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.

翻译：近年来，在轨卫星数量迅速增长，以星链为代表的巨型星座可提供近全球覆盖的低延迟通信服务。然而，并非所有卫星通信应用场景都具有严格的延迟要求；地球观测和远程物联网等业务即属此类。这类业务对服务质量中的延迟指标要求较为宽松，使得通过稀疏星座结合容迟网络协议提供服务成为可能。在必须通过卫星网络传输的数据量快速增长背景下，星地链路容量不足成为关键挑战。这促使学界提出采用可提供高数据速率的自由空间光通信技术。然而，FSO通信极易受天气因素干扰，导致某些通信窗口存在能量效率低下的问题。考虑到卫星的能量受限特性，开发提升能量效率的方案具有重要价值。本研究提出了静态与自适应两类方案，以在维持交付率与最大化能量效率之间取得平衡。所提方案涵盖阈值方案、启发式排序算法以及基于强化学习的方案三大类别。研究在不同数据量、云层分布配置及历史气象数据案例下对各方案进行评估。结果表明：相较于自适应技术，静态方案在动态环境下的交付率表现较差，但自适应方案性能的提升是以增加系统复杂性和星上计算量为代价的。