Energy Efficient Traffic Scheduling For Optical LEO Satellite Downlinks
🤖 AI Summary
This study addresses the significant impact of weather conditions on low Earth orbit (LEO) optical satellite downlinks, which poses a challenge in simultaneously achieving high data delivery rates and energy efficiency under stringent power constraints. Focusing on delay-tolerant applications such as Earth observation and Internet of Things (IoT), the work proposes a multi-class scheduling strategy that integrates threshold-based mechanisms, heuristic prioritization, and reinforcement learning to balance delivery reliability and energy consumption. For the first time, the trade-off between energy efficiency and delivery rate is evaluated using real historical meteorological data. Experimental results demonstrate that the proposed adaptive scheme substantially outperforms static strategies under dynamic weather conditions, albeit at higher computational cost, and case studies confirm the effectiveness and practicality of the approach.
Application Category
📝 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 \pgm{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 \pgm{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.
Problem
Research questions and friction points this paper is trying to address.
energy efficiency
optical satellite downlinks
delay-tolerant networking
free-space optical communication
LEO satellites
Innovation
Methods, ideas, or system contributions that make the work stand out.
energy-efficient scheduling
free-space optical (FSO) communications
delay-tolerant networking
reinforcement learning
LEO satellite downlinks
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