and remote Internet of Things (IoT) fall into this category. These relaxed delay quality of service (QoS) objectives allow services to be delivered using sparse constellations, enabled by delay-tolerant networking protocols. In the context of rapidly growing data volumes that must be delivered through satellite networks, a key challenge is having sufficient space-to-ground link capacity. This has led to proposals for using free-space optical (FSO) communications, which offer high data rates. However, FSO communications are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being energy inefficient. Given the energy-constrained nature of satellites, developing schemes to improve energy efficiency is highly desirable. In this work, both static and adaptive schemes were developed to balance maintaining the delivery ratio and maximizing energy efficiency. The proposed schemes fall into the following categories: threshold schemes, heuristic sorting algorithms, and reinforcement learning-based schemes. The schemes were evaluated under a variety of different data volumes and cloud cover distribution configurations as well as a case study using historical weather data. It was found that static schemes suffered from low delivery ratio performance under dynamic conditions when compared to adaptive techniques. However, this performance improvement came at the cost of increased complexity and onboard computations.