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.

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时延通信服务。然而，并非所有卫星通信应用场景都具有严格的时延要求；例如地球观测与远程物联网服务便属于此类。这类服务对时延的服务质量要求较为宽松，可通过容忍时延的网络协议，利用稀疏星座实现服务传输。在卫星网络需传输数据量快速增长背景下，核心挑战在于如何保障充足的星地链路容量。这促使学界提出采用具备高数据速率特性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中断，致使部分通信窗口存在能量效率低下的问题。鉴于卫星平台的能源受限特性，开发提升能量效率的调度方案具有重要价值。本研究提出了静态与自适应两类调度方案，以在维持传输成功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、启发式排序算法及基于强化学习的方案三大类别。研究通过多种数据量及云层分布配置场景进行评估，并采用历史气象数据进行案例验证。结果表明：相较于自适应技术，静态方案在动态环境下的传输成功率表现较差。但自适应方案性能的提升是以增加系统复杂度与星载计算量为代价的。