Edward F. Crawley’s research while affiliated with The American Institute of Aeronautics and Astronautics and other places

The new era of satellite communications will rely on thousands of highly flexible spacecraft capable of autonomously managing constellation resources, such as power or frequency. Previous work has focused on the automation of the individual tasks that compose the resource allocation problem (RAP). However, two aspects remain unaddressed: (1) A unified method that autonomously solves the RAP under nongeosynchronous conditions is still to be developed, and (2) the cost–benefit of using optimization methods remains to be studied. Note that these studies are critical for satellite operators to take appropriate decisions concerning the automation of communications constellations operations. To close this gap, this work proposes an adaptive framework to solve the RAP for high‐dimensional nongeosynchronous satellite constellations. The proposed framework uses a divide‐and‐conquer approach that solves each step of the RAP, leveraging different optimization algorithms at the subproblem level to produce a valid and efficient allocation of resources over long time horizons. When comparing the proposed method against scalable greedy solutions, the former achieves up to four times more constellation capacity and reduces the overall consumed power by up to a factor of 3. The cost–benefit analysis reveals which RAP subproblems should be prioritized depending on the operator's objectives. Studying diverse operational conditions, we find that optimization methods enhance capacity consistently yet might raise power consumption due to trade‐offs in the routing algorithms.

Robust routing strategies for satellite constellations are crucial for mitigating outage probability amidst satellite failures. While existing literature primarily addresses robust routing within the satellite network, this study introduces innovative strategies to enhance robustness in ground-satellite links. Leveraging established heuristic and optimization methods, these strategies are tailored to incorporate redundancy. Using the Starlink constellation with 200,000 users as an example, our findings illustrate a significant reduction in outage probability for end-users, albeit with a trade-off of reduced capacity. Specific approaches demonstrate the capability to sustain a substantial portion of throughput, maintaining 96% while reducing the outage probability up to 66% and 10% for internal and external failures, respectively.

With the reduced distance between satellites in modern megaconstellations, the potential for self-interference has emerged as a critical challenge that demands strategic solutions from satellite operators. The goal of this paper is to propose a cooperative framework that combines the Satellite Routing (i.e., mapping of beams to satellites) and Frequency Assignment (i.e., mapping of frequency spectrum to beams) strategies to mitigate self-interference both within and between satellites. This approach stands in contrast to current practices found in the literature, which address each problem independently and solely focus on intra-satellite interference. This study presents a novel methodology for addressing the Satellite Routing problem, specifically tailored for modern constellations to maximize capacity while effectively mitigating self-interference through the use of Integer Optimization. By combining this method with established Frequency Assignment techniques, the results demonstrate an increase in throughput of up to 138% for constellations such as SpaceX Starlink. Notably, the study reveals that relying on individual approaches to tackle interference may lead to undesired outcomes, underscoring the advantages of a cooperative framework. Through simulations, the study highlights the practicality and applicability of the proposed method under realistic operational conditions.

Satellite communications megaconstellations are revolutionizing Internet connectivity, serving millions of users worldwide. However, prior performance analyses are limited to Low Earth Orbit architectures and broad operational assumptions, not representative of the modern satellite environment. This study aims to breach this gap by analyzing all proposed communications constellations with more than 100 satellites using representative operational conditions and state-of-the-art resource allocation methodologies. The analysis underscores the pivotal role played by the number of satellites and link quality as key drivers of performance. Combining existing designs could serve up to 1.8 billion people at 2 Mbps.

Satellite communications are transforming the when and how people can access broadband Internet, enabling connectivity in markets unreachable by terrestrial networks, such as isolated regions or connectivity on-the-go. Part of the new architectures’ success relies on intricate hybrid constellation designs that combine multiple orbital shells at different altitudes, such as the SpaceX 4-shell LEO and the Boeing 10-shell LEO-MEO-HEO constellations. Under the new systems, satellite operators will need automated and scalable mechanisms able to efficiently group and distribute individual customers across satellites (the User Grouping problem) in order to maximize satellite utilization and achieve increased constellation capacity. While previous studies propose methods for single-altitude designs, algorithms for hybrid systems are yet to be developed. This work aims to breach this gap by 1) formulating the User Grouping problem for hybrid constellations as a Mixed Integer Linear problem, and 2) developing a scalable methodology tailored for high-dimensional scenarios. By using the SpaceX and the Boeing constellations as examples, this work demonstrates that the proposed approach can provide high quality solutions in feasible time for scenarios with up to 100,000 customers, which represents realistic operational conditions, with a minimum 77% reduction in maximum satellite load compared to methods for single-altitude designs.

While the Sun provides the Earth with the energy needed to sustain life, the volatility associated with this intense energy source generates solar weather, which can have devastating implications on Earth. Solar weather can result in data compromise, radio interference, premature satellite deorbit, and even failure of the power grid. To mitigate the negative effects of solar weather, constant observation of the entirety of the Sun’s surface is essential. This complete picture of the Sun’s ever-changing state will help scientists anticipate solar events that may negatively impact life on Earth. A heliocentric satellite constellation called the Solar Unobstructed Network-based First Long-term Outer-space Weather Effects Research (SUNFLOWER) Observatory is proposed to continuously monitor coronal mass ejections, sunspots, and coronal holes with a suite of science instruments capable of collecting data in various electromagnetic wavelengths. This report offers a holistic view of the mission and spacecraft architectures. The paper begins with a discussion of motivation, mission objectives, and influential past missions. Next, a high-level overview of the mission design flow, mission-level requirements, and cost and schedule estimation assumptions is explored. This is followed by an analysis of the stakeholders and associated value flows and identification of system boundaries. Next, high-level design decisions for critical components of the system architecture and project risks and risk mitigation strategies are discussed. Results for instrument selection, constellation design, and spacecraft design are presented along with the reasoning behind the recommended architectures and design decisions. The final result is an estimate of the overall mission cost and schedule—roughly $4B in FY2025 USD over an 18-year lifecycle beginning in FY2025. The conclusion summarizes the proposed constellation, composed of nine identical spacecraft—each containing a magnetograph, an extreme ultraviolet imager, and a coronagraph—in a Walker-Delta 54.7° configuration at one AU, with three spacecraft in each of three planes. This solution offers continuous 4π-steradian remote sensing coverage of the solar surface—including the poles—with daily communication of science and state-of-health data over Ka-band frequencies to Earth using 34-m ground stations within the Deep Space Network (DSN). To circumvent the significant burden that would be placed on DSN, a compelling and mutually beneficial case for investing in additional 34-m antennas is presented. The paper concludes with recommendations for future work on the SUNFLOWER Observatory.